Advances in Clean Energy Technologies: Select Proceedings of ICET 2020 9811602344, 9789811602344, 9789811602351

Table of contents :
Preface
About This Book
Contents
About the Editors
1 Experimental Investigation of Domestic Refrigerator Used as an Air Conditioner by Augmentation Method
1.1 Introduction
1.2 Experimental Methodology
1.2.1 Experimental Setup for Case-1
1.2.2 Experimental Setup for Case-2
1.3 Result and Discussion
1.4 Conclusion
References
2 Stabilizing Molten Salts Through Additives for High Temperature CSP Applications
2.1 Introduction
2.2 Experimental Setup and Procedure
2.3 Results and Discussion
2.4 Conclusion
References
3 Impact of Various Heterogeneous Catalysts on the Production of Biodiesel
3.1 Introduction
3.1.1 The Impact of Heterogeneous Catalyst on Biodiesel Production
3.1.2 Solid Base Catalyst
3.1.3 Solid Acid Catalysts
3.1.4 Enzymatic Catalysts
3.2 Conclusion
References
4 Investigations on the Use of Molten Oxides for High Temperature Heat Transfer in Solar Power Plants
4.1 Introduction
4.2 Materials and Methods
4.2.1 Preparation of Oxide Mixtures
4.2.2 Viscosity Measurement
4.2.3 Thermogravimetric Analysis
4.3 Results and Discussion
4.4 Conclusion
References
5 Non-invasive Measurement of Oxygenated Hemoglobin (SpO2) and Blood Pressure
5.1 Introduction
5.2 Measurement Principles
5.2.1 Methods
5.3 Results
5.4 Conclusion
References
6 Investigation and Simulation of Rooftop Solar Photovoltaic System
6.1 Introduction
6.2 Problem and Challenges in Solar PV System
6.2.1 High Investment
6.2.2 Need for New Business Model (Good Incentives for People to Buy Rooftop Solar PV)
6.2.3 Snail Trail Effect
6.3 Design Methodology
6.3.1 On-Grid PV Array Sizing
6.3.2 Shadow Analysis
6.4 Major System Components and Input Parameters of the Components
6.5 Calculations for 124.8 kW Plant
6.5.1 Basic Calculations
6.5.2 Return of Investment Calculations
6.5.3 Percentage of Money Saved on Electricity Bill on Average by Solar Energy
6.6 Design of 124.8 kW PV System.
6.6.1 Orientation
6.7 Results and Discussion
6.7.1 Balances and Main Result
6.7.2 Loss Diagram
6.7.3 Normalized Production
6.7.4 Shadow Analysis
6.7.5 Emissions Saved
6.7.6 Economic and Cost Analysis
6.8 Conclusion
References
7 Wood Plastic Composite: Emerging Material for an Environmental Safety—A Review
7.1 Introduction
7.2 Literature Survey
7.2.1 Waste Recycling to Form Wood Plastic Composite (WPC)
7.2.2 Effect of Filler Addition on Wood Plastic Composite (WPC)
7.2.3 Effect of Filler Size on Wood Plastic Composite (WPC)
7.2.4 Effect of Processing Parameters on Wood Plastic Composite (WPC)
7.2.5 Effect of Various Coupling Agents on Wood Plastic Composite (WPC)
7.2.6 Effect of Various Additives on Wood Plastic Composite (WPC)
7.2.7 Effect of Various Treatments on Wood Plastic Composite (WPC)
7.2.8 FTIR Analysis of Wood Plastic Composite (WPC)
7.3 Conclusion
References
8 Selection of Heat Exchanger Based on Performance and Applications for Efficient Heat Transfer
8.1 Introduction
8.2 Some Applications of Common HEs
8.3 Techniques for Enhancing Heat Transfer
8.3.1 Passive Technique
8.3.2 Active Technique
8.4 Different Fin Configuration Design Effects on Thermal Performance
8.5 Fin Spacing Effects on Thermal Performance
8.6 Waffle Height Effects on Thermal Performance
8.6.1 Effect of Waffle Height and Thickness
8.6.2 Finned-Tube Pattern Effects
8.7 Finned-Tube’s Pattern Configuration Effects on Thermal Performance
8.8 Effects of Using Nanofluids and Twisted Tape Insert in HEs
8.9 Conclusions
References
9 Review on Conventional and Advanced Sliding Mode Control Schemes for Uncertain Dynamic System
9.1 Introduction
9.2 Conventional SMC
9.2.1 Classical SMC
9.2.2 PID-SMC
9.2.3 FOPID-SMC
9.2.4 Fuzzy FOPID-SMC
9.3 Advanced Fractional-Order Adaptive Fuzzy SMC
9.4 Conclusion
References
10 Modeling and Simulation of a Spiral Type Hybrid Photovoltaic Thermal (PV/T) Water Collector Using ANSYS
10.1 Introduction
10.2 Methodology
10.3 Model Description
10.4 Condition and Operating Parameters
10.5 Results and Discussion
10.6 Validation
10.7 Conclusions
References
11 Development of Correlation for Efficiency of Incineration Plants Using Deep Neural Network Model
11.1 Introduction
11.2 Methodology
11.2.1 Data Collection
11.2.2 Model Formulation
11.2.3 Model Description
11.3 Results and Discussion
11.4 Conclusion
References
12 Smart Grid Initiatives Towards Sustainable Development: Indian and Worldwide Scenario
12.1 Introduction
12.2 Smart Grid Framework: Policy Initiatives and Measures Undertaken (Indian Scenario)
12.3 Smart Grid Framework: Policy Initiatives and Measures Undertaken (Worldwide Scenario)
12.4 Conclusion
References
13 Development and Performance Analysis of Pine Needle Based Downdraft Gasifier System
13.1 Introduction
13.2 Experimental Setup
13.3 Results and Discussions
13.4 Conclusions
References
14 Indian Energy Scenario and Smart Grid Development
14.1 Introduction
14.2 Present Energy Scenario in India
14.3 Energy Policy-Making Institutional Framework in India
14.4 Smart Grid System Prospects in India
14.5 Future Outlook
References
15 Applications of Machine Learning in Harnessing of Renewable Energy
15.1 Introduction
15.2 Machine Learning
15.3 Machine Learning in Wind Energy
15.3.1 Application of ML in Wind Energy
15.4 Machine Learning in Geothermal Energy
15.4.1 Application of ML in Geothermal Field
15.5 Machine Learning in Solar Energy
15.6 Machine Learning in Wave Energy
15.6.1 Application of ML in Wave Energy
15.7 Conclusion
References
16 Optimization of Tilt Angles for Solar Devices to Gain Maximum Solar Energy in Indian Climate
16.1 Introduction
16.2 Methodology
16.2.1 Solar Radiation
16.3 Result and Discussion
16.4 Conclusion
References
17 A Novel Concept of ‘Parapet Farming’ Using ‘Living Chain’ Hanging System Integrated with Drip Irrigation Technique
17.1 Introduction
17.2 Urban Agriculture and Rooftop Farming
17.2.1 Existing Challenges
17.3 Concept of ‘Vertical Farming’/‘Vertical Gardening’
17.4 Our Concept
17.4.1 Experiment
17.4.2 Salient Features of the Project Establishing Its Uniqueness
17.4.3 Limitations
17.5 Future Scope and Conclusion
References
18 Implementation of Control Strategy for PV-Powered Switched Reluctance Motor Drive for Pumping Applications
18.1 Introduction
18.2 Framework of Proposed System
18.3 Control Strategies Implemented
18.3.1 MPPT Control Algorithm
18.3.2 Direct Torque Control
18.3.3 Bidirectional Power Flow Control
18.4 Proposed System Design
18.4.1 Solar PV Array
18.4.2 Boost Converter Design
18.4.3 Modelling and Design of SRM
18.5 Simulation Results
18.6 Epilogue
References
19 Experimental Investigation of Equilateral Triangle-Shaped Solar Air Heater with Two Blackened Absorber Surfaces
19.1 Introduction
19.2 Experimental Setup
19.3 Data Reduction
19.3.1 Data Reduction for Efficiency
19.4 Computational Domain and Meshing
19.5 Result and Discussion
19.6 CFD Results
19.7 Conclusion
References
20 Experimental Investigation, Exergy Analysis, and CFD Simulation of Solar Air Heater Roughened with Artificial V-Shaped Ribs on Absorber Surface Artificial Roughness on Absorber Plate
20.1 Introduction
20.2 Experimental Set up
20.3 Data Reduction
20.3.1 Efficiency Analysis
20.3.2 CFD Simulation
20.4 Result and Discussion
20.4.1 Experimental Result
20.4.2 CFD Results and Plots
20.5 Conclusion
References
21 Energy Generation and Management for Rural Areas of Rajasthan Through Solar Photovoltaic System
21.1 Introduction
21.2 Estimation of Daily Global Radiation at Horizontal Surface Methodology
21.3 Estimation of Daily Global Radiation at Tilted Surface Methodology
21.3.1 Average Daily Global Radiation at Tilted Surface
21.3.2 Calculation of Ht
21.4 Cost Estimation and Formulation of Model Equation
21.4.1 Cost Estimation
21.5 Result Analysis
21.6 System Estimation Design and Potential Estimation of Energy Source
21.6.1 Load Estimation and Module Design of Padla Village
21.7 Conclusions
References
22 Analyzing Effects of Camber and Its Position on Various Parameters in NACA Designated Aerofoil Blades Under Dynamic Similarity
22.1 Introduction
22.2 Methodology and Problem Setup
22.2.1 Mathematical Model
22.2.2 K-omega SST Model:
22.3 Results and Discussions
22.4 Conclusions
References
23 Design of Closed-Loop Control of a Three-Phase Sine Wave Inverter Using High Gain DC–DC Converter for Renewable Energy Applications
23.1 Introduction
23.2 System Configuration and Control Strategy
23.2.1 Configuration of Converter
23.2.2 Configuration of Inverter
23.3 Simulink Model of the Proposed System
23.3.1 Simulink Model of Converter
23.3.2 Simulink Model of Inverter
23.4 Observations
23.5 Conclusion
References
24 Effect of the Cool Roof on the Indoor Temperature in a Non-conditioned Building of Hot–Dry Climate
24.1 Introduction
24.2 Analysis
24.3 Results and Discussion
24.4 Conclusion
References
25 Comparison Analysis of Maximum Power Point Tracking Techniques for a Solar Photovoltaic System
25.1 Introduction
25.2 Solar Photovoltaic System
25.3 Mathematical Modeling of Equations Involved in a PV System
25.4 Modeling of DC to DC Convertor
25.4.1 Modeling of Boost Converter in Simulink
25.5 MPPT Technique
25.5.1 Perturb and Observe MPPT Algorithm
25.5.2 Incremental Conductance Algorithm
25.6 Modeling and Simulation of P&O MPPT Controller
25.7 Modeling and Simulation of INC MPPT Controller
25.8 Result and Discussions
25.9 Conclusion
References
26 Effect on Solar PV Panel Performance Due to Varying Latitude in Northern Hemisphere
26.1 Introduction
26.2 Methodology
26.2.1 Simulation Software’s
26.3 Simulation Results
26.4 Seasonal Tilt Angle
26.5 Conclusion
References
27 Vibration Analysis of Rotating Machines: A Case Study
27.1 Introduction
27.2 FFT Analyzer
27.2.1 FFT Tools
27.2.2 FFT Terminology
27.2.3 Process
27.3 Case
27.4 Specifications
27.5 Observations
27.5.1 Overall Vibration Level
27.5.2 Spectra at Different Points
27.6 Results and Discussion
27.7 Conclusion
References
28 Estimation of Energy Generation and Daylight Availability for Optimum Solar Cell Packing Factor of Building Integrated Semitransparent Photovoltaic Skylight
28.1 Introduction
28.2 Method and Calculation
28.3 Result and Discussion
28.4 Conclusion
References
29 Optimal Design and Techno-Economic Analysis of a Microgrid for Community Load Applications
29.1 Introduction
29.2 Methodology Used for the Analysis
29.2.1 Locations and Weather Conditions of the Selected Site
29.2.2 Schematic Diagram of the Proposed System
29.2.3 Solar Array
29.2.4 Wind Turbine
29.2.5 Power Inverter
29.2.6 Utility Grid
29.2.7 Diesel Generator
29.3 Financial Matrices
29.4 Result and Discussions
29.4.1 Autonomous Mode of Operation
29.4.2 Grid-Connected Mode
29.4.3 Sensitivity Analysis
29.4.4 Optimum Sizing of the System
29.4.5 Future Scope
29.5 Conclusions
References
30 Effectiveness of Homogeneous and Heterogeneous Catalyst on Biodiesel Yield: A Review
30.1 Introduction
30.2 Effect of Heterogeneous Catalyst on Transesterification
30.3 Effect of Homogeneous Catalyst on Transesterification
30.4 Conclusion
References
31 Experimental Analysis of a Generator Set Operating on Di Diesel Fuel and Ethanol Fumigation at Different Loads
31.1 Introduction
31.2 Materials and Methods
31.2.1 Engine Setup
31.2.2 Uncertainty Analysis
31.3 Engine Operation Conditions and Experimental Test Procedure
31.4 Results and Discussions
31.4.1 Total Specific Consumption
31.4.2 Global Thermal Efficiency
31.4.3 Exhaust Gas Temperature
31.4.4 Smoke Opacity
31.4.5 Carbon Monoxide Emissions
31.4.6 NOx Emissions
31.5 Conclusion
References
32 Optimizing the Yield of Biodiesel Made from Waste Soybean Oil by Varying the Temperatures and Volumetric Ratios of Oil and Methanol
32.1 Introduction
32.2 Transesterification Process
32.3 Types of Transesterification Process
32.3.1 Acid-Catalyzed Reactions
32.3.2 Base-Catalyzed Reactions
32.3.3 Lipase Based Reactions
32.3.4 Difference Between Esterification and Transesterification
32.4 Factors Influencing Biodiesel Production
32.4.1 The Molar Ratio of Oil and Alcohol
32.4.2 Effect of Temperature
32.4.3 Effect of Reaction Time
32.4.4 Effect of Catalyst Concentration
32.4.5 Free Fatty Acid and Moisture Content
32.5 Experimentation
32.5.1 Titration of Oil Sample
32.5.2 Process of Transesterification
32.5.3 Separation and Washing
32.6 Observation and Results
32.7 Conclusions
References
33 Smart Agricultural Robot with Real-Time Data Analysis Using IBM Watson Cloud Platform
33.1 Introduction
33.2 Objective
33.3 Related Work
33.4 Proposed Novel Methodology
33.5 Integrated Sensors of Agrobot
33.6 IBM Watson Cloud Platform
33.7 Agrobot Prototype
33.8 Results
33.8.1 Integrating Sensors to IBM Watson Platform
33.8.2 Real-Time Data Analysis on IBM Watson
33.8.3 Data Flow from Sensors to Database on Node Red Tool
33.8.4 Data Storage in Cloudant Database
33.8.5 Agro Chatbot Using Watson Assistant
33.9 Conclusion and Future Scope
References
34 SPWM Control Scheme for CHB-MLI with Minimal Voltage THD
34.1 Introduction
34.2 Five-Level CHB-MLI Topology
34.3 Modulation Schemes
34.3.1 PD Modulation
34.3.2 POD Modulation
34.3.3 APOD Modulation Scheme
34.4 Simulation Results
34.5 Conclusion
References
35 IoT Communication Technologies for Smart Farming—A Review
35.1 Introduction
35.2 Related Work
35.2.1 Long Range (LoRa)
35.2.2 Sigfox
35.2.3 NB-IoT
35.2.4 LTE-M
35.3 Comparison of Performance Parameters for a Suitable IoT Communication Technology
35.3.1 Battery Life
35.3.2 Latency
35.3.3 Quality of Service
35.3.4 Coverage and Range
35.3.5 Computational Cost
35.4 Conclusion
References
36 Recurrent Neural Network Analysis for Accurate Extrapolation of the Wind Velocity
36.1 Introduction
36.2 Observation and Site Details
36.3 Power Law
36.4 Results and Discussion
36.5 Conclusion
References
37 Roof Top Agriculture with Rainwater Harvesting and Smart Irrigation System
37.1 Introduction and Methodology
37.1.1 Prepared Rooftop Area
37.1.2 Soil Moisture Sensor (FC-28)
37.1.3 GSM Module (SIM 800A)
37.1.4 12 V SPDT Relay
37.1.5 12 V Water Pump
37.1.6 ESP8266 Wi-Fi Module
37.1.7 Two Selection Switches
37.2 Architecture and Working of Proposed System
37.3 Conclusion
References
38 A Delay-Sensitive Cyber-Physical System Framework for Smart Health Applications
38.1 Introduction
38.2 Literature Survey
38.3 Proposed Work or Methodology
38.3.1 Novel Similarity Measure-Based Random Forest
38.4 Performance Analysis
38.5 Conclusion and Future Work
References
39 Analyze and Identify Smart City Applications and Their Existing Frameworks
39.1 Introduction
39.2 Smart City Applications
39.2.1 Smart Parking
39.2.2 Smart Energy
39.2.3 Smart Education
39.2.4 Smart Health
39.2.5 Smart Tourism
39.2.6 Smart Water
39.2.7 Smart Waste
39.2.8 Smart Buildings
39.2.9 Smart Home
39.2.10 Smart Finance
39.2.11 Smart Retail
39.2.12 Smart Manufacturing
39.3 Smart City Architectures
39.3.1 Tier Architecture for Smart City
39.3.2 Message Queue Architecture
39.3.3 Microservice Architecture
39.3.4 Serverless Architecture
39.4 Comparison of the Different Smart City Application Development Architectures
39.5 Conclusion
References
40 Prevention of Intrusion Attacks via Deep Learning Algorithm in Wireless Sensor Network in Smart Cities
40.1 Introduction
40.2 Literature Survey
40.3 Proposed Work
40.3.1 Comparison with Previous Works
40.4 Results and Discussion
40.5 Conclusion and Future Scope
References
41 Torque Ripple Reduction of a Solar PV-Based Brushless DC Motor Using Sliding Mode Control and H7 Topology
41.1 Introduction
41.2 Mathematical Modeling of BLDC Motor
41.3 Modeling of SMC for BLDC Performance Improvement
41.3.1 Power Losses Calculations
41.4 SMC and H7 Inverter Control Strategy for Speed, Torque Control, and Ripples Reduction
41.5 Results and Discussion
41.5.1 Case A: Constant Speed and Variable Torque Control of BLDC Motor
41.5.2 Case B: Constant Torque and Variable Speed Control of Solar PV-Based BLDC Motor
41.5.3 Case C: Variable Torque and Variable Speed Application of BLDC Motor
41.5.4 Case D: Application of BLDC Motor as Electrical Vehicle (EV) Load
41.6 Conclusion
Appendix
References
42 Density-Based Smart Traffic Light Control System for Emergency Vehicles
42.1 Introduction
42.2 Literature Review
42.3 Block Diagram
42.3.1 Specifications of Traffic Lights
42.3.2 Basic Prototype of Traffic Light System
42.4 Software Tool
42.5 Flowchart for Density-Based Vehicular System
42.6 Results and Discussion
42.7 Conclusion
References
43 Development of an Assessment Tool to Review Communication Technologies for Smart Grid in India
43.1 Introduction
43.2 Literature Review
43.3 Methodology
43.3.1 Assessment Approach
43.3.2 Algorithm
43.3.3 Flowchart
43.4 Assessment of Indian Smart Grid Pilots
43.4.1 Disclosures and Considerations
43.4.2 Assessment Results
43.4.3 Summary
43.5 Conclusions, Recommendations, and Future Directions
43.5.1 Conclusions
43.5.2 Recommendations
43.5.3 Future Directions
Appendix–1: Nomenclatures and Abbreviations
References
44 Simulation and Analysis of Building Integrated Photovoltaic System for Different Climate Zones in India
44.1 Introduction
44.1.1 Monocrystalline [11, 14]
44.1.2 Polycrystalline [14]
44.1.3 Thin film [14]
44.2 Methodology
44.2.1 PVsyst Software
44.3 Result and Discussion
44.3.1 Monthly Energy Variation Between Six Cities
44.4 Conclusion
References
45 CFD Analysis of Temperature Profile and Pattern Factor at the Exit of Swirl Dump Combustor
45.1 Introduction
45.2 Experimental Steps and Instrumentations
45.3 Calculations
45.4 Results and Discussion
45.5 Conclusions and Future Scope
References
46 Determining the Performance Characteristics of a White-Box Building Energy System Model and Evaluating the Energy Consumption
46.1 Introduction
46.2 Modelling Approach
46.3 Step Response of Building Energy System
46.4 Simulation Results
46.5 Conclusion
References
47 Battery Management System with Wireless Parameter Estimation in EV
47.1 Introduction
47.2 Battery Management System
47.2.1 CAN Communication
47.3 Wireless Battery Management System
47.4 Hardware Implementation
47.4.1 Battery Monitoring Module
47.4.2 Controller and Bluetooth Module
47.4.3 BMS Controller and Display Unit
47.5 Experimental Results
47.6 Conclusion
References
48 A Novel Cascaded ‘H’ Bridge-Based Multilevel Inverter with Reduced Losses and Minimum THD
48.1 Introduction
48.2 Novel CHB-Based MLI
48.2.1 Design and Working
48.2.2 Modulation Technique
48.2.3 Switching Losses
48.3 Simulation Results
48.4 Conclusion
References
49 Assessing Factors Influencing Supply Chain 4.0: A Case of Smart City Development
49.1 Introduction
49.2 Literature Review
49.2.1 Industry 4.0
49.2.2 Smart City
49.2.3 Supply Chain 4.0
49.3 Conclusion
References
50 Electrical Equivalent Model for Proton Exchange Membrane Fuel Cell Useful in On-Board Applications
50.1 Introduction
50.2 Electrical Equivalent Model for PEM Fuel Cell
50.2.1 Prediction Steps
50.2.2 Correction Steps
50.3 Experimental Setup Description
50.4 Experimental Results and Analysis
50.5 Conclusion
References
51 Predicting Waste to Energy Potential and Estimating Number of Transfer Station Based on Indore Waste Management Model: A Case of Indian Smart Cities
51.1 Introduction
51.2 Literature Review
51.3 Methodology
51.3.1 Stage 1: Estimation of Waste to Energy Potential (Bio-CNG and Biogas)
51.3.2 Stage 2 Estimation of Number of Transfer Station
51.4 Result and Discussions
51.5 Conclusion
References
52 Analysis of Thermal Energy Storage Mediums for Solar Thermal Energy Applications
52.1 Introduction
52.1.1 Working Cycle of TES Systems
52.1.2 Classification of TES Systems
52.1.3 Work Methodology
52.2 General Selection Criteria for a TES System
52.2.1 Phase Change
52.2.2 Encapsulation of PCMs
52.3 Applications of TES in Solar Thermal Systems
52.3.1 Solar Air Heater (SAH)
52.3.2 Solar Water Heater (SWH)
52.3.3 Solar Dryers
52.3.4 Other Emerging Applications
52.4 Discussion and Future Development Scope
References
53 Application of Concrete Filled Steel Tubes in Solar Module Mounting Structure
53.1 Introduction
53.1.1 Tracking Mechanism
53.1.2 Solar Module Mounting Structure
53.1.3 Loads on the Solar Module Mounting Structure
53.1.4 Concrete Filled Steel Torque Tubes
53.2 Methodology
53.2.1 Computation of the Load
53.2.2 Structural Analysis Using STAAD PRO
53.2.3 Finite Element Analysis
53.3 Results
53.4 Conclusion
References
54 Reduction of Over Current and Over Voltage Under Fault Condition Using an Active SFCL with DG Units
54.1 Introduction
54.2 Theoretical Analysis of Active SFCL
54.2.1 Structure of Active SFCL
54.2.2 Operating Principle of Active SFCL
54.3 Distribution System with DG Units Using Active SFCL
54.4 Simulink Results
54.4.1 Simulation Results During Normal Condition (Without Fault)
54.4.2 Reducing of Over Voltage Characteristics of Active SFCL During Fault Condition
54.4.3 Active SFCL, Over Current Suppressing Characteristic
54.5 Conclusion
References
55 Mathematical Modeling of Air Heating Solar Collectors with Fuzzy Parameters
55.1 Introduction
55.2 Mathematical Model of Solar Air Heater
55.2.1 Basic Concepts
55.2.2 Mathematical Model
55.2.3 Theorem
55.3 Result and Discussion
55.4 Conclusion
References
56 Performance of Machine Learning Approaches for Malicious Traffic Intrusion Detection in Network
56.1 Introduction
56.2 Related Work
56.3 Experimental Work and Results
56.4 Result Analysis
56.5 Conclusion
References
57 Applications of Synchrophasors Technology in Smart Grid
57.1 Introduction
57.2 Synchrophasors Technology
57.3 Synchrophasors-Based Wide Area Monitoring System
57.3.1 Phasor Measurement Units
57.3.2 Sub/Local Phasor Data Concentrator
57.3.3 Communication Network
57.3.4 Super PDC
57.3.5 Synchrophasors Based Application or Tools
57.4 Synchrophasors Applications
57.4.1 Real-Time Control and Monitoring:
57.4.2 Situational Awareness Coordination
57.4.3 Analysis/Estimate Planners
57.5 Case Study NR of Indian Power Grid Using Synchrophasors Data
57.6 Conclusion
References
58 Numerical Analysis of Performance Parameters and Exhaust Gas Emission of the Engine with Regular Air Intake System and with Insulated Air Intake System
58.1 Introduction
58.1.1 Chemistry of Combustion in SI Engine
58.1.2 Heat Transfer and Cooling of SI Engine
58.2 Methodology
58.2.1 Computational Fluid Dynamics
58.3 Modeling
58.4 Result and Discussion
58.4.1 Effect of Engine Thermal Losses on Intake Air Temperature Using Non-insulated or Regular Air Intake System
58.4.2 Effect of Engine Thermal Losses on Intake Air Temperature Using Insulated Air Intake System
58.5 Conclusion
References
59 Investigation of AI Based MC-UPFC for Real Power Flow Control
59.1 Introduction
59.2 MC-UPFC Switching Control
59.3 Modeling of MC-UPFC in Power System
59.3.1 MC-UPFC in Two Bus System
59.4 Fuzzy Logic Controller (FLC)
59.4.1 Implementation of PID Based FLC
59.4.2 Test Case-IEEE 14-Bus System with FLC Controller
59.5 Simulation and Results
59.6 Conclusion
59.7 Future Work
Appendix
References
60 Sizing and Performance Investigation of Grid-Connected Solar Photovoltaic System: A Case Study of MANIT Bhopal
60.1 Introduction
60.2 Energy Demand
60.3 Sizing of Solar PV System
60.3.1 Panel Generation Factor
60.3.2 Energy Generation from Solar PV Modules
60.3.3 Watt Peak Rating for PV Modules
60.4 System Designing
60.4.1 Number of Solar PV Modules Required
60.4.2 Number of Inverters Required
60.4.3 Solar PV Modules Arrangement
60.5 Performance Investigation
60.5.1 Energy Injected Into Grid
60.5.2 Performance Ratio (PR)
60.5.3 CO2 Emission Reduction
60.6 Life Cycle Evaluation of PV System
60.6.1 Energy Payback Period
60.6.2 Capacity Factor
60.7 Economic Analysis
60.8 Conclusion
References
61 Comparative Study and Trend Analysis of Regional Climate Models and Reanalysis Wind Speeds at Rameshwaram
61.1 Introduction
61.2 Data and Methods
61.2.1 Data
61.2.2 Methods
61.3 Results and Discussion
61.3.1 Validation
61.3.2 Cumulative Change and Temporal Trends of RCMs Wind Speeds
61.4 Conclusions
References
62 A Novel Islanding Detection Technique for Grid-Connected Distributed Generation Using KNN and SVM
62.1 Introduction
62.1.1 Islanding Detection Methods
62.2 Literature Survey
62.3 System Under Study
62.4 Proposed Islanding Detection Methodology
62.4.1 Data Acquisition
62.4.2 Data Pre-processing
62.4.3 Feature Extraction
62.4.4 Building Classification Model (Training Phase)
62.4.5 Testing Classification Model
62.5 Result and Discussion
62.6 Conclusion
62.7 Future Scope
References
63 A 150 kW Grid-Connected Roof Top Solar Energy System—Case Study
63.1 Introduction
63.2 PV Plant Location Information
63.3 Installed Plant Description
63.3.1 PV Modules
63.3.2 The Power Conditioning Units
63.3.3 Grid Connection
63.4 System Performance Analysis
63.4.1 Array Yield
63.4.2 Final Yield
63.4.3 Reference yield
63.4.4 PV Module Efficiency
63.4.5 System Efficiency
63.4.6 Performance Ratio (PR)
63.4.7 Capacity Utilization Factor (CUF)
63.4.8 PV Plant Losses
63.5 Cost of Energy for the PV Plant
63.6 Conclusion
References
64 Fuzzy SVM Classifier for Clothes Pattern Recognition
64.1 Introduction
64.2 Review of Literature
64.3 Functional Diagram and Methodolgy
64.3.1 Statistical Features Extraction (STA)
64.3.2 Recurrence Quantification Analysis (RQA)
64.3.3 Scale-Invariant Feature Transform (SIFT)
64.3.4 Discrete Wavelet Transforms (DWT)
64.3.5 Support Vector Machine (SVM)
64.4 System Description
64.5 Results and Discussions
64.5.1 Color Calculation
64.5.2 Detected Color (with Speaker)
64.5.3 Pattern Calculation
64.6 Conclusion
References
65 A Detailed Analysis of Municipal Solid Waste Generation and Composition for Haridwar City, Uttrakhand, India
65.1 Introduction
65.2 Materials and Methods
65.2.1 Description of Study Area
65.2.2 Sampling Methodology
65.2.3 Sorting Procedure
65.3 Results and Discussion
65.3.1 Estimation of MSW Generation and Its Management
65.3.2 Composition of MSW
65.4 Conclusion
References
66 Techno-Economic Analysis of Piezoelectric-Based Smart Railway Tracks
66.1 Introduction
66.2 Piezoelectric Effect
66.2.1 Compression-Type Piezoelectric System
66.2.2 Cantilever-Type Piezoelectric System
66.3 Sensitivity Analysis
66.3.1 Sensitivity Analysis of ComPES
66.3.2 Sensitivity of CanPES
66.4 Economic Analysis
66.4.1 Cost Estimation of ComPES Array
66.5 Techno-economic Differences of Two PES
66.6 Conclusion
References
67 JDMaN: Just Defeat Misery at Nagging—A Smart Application for Women Protection
67.1 Introduction
67.2 Literature Survey
67.3 Existing System
67.4 Proposed Methodology
67.4.1 Software’s
67.4.2 Hardware Devices
67.4.3 Working
67.5 Conclusion and Future Work
References
68 Control of PM Synchronous Motor with Hybrid Speed Controller with Gain Scheduling for Electric Propulsion
68.1 Introduction
68.2 Electric Propulsion System
68.3 Hybrid Gain-Scheduled PI Speed Controller
68.3.1 Gain-Scheduled PI Controller
68.3.2 Hybrid Fuzzy PI Controller
68.3.3 Hybrid Speed Controller with Gain Scheduling
68.4 Results and Discussion
68.5 Conclusions
References
69 Study on Effect of Draft Tube Diffuser Shape on Performance of Francis Turbine
69.1 Introduction
69.2 Geometric Modelling and Simulation
69.3 Pre-processing and Domain Specifications
69.4 Boundary Conditions
69.5 Results and Discussion
69.6 Conclusions
References
70 Dehydration of Vegetables Through Waste Heat of Vapour Compression Refrigeration System
70.1 Introduction
70.2 Material and Method
70.3 Result and Discussion
70.4 Conclusion
References
71 Peak Power Impact from Electric Vehicle Charging
71.1 Introduction
71.2 Methodology
71.3 Overview of Different EV Segments and Their Charging
71.3.1 Electric Bus Charging
71.3.2 Electric Car Charging
71.3.3 Electric Three Wheelers
71.3.4 Electric Two Wheelers
71.4 Forecast Peak Demand from Different Vehicle Segments in FAME
71.5 Conclusion and Way Forward
References
72 Integration of Multiple Energy Sources for Hybrid Smart Street Light System
72.1 Introduction
72.2 Smart Street Light Charging System
72.2.1 Solar Energy System
72.2.2 Wind Energy System
72.2.3 Mobile Radiation Energy System
72.3 Intensity Control by Sensing Vehicle Movement
72.4 Hybrid Street Light Sun, Wind and Mobile Radiation
72.5 Conclusion
References
73 Improving Cold Flow Properties of Biodiesels Using Binary Biodiesel Blends
73.1 Introduction
73.2 Problems Due to Poor CFP
73.3 Material and Methodology
73.3.1 Material
73.3.2 Experimental
73.4 Results and Discussion
73.4.1 Blends of JB with Diesel
73.4.2 Binary Blends of JB with MB
73.4.3 Binary Blends of PB with MB
73.4.4 Blends of JB with Ethanol
73.4.5 Blends of JB with Kerosene
73.5 Finding
73.6 Conclusions
References
74 Dual-Axis Solar Tracking System
74.1 Introduction
74.2 Solar Tracking System
74.3 GPS Receiver
74.4 Solar Tracker Design
74.5 Electronic Control System
74.6 Result and Discussion
74.7 Conclusion
References
75 CFD Analysis of Air Distribution for Suitable Position of Evaporator in Cold Chamber
75.1 Introduction
75.2 Experimental Setup
75.2.1 Specification
75.2.2 CFD Approaches
75.3 Result and Analysis
75.4 Conclusion
References
76 Role of Supercapacitor for Increasing Driving Range of Electric Vehicles Under Indian Climatic Conditions
76.1 Introduction
76.2 Theoretical Description of Supercapacitor
76.3 Model of the Hybrid EESS
76.4 Calculation of Driving Range
76.5 Data Collection and Identification of Temperature Zones of India
76.6 Results and Discussion
76.6.1 Driving Pattern of EV
76.6.2 Power Required Based on Temperature
76.6.3 Effect of Ambient Temperature on the SC Power
76.6.4 Effect of Ambient Temperature on the SOC of SC
76.6.5 Estimation of Range with Respect to Temperature
76.7 Conclusions
References
77 Noise Vulnerability Assessment for Kota City
77.1 Introduction
77.2 Study Area and Research Methodology
77.3 Observations
77.4 Results and Discussion
77.4.1 Noise Vulnerability Assessment for Kota City at 78 dB(A)
77.4.2 Noise Vulnerability Assessment for Kota City at 80 dB(A)
77.5 Conclusion
References
78 Application of Global Sensitivity Analysis to Building Performance Simulations for Screening Influential Input Parameters in a Humid Coastal Climate
78.1 Introduction
78.2 Climate of the Study Location
78.3 Sensitivity Analysis
78.3.1 Morris Method of Screening
78.4 Methodology
78.4.1 Model Input Parameters
78.4.2 Sampling Strategy
78.4.3 Building Energy Simulation
78.4.4 Sensitivity Analysis and Ranking of Parameters
78.5 Results and Discussion
78.5.1 Analysis of Morris Screening Results
78.5.2 Scope for Further Work
78.6 Conclusion
References
79 Two Decades of Urban Growth in Kota City: The Urban Heat Island Study
79.1 Introduction
79.2 Study Area
79.3 Research Methodology and Data Used
79.3.1 Research Methodology
79.3.2 Data Used
79.4 Results and Discussion
79.4.1 Land Surface Temperature (LST)
79.4.2 Correlation Between Land Surface Temperature versus NDBI
79.5 Conclusion
References
80 Anomaly Detection Systems Using IP Flows: A Review
80.1 Introduction
80.1.1 Types of Intrusion Detection Systems
80.1.2 Detection Methods
80.1.3 Types of Data
80.2 Flow Collection Process and Tools
80.2.1 Tools Used in Flow Collection Process
80.2.2 Standard Protocols for Handling Flows/Flow-Based Formats
80.2.3 Flow Exporters
80.2.4 Flow Collector
80.3 Techniques Used in Flow-Based Anomaly Detection
80.3.1 Statistical Techniques
80.3.2 Data Mining and Machine Learning Techniques
80.3.3 Deep Learning Techniques
80.3.4 Ensemble Learning
80.3.5 Outlier Techniques
80.3.6 Other Techniques
80.3.7 Combination of Techniques
80.4 Discussion and Research Gaps
80.5 Conclusion and Future Work
References
81 Performance Analysis of 250 kWP Roof Top Grid-Connected Solar PV System Installed at MANIT Bhopal
81.1 Introduction
81.2 System Description
81.3 Performance Parameters
81.4 Results and Discussion
81.5 Conclusion
References
82 An Ensemble Model of Machine Learning for Primary Tumor Prognosis and Prediction
82.1 Introduction
82.2 Proposed Approach
82.2.1 Preprocessing
82.2.2 Data Visualization
82.2.3 Feature Scaling
82.2.4 Train-Test Split
82.2.5 Machine Learning Algorithm
82.3 Experimental Setup
82.4 Performance Metrics
82.5 Conclusion
References
83 Implementing Fog Computing for Detecting Primary Tumors Using Hybrid Approach of Data Mining
83.1 Introduction
83.1.1 Emerging Technologies in Healthcare
83.2 Proposed System
83.2.1 IoT Data Accummulating Layer
83.2.2 Fog Layer
83.2.3 Cloud Layer
83.3 Experimental Setup
83.4 Results
83.4.1 Parameters Used to Evaluate Prediction Efficiency Are as Follows
83.5 Conclusion
References
84 Analysis on Filter Circuits for Enhanced Transient Response of Buck Converters
84.1 Introduction
84.2 Working and Design of Buck Converter
84.3 Design of Filter Circuits for Buck Converter
84.4 Simulation Results and Analysis
84.5 Conclusion
References
85 The Cause and Control of Failure of Hydraulic Turbine Due to Cavitation: A Review
85.1 Introduction
85.1.1 Types of Cavitation in Hydraulic Turbine
85.1.2 Evaluation of Cavitation Coefficient
85.2 Detection of Cavitation Erosion
85.3 Methods for Prevention of Cavitation in Hydroturbine
85.3.1 Air Injection Method
85.3.2 Material Coating
85.3.3 Turbine Setting
85.4 Effect of Other Parameters on Cavitation Characteristic
85.5 Conclusions
References
86 Classification and Synthesis of Nanoparticles: A Review
86.1 Introduction
86.2 Classification
86.2.1 Nanoparticle Based on Origin
86.2.2 Organic Nanoparticles
86.2.3 Inorganic Nanoparticles
86.3 Properties of Nanoparticles
86.3.1 Melting Point
86.3.2 Band Gap
86.3.3 Mechanical Properties
86.3.4 Magnetic Properties
86.4 Application of Nanoparticles in Daily Life
86.4.1 Use of Nanotechnology in Food
86.4.2 Nanoparticle Use in the Electronics
86.4.3 Nanoparticles in the Medical Industry
86.4.4 Nanoparticles as Catalyst
86.5 Conclusion
References
87 Marble and Granite Slurry Reuses in Industries
87.1 Introduction
87.2 Applications Sector of Marble Slurry
87.2.1 Utilization of Marble Slurry in Cement Manufacturing
87.2.2 Utilization of Marble Slurry Dust (MSD) in Road Construction
87.2.3 For the Manufacturing of Concrete
87.2.4 Utilization of Marble Slurry in Brick Manufacturing
87.2.5 Manufacture of Ceramic Tiles
87.2.6 Manufacture of Thermoset Resin Composites
87.2.7 Manufacture of Lime
87.2.8 Manufacture of Activated Calcium Carbonate
87.2.9 Hollow Blocks and Wall Tiles
87.2.10 Water Management
87.2.11 Visualization on Latent Fingerprint
87.2.12 Ceramic Artwork
87.2.13 Rubber Industry
87.3 Conclusion
References
88 Experimental Investigation on Thermal Performance of Solar Air Collector Provided with Corrugated Absorber
88.1 Introduction
88.2 Materials and Methods
88.3 Results and Discussions
88.4 Conclusions
References
89 Noise Vulnerability Assessment at 78 dB (A) for Kota City
89.1 Introduction
89.2 Study Area and Research Methodology
89.3 Observations
89.4 Results and Discussion
89.5 Noise Vulnerability Assessment for Kota City at 78 dB (A)
89.6 Conclusion
References
Author Index

Citation preview

Springer Proceedings in Energy

Prashant V. Baredar Srinivas Tangellapalli Chetan Singh Solanki   Editors

Advances in Clean Energy Technologies Select Proceedings of ICET 2020

Springer Proceedings in Energy

The series Springer Proceedings in Energy covers a broad range of multidisciplinary subjects in those research fields closely related to present and future forms of energy as a resource for human societies. Typically based on material presented at conferences, workshops and similar scientific meetings, volumes published in this series will constitute comprehensive state-of-the-art references on energy-related science and technology studies. The subjects of these conferences will fall typically within these broad categories: • • • • • • •

Energy Efficiency Fossil Fuels Nuclear Energy Policy, Economics, Management & Transport Renewable and Green Energy Systems, Storage and Harvesting Materials for Energy

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More information about this series at http://www.springer.com/series/13370

Prashant V. Baredar · Srinivas Tangellapalli · Chetan Singh Solanki Editors

Advances in Clean Energy Technologies Select Proceedings of ICET 2020

Editors Prashant V. Baredar Department of Energy Energy Centre, Maulana Azad National Institute of Technology Bhopal, Madhya Pradesh, India

Srinivas Tangellapalli Department of Mechanical Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar Jalandhar, Punjab, India

Chetan Singh Solanki Department of Energy Science and Engineering Indian Institute of Technology Bombay Mumbai, Maharashtra, India

ISSN 2352-2534 ISSN 2352-2542 (electronic) Springer Proceedings in Energy ISBN 978-981-16-0234-4 ISBN 978-981-16-0235-1 (eBook) https://doi.org/10.1007/978-981-16-0235-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

This book presents a pool of research and review articles on different aspects of engineering design from the 1st International Conference on Innovations in Clean Energy Technologies (ICET), which was organized by Energy Centre, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India, from August 27 to 28, 2020. The conference aims to provide a platform for academicians, scientists, and researchers across the globe to share their scientific ideas and vision in the areas of smart technologies based products, energy-efficient systems, solar and wind energy, carbon sequestration, green transportation, green buildings, energy materials, bioenergy, smart cities, and other related fields of energy. The ICET-2020 conference played a key role in setting up a bridge between academicians and industries. Due to the COVID-19 outbreak around the world, the meetings and gatherings were banned, besides strict immigration policy. Based on most authors’ appeal and health considerations, after careful discussion, the conference committee changed this event to an online conference. The conference presented more than 200 participants to interchange scientific ideas. During the two-day conference, researchers from academics and industries offered the most recent cutting-edge findings, went through several scientific brainstorming sessions, and exchanged ideas on practical socio-economic topics. This conference also provided an opportunity to establish a network for collaboration between academician and industry. The major emphasis was given on the recent developments and innovations in various fields of energy and clean technologies through plenary lectures. This book presents various chapters addressing the science and engineering of various clean technologies in the form of mathematical- and computer-based methods and models for designing, analyzing, and measuring the cleanliness of products and processes. This book brings together different aspects of engineering design and will be useful for researchers and professionals working in this field. We would like to acknowledge all the participants who have contributed to this volume. We also deeply express our gratitude to the generous support provided by, MANIT, Bhopal. We also thank the publishers and every staff and student volunteer of the department and institute who has directly or indirectly assisted in accomplishing v

vi

Preface

this goal. Finally, we would also like to express our gratitude to the Respected Director of MANIT, Dr. N. S. Raghuwanshi, for providing all kinds of support and blessings. Despite sincere care, there might be typos and always a space for improvement. We would appreciate any suggestions from the reader for further improvements to this book. Bhopal, India Jalandhar, India Mumbai, India October 2020

Dr. Prashant V. Baredar Dr. Srinivas Tangellapalli Dr. Chetan Singh Solanki

About This Book

Innovation, technology, material, resources, and various other factors have an important role in the design of a component. This book presents various chapters addressing the science and engineering of various clean technologies in the form of mathematical- and computer-based methods and models for designing, analyzing, and measuring the cleanliness of products and processes. Experimental, computational, and analytical aspects in the field of design are discussed in various chapters in this book. This book brings together different aspects of engineering design and will be useful for researchers and professionals working in this field. ICET-2020 was held at MANIT, Bhopal, India, in August 2020. Numerous scientists, researchers, and industry experts presented the future aspects of process/product design and papers on the latest researches and technologies. This conference provided a forum for research scholars, scientists, undergraduate, postgraduate, and industry experts to discuss the latest challenges and future needs in the areas of design and development of innovative clean technologies.

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2

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4

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9

Experimental Investigation of Domestic Refrigerator Used as an Air Conditioner by Augmentation Method . . . . . . . . . . . . . . . . Pankaj P. Gohil and Altafhusen Saiyed

1

Stabilizing Molten Salts Through Additives for High Temperature CSP Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Siddhesh C. Pawar, Varun Shrotri, and Luckman Muhmood

13

Impact of Various Heterogeneous Catalysts on the Production of Biodiesel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gaurav Dwivedi, Deviprasad Samantaray, Swayamsidha Pati, Suyasha Pandey, and Ambar Gaur

23

Investigations on the Use of Molten Oxides for High Temperature Heat Transfer in Solar Power Plants . . . . . . . . . . . . . . . Varun Shrotri and Luckman Muhmood

45

Non-invasive Measurement of Oxygenated Hemoglobin (SpO2 ) and Blood Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rajendra Naik Bhukya, Shoban Mude, and G. Sneha

57

Investigation and Simulation of Rooftop Solar Photovoltaic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arun K. Behura, Spandan Shah, Aman Kumar, and Gaurav Dwivedi

69

Wood Plastic Composite: Emerging Material for an Environmental Safety—A Review . . . . . . . . . . . . . . . . . . . . . . . . Nidhi Dwivedi, Amit Prem Khare, and Shamsul Haq

85

Selection of Heat Exchanger Based on Performance and Applications for Efficient Heat Transfer . . . . . . . . . . . . . . . . . . . . Kumari Deepika and R. M. Sarviya

101

Review on Conventional and Advanced Sliding Mode Control Schemes for Uncertain Dynamic System . . . . . . . . . . . . . . . . Shailu Sachan and Pankaj Swarnkar

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10 Modeling and Simulation of a Spiral Type Hybrid Photovoltaic Thermal (PV/T) Water Collector Using ANSYS . . . . . Naimish Kumar Baranwal and Mukesh Kumar Singhal

127

11 Development of Correlation for Efficiency of Incineration Plants Using Deep Neural Network Model . . . . . . . . . . . . . . . . . . . . . . Deepuphanindra Gannamani and Anuj Kumar

141

12 Smart Grid Initiatives Towards Sustainable Development: Indian and Worldwide Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sumeet K. Wankhede, Priyanka Paliwal, and Mukesh K. Kirar

153

13 Development and Performance Analysis of Pine Needle Based Downdraft Gasifier System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abhishek Agrawal and Divyanshu Sood

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14 Indian Energy Scenario and Smart Grid Development . . . . . . . . . . . Kunal Chakraborty, Sanchita Mukherjee, and Samrat Paul

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15 Applications of Machine Learning in Harnessing of Renewable Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chris Daniel, Anoop Kumar Shukla, and Meeta Sharma

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16 Optimization of Tilt Angles for Solar Devices to Gain Maximum Solar Energy in Indian Climate . . . . . . . . . . . . . . . . . . . . . Digvijay Singh, A. K. Singh, S. P. Singh, and Surendra Poonia

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17 A Novel Concept of ‘Parapet Farming’ Using ‘Living Chain’ Hanging System Integrated with Drip Irrigation Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dibyendu Bhowmick and Shilpi Saha 18 Implementation of Control Strategy for PV-Powered Switched Reluctance Motor Drive for Pumping Applications . . . . . T. Sai Rakshitha, E. Shiva Prasad, D. S. G. Krishna, and K. Sravani 19 Experimental Investigation of Equilateral Triangle-Shaped Solar Air Heater with Two Blackened Absorber Surfaces . . . . . . . . Rahul Kumar, Shri Krishna Mishra, Hitesh Kumar, Rachit Saxena, and Anoop Kumar Pathariya 20 Experimental Investigation, Exergy Analysis, and CFD Simulation of Solar Air Heater Roughened with Artificial V-Shaped Ribs on Absorber Surface Artificial Roughness on Absorber Plate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shri Krishna Mishra, Rahul Kumar, Renu Joshi, Hitesh Kumar, and Nishant Saxena

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21 Energy Generation and Management for Rural Areas of Rajasthan Through Solar Photovoltaic System . . . . . . . . . . . . . . . . Rachit Saxena, Sonal Saxena, Hitesh Kumar, Shri Krishna Mishra, and Rahul Kumar 22 Analyzing Effects of Camber and Its Position on Various Parameters in NACA Designated Aerofoil Blades Under Dynamic Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rohit Gupta, Prashant Baredar, and Sanjeev Kumar Bhukesh 23 Design of Closed-Loop Control of a Three-Phase Sine Wave Inverter Using High Gain DC–DC Converter for Renewable Energy Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pranjit Kumar Roy, Pradip Kumar Saha, and Ashoke Mondal 24 Effect of the Cool Roof on the Indoor Temperature in a Non-conditioned Building of Hot–Dry Climate . . . . . . . . . . . . . . Mohan Rawat, R. N. Singh, and S. P. Singh 25 Comparison Analysis of Maximum Power Point Tracking Techniques for a Solar Photovoltaic System . . . . . . . . . . . . . . . . . . . . . Vivek Kumar, Archana Soni, Markapuram Srinivasa Rao, and Sanjeev Kumar Bhukesh 26 Effect on Solar PV Panel Performance Due to Varying Latitude in Northern Hemisphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pooja Rawat and Archana Soni 27 Vibration Analysis of Rotating Machines: A Case Study . . . . . . . . . Atul Gautam, Pramod Kumar Sharma, Prashant Baredar, Vilas Warudkar, J. L. Bhagoria, Siraj Ahmed, and Sagar Balkrishna Sutar 28 Estimation of Energy Generation and Daylight Availability for Optimum Solar Cell Packing Factor of Building Integrated Semitransparent Photovoltaic Skylight . . . . . . . . . . . . . . . Digvijay Singh and S. P. Singh

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29 Optimal Design and Techno-Economic Analysis of a Microgrid for Community Load Applications . . . . . . . . . . . . . . . Venkatesh Boddapati and S. Arul Daniel

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30 Effectiveness of Homogeneous and Heterogeneous Catalyst on Biodiesel Yield: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bharat Singh, Siddharth Jain, and Brijesh Gangil

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31 Experimental Analysis of a Generator Set Operating on Di Diesel Fuel and Ethanol Fumigation at Different Loads . . . . . . . . . . J. Ramachander, S. K. Gugulothu, G. Ravikiran Sastry, and S. Rafiuzzama

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32 Optimizing the Yield of Biodiesel Made from Waste Soybean Oil by Varying the Temperatures and Volumetric Ratios of Oil and Methanol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mandar Chikhalikar, Srinivasa Rao Markapuram, Rushikesh Kamble, Bhupen Tiwari, and Kavita Gidwani

403

33 Smart Agricultural Robot with Real-Time Data Analysis Using IBM Watson Cloud Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prathyusha Thatipelli and R. Sujatha

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34 SPWM Control Scheme for CHB-MLI with Minimal Voltage THD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Harish Karneddi, Kavali Janardhan, Aditya Sirsa, Amit Ojha, Sanjeev Singh, and Arvind Mittal

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35 IoT Communication Technologies for Smart Farming—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sujatha Rajkumar, Karnan Rajendran, and Sailesh Suresh

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36 Recurrent Neural Network Analysis for Accurate Extrapolation of the Wind Velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atul Gautam, Vilas Warudkar, and J. L. Bhagoria

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37 Roof Top Agriculture with Rainwater Harvesting and Smart Irrigation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akkenaguntla Karthik, A. V. Pavan Kumar, T. M. Manohar Reddy, Anumula Amarnath, and Banka Sai Reddy

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38 A Delay-Sensitive Cyber-Physical System Framework for Smart Health Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rupinder Kaur, Prabh Deep Singh, Rajbir Kaur, and Kiran Deep Singh

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39 Analyze and Identify Smart City Applications and Their Existing Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prabh Deep Singh and Rajbir Kaur

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40 Prevention of Intrusion Attacks via Deep Learning Algorithm in Wireless Sensor Network in Smart Cities . . . . . . . . . . Deepak Choudhary and Roop Pahuja

501

41 Torque Ripple Reduction of a Solar PV-Based Brushless DC Motor Using Sliding Mode Control and H7 Topology . . . . . . . . D. V. N. Ananth and D. A. Tatajee

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42 Density-Based Smart Traffic Light Control System for Emergency Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. B. Shylashree, Monika Divakar, Neha R. Navada, and A. N. Nagashree

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Contents

xiii

43 Development of an Assessment Tool to Review Communication Technologies for Smart Grid in India . . . . . . . . . . . Jignesh Bhatt, Omkar Jani, and V. S. K. V. Harish

563

44 Simulation and Analysis of Building Integrated Photovoltaic System for Different Climate Zones in India . . . . . . . . . . . . . . . . . . . . Priyanka Rai, Archana Soni, and Rushikesh Kamble

577

45 CFD Analysis of Temperature Profile and Pattern Factor at the Exit of Swirl Dump Combustor . . . . . . . . . . . . . . . . . . . . . . . . . . Yogesh Bhawarker, Prakash Katdare, Manish Kale, Hitesh Kumar, Shri Krishna Mishra, and Rahul Kumar 46 Determining the Performance Characteristics of a White-Box Building Energy System Model and Evaluating the Energy Consumption . . . . . . . . . . . . . . . . . . . . . . . V. S. K. V. Harish, Amit Vilas Sant, and Arun Kumar 47 Battery Management System with Wireless Parameter Estimation in EV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Vishnu, Amit Ojha, and R. K. Nema 48 A Novel Cascaded ‘H’ Bridge-Based Multilevel Inverter with Reduced Losses and Minimum THD . . . . . . . . . . . . . . . . . . . . . . Madhusudhan Pamujula, Amit Ohja, Pankaj Swarnkar, R. D. Kulkarni, and Arvind Mittal

589

605

617

627

49 Assessing Factors Influencing Supply Chain 4.0: A Case of Smart City Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hritika Sharma, Saket Shanker, and Akhilesh Barve

641

50 Electrical Equivalent Model for Proton Exchange Membrane Fuel Cell Useful in On-Board Applications . . . . . . . . . . . Sujit Sopan Barhate and Rohini Mudhalwadkar

649

51 Predicting Waste to Energy Potential and Estimating Number of Transfer Station Based on Indore Waste Management Model: A Case of Indian Smart Cities . . . . . . . . . . . . . Ankit Tiwari and Pritee Sharma

663

52 Analysis of Thermal Energy Storage Mediums for Solar Thermal Energy Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shivansh Aggarwal, Rahul Khatri, and Shlok Goswami

679

53 Application of Concrete Filled Steel Tubes in Solar Module Mounting Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jitendra Pratap Singh and Ajay Kumar

693

xiv

Contents

54 Reduction of Over Current and Over Voltage Under Fault Condition Using an Active SFCL with DG Units . . . . . . . . . . . . . . . . G. Sasi Kumar, G. Radhika, and D. Ravi Kumar

707

55 Mathematical Modeling of Air Heating Solar Collectors with Fuzzy Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Purnima Pandit, Prani R. Mistry, and Payal P. Singh

721

56 Performance of Machine Learning Approaches for Malicious Traffic Intrusion Detection in Network . . . . . . . . . . . . Madhavi Dhingra, S. C. Jain, and Rakesh Singh Jadon

737

57 Applications of Synchrophasors Technology in Smart Grid . . . . . . . Marwan Ahmed Abdullah Sufyan, Mohd Zuhaib, and Mohd Rihan

745

58 Numerical Analysis of Performance Parameters and Exhaust Gas Emission of the Engine with Regular Air Intake System and with Insulated Air Intake System . . . . . . . . . . . . Sanjay Chhalotre, Prem Kumar Chaurasiya, Upendra Rajak, Rashmi Dwivedi, R. V. Choudri, and Prashant Baredar

759

59 Investigation of AI Based MC-UPFC for Real Power Flow Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Boopalan, V. Saravanan, and T. A. Raghavendiran

777

60 Sizing and Performance Investigation of Grid-Connected Solar Photovoltaic System: A Case Study of MANIT Bhopal . . . . . Arvind Mittal, Radhey Shyam, and Kavali Janardhan

793

61 Comparative Study and Trend Analysis of Regional Climate Models and Reanalysis Wind Speeds at Rameshwaram . . . . . . . . . . B. Abhinaya Srinivas, Garlapati Nagababu, Hardik Jani, and Surendra Singh Kachhwaha

805

62 A Novel Islanding Detection Technique for Grid-Connected Distributed Generation Using KNN and SVM . . . . . . . . . . . . . . . . . . . Poonam P. Tikar, Ravishankar S. Kankale, and Sudhir R. Paraskar

819

63 A 150 kW Grid-Connected Roof Top Solar Energy System—Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Achala Khandelwal and Pragya Nema

833

64 Fuzzy SVM Classifier for Clothes Pattern Recognition . . . . . . . . . . . Abhishek Choubey, Shruti Bhargava Choubey, and C. S. N. Koushik 65 A Detailed Analysis of Municipal Solid Waste Generation and Composition for Haridwar City, Uttrakhand, India . . . . . . . . . . Kapil Dev Sharma and Siddharth Jain

843

855

Contents

xv

66 Techno-Economic Analysis of Piezoelectric-Based Smart Railway Tracks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manvi Mishra, Priya Mahajan, and Rachana Garg

869

67 JDMaN: Just Defeat Misery at Nagging—A Smart Application for Women Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Jayanthi, N. M. Deepika, G. Nishwitha, and K. Mayuri

887

68 Control of PM Synchronous Motor with Hybrid Speed Controller with Gain Scheduling for Electric Propulsion . . . . . . . . . Amit V. Sant and V. S. K. V. Harish

899

69 Study on Effect of Draft Tube Diffuser Shape on Performance of Francis Turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lakshman Suravarapu and Ruchi Khare

913

70 Dehydration of Vegetables Through Waste Heat of Vapour Compression Refrigeration System . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ankur Nagori, Rubina Chaudhary, and S. P. Singh

921

71 Peak Power Impact from Electric Vehicle Charging . . . . . . . . . . . . . Chandana Sasidharan and V. S. K. V. Harish 72 Integration of Multiple Energy Sources for Hybrid Smart Street Light System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anurag Choubey and Hitesh Kumar 73 Improving Cold Flow Properties of Biodiesels Using Binary Biodiesel Blends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Krishna Kant Mishra, Mukesh Kumar, Ravikant Ravi, Amol Saini, Kunal Salwan, and Mahendra Pal Sharma 74 Dual-Axis Solar Tracking System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rahul Shaw, Swarup Kumar Das, and Sajjan Kumar 75 CFD Analysis of Air Distribution for Suitable Position of Evaporator in Cold Chamber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sushil Kumar Maurya, Rahul kumar, Shri Krishna Mishra, Himanshu Vasnani, and Hitesh Kumar 76 Role of Supercapacitor for Increasing Driving Range of Electric Vehicles Under Indian Climatic Conditions . . . . . . . . . . . Vima Mali and Brijesh Tripathi

931

941

951

963

973

987

77 Noise Vulnerability Assessment for Kota City . . . . . . . . . . . . . . . . . . . 1001 Kuldeep, Sohil Sisodiya, and Anil K. Mathur 78 Application of Global Sensitivity Analysis to Building Performance Simulations for Screening Influential Input Parameters in a Humid Coastal Climate . . . . . . . . . . . . . . . . . . . . . . . . 1011 Souryadeep Basak and Aviruch Bhatia

xvi

Contents

79 Two Decades of Urban Growth in Kota City: The Urban Heat Island Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025 Payal Panwar, Sohil Sisodiya, and Anil K. Mathur 80 Anomaly Detection Systems Using IP Flows: A Review . . . . . . . . . . 1035 Rashmi Bhatia, Rohini Sharma, and Ajay Guleria 81 Performance Analysis of 250 kWP Roof Top Grid-Connected Solar PV System Installed at MANIT Bhopal . . . . . . . . . . . . . . . . . . . 1051 Arvind Mittal, Radhey Shyam, and Kavali Janardhan 82 An Ensemble Model of Machine Learning for Primary Tumor Prognosis and Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1059 Tejinderdeep Singh, Prabh deep Singh, and Rajbir Kaur 83 Implementing Fog Computing for Detecting Primary Tumors Using Hybrid Approach of Data Mining . . . . . . . . . . . . . . . . 1067 Jasdeep Singh, Sandeep Kad, and Prabh Deep Singh 84 Analysis on Filter Circuits for Enhanced Transient Response of Buck Converters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1081 Karthik Ramireddy, J. V. A. R. Sumanth, T. R. S. Praneeth, and Y. V. Pavan Kumar 85 The Cause and Control of Failure of Hydraulic Turbine Due to Cavitation: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1099 Md. Mustafa Kamal, Ali Abbas, Ravi Kumar, and Vishnu Prasad 86 Classification and Synthesis of Nanoparticles: A Review . . . . . . . . . 1113 Anna Raj Singh, M. Maniraj, and Siddharth Jain 87 Marble and Granite Slurry Reuses in Industries . . . . . . . . . . . . . . . . 1127 S. S. Godara, Mohit Kudal, Tikendra Nath Verma, Gaurav Dwivedi, and Shrey Verma 88 Experimental Investigation on Thermal Performance of Solar Air Collector Provided with Corrugated Absorber . . . . . . 1137 Suman Debnath, Mukesh Kumar, Vikas Kumar, Amol Saini, Kunal Salwan, and Ravikant Ravi 89 Noise Vulnerability Assessment at 78 dB (A) for Kota City . . . . . . . 1147 Kuldeep, Sohil Sisodiya, and Anil K. Mathur Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1161

About the Editors

Dr. Prashant V. Baredar besides being the Chairman and Professor at the Department of Energy, Energy Centre MANIT, Bhopal, is also an educator, innovator, researcher, and author. He did his Ph.D. in Thermal Engineering, Integrated Energy System. He has successfully led 8 projects which involves sensitivity analysis and optimization of hybrid system of solar, wind and biomass’ and evaluated downdraft gasifier in RGPV Campus using different biomass briquettes for electricity generation for Madhya Pradesh Council of Science and Technology (MPCST). He carried out electricity generation project entirely based on captivating wind power for Ministry of New and Renewable Energy (MNRE). His constant and unfailing devotion earned him best faculty award in 2010 by honorable Chairman BOG and Best Researcher award in 2017 by CRS Education Expo NCR Noida. He has also published over 100 research papers in reputed international and national journals and conferences; authored five books and three books chapter on mechanical engineering, biodiesel and renewable energy resources. He also has two patents to his credit. He has also guided 43 M.Tech. Theses and eight Ph.D. theses so far. He has done several sponsored and consultancy projects in energy sector for Madhya Pradesh Urja Vikas Nigam, NHDC and Municipal Corporation. Dr. Srinivas Tangellapalli besides being an Associate Professor at Department of Mechanical Engineering in Dr. B. R. Ambedkar NIT Jalandhar, is also an educator, researcher and author. He received his Ph.D. from JNT University College of Engineering, Hyderabad, in 2008 and was a Postdoctoral fellow at University of Ontario Institute of Technology (UOIT), Canada, in 2010 and 2013. He has completed various research projects funded by CSIR, SERB and NSERC. He has four patents and one copyright to his credit. He has authored three books, ten book chapters, 100 journal publications, and 85 conference proceedings. His current areas of research are solar poly-generation, combined power and cooling systems, Kalina power systems, combined cycle power generation and solar-biomass hybrid systems. He has given 60 invited lectures and has supervised five Ph.D. students, 29 M.Tech. thesis and 35 UGC projects. He also has an industrial experience of 3 years and 9 months in a machine tool company. xvii

xviii

About the Editors

Dr. Chetan Singh Solanki besides being a Professor at the Department of Energy Science and Engineering, IIT Bombay, is also an educator, innovator, researcher, entrepreneur, author and philosopher. He received his Ph.D. from IMEC (Ketholik University) Leuven, Belgium, a leading R&D and innovation hub in micro and nanoelectronics. Dr. Solanki has done remarkable work in the solar sector and being one of the Principal Investigators at The National Center for Photovoltaic Research and Education (NCPRE) is currently leading two projects of national importance on the dissemination of affordable solar technology. This project aims to provide R&D and education support for India’s ambitious 100 GW solar mission. He also fathers the Solar Urja through Localization for Sustainability (SoULS) project at IIT Bombay, which aims to provide solar study lamp to every child in rural India as part of its ‘Right to Light’ mission. He has also won the European Material Research Society’s young scientist award in 2003 and IIT Bombay’s Young Investigator Award in 2009. He has published over 100 research papers in reputed international journals, authored 4 books on solar and renewable energy and received first prize from the Ministry of New and Renewable Energy (MNRE), Govt. of India in 2011 for his book, ‘Renewable Energy Technologies—A Practical Guide for Beginners’ (Hindi). He also has four US patents to his credit. Being an active member of several national committees related to Solar Technology he still finds time to practice yoga, breathing exercises and meditation for physical and mental well-being and promotes “being happy under all situations”.

Chapter 1

Experimental Investigation of Domestic Refrigerator Used as an Air Conditioner by Augmentation Method Pankaj P. Gohil and Altafhusen Saiyed

Abstract Human comfort demand is raised day to day due to climate change, global warming and other environmental issues. Conventional air conditioners provide the human comfort; however, other side it consumes high electricity. So, main objective of the study is to reduce the power consumption. In this study, the domestic refrigerator compressor is interconnected with separate indoor unit of air conditioner. In addition, also one more capillary tube is augmented with the existing refrigeration unit with air conditioner indoor unit. An attempt has been made to investigate the performance parameters for both cases with four different coiling cabinet volumes of 44.4, 3.41, 2.25 and 1.5 m3 . The results showed that more refrigeration effect is achieved with augmented one capillary tube in second case condition and the temperature and relative humidity have obtained as 23 °C and 50%, respectively. The power consumption compared to same capacity of existing air conditioner is obtained with 1/3rd times of power for small cooling cabinet area. Keywords Vapor compression refrigeration cycle · Air conditioner · Refrigerator · Augmentation method · Power consumption · Refrigeration effect

Nomenclatures TR heo hei hco hci

Tons of refrigeration Enthalpy at evaporator outlet in kJ/kg Enthalpy at evaporator inlet in kJ/kg Enthalpy at compressor outlet in kJ/kg Enthalpy at compressor inlet in kJ/kg

P. P. Gohil (B) Mechanical Engineering Department, Sarvajanik College of Engineering and Technology, Surat, India e-mail: [email protected] A. Saiyed Mechanical Engineering Department, Vidhyadeep Institute of Engineering and Technology, Surat, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_1

1

2

P. P. Gohil and A. Saiyed

1.1 Introduction Human comfort demand is raised day to day due to the global warming, climate change and others. Main factors affecting to the human comfort areas; air temperature, humidity, circulation and hygiene. These requirements of human can be fulfilled by air conditioner [1]. Power consumption and environmental imbalance are main critical issues in the field of air conditioner. Total electricity produced all over the world is consumed for the refrigeration and air conditioning purpose of about 30% [2]. Normally, the power consumption of 1 tons of refrigeration (TR) of split air conditioner is around 1 kWh; it shows that every hour one unit of electricity is consumed. And other side, domestic refrigerator units are typically characterized by low cooling capacities (50–250 W) and low quantities of charge (20–200 g) considered. A conventional domestic refrigerator runs on vapor compression refrigeration (VCR) cycle. It comprises main four components viz. compressor, condenser, capillary tube and evaporator, and accessory part is used as dryer. The similar components are also used in a domestic air conditioner system. In addition, accumulator is provided in the air conditioner for better performance. Rather than, there is vast difference in design of cooling load, capacity and size of component and also in types of refrigerant. The geometrical parameters of the components of refrigerator system are also considered other main factors to affect the performance value. The schematic diagram of vapor compression refrigeration cycle is illustrated in Fig. 1.1, which commonly used in both domestic refrigerator and air conditioner. In the past, many of researchers were carried out the studies on the subjects of optimization, effectiveness and performance of the individual refrigerator and air conditioner systems. Moreover, the researchers also made efforts to run the air

Fig. 1.1 Schematic diagram of VCR cycle [2]

1 Experimental Investigation of Domestic Refrigerator Used …

3

conditioner by using different source of energy, i.e., energy available in exhaust gases [3, 4]. Among few researchers worked on the combined system, these studies are mostly on conservation and utilization of cooling and heating of the system. Himanshu et al. attempted the feasibility study of refrigerator cum air conditioner and conserved the excessive cooling of defrosting unit of refrigerator area and utilized it in conditioning of air, where water is circulated and this circulation pipe takes place in the freezer tray [5]. In addition, an experimental study was performed by Kongre et al. [1] air conditioner cum water dispenser and investigated the performance of the water and air/water cycle running on the system. It was found that room temperature decreased from 28 to 20 °C, and hot water temperature increased from 28 to 58 °C. Moreover, cold water temperature decreased from 30 to 5 °C which maintains the COP in between 4 and 5. In the same line of work, the heat exchanger is used in between compressor and condenser [6]. The result showed that water enters as water refrigerant heat exchanger, and it was heated up to 50 to 55 °C within 2 min and leads to save energy. The study of intercooler is also used in between refrigerator and air conditioner was investigated by Doyong and Jeong [7]. The adoption of diffuse and sub cooling techniques leads to increase in the COP of the system [8]. In this study, analytical method was attempted, and the results showed that improvement in the COP of the system obtained by 27.58%. Shashank et al. [9] discussed the different parameters, such as capillary tube length, bore diameter, coil pitch, number of twist and twist angle. It was found that pitch and diameter did not create any effect in case of helical coiled capillary tube. While, in case of serpentine coiled tube, pitch and diameter affected performance of the system. One relevant study was also carried out with different tube parameters by Nishant et al. [10] and showed there is a possibility to improve COP of the system. The ellipse shaped vapor coil was also used to increase the heat transfer rate [11]. This result showed that there was a minor decrease in system pressure, and COP was also improved. Researchers have been investigated the capillary tube at specific condition of adiabatic [12–14]. Al-Rashed [15] carried out the study on the investigation of performance by different refrigerants. Various other parameters, such as expansion device capacity, quantity of charge and ambient temperature were also examined [16]. Basha [17] studied the optimum condenser length for 165 L domestic refrigerator for different size condenser by experimental method. The result discussed that the COP found was 8.298 and 8.890 for condenser lengths of 6.1 m and 7.01 m, respectively. The maximum COP obtained was 8.51 at condenser length of 7.01 m. Researches carried out the work mainly on to optimize the units of refrigerator and air conditioner which would overcome the problem of electricity consumption as well as environmental balance. It is toward the reduction in power consumption of the system [2]. Based on the reports shown that in actual working of refrigerator, compressor shuts off as the desired temperature is obtained by the system. The compressor of the refrigerator in the night period almost remains in non-working condition, and air conditioner is mostly used in the same time period. It is, therefore, the compressor can be utilized for the air conditioning purpose, and power consumption could be minimized.

4

P. P. Gohil and A. Saiyed

The paper presents an attempt to investigate the power consumption of an air conditioner by using the compressor of refrigerator and analyze the power consumption of multifunctional systems.

1.2 Experimental Methodology In order to investigate the performance parameter of an air conditioner running on a compressor of refrigerator with necessary modifications, the experimentation has been carried out. The experiments were conducted under two different cases: (i) Case-I: Existing refrigerator with indoor unit of air conditioner, and (ii) Case-II: Augmenting capillary unit with case-I. In order to perform the investigation of performance parameters of the multifunctional systems, a refrigerator of 165 L capacity made by Voltas and air conditioner unit having capacity of 0.8 TR are selected. The compressor used is low capacity hermetically sealed refrigerator compressor. The detail specification of both units is provided in Table 1.1. The mathematical expressions are used in calculation of performance parameters for both cases, and the detail of same is provided as [18]. Mass flow rate = 210/RE

(1.1)

Refrigeration effect (RE) = Enthalpy difference of evaporator inlet and outlet, h eo − h ei

(1.2)

Compressor work (Wc) = Enthalpy difference of compressor suction and discharge hceo − h ci (1.3) Table 1.1 Detail of considered refrigerator and indoor unit of air conditioner for the study

Refrigerator Volume

165 L

Compressor input current

1.2 A

Compressor input voltage

230 V

Length of evaporator

381 mm

Length of condenser

660 mm

Indoor unit of air conditioner Capacity

0.8 TR

Length of evaporator

1270 mm

Blower motor rating

75 W

Air flow volume

16.5 m3 /h

1 Experimental Investigation of Domestic Refrigerator Used …

5

1.2.1 Experimental Setup for Case-1 First, the performance test has been carried out on a single compartment refrigerator of 165 L volume with refrigerant R-12. And an indoor unit of air conditioner of 0.8 TR capacity is combined with the same. The concept of use of only indoor unit of air conditioner as available and the schematic diagram of case-1 is shown in Fig. 1.2. The experimental setup was prepared in the laboratory, and the same picture is illustrated in Fig. 1.3. It is clearly seen from Figs. 1.2 and 1.3, the experimental setup in which evaporator of air conditioner is connected parallel with refrigerator’s evaporator by using only one capillary of refrigerator having 2743 mm length and 0.635 mm bore. Among four valves, two valves are provided at the ends of both evaporators 1 and 2 to separate systems for individual running. A cycle with capillary tube and evaporator-1 is for the refrigeration purpose and capillary and evaporator-2 is for air conditioning. If the system works as an air conditioner, the valves of evaporator-1 remain closed, and when it works as the refrigerator, the valves of evaporator-2 will remain closed. After the completion of fabrication of system, it is evacuated and filled with refrigerant R-134a and observed the performance readings of pressure, temperature and air flow from constructed setup with four different considered cooling cabinet volumes of 44.4, 3.41, 2.25 and 1.15 m3 . In order to implement the augmentation method with the case-1 system, one capillary tube is added with the same system and observed all same readings are considered as case-2.

Fig. 1.2 Schematic diagram of system for case-1

6

P. P. Gohil and A. Saiyed

Evaporator unit of Spilt air-conditioner

Condenser

Capillary tube Pressure gauge

Compressor

Fig. 1.3 Picture of complete experimental setup of case-1

1.2.2 Experimental Setup for Case-2 The schematic diagram of the case-2 is illustrated in Fig. 1.4. In this cycle, there are single compressor, single condenser, two capillary tubes and two evaporators. Additional capillary tube having 914 mm long and 1.397 mm bore is provided with existing capillary-1 of refrigerator. It also contains four valves at the inlet of capillary tube and at the inlet and outlet of both evaporators 1 and 2. Moreover, the performance readings of pressure, temperature and air flow from constructed setup with four different control volumes of 44.4, 3.41, 2.25 and 1.5 m3 have been observed. The photograph of capillary tube is used for case-2 is shown in Fig. 1.5. The performance analysis and calculations were performed to investigate all the parameters of performance for the both case I and II conditions.

1.3 Result and Discussion As a first step, the procedure of observation of reading was initiated with measurement of power consumption and other parameters of existing considered refrigerator of

1 Experimental Investigation of Domestic Refrigerator Used …

7

Fig. 1.4 Schematic diagram of system for case-2

Fig. 1.5 Picture of complete experimental setup of Case-2

capacity 165 L. It has found that compressor requires 1.2 A and 230 V power. The primary readings were taken before the starting of experiment for two cases 1 and 2. In first case, by considering the average load for two persons as 76.2 W and in volume of system is 44.4 m3 , an experiment was performed. In this case, two types of readings were taken, one is when the blower is OFF and other is with blower ON, and the readings were recorded after running of blower for 10 min. The summary of results case-1 is shown in Table 1.2.

8

P. P. Gohil and A. Saiyed

Table 1.2 Summary results for case-1 S. No.

Parameters

Blower off

Blower on

1

Compressor suction pressure, P1

207 kPa

341 kPa

2

Compressor discharge pressure, P2

2034 kPa

2206 kPa

3

Evaporator inlet pressure, P3

221 kPa

248 kPa

4

Atmosphere temperature, T a

32 °C

32 °C

5

Condenser inlet temperature, T 1

87.5 °C

91.6 °C

6

Condenser outlet temperature, T 2

63.9 °C

67.5 °C

7

Refrigerator evaporator inlet temperature, T 3

6.6 °C

9.8 °C

8

Refrigerator evaporator outlet temperature, T 4

15.5 °C

10.3 °C

9

Air conditioner evaporator inlet temperature, T 5

6.8 °C

8.8 °C

10

Air conditioner evaporator outlet temperature, T 6

20.6 °C

35.5 °C

Based on the results measured, the obtained value of temperature at inlet of evaporator of air conditioner is 6.8 and 8.8 °C, when the blower is OFF and ON, respectively. Finally, it is concluded that the power consumption of compressor with case-I condition is current of 1.35 A and voltage of 230 V, i.e., 310.5 W h. It is slightly higher than the existing refrigerator consumption of 276 W h; however, it is 1/3rd times lesser compared to 0.8 TR air conditioner consumption. The same procedure was adopted, and the readings were recorded for the case-2 condition. The summary of the results is provided in Table 1.3. It is apparent from Tables 1.2 and 1.3 that suction and discharge of compressor as well as evaporator pressures were significantly noticed lower in case-2 than that of case-1 in the same atmospheric temperature. Addition of capillary tube also reduced temperature of condenser and temperature of both evaporators on both ends. The power consumption is found to be with case-2 condition is 315 W h. It is Table 1.3 Summary result for case-2 S. No.

Parameters

Blower off

Blower on

1

Compressor suction pressure, P1

124 kPa

172 kPa

2

Compressor discharge pressure, P2

1703 kPa

1930 kPa

3

Evaporator inlet pressure, P3

138 kPa

193 kPa

4

Atmosphere temperature, T a

32 °C

32 °C

5

Condenser inlet temperature, T 1

73.9 °C

82.1 °C

6

Condenser outlet temperature, T 2

59.4 °C

64.1 °C

7

Refrigerator evaporator inlet temperature, T 3

1.5 °C

4.6 °C

8

Refrigerator evaporator outlet temperature, T 4

16.1 °C

30.4 °C

9

Air conditioner evaporator inlet temperature, T 5

2 °C

3.5 °C

10

Air conditioner evaporator outlet temperature, T 6

22 °C

34 °C

1 Experimental Investigation of Domestic Refrigerator Used …

9 145 125

221

25

152

Suction Pressure(kPa)

20

Compressor work(kJ/kg)

Case:1 Single Capillary

Refrigeration effect(kJ/kg)

Case:2 Double Capillary

Fig. 1.6 Comparison between Case-1 and Case-2 for different parameters

minimum difference in value with case-1 condition. It leads to more refrigeration effects achieved with case-2 with the same power utilization. Moreover, the similar sets of readings were also measured for the other cooling volumes as 3.41, 2.25 and 1.5 m3 . Figure 1.6 illustrates the comparison of results of suction pressure, theoretical compressor work and refrigeration effect for case-1 and case-2 condition. It is clearly shown in Fig. 1.6 that the suction pressure and theoretical compressor work are found to be less for case-2 compared with case1. To achieve the same temperature across the evaporator, compressor consumes comparatively less work as additional capillary tube is provided. The refrigeration effect is found more to be 145 kJ/kg for case-2 compared to 125 kJ/kg of case-1. It may be due to augmented additional specific length of capillary tube with the case-1, and it leads to decrease the temperature. The percentage of refrigeration effect is achieved by 16% more in case-2. In addition, the same parameters are observed with different considered cooling cabinet volumes to find the suitable area for optimum comfort condition. The result is shown in Fig. 1.7. It is clearly shown in figure that the refrigeration effect increases with cooling cabinet decrease for both cases. It can clearly be seen from above reading that case-2 is more suitable condition for running the combined system. Considering the result analysis, the nine sets of reading were recorded after every 10 min with case-2 conditions for investigating the performance in long time period, and the recorded results are illustrated in Table 1.4. It is seen from Table 6 last set of reading shows the maximum temperature difference of 8 °C between atmosphere and cabinet area as well as pressure difference of 2758 kPa. The system runs with 23 °C with air flow of 1.2 m/s, and 50% relative humidity was obtained without affecting the performance of actual refrigeration system.

10

P. P. Gohil and A. Saiyed 140 120

241

145

125

245 186

176

25

Suction pressure(kPa)

44.4 m^3

Refrigeration effect(kJ/kg)

3.41 m^3

2.25 m^3

25

20

20

Compressor work(kJ/kg)

1.5 m^3

Fig. 1.7 Parameter variation for different volumes

Table 1.4 Results of Case-2 Set of readings

Compressor inlet pressure (kPa)

Compressor discharge pressure (kPa)

Atmospheric temperature (°C)

Cabinet temperature (°C)

1

241

2206

32

28

2

172

1620

34

32

3

172

1930

33

30

4

186

1862

34

33

5

290

2537

32

30

6

241

2275

32

28

7

207

1496

31

27

8

348

3103

34

23

1.4 Conclusion An attempt has been made to investigate the feasibility for running the air conditioning evaporator unit by existing refrigerator system and concluded based on the experimental results that, a human comfort can be achieved by running a same cycle of refrigerator requires 230 V, with 1.2 A current requirement. The following findings are drawn from the investigation: (i)

The system provides the temperature of 23 °C, air flow of 1.2 m/s and 50% of relative humidity without affecting the performance of actual refrigeration system.

1 Experimental Investigation of Domestic Refrigerator Used …

(ii)

(iii)

11

The power consumption can be reduced to 1/3rd times of existing same capacity of air conditioner. This study may be more viable for small confined area. However, there is a problem of compressor tripping. It could be eliminated by designing the proper components are to be proposed in future work.

References 1. U.V. Kongre, A.R. Chiddarwar, P.C. Dhumatkar, A.B. Aris, Testing and performance analysis on air conditioner cum water dispenser. Int. J. Eng. Trends Technol. (IJETT) 4(4) (2013) 2. A.S. Dhunde, K.N. Wagh, P.V. Washimkar, An effective combined cooling with power reduction for refrigeration cum air conditioner, air cooler and water cooler: a review. Int. J. Eng. Res. General Sci. 4(2), Mar-Apr 2016. ISSN 2091-2730 (2016) 3. Iyer, R., Gohil, P., Nagarsheth, H., Channiwala, S., Development of a vapor compression air conditioning system utilizing the waste heat potential of exhaust gases in automobiles. SAE Technical Paper 2005-01-3475 (2005). https://doi.org/10.4271/2005-01-3475. 4. P. Gohil, S.A. Channiwala, Feasibility of design and operation of exhaust driven turbine to run an air conditioning system. Adv. Mech. Eng. (AIME), New Delhi, pp. 888–893 (2006) 5. Himanshu, Upadhyay, K., Sehgal N., Jaraut, M., Gautam, P., Kalra, S., Gupta, S.K.: Feasibility study and development of refrigerator cum air conditioner. Int. J. Sci. Res. Publ. 4(12) (2014). ISSN 2250-3153 (2014) 6. Liu, F., Huang, H., Yingjiang, M., Zhuang, R.: Research on the air conditioning water heater system. In: International Refrigeration and Air Conditioning Conference, paper 893 (2008) 7. H. Doyong, J.H. Jeong, Performance characteristics of a combined air-conditioner and refrigerator system interconnected via an intercooler. Int. J. Refrig. 49, 57–68 (2015) 8. Upadhyay, N.: Analytical study of vapour compression refrigeration system using diffuser and sub cooling. IOSR J. Mech. Civil Eng. (IOSR JMCE). e-ISSN: 2278-1684, p-ISSN: 2320– 334X, vol. 11, Issue 3 Ver. VII (May–June 2014), pp. 92–97 (2014) 9. Shashank, S., Chauhan, P. S.: Effect of capillary tube on the performance of a simple vapour compression refrigeration system. IOSR Journal of Mechanical and Civil Engineering (IOSRJMCE) e-ISSN: 2278–1684, 11(3), pp. 05–07 (2014) 10. Tekade, N.P., Wankhede, U.S..: Selection of spiral capillary tube for refrigeration appliances. Int. J. Modern Eng. Res. (IJMER) 2(3), 1430–1434 (2012). 11. Chandramouli, J., Sreedhar, C., Subbareddy, E.V.: Design, fabrication and experimental analysis of vapour compression refrigeration system with ellipse shaped evaporator coil. Int. J. Innov. Res. Sci. Eng. Technol. 4(8) (2015) 12. P.K. Bansal, A.S. Rupasinghe, An empirical model for sizing capillary tubes. Int. J. Refrig. 19, 497–505 (1996) 13. O. Garcia-Valladares, Review of numerical simulation of a capillary tube using refrigerant mixtures. Appl. Therm. Eng. 24, 949–966 (2004) 14. C. Zhang, G. Ding, Approximate analytic solutions of adiabatic capillary tube. Int. J. Refrig. 27, 17–24 (2004) 15. A.A.A.A. Al-Rashed, Effect of evaporator temperature on vapor compression refrigeration system. Alexandria Eng. J. 50, 283–290 (2011) 16. E. Bjork, P. Bjorn, Performance of a domestic refrigerator under influence of varied expansion device capacity, refrigerant charge and ambient temperature. Int. J. Refrig. 29, 789–798 (2006) 17. Basha, T. M.: Optimum length of a condenser for domestic vapor compression refrigeration system. Int. J. Sci. Eng. Technol. Res. (IJSETR) 4(2) (2015) 18. ASHRAE Handbook Fundamental (1997)

Chapter 2

Stabilizing Molten Salts Through Additives for High Temperature CSP Applications Siddhesh C. Pawar, Varun Shrotri, and Luckman Muhmood

Abstract Solar salt, an equimolar concentration of KNO3 and NaNO3 is currently being used as the heat transfer fluid in concentrating solar power plants. It decomposes around 600 °C. Hitec® salt, another potential salt mixture, is not stable beyond 538 °C. There has been work directed toward improving its high temperature stability of Hitec® salt by adding a chloride component. It was observed that the stability of the mixture increased by 50 °C enabling its use at temperatures above 600 °C. A new ternary mixture comprising of Ca(NO3 )2 –KNO3 –NaNO3 termed as “Base salt” was prepared in the lab which has a significantly lower freezing temperature (145 °C), and the high temperature stability is above 600 °C. In the current work, additions of other components like NaCl, KCl, LiCl, and CaCl2 in various proportions were done to the Base salt and the thermogravimetric studies were carried out in a custom-made TGA set up to analyze any further improvement in high temperature stability of the mixture. Among all the chloride additions, 5% sodium chloride (NaCl) and 5% NaCl + 5% KCl addition by weight to the Base salt proved to be most promising. Keywords CSP · Molten nitrate salt · Thermal stability

2.1 Introduction Solar energy is one of the best alternatives to produce electric energy in more sustainable manner. Solar radiation can be directly converted into electricity by means of solar-photovoltaic cells. The indirect conversion of solar energy into electrical energy is achieved by concentrated solar power plants (CSP’s). In this technology, the solar S. C. Pawar (B) · V. Shrotri · L. Muhmood Department of Mechanical Engineering, K.J. Somaiya College of Engineering, Vidyavihar, Mumbai 400077, India e-mail: [email protected] V. Shrotri e-mail: [email protected] L. Muhmood e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_2

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beam radiation is concentrated using parabolic/flat reflectors on a receiver which carries the heat transfer fluid (HTF). Such high temperature heat transfer fluid is further used to drive a conventional Rankine cycle to produce electricity. In suitable geographical locations, CSP’s along with high energy density thermal energy storage (TES) facility can produce electricity during non-sunshine hours (approx. 15 h). Thus, increasing efficiency and reliability on the renewable source of energy [1]2. The current generation of parabolic trough power plants uses organic oils (mixture of diphenyl oxide and biphenyl oxide) as heat transfer fluids (HTF). The freezing point and upper temperature limit of this oil are 12 °C and 393 °C, respectively [3]. These plants cannot operate above the upper temperature limit of heat transfer fluid (HTF), and hence, the live steam temperature in such systems remains close to 370 °C reducing the gross efficiency of the Rankine cycle. The reduced limit on cycle efficiency increases the levelized cost of electricity (LCOE) [3]. Inorganic salts have the potential to be used above 400 °C due to their high temperature stability and are increasingly replacing the organic oils in this application. The nitrate salt mixtures make excellent HTFs due to their lower viscosity, high volumetric heat capacity, compatibility with the environment, high operating and decomposition temperature, and non-toxic nature [4]. Previous studies have found that levelized cost of electricity (LCOE) reductions of up to 16.4% can be expected when substituting thermal oil with molten salt as HTF [5]. However, high freezing point is a disadvantage as an alternate arrangement for thawing needs to be done to prevent the solidification of salt in the receiver tubes [6]. These salts are eutectic mixtures of alkali nitrates. The commonly used salt in CSP’s is “Solar salt” which is binary mixture of 40% KNO3 –60% NaNO3 by weight. This salt can be used without considerable decomposition till 600 °C [7]. The freezing temperature is comparatively high around 223 °C [8]. To resolve this issue, a new salt called Hitec®, a ternary mixture of 53% KNO3 –40% NaNO2 –7% NaNO3 have been devised by the Reg. U.S. Patent—Coastal Chemical Company, which has significantly lower melting point around 142 °C [9] and high temperature working limit is around 538 °C [10]. Experiment have been conducted [11] to increase the high temperature stability of Hitec® without altering the melting point of 142 °C by adding lithium chloride (LiCl), potassium chloride (KCl), calcium chloride (CaCl2 ), zinc chloride (ZnCl2 ), sodium chloride (NaCl), and magnesium chloride (MgCl2 ). Out of all these additions, potassium chloride (KCl) and lithium chloride (LiCl) were considered for further studies as they did not alter the melting point much. 5% lithium chloride addition was most promising as the endothermic peak shifted to around 79 °C. Hitec starts decomposing rapidly at 610 °C, with 5% addition of KCl, it decomposes around 648 °C, with 5% LiCl it decomposes at 685 °C and with 10% LiCl it decomposes at 666 °C. Hence, the short-term high temperature stability of nitrates is found to increase by chloride additions. The improvement in the high temperature stability of these salt systems is desirable to increase the live steam temperature in the Rankine cycle. Increased live steam temperatures result in increase of the gross efficiency of the cycle which reduces the collector area required per MW of plant rating. The levelized cost of electricity

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(LCOE) reduces increasing the plant reliability. In this context, a new ternary salt mixture comprising of 44% KNO3 –32% Ca(NO3 )2 –24% NaNO3 by weight (Base Salt) was prepared in the metallurgy lab of K J Somaiya College of Engineering, Mumbai. This mixture has a freezing temperature of 145 °C, and the high temperature stability is above 600 °C. The challenge is to increase the decomposition temperature of this salt keeping its melting point comparable to Hitec®. Metal chlorides such as sodium chloride (NaCl), potassium chloride (KCl), lithium chloride (LiCl), and calcium chloride (CaCl2 ) have high melting points and are found to be stable above 700 °C. Hence, the chloride additions to the new Base salt mixture is expected to increase the high temperature stability.

2.2 Experimental Setup and Procedure The Base salt is made from Ca(NO3 )2 , KNO3 , and NaNO3 powders of purity higher than 98% obtained from “AB Enterprises” and “Alpha Chemika,” based in Mumbai. A custom-made thermogravimetric analysis (TGA) setup in K.J Somaiya college of Engineering that was used to study the high temperature stability of the Base salt and Base salt with chloride additions is shown in Fig. 2.1. The experimental setup consists of a precision mass balance (Metler Toledo) which has provision to connect and record data (mass) onto an external computer with 102 readings recorded per minute, a customized vertical tube furnace with temperature range up to 1200 °C, a K-type thermocouple with range up to 1200 °C along with data recorder of Simex, (Multicon CMC-99) to record temperature at regular interval, water circulator to prevent overheating of the steel parts of the vertical tube furnace and crucibles made of SS316L grade [12]. To get the sample ready for TGA, the Base salt sample was heated for 2 h to 200 °C to allow melting and homogeneity to be reached [11]. A total of 1gram of salt (including chloride salt/salts) was then taken in SS316L crucibles. The crucible was attached to the bottom of the precision mass recorder with the help of a thin SS304 grade wire of diameter 1 mm and loaded in the vertical tube furnace. Thermocouple connected to temperature recorder was inserted in the vertical tube furnace to record the actual temperature inside the furnace. A computer connected to the precision mass balance gives continuous output of the instantaneous mass of the crucible containing salt. The vertical tube furnace was initially flushed with argon gas at flow rate of 200 mL/min for 20 min while heating it to around 150 °C to remove air and moisture present in it. The initial mass of the salt was recorded. The salt sample was then lowered into the furnace with help of string as mentioned earlier. The furnace was heated to 800 °C at a heating rate of 10 °C/min [5]. The salt mass and its corresponding temperatures were recorded after every 0.6 s. The corresponding percentage mass loss was found by mathematical calculations and its graph with respect to temperature was plotted to get an idea of high temperature stability of the salt.

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Fig. 2.1 Layout of experimental setup

Five different salt mixtures viz. Base salt (44% KNO3 –32% Ca(NO3 )2 –24% NaNO3 by weight), Base salt + 5% NaCl by weight, Base salt + 5% KCl by weight, Base salt + 5% LiCl by weight and Base salt + 10% CaCl2 by weight were prepared. The TGA for these mixtures was carried out as per the procedure discussed above.

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2.3 Results and Discussion The observations of thermogravimetric analysis of Base salt and Base salt + chloride additions is presented in graphical format in Fig. 2.2. For comparison, the temperature at which 3% of the total mass of the salt sample is lost is considered as the decomposition temperature of the sample [6]. As observed in Fig. 2.2, the decomposition, i.e., temperature at which 3% mass loss of Base salt occurs is around 663 °C. This temperature (663 °C) is considered as a standard reference, and the chloride additions are expected to push decomposition temperature ahead of the considered standard value. By addition of 5% sodium chloride (NaCl) by weight to Base salt, the decomposition is observed at 672 °C and with 5% KCl by weight, it is around 667 °C. The addition of 5% lithium chloride (LiCl) by weight, does not alter the decomposition temperature much, as 3% mass loss is observed at 661 °C. The addition of 10% calcium chloride (CaCl2 ) by weight reduced the decomposition temperature to 600 °C. The observation from these set of experiments are altogether shown in Table 2.1. Hence, out of all the chloride additions, NaCl and KCl showed a little improvement in the high temperature stability of the Base salt. Hence, these were considered for further study. Two new combination of salts were prepared, in one case, 10% NaCl by weight was added to the Base salt and in other case 5% NaCl + 5% KCl by weight was added to Base salt. The TGA process was repeated for these two new mixtures, and the results are shown in the following Fig. 2.3. TGA graphs of "Base salt" and "Base salt + metal Chloride" additions

% Mass remaining

Base salt

base+5%NaCl

Base+5%LiCl

base+10%CaCl2

Base+5%KCl

101 100 99 98 97 96 95 94 93 92 91 90 89 88 87 86 85 100

150

200

250

300

350

400 450 500 Temperature°C

550

600

650

700

750

800

Fig. 2.2 TGA of base salt, base salt + 5% NaCl, Base salt + 5% LiCl, base salt + 5% KCl, base salt + 10% CaCl2

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Table 2.1 Decomposition temperature of base salt compared with other mixtures

Decomposition temperature (3% mass loss)

Base salt

Base salt + 5% NaCl

Base salt + 5% KCl

Base salt + 5% LiCl

Base salt + 10% CaCl2

663 °C

672 °C

667 °C

661 °C

600 °C

TGA graphs of "Base salt" and "Base salt + NaCl /KCl" additions in varoius proportions

% Mass remaining

Base salt

base+10%NaCl

base+5%NaCl+5%KCl

101 100 99 98 97 96 95 94 93 92 91 90 89 88 87 86 85 100

150

200

250

300

350

400

450

500

550

600

650

700

750

800

Temperature°C

Fig. 2.3 TGA of Base salt, Base salt + 10% NaCl, Base salt + 5% KCl + 5% NaCl

From Fig. 2.3, it can be observed that the increased addition of NaCl to the Base salt does not affect the thermal stability much, as 3% mass loss is observed around the same temperature of 672 °C. With 5% KCl + 5% NaCl addition by weight to the base salt, the thermal stability of mixture is pushed further by 32 °C, the salt decomposes at 695 °C. This is considerable improvement in high temperature stability of the Base salt. The observations form this set of TGA experiments is summed up in Table 2.2. Hence, the quinary mixture, i.e., the 5-component mixture of Base salt (made of 3 components) with 5% KCl + 5% NaCl by weight, is found to be more stable at Table 2.2 Decomposition temperature of Base salt, Base salt + 10% NaCl, Base salt + 5% NaCl + 5% KCl

Decomposition temperature (3% mass loss)

Base salt

Base salt + 10% NaCl

Base salt + 5% KCl + 5% NaCl

663 °C

672 °C

695 °C

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19

higher temperature as compared to a quaternary (4 component) mixture of Base salt + 10% NaCl. Generally, it is being observed that the increase in decomposition temperature of nitrate by addition of chlorides is followed by increase in melting point of the eutectic salt mixture [11]. Studies were carried out to observe the change if any, in the melting point of the salt mixture. The cooling behavior of Base salt, Base salt + 5% NaCl, and Base salt + 10% NaCl was studied. The salt samples were initially heated to 300 °C at heating rate of 10 °C/min in a muffle furnace. After removing it from furnace, a K-type thermocouple was inserted in it and it was allowed to cool naturally. The temperature was recorded at every 0.5 s, and the Time versus Temperature graph was plotted. The cooling behavior of Base salt and Base salt + 5% NaCl, Base salt + 10% NaCl is shown in Fig. 2.4. The point where the slope decreases or the temperature value does not change with time, i.e., the graph shows a horizontal behavior is the point where the salt starts to freeze. The freezing point of Base salt can be observed at 145 °C, whereas with 5% NaCl by weight, it is 151 °C, and with 10% NaCl by weight, it is 158 °C. Table 2.3 gives the overall idea of the decomposition temperatures as well as the melting points of the considered salt mixtures. Cooling curves of the salt mixtures 300 275

Base salt

Base+5%NaCl

Base+10%NaCl

250

Temperature °C

225 200 175 150 125 100 75 50 25 0 0

500

1000

1500

2000

2500

3000

3500

4000

4500

Time (seconds)

Fig. 2.4 Cooling graphs of Base salt, Base salt + 5% NaCl and Base salt + 10% NaCl

5000

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S. C. Pawar et al.

Table 2.3 Melting points of the salt mixtures Base salt

Base salt + 5% NaCl

Base salt + 10% NaCl

Decomposition Temperature (°C)

663

672

672

Melting point (°C)

145

151

158

Table 2.4 Comparison of melting points, decomposition temperature, and operating range of the salt mixtures Decomposition temperature (°C)

Base salt

Base salt + 5% NaCl

Base salt + 10% NaCl

663

672

672

Melting point (°C)

145

151

158

Operating range (°C)

518

521

514

2.4 Conclusion The decomposition temperature and melting temperatures observed from the above set of TGA and cooling curve experiments are listed in Table 2.4. Form the table, it is clear that the addition of 5% sodium chloride (NaCl) component by weight to the Base salt mixture (44% KNO3 –32% Ca(NO3 )2 –24% NaNO3 ) pushes the decomposition temperature ahead by 9 °C, and the melting point increases by 6 °C. Thus, the overall operating range of the mixture improves by just 3 °C. The addition of 10% sodium chloride (NaCl) component by weight to the Base salt mixture (44% KNO3 –32% Ca(NO3 )2 –24% NaNO3 ) pushes the decomposition temperature ahead by 9 °C. Consequently, the melting point is also pushed further by 13 °C. The overall range of operation reduces by 4 °C. Hence, it can be observed that the addition of NaCl to Base salt improves the high temperature stability up to a certain limit. The increased proportion of NaCl does not increase the decomposition temperature any further. On the other hand, the melting point also increases with NaCl addition and keeps on increasing with increased proportion of NaCl reducing the operating range of the salt mixture. The addition of 5% NaCl + 5% KCl to the Base salt mixture pushes the decomposition temperature ahead by almost 32 °C. This is a considerable improvement in the thermal stability of the Nitrate-based Base salt mixture. The melting behavior of this quinary mixture will be studied further. Also, the phases which lead to this increase in high temperature stability will be investigated. Acknowledgements The authors would like to acknowledge the Department of Science and Technology (SERB/EMR/2016/002784) for funding the project.

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References 1. T. Bauer, N. Breidenbach, N. Pfleger, D. Laing, M. Eck, ASES Conference Solar (2012) 2. N. Khandelwal et al., Recent developments in integrated solar combined cycle power plants. J. Thermal Sci. 29(2), 298–322 (2020) 3. R.W. Bradshaw, N.P. Siegel, Molten nitrate salt development for thermal energy storage in parabolic trough solar power systems. Energy Sustain. 43208 (2008) 4. V.M.B. Nunes et al., Molten salts as engineering fluids—a review: Part I. Molten alkali nitrates. Appl. Energy 183, 603–611 (2016) 5. R.I. Olivares, The thermal stability of molten nitrite/nitrates salt for solar thermal energy storage in different atmospheres. Sol. Energy 86(9), 2576–2583 (2012) 6. A.G. Fernández et al., Thermal characterization of HITEC molten salt for energy storage in solar linear concentrated technology. J. Therm. Anal. Calorim. 122(1), 3–9 (2015) 7. C.A. Pan et al., Identification of optimum molten salts for use as heat transfer fluids in parabolic trough CSP plants. A techno-economic comparative optimization, in AIP Conference Proceedings, vol. 2033, No. 1. AIP Publishing LLC (2018) 8. SQM’s Solar-salts-Book. 9. MSR—HITEC Heat Transfer Salt. 10. Coastal Chemical Company, Hitec© Heat Transfer Salt (Technical data sheet, 2009) 11. T.O. Dunlop et al., Stabilization of molten salt materials using metal chlorides for solar thermal storage. Sci. Rep. 8(1), 1–7 (2018) 12. F. Subari, H.F. Maksom, A. Zawawi, Corrosion behavior of eutectic molten salt solution on stainless steel 316L. Procedia-Soc. Behav. Sci. 195, 2699–2708 (2015)

Chapter 3

Impact of Various Heterogeneous Catalysts on the Production of Biodiesel Gaurav Dwivedi, Deviprasad Samantaray, Swayamsidha Pati, Suyasha Pandey, and Ambar Gaur

Abstract The world which we are momentarily breathing in is wavering every single day. At this point, we are subjected to rapid urbanization and because of it, there is always a need for energy as the augmentation of our realm and its prosperity hinges on the growth of energy. Thus, to encompass the energy which remains almost always insatiate to us, we depend on various fossil fuels to meet the energy demands to support our economic and social growth in the time of global precariousness exchange. Though we have used fossil fuels to allocate the energy around the globe as its requirement is increasing, it is becoming a rather daunting task for us to even have a quality life. Therefore, it is time now to look for alternate sources of energy which could replace fossil fuels that too at a decent price and lasts us for a longer period. A viable answer to our misery can be biodiesel. This fuel can be fabricated even utilizing waste frying oils by incorporating a varied variety of catalysts to accelerate the production of propellant. Also, various researches aid that it is capable to alleviate various greenhouse gases which are primarily present in our globe’s environment. This review focuses on the work of various researchers who toiled over biodiesel manufacture via transesterification. Based on different doings, using heterogeneous catalysts for the synthesis of biofuel can be a better way as it is environmentally friendly. Moreover, this benign need no washing from water and separation of product from the catalyst is rather simpler. This current paper is a survey of the advances made in the growth and progression of heterogeneous catalysts which can be befitting for the manufacture of biodiesel. Keywords Biodiesel · Heterogeneous catalysts · Transesterification

G. Dwivedi · A. Gaur Energy Centre, MANIT, Bhopal, India D. Samantaray (B) · S. Pati Department of Microbiology, CBSH, OUAT, Bhubaneswar, Odisha, India S. Pandey Mechanical Department, University Institute of Technology, RGPV, Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_3

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3.1 Introduction When the biological matter such as edible or non-edible cooking oils, fats, and blubber undergoes the process of transesterification, the resultant propellant is biodiesel or biofuel which is not only an inexhaustible resource for energy production but is also ecological owing to its reduced emissions of carbon monoxide unburned hydrocarbons and almost no sulfur content [1]. This fuel does not require the making of a separate engine, and it can be used in the very tantamount paraphernalia as diesel oil made from crude oil. It can reduce dependency on oil imports and increase energy security of India [2]. There have been many feedstocks suggested by the various researchers which are classified according to the way they developed. First-generation feedstock includes vegetable oil derived from soybean, sunflower, safflower, mustard, etc., but there are several social and economic challenges with it [3]. Second-generation feedstock includes non-edible oil like jatropha curcas, pongamia oil, neem, and mahua [4, 5]. Third-generation feedstock includes microalgae and synthesis of biodiesel from which it is a costly affair. Various types of oils are used for biodiesel production in India. About 52% of biodiesel productions utilize soyabean oil. Canola oil and corn oil contribute 13% to biodiesel production. Waste frying oil and yellow grease are utilized for 12% of biodiesel production in India. Biodiesel is mostly used as amalgamation with petroleum diesel in the ratio of 2%, 5%, 20%, etc., which is also mentioned as B2, B5, B20, respectively [6]. Catalysts play an important role in biodiesel production as it accelerates the transesterification process and enhances biodiesel yield. The transesterification process can be mediated by homogeneous and heterogeneous catalysts. A homogeneous catalyst, such as NaOH, KOH, H2 SO4 , has the same phase as a reaction mixture and also has good performance and cost-effectiveness [7]. However, homogeneous catalysts have drawbacks like soap formation, catalyst leaching, corrosive nature costly separation, and emulsion formation [7–9]. Homogeneous catalysts are not useful for continuous biodiesel production due to separation issues and also consume more energy [10]. Heterogeneous catalysts can be an option to do away these limitations. Heterogeneous catalysts are mostly derived from solid biomass, which ensures sustainable and environmentally friendly catalysts. It also has higher reusability and durability than a homogeneous catalyst [8].

3.1.1 The Impact of Heterogeneous Catalyst on Biodiesel Production Generally, the end product of the transesterification comprises the substantive aggregate of water which was severely blended with the low-fat ingredients and arduous to eradicate. Thus, water competes against the alcohol and inhibits the manufacture of the fuel. Therefore, to overcome this problem, heterogeneous catalysts must be ensued or used for the sake of completion of the reaction process [11].

3 Impact of Various Heterogeneous Catalysts on the Production …

25

Also for the mass scale production of biodiesel in the business sector, biomass fuel comes to fruition by heterogeneous catalysts because of their superior activity and refinement, enhanced re-utilization, trimming in manufacturing paces, and wastes. The catalytic activity of catalysts is pivoted on the fortitude and kind of innate basic or acerbic peculiarities. Biomass fuel fabrication using heterogeneous catalysts rompers on the assorted reaction criterions such as the time taken for the overall reaction process to complete, temperature, alcohol to oil molar ratio, the magnitude of catalyst used, and speed at which the reactants are stirred [12]. Heterogeneous catalysts can help to accelerate the esterification and transesterification process simultaneously, also it require less washing, can provide more surface area, and have a simpler purification process than the homogeneous catalysts. However, due to three phases with oil and alcohol result in hindrance in the diffusion process and slow reaction rate [13]. This review analyses the narrative of incessant biodiesel fabrication processes, by means of transesterification reaction using acid, base, acid–base heterogeneous catalysts, and enzymatic catalysts so that accurate catalyst and ideal reaction constraints possibly be determined.

3.1.2 Solid Base Catalyst Solid base catalyst displays sublime activity for transesterification of triglycerides and is only used when the entire proportion of free fatty acid adds up to less than 1%. Various preparation methods and applications of different solid base catalysts are mentioned as follows: Sharma et al. [14] examined contemporary progress made on the utilization of variegated alkali catalysts for an efficacious and non-polluting way of formation of biodiesel. This methodology involved calcinations at 100–200 °C to enhance the alkaline nature and pore size of the catalysts. The result indicated that at a higher molar ratio of alcohol to oil, loading alumina with KNO3 and Eu2 O3 gave about 90% transformation, whereas with KF and KOH and Al2 O3 yields obtained were 90%, 91%, and 96%, respectively. Also, the rate of conversion for zeolites varied from 85 to 95% with longer reaction times [14]. Kay and Yasir [15] studied different ways to fabricate biodiesel from low-quality crude Jatropha oil. With the molar ratio of methanol to the oil of 20:1, at 70 °C with 5 wt% of natural zeolites as the catalyst, a total of 6 h was taken for the completion of reaction giving about 96.5 wt% of ester content. Bet-Moushoul et al. [16] worked on the coalescence of CaO–AuNPs nanocatalysts and emphasized the usefulness of AuNPs deputed against CaO to fabricate biodiesel. Best scrutinized reaction conditions for 94–97% of FAME production were 9:1 methanol to oil molar ratio, with 65 °C reaction temperature and 0.3% catalysts loading. This catalyst could be reused for at least 10 times without considerable downfall of operative skills. Wu et al. [17] sought after sewage sludge to produce biomass propellant through KOH/activated carbon as solid base catalysts which was composed via wet impregnation technique at the temperature of 65 °C

26

G. Dwivedi et al.

with the mass ratio of methanol to the sludge of 10:1 (w/w). This technique resulted in higher FAME produce when 3% (wt/wt) of catalyst along with 25% mass proportion of KOH was used. De Luna et al. [18] generated a way to utilize a novel heterogeneous Na-pumice base catalyst for transesterification of soybean oil by studying reaction kinetics and catalyst reusability. The accelerator used in this experiment was prepared by wet impregnation method using 3 M NaOH solution. This catalyst was then washed and at a temperature of 60 °C with methanol to oil molar ratio of 24:1 with 13 wt 5 of catalyst within 2.75 h gave 99% of biodiesel. Pandit and Fulekar [19] proved that scallops waste like eggshell can be extremely functional heterogeneous catalysts. Calcium oxide (CaO) nanocatalyst of 75 nm size and 16.4 m2 g−1 surface areas testified by various techniques like X-ray powder diffraction, scanning electron microscope (SEM), etc., confirmed that they could transesterify dry biological matter into methyl esters (biodiesel). The reaction was carried out at 1.7 wt% of the catalyst within 3.6 h. Nearly, 86.41% of biodiesel was obtained from this methodology. Munguia et al. [20] incorporated ZnAl hydrotalcite-like compounds with 0.0, 0.10, and 0.25 molar ratios of Zr and Al via co-precipitation method for biodiesel synthesis, and within 2 h of reaction time gave 82.1% of biodiesel. In another study, a mussel shell (Pernaviridis) as primal matters for CaO heterogeneous catalyst for biodiesel fabrication from palm oil by roasting it at 900 °C for 3 h. Further, Perna viridis was amalgamated with activated carbon with C to CaO ratio of 2:3 followed by saturation with NaOH solution. This catalyst produced 95.12% of biomass propellant just with 7.5 wt% of catalyst in 3 h at 65 C when alcohol to oil molar ratio was 5:1 [21]. Li et al. [22] used hydrothermally liquefied microalgae chlorella for the production of biodiesel. The bio-crude fabrication process instilled upgrading of Al-SBA-15, CuO/Al-SBA-15, ZuO/Al-SBA-15, and CuO-ZnO/Al-SBA-15 catalysts and fuel in a facile way, without the addition of H2 . At 400 °C, 25.1–65.7% of biodiesel was obtained when only 7.68 wt% of catalyst was used. Musil et al. [23] broadened our perspective of butanolysis. In this study, a juxtaposition of potassium hydroxide and potassium tert-butoxide as the catalyst for the manufacturing of biodiesel from rapeseed oil was drawn. At a temperature of 30 °C, the KOH catalyst proved to be better than the tert-butoxide catalyst and promoted easy transmutation of FAME into FABE at a very inexpensive rate and in very less time of only 30 min . Ayoola et al. [24] provided obsolete ways to fabricate biodiesel from palm kernel oil by employing CaO derived from waste turkey bones by reducing the bones to a very craggy size of 150 mm accompanied by roasting at 800 °C for 3 h to amplify its catalytic pursuit. This process results in high production of biodiesel just within 1–3 h of reaction time at only 1–7 wt% of the catalyst with 8:14 methanol to oil molar ratio. Kesserwan et al. [25] used hybrid CaO/Al2 O3 in the form of aerogel and found out 3:1 CaO/Al2 O3 was giving the best results with biodiesel yield of 89.9% and purity as high as 98%. Also, low CaO content in catalyst showed less soap formation. Gohain et al. [26] carried out a sustainable approach by preparing heterogeneous catalysts from waste biomass of Tectona grandis leaves. The catalyst was suggested as an alternative to the industrial base catalyst. It provided the basic site of alkali and alkaline earth metals that was able to 100% FAME conversion at 2.5 wt % of catalyst [8]. Aryasomayajula Venkata Satya Lakshmi et al. [9] used CaO derived from waste eggshell for continuous biodiesel

3 Impact of Various Heterogeneous Catalysts on the Production …

27

production as a heterogeneous catalyst from rubber seed oil. A maximum biodiesel yield of 97.84% was achieved from alcohol to oil ratio 9:1 which was less than the alcohol to oil ratio from batch biodiesel production [9]. Balajii and Niju [10] produced biodiesel from Ceiba pentandra oil using green and heterogeneous catalyst derived from the banana peduncle. Biodiesel production required only 1.978 wt% of catalyst concentration to carry out the transesterification process with 98.69 ± 0.18% FAME yield. Elias et al. [7] used bi-functional heterogeneous catalyst CaO/Al2 O3 synthesized from the co-precipitation method for biodiesel production from waste sunflower oil and which resulted in a 98% yield. The ratio of CaO/Al2 O3 by weight was 4:1. Arumugam and Sankaranarayanan [13] used sugarcane leaf ash as a base heterogeneous catalyst to produce biodiesel from Calophyllum inophyllum oil. The catalyst was reused up to 10 cycles in which a 22% decrease was observed from the initial results. The maximum yield was 97% with 5 wt% of catalyst concentration.

3.1.3 Solid Acid Catalysts Solid acid catalysts are capable of contemporaneous esterification and transesterification and make it possible to discrete the catalyst from the reaction products quite effortlessly. Thus, they are used in varied reactions to fabricate biomass propellant. Cottonseed oil and methyl alcohol were looked upon as the viable provenance for the fabrication of biomass fuel. Shu et al. [27] devised a carbon-based solid acid catalyst for accelerating the reaction process by the sulfonating of carbonized edible oil asphalt. By only employing 0.2 wt% of catalyst, more acidic sites came into being which gave 89–93% of biodiesel at 260 °C when alcohol to oil molar ratio was 18.2:1 within 3 h. Zhang et al. [28] researched in the development of diminutive-globular a-zirconium phosphate catalysts with a diameter of 5–45 lm and sphericity of 0.80 for colossal production of biodiesel. 92.9% of biodiesel was obtained at 120 °C when the only 22 g of the catalyst was employed. The whole reaction was completed within 12 h. Soetaredjo et al. [29] advanced to use KOH/bentonite as a catalyst. Multiple concoctions of KOH and betonite were made to examine which proportion gave the highest yield. When KOH and betonite were present in ratios of 1:4, the highest yield of 90.7% at 60 °C with a 6:1 molar ratio of alcohol to oil in just 3 h was obtained. Wu et al. [30] worked on a method which could by using Ti3 AlC2 and SO4 2− /Ti3 AlC2 ceramic as acid catalysts esterify the benzoic acid. Rate of conversion was 80.4 with 99% of selectivity at 120 °C within 34 h of time to complete the reaction process. Pileidis et al. [31] used sulphonic groups as heterogeneous catalysts to improve the biological matter procured from hydrothermal carbons from catalytic conversion of levulinic acids into ethyl levulinates at much-unprecedented rate. For the reaction varied variety of biological matter derivatives were taken into accounts, namely cellulose, glucose, and rye straw in amongst which 5–6.4 wt% of glucose at 60 °C showed commendable transubstantiation and selective character, giving 91.5% of produce in 1 h time. Guldhe et al. [32] toiled over oleaginous scenedesmussp which was fostered

28

G. Dwivedi et al.

phototrophically in an open tarn and later bone-dried and utilized for the fabrication of biomass-based fuel by incorporating tungstated zirconia. Transesterification was carried out utilizing microwave technique and sonication at 50 °C and 100 °C, respectively. Amongst the two processes sonication resulted in the production of the colossal amount of biodiesel. Ortiz et al. [33] attested that absolute transmutation of glycerol is possible if supercritical water is used instead of nickel-based catalyst. This scrutiny epitomizes that amelioration of glycerol from supercritical water could be achieved at temperatures ranging from 600 to 800 °C accompanied with colossal production of hydrogen at about 2.4 s of residence time. Baroi and Dalai [34] laid prominence on the cohesiveness of biodiesel fabrication from green seed canola oil by using both homogeneous and heterogeneous acid catalysts. Results testified that the heterogeneous process at 200 °C with alcohol to oil molar ratio of 25.84:1 within 6.25 h turned out to be much guarded and more ecologically harmless process and thus giving about 92.6–995 of biodiesel. Malins et al. [35] performed arylation of activated carbon with 4-sulfobenzenediazonium chloride which leads to the manufacturing of 4-sulfophenyl activated carbon-based solid acid catalyst (ACPhSO3H) of 10.1 nm of average pore diameter, 114 m2 /g useful floor space with 0.29 cm3 /g pore volume, and 0.72 mmol H+/g of PhSO3H density, making it a potent accelerator, producing 95% of biomass fuel in 7 h. Dhawane et al. [36] adopted the Taguchi method which involved the use of ferromagnetic iron (II) doped carbonaceous catalyst for complete utilization of rubber seed oil. 96.31% of biodiesel was procured at 400 C with 5 wt% of catalyst within 1–5 h. Ishola et al. [37] using ferric sulfate as a heterogeneous acid catalyst brought down the acerbic value of palm kernel oil and esterified PKO. The conversion was of highly augmented order and modeled by adopting response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). This methodology gave about 95.2% of yield for 6.10 wt% of catalyst at 600 °C when alcohol to oil molar ratio was 2.96:1 in 15–25 min with a reduction in acid value. Guldhe et al. [38] works showcased encouraging the possibility of utilizing Chromium-aluminum mixed oxide (CRAL) as a heterogeneous catalyst. On using 15 wt% of CRAL as catalyst, microalgal lipids could be transesterified into biomass-based fuel at 80 °C for 20:1 alcohol to oil molar ratio within 4 h thus giving almost 98.28% FAME conversion. Moreover, the catalyst could be used up to four batches with almost the same production capability. Akkarawatkhoosith and Jaree [39] for the biodiesel fabrication layered tin oxide on microchannel outer periphery via chemical bath deposition (CBD) method within 24 h at 80 °C by keeping the congregation of SnCl4 ·5H2 O at 0.48 M. Thus, to obtain 90% produce of biodiesel less than 35 g of catalyst should be used by maintaining alcohol to oil molar ratio at 30:1 for 10 min. D’Souza et al. [40] implemented a series of toiling operations to convert waste cooking or frying oil into a biomass-based fuel in just one step by aiding iron oxide on sulphated graphene oxide (GO-Fe2 O3 eSO3 H) which initiates the esterification of oleic acid using ethyl alcohol in the molar ratio of 12:1 at 100 °C for 4 h. GO-Fe2 O3 eSO3 H could be magnetically separated and showed higher operative skills when it came to the mass production of biomass-based fuel. Reinoso and Tonetto [41] tirelessly worked over, fuel bio-additives manufacturing

3 Impact of Various Heterogeneous Catalysts on the Production …

29

by acidic ion exchange resin in a batch type of reactor, where acetic acid was esterified with glycerol, and 99.6% of conversions were attained when the reaction was carried out at 120 °C for 240 min with 9:1 alcohol to oil molar ratio when only 4 wt% of catalyst was used. The catalyst could be used five more times in a catalytic cycle without the need for resuscitation. Furthermore, there is no need for filtering of the discerned active species. De Lima et al. [42] executed the following procedure in which the basic heterogeneous catalysts of organic amines were implanted on MCM-41 by the co-condensation method. 100% of biodiesel yield was generated at 70 °C with 1:12 oil to methanol molar ratio and 0.15 g of catalyst loading for 2 h. Lim et al. [43] palm empty fruit bunch biochar used to extract solid acid catalyst by sulfonation process with the help of 4-benzenediazonium sulfonate. The catalyst was calcinated at different range, and it was observed that low-temperature calcined catalyst at 200 °C gave an optimum biodiesel yield conditions also the result showed better efficiency then sulfonation by H2 SO4 . Deeba et al. [44] performed an extraction process for catalyst on waste yeast residue which showed acidic nature with FAME yield of 96.2% from waste cooking oil. The produced FAME has good oxidation stability. Catalyst had high catalytic activity and thermal stability with reusability up to four cycles. Ballotin et al. [45] used an amorphous sulfonated carbon nanostructure derived from bio-oil showed the potential of renewable and low-cost chemical catalysts. Low activation energy and yield of 97% was achieved.

3.1.4 Enzymatic Catalysts Yan et al. [46] toiled over a novel and vigorous recombinant P. pastoris yeast wholecell biocatalyst with intracellular overextension of Tll which was matured for the utilization in making of biodiesel by incorporating waste cooking oils. The reaction was carried out at 60 °C when only 2% of the catalyst by wt% was incorporated at alcohol to oil molar ratio of 8:1 within 6–10 h of reaction time. This catalyst could be used up to three times. Riadi et al. [47] based on numerous determinants like the amount of catalyst (Ozone—5.8%, KOH–1.5%, and ash—17.35), the temperature (30–60 °C) and reaction time (3 h) analyzed a way of synthesizing biofuel. This technique was decisive in producing 85.722 mg/l of short-chain methyl esters and 655.286 mg/l of long-chain methyl esters. Amoah et al. [48] plodded away on the study which presents lipid unsheathed from Chlamydomonas sp. JSC4 which is composed of a prominent blend of numerous lipids, using it as catalyst gives about 97% of FAMEs at 30 °C at 4:1 alcohol to oil molar ratio in 32 h. Here, water is very pivotal for actuating the sites for operative proficiency. Guldhe et al. [49] sought after lipases procured from multifarious microbial sources which were applied in the metamorphosis of oil to biofuel as they were more ecological when juxtaposed to the standard proposition of chemical transformation due to clement reaction state. Only at 2 wt% of the catalyst, about 97% of biodiesel was obtained at 60 °C when alcohol to oil molar ratio was 8:1. The entire reaction took about 6–10 h for completion. This process ensures the generation of a low quantity of waste water. Imanparast

30

G. Dwivedi et al.

et al. [50] used a novel catalyst which contained lipase and was able to achieve good catalytic activity that resulted in a yield of 85%, high saturated fatty acid chains, and good reusability up to three cycles, however, ration time 17 h. Various heterogeneous catalysts, their characteristics, and with transesterification process parameter have been described in Tables 3.1 and 3.2.

3.2 Conclusion In this review, variant types of catalysts, solid acidic or solid basic or enzymatic have been studied. Several investigations related to biodiesel production were carried out, and the result confers a handful of the attributes of various catalysts. Solid base catalysts can be used multiple numbers of times also their reaction rate is much faster in comparison to acid-catalyzed transesterification and requires lower energy but there is a highly likely chance of getting the catalyst poisoned when exposed to surrounding air, and it is highly sensitive to FFA content in the oil. Speaking of solid acid catalysts, they are quite insensitive to FFA and water content in the oil, and it can undergo both esterification and transesterification simultaneously ensuring that products get easily segregated from catalysts. But the drawback of using them is that the rate of reaction is low, and also they have adverse side reactions, demands more energy and leads to contamination of the product if the leaching of active sites of the catalyst occurs. On another hand, an enzymatic catalyst does not let saponification occur and is eco-friendly but at the same time, rate of reaction is quite slow and is highly sensitive to alcohol, typically methanol as it can deactivate the enzyme. Thus, after overviewing all kinds of catalysts, the need for the hour is to ameliorate them so they contribute successfully in the production of biomass propellant at the commercial level.

Synthesis process

Rapid sol–gel method

Catalyst source

Hybrid CaO/Al2 O3 aerogel

700

5

Calcination temperature Calcination time (°C) (h)

Table 3.1 Source, synthesis process, and characteristics of various heterogeneous catalyst

–

Reuse (number of cycles)

Reference

1. More specific surface [25] area than alcogel but less than pure alumina 2. Weaker response to sintering process than alcogel 3. Aluminum to calcium ratio has a linear relationship with crystallite size 4. Mesoporous sites 5. As the molar ratio increases, the basicity of catalyst increases (continued)

Catalyst characteristics

3 Impact of Various Heterogeneous Catalysts on the Production … 31

Synthesis process

–

–

Catalyst source

Calcined tectona grandis leaves

CaO derived from calcined eggshells

Table 3.1 (continued)

900

700

4

4

Calcination temperature Calcination time (°C) (h) 5 (minimum)

Reuse (number of cycles)

Reference

1. Derived catalyst had [9] a rod-shaped structure at its surface 2. Catalyst has a composition of the main component of Ca and O2 with a small amount of Mg and C (continued)

1. Metal oxide and [8] carbonates like CaO and CaCO3 were present; thus, catalyst has high basicity 2. The presence of K, Ca, Si, Mg, and Na 3. Strong to moderate basic site in the catalyst 4. Up to 9% weight loss at the temperature of 700 °C 5. Microporous and mesoporous sites were present

Catalyst characteristics

32 G. Dwivedi et al.

Synthesis process

Calcined banana peduncle –

Catalyst source

Table 3.1 (continued)

700

4

Calcination temperature Calcination time (°C) (h)

Reuse (number of cycles)

Reference

1. Compound like alkali [10] oxide, metal oxide, and earth metal oxides were present 2. K-based compound was present in green ash 3. Huge perforation was observed 4. High basicity due to alkaline compounds (continued)

Catalyst characteristics

3 Impact of Various Heterogeneous Catalysts on the Production … 33

Synthesis process

Co-precipitation method

Catalyst source

CaO/Al2 O3

Table 3.1 (continued)

750

6

Calcination temperature Calcination time (°C) (h) 2

Reuse (number of cycles)

Reference

1. The decomposition [7] of hydroxide into oxides leads to a 40% decrease in the weight of catalyst at the temperature range of 750–900 °C 2. SEM–EDX analysis shows the composition of 80% of the catalyst has 41.41 wt% of Ca, 49.62% of O, and more than 8.97% of Al 3. The porosity of 10–100 nm was observed 4. An increase in surface area and a decrease in the pore volume of Al2 O3 were observed after calcination 5. A decrease in the active site was observed which may be caused by the crystallization of CaO (continued)

Catalyst characteristics

34 G. Dwivedi et al.

Synthesis process

–

Catalyst source

Residual ash from sugarcane leaf

Table 3.1 (continued)

–

–

Calcination temperature Calcination time (°C) (h) 10

Reuse (number of cycles)

Reference

1. Porous, spongy ash [13] was observed after calcination 2. FTIR analysis shows that the absorption band was between 3905.9 and 464.6 cm−1 3. The presence of Cl group, Cao, CaCO3 MgO, etc., was observed in ash 4. Crystalline silica was present (continued)

Catalyst characteristics

3 Impact of Various Heterogeneous Catalysts on the Production … 35

Synthesis process

Sulfonation process

Catalyst source

Oil palm empty fruit bunch

Table 3.1 (continued)

200

2.5

Calcination temperature Calcination time (°C) (h) –

Reuse (number of cycles)

Reference

1. The mesoporous [42] structure was developed due to calcination 2. Sulfonication process results in high surface area capacity for the acid group 3. Reduction in porosity and surface area was observed at high calcination temperatures 4. The sulfonation process increases carbon and oxygen content 5. Acid density decrement was observed at higher calcination temperatures (continued)

Catalyst characteristics

36 G. Dwivedi et al.

Synthesis process

Sulfonation process

Catalyst source

Yeast residue based

Table 3.1 (continued)

–

–

Calcination temperature Calcination time (°C) (h) 4

Reuse (number of cycles)

Reference

1. Weak acid sites were [43] observed due to the presence of—OH group 2. Strong acid sites were observed due to the presence of—SO3 H and—SO2 group which was also responsible for the high acidity of the catalyst 3. The amorphous nature of the catalyst was observed 4. The grain size was more than 10 μm 5. Catalyst has high accessibility to active sites and a high diffusion rate (continued)

Catalyst characteristics

3 Impact of Various Heterogeneous Catalysts on the Production … 37

Synthesis process

–

Catalyst source

Carica papaya stem

Table 3.1 (continued)

700

4

Calcination temperature Calcination time (°C) (h) 5

Reuse (number of cycles) 1. Metal carbonate oxide converted to metal oxide resulted in high basicity of derived catalyst 2. An increased surface area was observed after calcination 3. K was a major component along with Ca, Mg, and Si. K2 O was mainly responsible for catalyst basicity 4. Catalyst has a hydrophobic nature

Catalyst characteristics

(continued)

[50]

Reference

38 G. Dwivedi et al.

Synthesis process

Physical covalent attachment method

Catalyst source

SBA-15@oleate@lipase

Table 3.1 (continued)

550

6

Calcination temperature Calcination time (°C) (h) 5

Reuse (number of cycles) 1. The presence of lipids in catalyst decreases surface area, pore size, and pore volume 2. A homogeneous monolayer of biocatalyst was observed 3. The biocatalyst nanosized particles have rod-shaped particles, with hexagonal array mesostructure 4. The presence of C-N and C=N was observed

Catalyst characteristics [49]

Reference

3 Impact of Various Heterogeneous Catalysts on the Production … 39

Sunflower waste cooking oil

Waste cooking oil

Hybrid CaO/Al2 O3 aerogel

Calcined tectona grandis leaves

SBA-15@oleate@lipase

Oil derived from cyanobacterium synechococcus

34

2

Scenedesmus obliquus lipid

4

Yeast oil 2

4

Waste cooking oil

Yeast residue based

Waste cooking oil

20

Oil palm empty fruit bunch Palm fatty acid distillate

Carica papaya stem

5

4

Waste palm oil

Calophyllum inophyllum oil

4

Waste sunflower oil

CaO/Al2 O3

Residual ash from sugarcane leaf

Ceiba pentandra oil 1.978

5

2.5

1

Catalyst wt%

-

60

60

70

60

110

64

65

65

65

65

Room temperature

65

Reaction temperature (°C)

Process parameters of transesterification

Calcined banana peduncle

CaO derived from calcined Rubber seed oil eggshells

Feedstock

Catalyst source

Table 3.2 Feedstock and transesterification process parameters

17

3

3

8

6

7

-

4

4

1

4

4

4

Retention period (hour)

3.4:1

9:1

9:1

10:1

10:1

–

19:1

9:1

9:1

9.20:1

9:1

6:1

11:1

Alcohol to oil ratio

85

93.33

95.23

94.8

96.2

98.1

97

89

[49]

[50]

[43]

[42]

[13]

[7]

[10]

98.69 ± 0.18 98

[9]

[8]

[25]

Reference

97.84

100

89

Yield (%)

40 G. Dwivedi et al.

3 Impact of Various Heterogeneous Catalysts on the Production …

41

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Chapter 4

Investigations on the Use of Molten Oxides for High Temperature Heat Transfer in Solar Power Plants Varun Shrotri and Luckman Muhmood

Abstract The current drive for replacing conventional energy with renewables has put a huge demand on solar energy. Solar thermal technologies have greater potential to deliver this energy demand. However, at present, the heat transfer fluids used in concentrating solar power (CSP) plants include molten salts which have a relatively low decomposition temperature ( 2000). Steady-state condition is applied for the whole simulation. For turbulent flow, k-ε standard model is used in which the value of turbulence intensity is 5% which is estimated by equation [16] I = 0.16. Re(−1/8) for flow rate of 66 and 99 L/h for which flow becomes turbulent. No-slip condition is applied to the walls. Simulation was performed using convergence criteria of 10–4 . The nodes and elements are 305,639 and 1,255,938, respectively. Following properties of water has been taken: Density (ρ) = 998.2 kg/m3 ; Specific heat capacity (C) = 4200 kJ/kg K; Viscosity (μ) = 0.001003 (kg/m s).

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Inlet water temperature is 300 K, and outlet pressure is atmospheric pressure. For the stagnant air, the free convection heat transfer coefficient on the PV panel is 10 W/m2 K. Model is simulated on heat flux (I) of 800, 900, and 1000 W/m2 while water flow rate (Q) is taken 16.5,33,66 and 99 L/h. The electrical efficiency of PV panel, as a function of cell temperature, is obtained by the following equation [17]. ηel = ηref (1 − 0.0045(Tc − 298))

(10.5)

where ηref is the efficiency of the PV module at the reference temperature [18], and T c represents cell temperature. The thermal efficiency of collector is obtained by the following equation [19]. ηth =

ρ QC(Tout − Tin ) IA

(10.6)

where A is the surface area of the PV panel; T in —inlet temperature of water (K); T out —outlet temperature of water (K); I—heat flux (W/m2 ).

10.5 Results and Discussion The first simulation is performed using ANSYS STEADY STATE HEAT TRANSFER on PV panel without the cooling for different heat flux with natural convection (convection heat transfer coefficient of 10 W/m2 K) and following temperatures were found out. The maximum temperature was 350 K, which was found out on 1000 W/m2 heat flux (Table 10.2). Figure 10.3 shows the temperature distribution in PV panel. The temperature is more on the central region while less on corners because of natural convection from the sides. But temperature variation is not too much varied from 344.75 to 345 K. The second simulation was performed with a water collector using ANSYS FLUENT, and the following results were found out on different flow rates. As we can see from Fig. 10.4, temperature variation with different heat flux in PV panel is more at lower flow rates, but as the flow rate increased, temperature variation decreased with different heat flux. Least variation was found out on 99 L/h flow rate. A relative drop in temperature with flow rate was more when the flow rate changed from 16.5 to Table 10.2 Variation in temperature of PV panel on different heat flux without cooling

Heat flux(W/m2 )

Temperature (K)

800

340

900

345

1000

350

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Fig. 10.3 Temperature distribution in PV panel on 900 W/m2 heat flux without cooling

PV panel surface temperature

350 340 330 320

I= 800 W/m2

310

I= 900 W/m2

300

I= 1000 W/m2

290 280

16.5

33

66

99

Water flow rate (Q) in L/h Fig. 10.4 Average temperature of PV panel after cooling on different flow rate

33 L/h. For 16.5 and 33 L/h, flow was laminar while for 66 and 99 L/h flow become turbulent. The average area temperature was taken for the calculation. Figure 10.5 shows the temperature of the water outlet from the spiral type collector. Outlet temperature from the water collector is less than in comparison to PV panel temperature. The highest temperature on PV panel was 343.47 K, and lower temperature was 304.63 K, while the highest temperature of outlet water was 333.17 K, and the lowest temperature was 304.25 K. So, for getting high-temperature water from outlet, we should reduce flow rate at any given heat flux. Figure 10.6 shows temperature contour on PV panel surface with cooling by flow rate of 66 L/h. The value of temperature is maximum in the central part, and it gradually decreases. So, it is an efficient cooling comparison to other types of flow like- serpentine flow and direct flow, where the temperature is not uniform [5]. Other reason for uniform temperature is cross section of the collector, which has surface

N. K. Baranwal and M. K. Singhal

Water Collector outlet temperature

134

340 335 330 325 320 315 310 305 300 295 290 285

I= 800 W/m2 I= 900 W/m2 I= 1000 W/m2

16.5

33

66

99

Water flow rate (Q) in L/h Fig. 10.5 Average outlet temperature from water collector on different flow rates

Fig. 10.6 Temperature distribution on PV panel surface on 900 W/m2 heat flux and flow rate of 66 L/h

contact with PV panel while in round tube cross-section; it will be a line contact with collector. In Fig. 10.7, temperature distribution on water collector wall is given. As we can see in the figure, a cool pipe is adjacent to the hot pipe, so temperature variation is not too much throughout while in most other designs adjacent pipe temperature is same, which make a very large variation of temperature in PV panel from one region to another region. Electrical and thermal efficiency on different flow rate and different heat flux is given in Table 10.3.

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Fig. 10.7 Temperature distribution in water collector wall on 900 W/m2 heat flux and flow rate of 66 L/h

As we can see from table, overall efficiency is increasing with increasing the flow rate, but the outlet temperature of the water is decreasing, so outlet water will be at a lower temperature with increasing flow rate. The increasing flow rate will get a negligible temperature change of outlet water comparison to the inlet temperature. The maximum overall efficiency is 53.66%, which is at 800 W/m2 heat flux and flow rate of 99 L/h. So, with the result, we can think that if we increase more flow rate, we can get more overall efficiency but change in inlet and outlet temperature of water from collector will be negligible and we would not get any thermal output from the system. So, the optimum flow rate for both electrical and thermal efficiency would be between 66 and 99 L/h flow rate. The comparison of electrical efficiency with cooling and without cooling on different flow rates is given in Fig. 10.8. There is a large increment in efficiency when the flow rate increases from 16.5 to 33 L/h compared to other flow increments. It is also seen that after 66 L/h flow rate increment in electrical efficiency with increment in flow rate is not very large. So, 66 L/h flow rate will be reasonable to run the system for getting more electrical power. Also, as we can see from Fig. 10.8, there is maximum steepness in slope at 16.5 L/h, so with the change of heat flux, electrical efficiency changes drastically on this flow rate. From Fig. 10.8, it is clear that cooling impacts too much on electrical efficiency of PV panel on high heat flux; it will save a lot of electric power with comparison to without cooling.

13.37

14.84

15.35

15.52

33

66

99

38.14

38.15

38.08

39.65

53.66

53.5

52.92

53.02

15.48

15.28

14.71

13.06

ηel (%)

ηoverall (%)

ηel (%)

ηth (%)

I = 900 W/m2

I = 800 W/m2

16.5

Flow rate (L/h)

38.14

38.15

38.08

39.65

ηth (%)

Table 10.3 Electrical, thermal, and overall efficiency with different heat flux and flow rate

53.62

53.43

52.79

52.71

ηoverall (%)

15.44

15.22

14.59

12.75

ηel (%)

I = 1000 W/m2

38.14

38.26

38.08

39.65

ηth (%)

53.58

53.48

52.67

52.4

ηoverall (%)

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16.00

Electrical efficiency (%)

15.52 15.35 15.00

15.48 15.28

14.84

14.71

15.44 15.22

With cooling at 99 L/h

14.59

With cooling at 66 L/h

14.00

With cooling at 33 L/h 13.37

13.00

13.06

12.98

12.62

12.75

With cooling at 16.5 L/h Without cooling

12.26 12.00

800

900

1000

Heat flux in W/m2 Fig. 10.8 Comparison of electrical efficiency of PV panel with and without cooling on different flow rates

10.6 Validation From Table 10.3 and Fig. 10.8, it shows that electrical efficiency varies from 13 to 15% while relative increase in electrical efficiency 3–18%, and thermal efficiency was 38– 39% approximately depending on different input conditions. From the Zayafiaqah et al. [20], it has been seen that with air cooling 3% power saved while in water cooling, 14% of electrical power is saved using direct flow type water collector. Sachit et al. [21] developed different designs of water collectors and found out that a spiral flow type water collector has 13.8% electrical efficiency and the value of thermal efficiency is 54.6% on 800 W/m2 and 147.6 L/h of water flow. Another group of researchers Masoud et al. [22] worked on different designs and also validated that spiral type flow was the best design. He has simulated on 30, 60, 90, and 180 L/h of the flow of water with a circular and square cross section and found that electrical efficiency was varied 11.3–11.9%. In contrast, thermal efficiency varied from 30 to 48% efficiency. Also, many western countries commercialized PV/T water system and found out that electrical efficiency is varied from 13 to 16%. Among those company, the multi-solar system [23], which is Israel-based company claims that it has 15% electrical efficiency, and overall efficiency is 85%. The company also claims that it also increases the lifespan of the PV panel more than 25 years.

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10.7 Conclusions PV/T collector spiral flow with semicircular cross section was simulated using ANSYS. PV panel surface and water outlet temperature were evaluated on heat flux 800,900, and 1000 W/m2 , atmospheric and water inlet temperature were 300 K, and flow rates were 16.5, 33,66 and 99 L/h. The first simulation was performed on ANSYS STEADY STATE TRANSFER without using a collector, and the second simulation was performed with cooling by the help of a spiral flow type water collector. It was found that increasing the flow rate of water from 16.5 to 99 L/h temperature of panel surface decreases drastically which results in increasing of relative electrical efficiency approximately 20% for 1000 W/m2 heat flux, but outlet temperature of water decreases. So for heating purposes, we need an auxiliary heater so that desired heated water we shall get for use. The maximum thermal efficiency was 39.65% on 16.5 L/h and maximum electrical efficiency 15.48% on 99L/h. For optimum power output, system should be operated between 33 and 66 L/h of flow rate. Also, the maximum temperature zone was in the central part of the PV panel, so we can use more passes in the central zone for getting more uniform temperature in the PV panel.

References 1. H.A. Zondag, D.W. de Vries, W.G.J. van Helden, R.J.C. van Zolingen, A.A. van Steenhoven, The yield of different combined PV-thermal collector designs. Sol. Energy 74(3), 253–269 (2003) 2. J.I. Bilbao, A.B. Sproul, Detailed HPVT-water model for transient analysis using RC networks. Sol. Energy 115, 680–693 (2015) 3. S. Dubey, G.N. Tiwari, Thermal modelling of a combined system of photovoltaic thermal (PV/T) solar water heater. Sol. Energy (2008) 4. A. Ibrahim, M. Othman, M. Ruslan, M. Alghoul, M. Yahya, A. Zaharim, et al., Performance of photovoltaic thermal collector (PVT) with different absorber design. WSEAS Trans. Enviro. Dev. 5(3), 321–330 (2009) 5. M.M. Sardouei, H. Mortezapour, Temperature distribution and efficiency assessment of different PVT water collector designs. S¯adhan¯a 43(6), 1–13 (2018) 6. A.K. Bhargava, H.P. Garg, R.K. Agarwal, Study of a hybrid solar system-solar air heater combined with solar cells. Energy Convers. Manag. 31(5), 471–479 (1991) 7. C.P. Mohanty, A.K. Behura, M.R. Singh, B.N. Prasad, A. Kumar, G. Dwivedi, P. Verma, Parametric performance optimisation of three sides roughened solar air heater. Energy Sour. Part A Recov. Util. Environ. Effects (In Press, 2020) 8. P. Verma, G. Dwivedi, A.K. Behura, D.K. Patel, T.N. Verma, A. Pugazhendhi, Experimental investigation of diesel engine fueled with different alkyl esters of Kranja oil, vol. 275 (2020), p. 117920 9. A.K. Behura, H.K, Gupta, Efficient direct absorption solar collector using Nanomaterial suspended heat transfer fluid. Mater. Today Proc. 22, 1664–1668 (2020) 10. A. Kumar, K.C. Nikam, A.K. Behura, An exergy analysis of a 250 MW Thermal power plant. Renew. Energy Res. Appl. 1, 197–204 (2020) 11. B.N. Prasad, A.K. Behura, L. Prasad, Fluid flow and heat transfer analysis for heat transfer enhancement in three sided artificially roughened solar air heater. Sol. Energy 105, 27–35 (2014)

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12. A.K. Behura, B.N. Prasad, L. Prasad, Heat transfer, friction factor and thermal performance of three sides artificially roughened solar air heaters. Sol. Energy 130, 46–59 (2016) 13. A.K. Behura, H.K. Gupta, Use of nanoparticle-embedded phase change material in solar still for productivity enhancement. Mater. Today Proc. (In Press, 2020) 14. N. Hamrouni, M. Jraidi, A. Chérif, Solar radiation and ambient temperature effects on the performances of a PV pumping system. Revue Des Energies Renouvelables 11(1), 95–106 (2008) 15. https://www.engineeringtoolbox.com/hydraulic-equivalent-diameter-d_458.html 16. https://www.afs.enea.it/project/neptunius/docs/fluent/html/ug/node238.htm 17. A. Tiwari, M.S. Sodha, Performance evaluation of solar PV/T system: an experimental validation. Sol. Energy. 80(7), 751–759 (2006) 18. C.G. Popovici, S.V. Hudi¸steanu, T.D. Mateescu, N.C. Chereche¸s, Efficiency improvement of photovoltaic panels by using air cooled heat sinks. Energy Procedia 85, 425–432 (2016) 19. N. Amrizal, D. Chemisana, J. Rosell, Hybrid Photovoltaic-thermal solar collectors dynamic modelling. Appl. Energy 101, 797–807 (2013) 20. Z. Syafiqah, N.A.M. Amin, Y.M. Irwan, M.S.A. Majid, N.A. Aziz, Simulation study of air and water cooled photovoltaic panel using ANSYS (2017). https://doi.org/10.1088/1742-6596/908/ 1/01/2074 21. F.A. Sachit, N. Tamaldin, M.A.M. Rosli, S. Misha, A.L. Abdullah, Current progress on flatplate water collector design in photovoltaic thermal (PV/Y) systems: a review. J. Adv. Res. Dyn. Control Syst. 10(04) (2018) 22. M.M. Sardouei, H. Mortezapour, K.J. Naeimi, Temperature distribution and efficiency assessment of different PVT water collector designs (2018). https://doi.org/10.1007/s12046-0180826-x 23. https://www.millenniumsolar.com/

Chapter 11

Development of Correlation for Efficiency of Incineration Plants Using Deep Neural Network Model Deepuphanindra Gannamani and Anuj Kumar

Abstract In the present era, production of municipal solid waste (MSW) has become unrestrained due to rapid growth in population and urbanization. Therefore, people are facing various challenges such as health and environmental safety. But, this huge potential of MSW can be used as a promising source for electricity production to reduce the greenhouse gas (GHG) emissions. Incineration is well-known technique which has been extensively used to produce economically affordable energy from MSW. The purpose of the incineration plant is to get the maximum desirable outputs (heat and power) out of waste and minimize undesirable outputs (emissions and bottom ash). The value of heat or power recovered from waste burning in incineration plant depends on the heating value of the waste. Determining this heating value of each waste sample has been considered as complex and time consuming task due to different moisture, ash, and chemical composition. Under the present study, an attempt has been made to develop a correlation to calculate efficiency of the plant using composition of waste. In order to develop this correlation, concept of deep neural network model from machine learning has been used in this paper. The developed application may be useful for plant design engineer to predict the performance of plant for given range of parameters. Keywords Municipal solid waste · Incineration Plant · Efficiency of plant · Composition of waste · Deep neural networks

Abbreviations f 1NN f DNN k x y

Single layer in the neural network Combination of all f 1NN No. of layers involved in the neural network Input values to the network Output value of the network for that specific x value

D. Gannamani · A. Kumar (B) Vellore Institute of Technology, Vellore 632014, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_11

141

142

w* κ W η WD κWD λ

D. Gannamani and A. Kumar

Output weights Loss fraction List of all parameters Learning Rate of the model Weight decay Loss fraction added by weight decay Normalization constant

11.1 Introduction Across the globe, world is facing a serious problem of growth in the pollution rate. As per survey conducted in mid-2019, the range of pollution index has been recorded as 93.20–11.57 for entire world [1]. This pollution increases the rate of global warming which leads to problem of climate change. Using of the fossil fuels for the producing of energy is one of the major causes for the global warming. In order to produce sustainable and clean energy without causing global warming effect, bioenergy is seen, more and more, as a promising and largely untapped renewable energy resource, and its potential environmental and economic benefits are becoming more apparent as technological improvements continue to emerge. Bioenergy makes the largest renewable contribution to global energy supply. Bioenergy accounted for 70% of the renewable energy consumption and 12.4% (including the 7% share of traditional use of biomass) to total final energy consumption as of the end of 2017. Modern sustainable bioenergy (excluding the traditional use of biomass) contributed an estimated 5.0% to the global supply of heat, 3% in transport and 2.1% to global electricity supply [2]. In order to produce electricity and/or heat, bioenergy projects often depend on solid fuels such as municipal solid waste (MSW), residues from agricultural and forestry processes, and purpose-grown energy crops. In 2016, the world’s cities generated around 2 billion tonnes of solid waste which is expected to increase by 70% in 2050 due to rapid population growth and urbanization [3]. It has been found that 80% of waste generated is sent for uncontrolled landfill. When landfilled, the organic portion of MSW can release large amounts of methane, which, if not captured or used, can contribute significantly to global warming. Generating renewable energy from waste is one of the stages for waste management. In this stage, a large fraction of MSW is treated in waste to energy (WTE) plants commonly known as “Incinerator.” Use of incinerator also avoids sending a large amount waste to landfill. Incineration is a type of waste treatment process which includes the direct combustion of the waste for recovering energy [4]. During this process, the treated waste is converted into intermediate burned matter (IBM), gases, particles, and heat which can be used for electricity generation. In order to eradicate the pollutants, the flue gases are first treated before going in to atmosphere. Almost 95–96% of mass of waste is reduced in the incineration process [5]. This amount of reduction depends

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on the degree of the recovery and composition of waste used. Composition of waste depends on various factors like season of the year, the habits or culture of the community, people’s educational or economic status and the geographical location [6]. Due to this large variation waste composition, estimation of efficiency of plant is complex and time consuming task. In last couple of years, numerous investigations have been carried out to assess the different aspect of MSW incineration plant such as plant efficiency, emissions, effect of waste amount on plant efficiency, etc. Chen and Chen [7] carried out a study to estimate environmental performance of large MSW plants using neural networks. Data envelopment analysis (DEA) type of network was employed to develop neural network model. Different parameters such as power capacity used, operation cost, and operation time were considered as input to the neural network model to estimate environment efficiency of the MSW incineration plants. Moreover, amount of hazardous waste emissions, municipal waste suspended, bottom ash, and opacity were also predicted using this model. Shu et al. [8] investigated the characteristics of waste collected during various seasons of the year from different sites of the Taiwan. Four models of multilayer perceptron (MLP) neural network was developed to predict lower heating value (LHV) of MSW based on measured data elemental analysis (ultimate analysis), dry- and wet-base physical composition analysis, and proximate analysis. This study concluded that LHV values were predicted most accurately using elemental analysis model. Moreover, the wet-base physical composition model was found to be the easiest and most economical. Hosseinpour et al. [9] carried out an investigation to determine cetane number (CN) of the biodiesel (fatty acid methyl esters) using artificial neural networks (ANN). A model using basal partial least square (PLS) method adapted by ANN was developed to predict CN of biodiesel based on carbon percentage as input parameter. A comparison has also been carried out between ANN—adapted PLS method and standard PLS method using around 135 testing data sets. Results obtained from both methods were found to be in good agreement with the experimental one. Giantomassia et al. [10] made an attempt to develop an algorithm for online prediction of the steam production of a municipal solid waste incinerator over a specified time horizon. Radial basis function networks have been used to develop this learning algorithm. In order to update all the parameters of the networks, the pruning strategy of the minimal resource allocating network technique along with an adaptive extended Kalman filter have also been used to predict the steam production. Many researchers have investigated the efficiency of MSW plants on the basis of different aspects of the plants. However, application of concept of deep neural network has not been reported to estimate efficiency of MSW plant so far. Therefore, an attempt has been made to investigate efficiency of plant based on elemental composition of waste using concept of deep neural network in the present paper. Deep neural network is a type of artificial neural networks which is a part of a huge family of machine learning. This may help the site engineer or a design engineer in estimating the efficiency with a short time and concentration. The organization of this paper is divided into four sections. First section is introduction in which brief discussion of studies carried out earlier in this area has been

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discussed. Subsequently, the methodology covering data collection, data organization, model formation, and model discussion is discussed in second section. Results of the present study are discussed in the third section. In this third section, the outcome of the model developed under this paper is compared and analyzed with the theoretical values based on mean square root error. Last and fourth section discusses the conclusion of the paper.

11.2 Methodology 11.2.1 Data Collection In order to develop neural network model, around 80% of data collected has been used to train the model whereas rest of them for testing of model. The present study has been carried out using data collected from the earlier studies [10–19] as well as two different sites of Andhra Pradesh as given in Tables 11.1 and 11.2, respectively.

11.2.2 Model Formulation The performance evaluation of incineration plants takes into consideration numerous parameters. Deep neural networks generate efficiency of the considered plant based on pre-determined inputs and outputs. Unlike as in the regular neural network analysis, deep neural networks used predictive analysis to correlate the input to get desired outputs. The model follows the binary variables analysis to find the efficiency of the plants to verify that they are in the correct trend or the off the trend. Deep neural network helps in the estimation of efficiencies by comparing the inputs and outputs of each against each other value. Depending on achieving the desired objectives, the use of binary variables allows the choice of productive units. In predictive analysis, the prediction of the unknown variables yi (i ∈ R) is done by the known x i (i ∈ R). By depending on the values of the output, it is segregated as classification or regression task. The number of free parameters in deep neural networks is growing as compared with single-layer network, as it is the network’s versatility to represent highly nonlinear functions. By stacking several single-layer networks into a deep neural network with k layers, we can formalize this mathematically as f D N N (x) = f 1N N ( f 1N N ( f 1N N (x))) = f 1N N . . . f 1N N (x)       k

(11.1)

k

In Eq. (11.1), the first layer is called as the input layer and the last layer is called as the output layer. And, all the layers in the between are called as hidden layers. According to the model, each layer’s dimension is not necessarily equal across all

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Table 11.1 Input and expected output values different composition MSW through literature C%

H%

O2 %

N2 % S%

Cl% Ash% GCV Moisture Efficiency Source (Kcal/kg) (%) (%)

20.63 2.040 15.90 0.99

0.09 0.32 04.97

2101.8

54.76

71.03

[10]

26.02 6.030 14.11 0.8

0.08 0.29 05.84

2627.3

46.85

67.17

[10]

25.71 3.100 19.77 0.76

0.02 0.17 05.71

2720.5

44.78

76.09

[10]

22.01 2.860 14.31 0.34

0.15 0.04 05.16

2316.8

55.14

70.67

[10]

17.89 2.630 10.81 0.46

0.14 0.14 06.04

1999.1

61.90

65.74

[10]

23.61 3.650 14.77 0.34

0.15 0.08 06.07

2646.4

51.34

72.66

[10]

37.42 5.140 29.91 1.19

0.13 0.66 25.56

3689.6

24.10

80.49

[11]

49.06 6.620 37.55 1.68

0.20 0.47 18.16

4821.8

28.29

80.99

[11]

46.78 5.920 45.55 0.32

0.09 0.32 01.34

4791.3

12.23

84.37

[11]

28.00 3.400 20.00 0.40

0.20 0.00 25.00

4567.8

25.00

86.64

[12]

46.11 6.860 28.12 1.26

0.23 8.84 09.23

4691.3

55.01

76.63

[13]

37.30 6.410 35.22 1.16

0.22 N/A 21.18

15,256

73.47

90.45

[14]

49.96 7.900 35.31 0.49

0.07 N/A 08.98

25,945

49.23

93.16

[14]

26.70 3.370 16.14 0.52

0.11 N/A 11.87

2610.3

41.30

75.04

[15]

27.19 3.430 16.43 0.53

0.11 N/A 10.27

2695.4

42.05

75.35

[15]

27.46 3.460 16.60 0.53

0.11 N/A 09.38

2733.1

42.46

75.45

[15]

51.87 6.490 40.48 0.07

0.00 N/A 01.08

4347.0

05.47

82.59

[16]

46.78 8.130 43.73 0.79

0.57 N/A 04.19

3119.3

54.68

64.23

[17]

56.29 5.510 36.67 0.90

0.34 N/A 11.00

4769.0

23.20

82.39

[18]

45.69 7.560 41.43 2.05

0.06 N/A 05.69

3399.4

Dry basis 78.26

[17]

48.35 7.310 41.98 0.91

0.03 N/A 02.03

3504.6

Dry basis 79.01

[19]

layers. Depending on the freedom of the layers, the size of the layer is determined. As a response, weight optimization in a deep neural network function is a complex task that requires gradient-based methods of optimization and regularization. The above selection leads to dense settings where there is no activation of a large portion of hidden units with zero output. On the other side, frequent network architectures are most often used with activation functions as they limit output to 0 or 1. Optimization in deep learning is similar to the general predictive analysis setup; the error is minimized through w∗ = arg min κ( f D N N (x; w), y).

(11.2)

w∈W

In Eq. (11.2), convex optimization is used by simple perceptrons to identify the weights w*, generally the solution for the optimization of the deep neural network is done by gradient-based numerical optimization which is similar to the single-layer neural network. By following Eq. (11.3), the partial derivatives of the loss parameters are determined with respect to loss k, and the parameters are modified to the decreased

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Table 11.2 Input and output composition values collected through site C%

H%

O2 %

N2 %

S%

Cl%

Ash%

GCV (Kcal/kg)

Moisture (%)

Efficiency (%)

33.4

4

25

0.4

0.2

0

14

3589.66

23

82.62

39.21

5.505

40.61

1.54

0.09

N/A

13.21

3780.75

6.35

83.35

42.18

6.41

27.52

4.25

0

N/A

18.2

4291.44

7.37

82.78

31.54

3.94

18.05

0.41

0.1

N/A

15.94

2910.82

30.01

77.87

20.45

2.66

15.13

0.63

0.13

N/A

10.33

2301.15

50.67

72.61

35.1

4.7

16.1

1.4

0.2

N/A

42.6

3678.29

45.8

77.32

42.55

6.11

50.1

1.14

N/A

N/A

15.47

3874.147

61.82

73.55

47.5

6.15

46.35

N/A

N/A

N/A

6.4

3599.47

6.71

80.69

78.4

5.74

9.94

1.44

0.07

N/A

4.3

7346.88

2.1

87.48

48.71

5.99

44.2

2.39

0.28

N/A

0.37

3605.68

18.84

78.65

40.51

4.38

13.23

0.68

2.67

N/A

25.05

3019.88

13.48

79.52

44.9

6.38

44.98

3.74

N/A

N/A

1.5

4855.821

1.44

85.44

30

5

24

1

0.1

N/A

25

6000

35

86.24

46.11

6.89

28.12

1.26

0.23

N/A

9.26

4180.15

55.01

74.09

22.1

3.1

14.6

0.5

0.1

0.2

11.4

2239.6

48.1

71.04

41.01

6.59

45.65

0.08

0.02

N/A

15.39

3774.147

Dry basis

82.46

50.44

8.8

36.6

0.08

0.02

N/A

9.29

4247.07

Dry basis

79.49

44.14

7.56

45.69

0.2

0.01

N/A

3.52

4755.821

Dry basis

83.85

48.93

6.96

40.39

2.96

0.26

N/A

2.44

3510.68

Dry basis

79.56

57.43

7.87

26.01

2.45

0.48

N/A

14.46

4869.07

Dry basis

82.13

33.99

5.68

34.5

1.59

0.17

N/A

44.26

3600.89

Dry basis

83.84

75.77

14.31

5.37

0.12

0.02

N/A

8.49

7246.88

Dry basis

79.79

74.57

10.97

10.69

0.16

0.01

N/A

2.65

7200.69

Dry basis

82.90

19.61

2.83

49.56

0.74

0.03

N/A

14.66

2100.55

Dry basis

87.05

48

6.4

37.6

2.6

0.4

N/A

5

3498.77

Dry basis

80.57

47.8

6

38

3.4

0.3

N/A

4.5

3440.66

Dry basis

81.09

values w ←w−η

 δ κ( f (xi, w), yi) δw i

(11.3)

The learning rate in Eq. (11.3) decides the size of the layer during the optimization process. This very well explains the first gradient-based numerical optimization. Now, after that the second main step is regularization of the error which will give the output efficiency value with a least mean error as possible. Even though having a good learning rate of the training data in the network, there will be decrease in the speed of the process which will increase the runtime which happens due to the change

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of the direction of gradient by the change in direction of the parameters. Hence to overcome this, the regularization of the network is done. During regularization of the deep neural networks on a one side, one looks at a huge number of free parameters, as this enables extremely nonlinear relationships to be represented. It, on the other side, makes the network more likely to over fit. The following are the most commonly used solutions for the regularization to the weights in the deep neural networks they are: Weight decay adds a loss function regularization concept, vilifying large network weights. W denotes the list for all parameters and thus shifts the loss function to κW D =

 δ λ κ( f (xi, w), yi) + w2 . δw 2 i

(11.4)

Subsequently, the gradient uses a new rule given by Eq. (11.5) as w ←w−η

 δ κ( f (xi, w), yi) + λw2 . δw i

(11.5)

As a measure, the boundaries of the decision are clearer, thus enabling network generalization. In order to increase stability of neural network, batch normalization is also performed to normalize output of a previous activation layer.

11.2.3 Model Description Under present study, deep neural network model has been developed using Python3. Figure 11.1 shows the flowchart of methodology adopted for the present study. In order to initialize the solution, different libraries, i.e., numpy, tensorflow, and pandas are to initialize the model. After initializing the model, the data is inserted in to the main loop of the model for training, as shown in Fig. 11.2. During training process, the data has been run multiple times through code. After training, the model is all set for testing using testing data. Figure 11.3 shows the summary of the model developed in which density of layers are very helpful to find how many times the regression of the model is taking place. The main source code of the neural network is shown in Figs. 11.2 and 11.3.

11.3 Results and Discussion Going through the output of the above-mentioned code and data, it is given in Fig. 11.4.

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Fig. 11.1 Flowchart of methodology adopted for the model developed under present study

Fig. 11.2 Model code in python3 during developing

Output of this model developed has been compared with the efficiency of testing data given in Tables 11.1 and 11.2. In output of code, first values are the efficiencies of the testing data and second values are the output of model. It has been found that the prediction of the model is on similar lines with efficiency of testing data. The mean absolute error and mean squared error have been obtained as 1.28 and 3.63%, respectively.

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Fig. 11.3 Summary of the developed model

Fig. 11.4 Output values predicted by the model

11.4 Conclusion A lot of research on municipal solid waste (MSW) usage in different forms is being carried out. In the MSW utilization, incineration is one of the leading and best working process. Efficiency of incineration plant is one of the most critical parameter that every plant engineer faces challenges to estimate if only composition of elements is known. Therefore, an attempt has been made to develop a neural network model to predict the efficiency MSW plant for different element compositions. Data collected from site as well as literature has been used to develop and test this neural network model. Results obtained by this model have been compared with the testing data. As a result, the mean absolute error and mean squared error have been obtained as 1.28% and 3.63%, respectively. These errors are found within permissible limits. Therefore,

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this model may be useful for the development of web/mobile-based application to estimate the efficiency of MSW plant. Moreover, this model may also be useful for plant engineers and other plant stakeholders to estimate the efficiency of plant based on elemental compositions.

References 1. Pollution Index by Country 2019 Mid-Year. https://www.numbeo.com/pollution/rankings_by_ country.jsp. Last accessed 2019/12/31 2. Bioenergy: sustainable renewable energy. https://www.energy.vic.gov.au/renewable-energy/ bioenergy/bioenergy-sustainable-renewable-energy. Last accessed 2019/12/17 3. Urban Development Series—Knowledge Papers. https://siteresources.worldbank.org/INT URBANDEVELOPMENT/Resources/336387-1334852610766/Chap3.pdf. Last accessed 2020/01/10 4. Countries that Produce the Most Waste. https://www.investopedia.com/articles/markets-eco nomy/090716/5-countries-produce-most-waste.asp. Last accessed 2020/01/25 5. Waste to energy in India. https://www.eai.in/ref/ae/wte/wte.html. Last accessed 2020/02/25 6. A beginner’s guide to neural networks and deep learning. https://pathmind.com/wiki/neuralnetwork. Last accessed 2020/01/25 7. Y. Chen, C. Chen, The privatization effect of MSW incineration services by using data envelopment analysis. Waste Manag. 32(3), 595–602 (2012) 8. H. Shu, H. Lu, H. Fan, M. Chang, J. Chen, Prediction for energy content of Taiwan municipal solid waste using multilayer perceptron neural networks. J. Air Waste Manag. Assoc. 56(6), 852–858 (2006) 9. S. Hosseinpour, M. Aghbashlo, M. Tabatabaei, E. Khalife, Exact estimation of biodiesel cetane number (CN) from its fatty acid methyl esters (FAMEs) profile using partial least square (PLS) adapted by artificial neural network (ANN). Energy Convers. Manag. 124, 389–398 (2016) 10. A. Giantomassi, G. Ippoliti, S. Longhi, I. Bertini, S. Pizzuti, On-line steam production prediction for a municipal solid waste incinerator by fully tuned minimal RBF neural networks. J. Process Control. 21(1), 164–172 (2011) 11. M. Kraus, S. Feuerriegel, A. Oztekin, Deep learning in business analytics and operations research: models, applications and managerial implications. Eur. J. Oper. Res. 281(3), 628–641 (2019) 12. J. Brau, M. Morandin, T. Berntsson, Hydrogen for oil refining via biomass indirect steam gasification: energy and environmental targets. Clean Technol. Environ. Policy 15(3), 501–512 (2013) 13. M.H.M. Yusoff, R. Zakaria, Combustion of municipal solid waste in fixed bed combustor for energy recovery. J. Appl. Sci. 12(11), 1176–1180 (2012) 14. G. Nordi, R. Palacios-Bereche, A. Gallego, S. Nebra, Electricity production from municipal solid waste in Brazil. Waste Manage. Res. 35(7), 709–720 (2017) 15. B. Dasgupta, M. Mondal, Bio energy conversion of organic fraction of Varanasi’s municipal solid waste. Energy Procedia 14, 1931–1938 (2012) 16. N. Khairuddin, L. Abd Manaf, M. Hassan, W. Wan Abdul Karim Ghani, N. Halimoon, Biogas harvesting from organic fraction of municipal solid waste as a renewable energy resource in malaysia: a review. Polish J Environ Stud 24, 1477–1490 (2015) 17. S. Kerdsuwan, K. Laohalidanond, W. Jangsawang, Sustainable development and eco-friendly waste disposal technology for the local community. Energy Procedia 79, 119–124 (2015) 18. N. Gnanapragasam, B. Reddy, M. Rosen, Optimum conditions for a natural gas combined cycle power generation system based on available oxygen when using biomass as supplementary fuel. Energy 34(6), 816–826 (2009)

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19. W. Tsai, An Analysis of Operational Efficiencies in the Waste-to-Energy (WTE) Plants of Kaohsiung Municipality (Taiwan). Resources 8(3), 125 (2019)

Chapter 12

Smart Grid Initiatives Towards Sustainable Development: Indian and Worldwide Scenario Sumeet K. Wankhede, Priyanka Paliwal, and Mukesh K. Kirar

Abstract The electrical power sector globally is experiencing an advanced evolution and expansion of the conventional electrical grid. This transformation is in response to the advanced technologies and the latest environmental policies. The conventional grid has many alarming problems associated with it such as harmful emissions, poor efficiency, poor reliability, poor monitoring and control, lacking smart field devices and sensors, etc. Thus, the smart grid has evolved as a vital tool to address the shortcomings of the conventional power grid with increased reliability, efficacy, security, and sustainability. However, the implementation of smart grid technologies has been a challenging issue. The electricity regulatory commissions (ERCs) of the respective countries are undergoing frequent amendments in the electricity regulating policies and standards. Thus, to assess the impact of initiatives taken in the implementation of smart grid policies requires comprehensive analysis. This work presents a review of the initiatives undertaken by the electricity regulating utilities for the prolific implementation of smart grid technologies. Various policies and measures taken by the respective countries are also presented in this work, enlisting the objective of each measure. Keywords Smart Grid · RES · DER

12.1 Introduction It is a glorious time for the power sector industry, as the transformational changes restructured the process of power generation, transmission, and distribution to the consumers. The modern evolution of the power sector industry has originated the term ‘Smart Grid’ which has emerged widely and critically approved by the electrical power industry across the globe. One of the most vital component of the smart grid is the distributed energy resources (DER) comprising renewable energy sources (RES). The degrading environmental conditions (harmful emissions) and depleting S. K. Wankhede (B) · P. Paliwal · M. K. Kirar Maulana Azad National Institute of Technology, Bhopal, MP 462003, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_12

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fossil fuel reserves have led to the transition from conventional power generation to RES-based DER generation. A steep transition from centralized generation to RES-based DER is observed in the past few years [1]. Solar photovoltaic (Solar PV) and wind power have emerged as a stepping stone on the path of sustainable energy development programs worldwide. Countries are seeking to achieve renewable portfolio standards by enhancing strategic measures and policy implementation. The increasing rate of DER penetration required suitable standards and guidelines for interconnection with the grid. Thus, electricity regulating policies of the respective countries underwent frequent amendments and revision [2, 3]. A smart grid comprises of multi-objective components [1], each component has its significance in enhancing the grid capabilities, Fig. 12.1 lists some of these components. The smart grid initiatives are focused on upgrade of the conventional grid with advanced transmission and distribution systems. Smart grid initiatives have deployed several advanced concepts such as substation and distribution automation, wide area monitoring system (WAMS), advanced metering infrastructure (AMI), cyber security, energy storage systems, electric vehicle, RES integration, power quality management, Internet of things (IoT), supervisory control and data acquisition/Energy management system (SCADA/EMS) units, etc. The different components of the smart grid function collectively, exhibiting superior coordination, and

Internet of Things (IOT)

SCADA/ EMS

SubstaƟon & DistribuƟon AutomaƟon

Power Quality Managem ent

Wide Area Monitoring System (WAMS) SMART GRID AMI (Smart

Renewable Energy

Metering)

Electric Vehicle

Cyber Security Energy Storage

Fig. 12.1 Smart grid and its components

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control. Thus, making the grid well structured, more reliable, and customer friendly. With the upgrade of monitoring and control structure, the faulty conditions can be rectified or avoided with superior action of control, making the conventional grid a smart grid [4]. A smart grid is an electrical grid incorporated with advanced automation systems enhancing the real-time monitoring and control of grid parameters, RES-based DER integration, infrastructure for the electric vehicle, data security from cyber attack, and secure communication channels for bi-directional flow of information [1–3, 5, 6]. The drivers of the smart grid are as follows: 1. 2.

3. 4. 5.

Deployment of DER and producing sustainable energy and contributing to the reduction of harmful emissions [7]. To develop a smart interface platform by integrating technologies such as SCADA/EMS, WAMS, smart metering with advanced bi-directional communication channels and distribution automation, cyber security, etc. [8, 9]. To provide an environment and infrastructure for the excessively growing market of electric vehicle and battery energy storage [10, 11]. To escalate the application of Internet of things (IoT) [12] and improve issues such as grid efficiency, power quality [13], etc. To enhance grid reliability, meeting the viability of economic constraints [14].

One major component of the smart grid development program is consumer empowerment. In which, the consumers are marked as active entities by providing the infrastructure and facilities. This establishes a bilateral relationship between utilities and consumers, which results in effective demand side management (DSM). And results in the empowerment of prosumers and consumers [15]. The integration of smart grid components (Fig. 12.1) is a well-known and widely addressed issue in the literature. It has been a challenging task to implement smart grid technologies. This work is majorly focused on the initiatives taken by different countries in implementing smart grid technologies. The measure took and policies implemented are discussed in detail with special context to the Indian scenario. The objectives in support of renewable portfolio standards are listed providing the motive for an increase in smart grid activities. A collective worldwide comprehensive review of the current status and future prospect of development of smart grid activities is provided in this work.

12.2 Smart Grid Framework: Policy Initiatives and Measures Undertaken (Indian Scenario) Indian smart grid forum (ISGF) is established by the government of India in May 2010 aiming to accelerate the deployment of smart grid technologies in India. The government of India has launched the National smart grid mission (NSGM) in March 2015. The prime objective of NSGM is to strengthen the existing power system infrastructure and make it more economical and efficient. To strengthen the mission,

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16 smart grid pilot projects, and 1 smart city research and development platform are in the implementation phase [16]. Indian has a clear vision to deploy smart grid technologies through the smart grid development roadmap discussed in Table 12.1. Ministry of renewable energy of India aims to accomplish a target of installing 175 GW power from renewable energy sources by the year 2022. The total installed capacity of 87.02 GW is achieved by Mar 2020, and India is ranked 5th globally in terms of total renewable power installed [17]. The world’s largest 2000 MW solar park, named ‘Shakti Sthala’ is developed in Karnataka which is spread across 13,000 acres. The initiatives put into action by India towards the growth in the renewable energy environment are stated as follows [17–19]: Table 12.1 Smart grid development roadmap through implementing smart grid technologies in India [17–19] S. No.

Objectives

Targets 2022

2027

1

Quality power access to The aim is to provide 24 * 7 all power supply in urban areas and at least 12 h a day in rural sectors

To provide secure, reliable, and quality power to all

2

Regulating policies and The aim is to provide the tariff rates consumers to have choice of electricity supplier in metro cities and selected urban areas. To support an environment for active consumer participation by regular monitoring of policies and tariff rates in context to DER integration

To provide all the consumers to choose among the multiple options of power suppliers across country. The prosumer being marked as active entities with direct supervision on the power feed/absorbed through net metering

3

Renewable energy and energy efficiency

The aim to have an installed capacity of 175 GW by RES

Implementation of dynamic energy efficiency programs

4

Deployment of smart grid technologies

Implementing of stronger communication channel for advanced metering infrastructure (AMI), micro-grid development for villages and industrial parks

All substation automation, AMI across country, around 20,000 developed micro-grids

5

Electric vehicles and energy storage

To develop and implement the road map structure for electric vehicle environment in some urban cities

To have a significant production of electric vehicles along with a developed infrastructure for their smooth interface. To provide charging station in all cities and national highways

6

AT&C loss reduction

To achieve the reduction in AT&C losses to under 12%

To achieve the reduction in AT&C losses to under 3%

12 Smart Grid Initiatives Towards Sustainable Development …

1.

2.

3.

4.

5.

6.

157

Feed-In tariff: Here, the DER owning utilities are provided with a guaranteed payment for the power they produce. A fixed standard tariff rate is made available to the DER utilities to provide investors financial perks making DER a low-risk investment. Tariff rates are monitored by the regulating utility from time to time. Accelerated depreciation: This financial scheme provides a fiscal incentive to investors to claim 40% of depreciation in the first year, helping the DER utilities to write off their capital cost early. In India, around 70% wind power projects are built on the accelerated depreciation scheme. Generation-based incentives: This financial incentive scheme provides the tariff rates for the power produced by RES-based DER. Wind power-based DER is provided with the standard tariff rate for the power they inject into the grid which is decided by the regulating committee. DER utilities could only be benefitted if they continue their generation for a period between 4 and 10 years. Under this scheme disbursement of the incentives is provided in parts and it must be within the one-fourth of the total amount of incentive provided in a fiscal year. Viability gap funding (VGF) for solar: The solar energy corporation of India (SECI) purchase the power produced by solar PV-based DER at a fixed tariff level. The DER utilities would make their bids following which the payment of VGF is generated. The VGF is limited to Rs. 2.5 crore/MW maximum. The latest reforms have caused significant cut down in prices of tariff plans, for solar PV-based DER. Net metering: Net metering enables the facility to consumers of electricity with DER (solar PV) at the local end to sell excess generated power to the distribution utilities. Smart meters are installed which runs on AMI accounting for both power absorbed and power feed. All the state electrical utilities have developed the net metering policies and slowly started implementing. Renewable energy certificate: Renewable purchase obligations (RPO) are defined by the respective state electricity regulators (SERCs) in India. The RPO provides financial support to the DER utilities. With the reduction in the cost of semiconductor material in a solar PV cell, the generation from solar PVbased DER has got significantly cheaper than conventional power generation techniques. Solar tariffs have decreased by 64% in the last 6 years [20].

India has immense potential to harness its renewable energy capacity due to its favorable atmospheric condition. Till now, India has only harvested 0.25% of its total renewable energy potential. Thus, this deficit opens up an immense opportunity to be explored in the future and reducing the dependence on the conventional generation.

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12.3 Smart Grid Framework: Policy Initiatives and Measures Undertaken (Worldwide Scenario) The annual growth of 2.2% electric power is expected from 2012 to 2035, according to a report from the international energy agency (IEA). The ever-rising electricity demand has led to the infusion of a higher budget by the government in the power sector by respective countries. To upgrade the conventional grid into the smart grid requires a lot of infrastructure investment. Hence, the smart grid development programs are kept on the priority list and a higher sum of the budget is allotted to it by the countries. Different countries suggested different definitions of the smart grid, varied on their policy framework and regulatory guidelines. Countries like the USA, China, India, Germany, Russia, and Japan are the leading producer of Co2 globally. Hence, a much needed and dedicated efforts are made by these countries for sustainable and eco-friendly growth [3]. Table 12.2 enlists the various initiatives taken with underlining objectives for enhancing smart grid development programs by different countries. The policies framed/implemented enroute to the smart grid development roadmap are also discussed in Table 12.2.

12.4 Conclusion The objective to attain sustainable green energy with lesser environmental impacts is the goal of all the smart grid development programs. The periodical up-gradation of the power sector has efficiently improved the quality of access to electricity to the consumers. With these advancements, hand in hand goes the investment cost in this sector. The investment in the power sector is peaking globally with supportive measures from electrical utilities and the government authorities. For the smooth operation of the grid, all the policies and measures undertaken have to be monitored at regular intervals to estimate its effectiveness. And if any amendments required, should be taken timely by the utilities. DER interconnection standards and guidelines such as IEEE P1547, California Rule 21, UL 1741 SA, and IEC 61727 are under intensive revision. With the advancing technology comes new challenges in the implementation of those technologies. The major threats which arise with DER integration and needs to be mitigated are as follows [24]: to maintain grid stability in the occurrence of any fault in the system, to control line active and reactive power flows, protection devices need to be coordinated and control because of the bi-directional power flows in the grid, arising power quality issues due to inverter-based power injections in the grid, temporary over-voltages (TOVs) under light loading conditions, deterioration of on-load tap changers (OLTCs), voltage regulators, and capacitor banks due to frequent operation, etc. Hence, it opens up a widespread field to be explored by the researchers.

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Table 12.2 Initiatives taken for enhancing smart grid activities world-wide [4, 20–23] S. No.

Country

Objectives

Policy’s implemented/targeted

i.

European Union [19] Implementing smart • 80% of households will be metering, reduce greenhouse incorporated with smart gases (GHG), increase meters by the year 2020 • The European Union on its renewable generation, and energy and climate policy improve energy efficiency of 2030 has targeted a total reduction of 40% in domestic GHG emissions • To obtain renewable power penetration to 20% in the grid by the end of 2020

ii.

USA [21]

Environment protection • Developing organizations through reduced emissions, like Office of Electricity energy by renewable sources Delivery and Energy at higher efficiency, and Reliability, Smart Grid considering economic Consumer Collaborative constraints for implementing the sustainable smart grid benefits • Major stepping stone in deploying smart grid activities by forming organizations like – Smart Grid Investment Grant program (SGIG) and the Smart Grid Demonstration Program (SGDP) • Providing heavy investment grants in implementing smart grid technologies stipulating through the American Recovery Act of 2009

iii.

Canada [20]

Reduce harmful emission of gases

• Target for 2020 is the reduction in harmful emissions of gases by 17% below 2005 levels • Running major smart grid pilot projects in the Ontario, Provinces of Quebec, and other provinces • Forming organizations like National Smart Grid Technology and Standards Task Force, Smart Grid Canada, Natural Resources Canada, for facilitating smart grid developments (continued)

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Table 12.2 (continued) S. No.

Country

Objectives

Policy’s implemented/targeted

iv.

South Korea [22]

Self-dependent on generating total required power through sustainable generation Aim to reduce harmful emissions

• To obtain renewable power penetration to 11% in the grid and reduction in the Transmission and Distribution (T&D) losses to 3% by the end of 2022 • Target for 2020 is the reduction in harmful emissions of gases by 30% • All households will be incorporated with smart meters by the year 2020 • Formation of Smart Grid Promotion Act for enhancing and successfully deploying smart grid activities and projects

v.

China [23]

Energy conservation, lower harmful emissions, diverse development, expanding international cooperation, increasing domestic resources

• For harnessing and utilizing the total renewable capacity amended Renewable Energy Law of 2009 is passed • Formation of special Smart Grid Science and Technology Industrialization Projects under the planning of 12th Five-Year Plan • Formation of organization like the National Development and Reform Commission (NDRC) to monitor smart grid development activities

vi.

Australia [23]

Increased use of RES-based DER for power production

• It has the privileged to be the first country to form the law for achieving renewable energy portfolio standards • To obtain renewable power penetration to 20% in the grid by the end of 2020

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References 1. India Smart Grid Forum (ISGF), The Smart Grid Handbook for Regulators and Policy Makers. https://www.indiasmartgrid.org/. Nov 2017. Last accessed 13 Aug 2018 2. A. Izadian, N. Girrens, P. Khayyer, Renewable energy policies: a brief review of the latest U.S. and E.U. policies. IEEE Ind. Electron. Mag. 7(3), 21–34 (2013) 3. M.L. Tuballa, M.L. Abundo, A review of the development of Smart Grid technologies. Renew. Sustain. Energy Rev. 59, 710–725 (2016) 4. Smart Grid Canada, Global Smart Grid Federation Report (2012). Last accessed 15 Aug 2018 5. REN21, 2020, Renewables 2020 Global Status Report. https://www.ren21.net/status-of-ren ewables/global-status-report/, June 2020. Last accessed 15 June 2020 6. www.nist.gov [ONLINE]. Available: https://www.nist.gov/engineering-laboratory/smart-grid. Last accessed 15 Aug 2019 7. Y. Yeliz, A. Onen, S.M. Muyeen, A.V. Vasilakos, I. Alan, Enhancing smart grid with microgrids: challenges and opportunities. Renew. Sustain. Energy Rev. 72, 205–214 (2017) 8. R. Bayindir, I. Colak, G. Fulli, K. Demirtas, Smart grid technologies and applications. Renew. Sustain. Energy Rev. 66, 499–516 (2016) 9. K. Yasin, A survey on smart metering and smart grid communication. Renew. Sustain. Energy Rev. 57, 302–318 (2016) 10. S.F. Tie, C.W. Tan, A review of energy sources and energy management system in electric vehicles. Renew. Sustain. Energy Rev. 20, 82–102 (2013) 11. M.A. Hannan, M.M. Hoque, A. Mohamed, A. Ayob, Review of energy storage systems for electric vehicle applications: issues and challenges. Renew. Sustain. Energy Rev. 69, 771–789 (2017) 12. M.S. Hossain, N.A. Madlool, N.A. Rahim, J. Selvaraj, A.K. Pandey, A.F. Khan, Role of smart grid in renewable energy: an overview. Renew. Sustain. Energy Rev. 60, 1168–1184 (2016) 13. A.S. Bubshait, A. Mortezaei, M.G. Simões, T.D.C. Busarello, Power Quality enhancement for a grid connected wind turbine energy system. IEEE Trans. Ind. Appl. 53(3), 2495–2505 (2017) 14. P. Priyanka, N.P. Patidar, R.K. Nema, Planning of grid integrated distributed generators: a review of technology, objectives and techniques. Renew. Sustain. Energy Rev. 40, 557–570 (2014) 15. N. Shaukat, S.M. Ali, C.A. Mehmood, B. Khan, M. Jawad, U. Farid et al., A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid. Renew. Sustain. Energy Rev. 81, 1453–1475 (2018) 16. indiasmartgrid.org [ONLINE]. Indian Smart Grid Forum. Available from https://indiasmar tgrid.org/nsgm.php. Last accessed 15 Sept 2019 17. www.cea.nic.in [ONLINE]. Central Electricity Authority (Govt. of India, 2020). Available (https://www.cea.nic.in/reports/monthly/installedcapacity/2020/installed_capacity03.pdf). Last accessed 15 June 2020 18. nsgm.gov.in [ONLINE]. Smart Grid Roadmap for India: Vision, Targets and Outcomes. Available https://www.nsgm.gov.in/sites/default/files/India-SmartGrid-Vision-Roadmap.pdf. Last accessed 15 Aug 2018 19. A. Sharma B.K. Saxena, K.V.S. Rao, Comparison of smart grid development in five developed countries with focus on smart grid implementations in India, in 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Kollam (2017), pp. 1–6. 20. E.C. Pischke, S. Barry, W. Adam, A. Alberto, E. Amarella, O.F. De, C. Suani, L. Oswaldo, From Kyoto to Paris: measuring renewable energy policy regimes in Argentina, Brazil, Canada, Mexico and the United States. Energy Res. Soc. Sci. 50, 82–91 (2019) 21. S.S. Akadiri, A.A. Alola, A.C. Akadiri, U.V. Alola, Renewable energy consumption in EU-28 countries: policy toward pollution mitigation and economic sustainability. Energy Policy 132, 803–810 (2019) 22. S. Kim, H. Lee, H. Kim, J. Dong-Hwan, K. Hyun-Jin, J. Hur, C. Yoon-Sung, H.K, Improvement in policy and proactive interconnection procedure for renewable energy expansion in South Korea, Renew Sustain Energy Rev 98, 150–162 (2018)

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Chapter 13

Development and Performance Analysis of Pine Needle Based Downdraft Gasifier System Abhishek Agrawal and Divyanshu Sood

Abstract Today’s Indian situation is facing an unprecedented energy crisis as India’s mainstream sources of energy continue to deteriorate with the limited stock of natural minerals posing a serious threat to the Indian economy. Out of available renewable sources of energy, biomass proves to be in satisfactory position for compensating voids for these natural resources. The present study deals with the performance analysis of throat less downdraft gasifier using pine needle as a feedstock material. The study investigates the various design modifications to allow efficient gasification of low-density fuel such as pine needle to eliminate the problem of agglomeration and channeling associated with it. In the present study, a comparison has been made between the performances of two type of grate design, i.e., flat plate and conical grate coupled with agitator rod and their combined effect on the flow rate of gases. The experiments and characterization of pine needle have been carried out at TERI Gram, New Delhi. It has been experimentally observed that by replacing flat grate with conical grate combined with agitator rod, the results got significantly improved. The results show 99.18% and 154.64% reduction in tar and dust, respectively. On the other hand, the gas flow rate and overall combustion efficiency improved by 59.07% and 62.5%, respectively. Keywords Pine needle · Proximate and ultimate analysis · Gasification · Thermal mode

13.1 Introduction Pine forests in India are spread over the Central Himalayan region and parts of Uttarakhand [1]. As per the report by Uttarakhand Forest Department, it was estimated that the state has approximately 3.43 lakh hectares of pine forests from which 20.58 lakhs tones of pine needles are produced annually [2]. It could be a cheap source of energy production and would also provide employment to local people. A. Agrawal (B) · D. Sood The Energy and Resources Institute, New Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_13

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Pine needles can be used as packing wool in fruits and vegetable boxes and also as bed material in cow shedding. A large amount of pine needle is available as litter in pine forests, and if mixed with the cow dung, it could be a good source of manure later. At some places, pine needle also used as check dams in which the needles are placed at 2 in. mesh coir net and form a cylindrical log [3]. The log is then used across eroding gullies, water streams that conserve moisture in the soil and also reduce calcium content in water. Pine trees are also a good source to produce chemical products such as pine oil, alpha-pinene, turpentine, and lingo-cellulose, and so on [4]. The motivation of using chir pine needle for heat as well as power generation is because of its inherent property which is harmful to the existence of existing forests in areas of Uttarakhand and Himachal Pradesh. One of the reasons is because of its high carbon content which intensifies the forest fire [5]. Also, the pine needles form a layer over the ground which prevents the rainwater from being absorbed into the soil which leads to early depletion of groundwater level. However, the irony is that the high carbon content which makes it a highly combustible can be a useful property during the gasification process. Table 13.1 shows the different types of gasification endothermic and exothermic reactions occur inside the gasifier reactor chamber. The use of pine needle as feedstock for biomass gasification is in the niche stage and is yet to be explored. Kala and Subbarao [8] estimated the feedstock potential of pine needle for power generation in the Central Himalayan state of Uttarakhand, India [8]. It was estimated that the annual gross pine needle yields 1.9 million tons while the annual net pine needle yield at 1.33 million tons. Few studies have been performed by researchers to evaluate the performance of gasifier and quality of producer gas by exploiting chir pin needle as feedstock. Dhaundiyal and Tewari [9] evaluated the performance of throat less gasifier using pine needle as feedstock for power generation and found that 12.8% and 0.1–0.5% of carbon dioxide and carbon monoxide, respectively, was emitted from the engine exhaust [9]. The similar kind of study was also performed by Dhaundiyal and Gupta [10] in which the producer gas from pine needle was generated and was used to run the engine [10]. The combustion efficiency was 76.66%. Kumar and Randa [11] also used chir pine needle to Table 13.1 Different gasification reactions in reactor [6, 7] Carbonation

Oxidation

Water gas shift

Methanation

1. C + CO2 ↔ 2CO + 172 kJ/mol (Bouduard reaction) 2. C + H2 O ↔ CO + H2 + 131 kJ/mol (Water gas reaction) 3. C + 2H2 ↔ CH4 —72.8 kJ/mol (Hydrogasification) 4. C + 1/2 O2 → CO—111 kJ/mol

1. C + O2 → CO2 —394 kJ/mol 2. CO + ½ O2 → CO2 —284 kJ/mol 3. CH4 + 2O2 ↔ CO2 + 2H2 O—803 kJ/mol 4. H2 + ½ O2 → H2 O—242 kJ/mol

CO + H2 O ↔ 1. 2CO + 2H2 → CO2 + CH4 + CO2 — H2—41.2 kJ/mol 247 kJ/mol Steam reforming 2. CO + 3H2 ↔ CH4 + H2 O— 1. CH4 + H2 O 206 kJ/mol ↔ CO + 3H2 + 206 kJ/mol 3. CO2 + 4H2 → CH4 + 2H2 O— 2. CH4 + ½ O2 165 kJ/mol → CO + 2H2 — 36 kJ/mol

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analyze the quality of producer gas. They performed 5 gasifier runs with different equivalence ratios to analyze the flow rate of producer gas and gas composition [11]. Based on the experiments, the authors suggested an optimized equivalence ratio of 0.251 for a downdraft gasifier. Syed et al. [12] calculated the cold gas efficiency of different feedstock using thermodynamic equilibrium approach within the entrained flow gasifier [12]. On the dry and ash-free basis, the efficiencies of RCT coal, pine needle, plywood, and lignite were 68.5%, 76.0%, 76.5%, and 74.0%, respectively. The State Government of Uttarakhand has formulated a policy titled “Policy for Power Generation from Pine Leaves and Other Biomass—2018” to promote energy production from pine needles [13]. This comprehensive policy aims to accelerate the growth of biomass-based power production in Uttarakhand by providing suitable leverages and benefits to the stakeholders. The objective behind the policy is to effectively utilize the pine needles to mitigate the forest fire that significantly affects the flora and fauna and also to reduce the ecological damage caused due to pine needles. The policy envisages achieving following targets by 2030: 1. 2. 3.

Setting up 1 MW plant by 2019 Setting up 5 MW plant by 2021 Setting up 100 MW plant by 2030.

Any practice made under the policy regulation will be considered as industry and entitled to the benefits prescribed under the prevailing Industrial Promotion Policy of Government of India and Uttarakhand micro, small, medium enterprise policy-2015.

13.2 Experimental Setup The setup consists of a downdraft gasifier having 400 mm effective diameter and 63.5 mm thick ceramic insulation inside the reactor and 1370 mm reactor height. The gasifier system consists of a hopper, reactor chamber, grate, and ash pit. Pine needle being a low-density feedstock, the system is installed with a suction blower, as shown in Fig. 13.1. Dust collector and cyclone were also fitted into the system to collect the dust and particulate matters travelling with the producer gas coming out of the outlet of the reactor chamber. Thermocouples were installed in the system and connected to a data logger to record the temperatures at different positions. Four nozzles were made equidistant from each other at a 1000 mm distance from the top of the reactor in order to combust the fuel at the beginning. Venturi was placed in between the cyclone and flaring to measure the gas flow rate. In addition, digital pressure differential manometers were also used to measure pressure at different points. The gas after passing through the cyclone and dust collector contains significantly less amount of dust and particulates matters, however, had a high temperature. Flaring was present after the cyclone, to burn the producer gas. After conducting a series of gasifier runs, certain modifications were made in existing design. The design of the gasifier and its accessories were modified so as to facilitate the proper gasification of low-density biomass, i.e., pine needle. The grate

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Fig. 13.1 Schematic of experimental setup developed for the present study

was placed below the hearth zone to descend down the ash into the grate basket preventing the ash from residing into the combustion zone for too long. Also, it maintains the gas production rate by controlling the drop-in reactor pressure. The design of the grate should be such that it allows ash to fall through it smoothly without bypassing the raw fuel into the ash pit.

13.3 Results and Discussions The characterization of pine needle was conducted at the biomass lab. The ultimate and proximate analysis of pine needle was compared with other commonly used feedstock materials for biomass gasification, demonstrated by Table 13.2. The results produced from the analysis and literature showed comparable values except for the bulk density. As wood is the traditional fuel for biomass gasifier, therefore, properties of pine needle and paddy straw were compared with the wood. Ash content of pine needle was in the same range as of wood while in paddy straw it was quite high. The calorific value of pine needle was almost the same as that of wood. It signifies that pine needle was quite suitable to extract energy out of it. However, very low bulk density is the major constraint to gasify loose biomass such as pine needle. In addition, paddy straw has very high ash content (11–29%) and silica content (84%) as compared to wood and pine needle. This shows gasification of paddy straw is quite challenging as compared to a pine needle. Therefore, pine needle could be considered as a promising fuel for the loose biomass gasification. Figures 13.2 and 13.3 demonstrate the temperature achieved inside the reactor and gas outlet temperature. It has been observed that although the temperature inside

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Table 13.2 Proximate and ultimate analysis of selected feedstock Properties

Wood

Pine needle

Literature Authors’ Literature values experimental values [9, [14–16] values 10, 17] Ash content 0.2–3 (%)

0.41

Paddy straw Authors’ Literature Authors’ experimental values experimental values [18–20] values

0.5–5.4

3.62

11–29

7.85

8.5

6–18

9.4

Moisture 8–17 content (%)

12.4

4–5.5

Volatile matter (%)

80–87

83.06

67–82.3

76.04

61–65

79.3

Fixed carbon (%)

11–18

4.12

15.5–17

11.7

15–29

3.4

C (%)

46–52

49.128

50–54

44.188

33–45

40.409

H (%)

5–7

6.492

5.5–7

5.244

4–7

N (%)

0.1–0.6

0.357

0.15–0.6

0.902

0.9–1.7

O (%)

40–45

44.023

32–41

49.666

46.71

52.899

HHV (MJ/kg)

18–20

15.5

19–20

20.23

14–15

12.3

Bulk density (kg/m3 )

188–277

Fig. 13.2 Flat plate grate

94.33

70–80

5.529 1.163

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Fig. 13.3 Conical grate

the reactor is higher in case of flat plate grate. However, lower gas outlet temperature was achieved when compared with the gas temperature in case of the conical grate. This was due to the accumulation of burnt feedstock over the grate that hinders the flow of gases, hence the lower temperature at the reactor outlet. The grate was shaken periodically to allow the ash to descend into the ash pit. In addition, it can also be revealed that whenever fresh feedstock filled into the reactor, the temperature increases abruptly. The mass flow rate is similar in both the cases, i.e., 13 kg/h. The system runs for an average of 5–6 h during an experiment with 3–4 h of smoke-free gas at flaring. Initially, when the system starts, it usually takes 1–2 h to reach the required temperature. Once the required temperature is achieved inside the reactor, the gas at the reactor outlet maintains a high temperature that leads to smoke-free gas at flaring. The modification in grate design, i.e., replacing flat plate grate with conical grate provides continuous gas flow as it reduces the accumulation of ash over the grate. Outcomes of both the cases are compared and given in Table 13.3 and can be concluded that the grate design and addition of agitator rod significantly improve the overall performance of the developed gasifier system.

13.4 Conclusions This paper concludes various modifications required to efficiently gasify pine needle to reduce the effect of channeling and agglomeration. While prospects of pine needle as feedstock for biomass gasification are very high, the challenges in technology implementation are also high. Because of its low density, the technology to capture its potential is in a niche state. With the modification in grate design combined with agitator rod, the following conclusions can be provided:

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Table 13.3 Performance comparison of gasifier in different conditions Properties

Flat plate grate Conical grate with agitator % Improvement

Ash (%)

7.2

3.5

Tar

(mg/Nm3 )

105.71

526.78

264.47

99.18

Dust (mg/Nm3 )

332.03

130.39

154.64

CV of gas (kJ/kg)

1.824

2.02

10.74 59.07

Gas flow rate

(m3 /h)

32.23

51.27

Max reactor temperature (°C)

787

755

4.23

Max gas outlet temperature (°C)

238

296

24.37

76.083

62.5

Gasifier combustion efficiency 46.82 (%)

1.

2.

3.

4.

The conical grate does not allow the ash to get accumulated over the surface of the grate. With timely shaking of the grate, the ash falls down into the ash pit that allows a smooth and continuous flow of producer gases at the outlet. The grate design and incorporation of the agitator rod significantly improves the thermal efficiency and flow rate of producer gases. There is a 62.5% improvement in gasifier thermal efficiency and 59.07% increase in gas flow rate. There is a potential reduction in tar and dust as well. The higher temperature of the producer gas facilitates the reduction in tar and passing the gas through dust collector and cyclone decreases the dust concentration. Due to its wide availability and support from Uttarakhand Government, pine needle has the potential to be considered for distributed power generation in the regions of Uttarakhand and Himachal Pradesh.

Acknowledgements The authors would like to thank the Indian Council of Agricultural Research, New Delhi for providing NASF funding to carry out this research. The authors would also like to thank The Energy and Resources Institute for providing a platform to establish the research facility and all other members at the organization involved in this project.

References 1. Forest fires impact typical Himalayan trees. https://india.mongabay.com/2019/05/forest-firesimpact-typical-himalayan-trees/. Accessed 14 July 14, 2020 (n.d.) 2. Pine needles based biomass gasifier “A Pilot Project”. https://ureda.uk.gov.in/pages/display/ 142-pine-needle-based-project. Accessed 18 Ap 2020 (n.d.) 3. (2) (PDF) Pine needle check dams for soil and water conservation. https://www.researchg ate.net/publication/341160365_Pine_needle_check_dams_for_soil_and_water_conservation. Accessed 14 July 2020 (n.d.).

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4. M. Chandran, A.R. Sinha, R.B.S. Rawat, Replacing controlled burning practice by Alternate methods of reducing fuel load in the Himalayan Long leaf Pine (Pinus roxburghii Sarg.) forests, in 5th International Wildland Fire Conference (2016) 5. A.S. Bisht, N.S. Thakur, Pine needle biomass gasification based electricity and cold storage systems for rural Himalayan region: optimal size and site. Int. J. Renew. Energy Technol. 8, 211 (2017). https://doi.org/10.1504/ijret.2017.10009899 6. V.S. Sikarwar, M. Zhao, P. Clough, J. Yao, X. Zhong, M.Z. Memon, N. Shah, E.J. Anthony, P.S. Fennell, An overview of advances in biomass gasification. Energy Environ. Sci. 9, 2939–2977 (2016). https://doi.org/10.1039/c6ee00935b 7. 5.1.3. Detailed Gasification Chemistry | netl.doe.gov. https://www.netl.doe.gov/research/coal/ energy-systems/gasification/gasifipedia/gasification-chemistry. Accessed 14 July 2020 (n.d.). 8. L.D. Kala, P.M.V. Subbarao, pine needles as potential energy feedstock: availability in the Central Himalayan State of Uttarakhand, India, in E3S Web Conf erence, vol. 23 (2017). https:// doi.org/10.1051/e3sconf/20172304001. 9. A. Dhaundiyal, P.C. Tewari, Performance evaluation of throatless gasifier using pine needles as a feedstock for power generation. Acta Technol. Agric. 19, 10–18 (2016). https://doi.org/ 10.1515/ata-2016-0003 10. A. Dhaundiyal, V.K. Gupta, The analysis of pine needles as a substrate for gasification, hydro Nepal. J. Water, Energy Environ. 15, 73–81 (2014). https://doi.org/10.3126/hn.v15i0.11299 11. A. Kumar, R. Randa, A. Professor, Experimental analysis of a producer gas generated by a chir pine needle (leaf) in a downdraft biomass gasifier. J. Eng. Res. Appl. 4, 122–130 (2014). www. ijera.com 12. S. Syed, I. Janajreh, C. Ghenai, Thermodynamics equilibrium analysis within the entrained flow gasifier environment. Int. J. Therm. Environ. Eng. 4, 47–54 (2011). https://doi.org/10. 5383/ijtee.04.01.007 13. P. Generation, P. Leaves, O. Biomass, I. Generation, C. Pine, L.M.T. Pine, V. Panchayat, L. Mt, P. Leaves, L. Mt, A.C. Residues, P. Generation, P. Leaves, O. Biomass, Government of Uttarakhand Energy Section-01 No. Dehradun (2018), p. 2, 06 (2018), pp. 1–32 14. E. Oveisi, S. Sokhansanj, A. Lau, J. Lim, X. Bi, F. Preto, C. Mui, Characterization of recycled wood chips, syngas yield, and tar formation in an industrial updraft gasifier. Environments. 5, 84 (2018). https://doi.org/10.3390/environments5070084 15. P.N. Sheth, B.V. Babu, Experimental studies on producer gas generation from wood waste in a downdraft biomass gasifier. Bioresour. Technol. 100, 3127–3133 (2009). https://doi.org/10. 1016/j.biortech.2009.01.024 16. H. Liu, R.J. Cattolica, R. Seiser, C. Hsien Liao, Three-dimensional full-loop simulation of a dual fluidized-bed biomass gasifier. Appl. Energy. 160, 489–501 (2015). https://doi.org/10. 1016/j.apenergy.2015.09.065 17. A.S. Bisht, S. Singh, S.R. Kumar, Pine needles a source of energy for himalayan region. Int. J. Sci. Technol. Res. 3, 161–164 (2014) 18. H. Zhou, A.D. Jensen, P. Glarborg, P.A. Jensen, A. Kavaliauskas, Numerical modeling of straw combustion in a fixed bed. Fuel 84, 389–403 (2005). https://doi.org/10.1016/j.fuel.2004.09.020 19. Rice straw, https://www.knowledgebank.irri.org/step-by-step-production/postharvest/rice-byproducts/rice-straw. Accessed 18 Apr 2020 (n.d.) 20. S. Krerkkaiwan, A. Tsutsumi, P. Kuchonthara, Biomass derived tar decomposition over coal char bed. Sci. Asia. 39, 511–519 (2013). https://doi.org/10.2306/scienceasia1513-1874.2013. 39.511

Chapter 14

Indian Energy Scenario and Smart Grid Development Kunal Chakraborty, Sanchita Mukherjee, and Samrat Paul

Abstract Energy demand post independence has faced a consistent pace with the growth of Indian economy. To maintain the national energy balance with sustainable development, it is time for various alternative energy resources with advanced devices like smart meters, smart grid model, etc., are implemented through different pilot projects only while the household is often underestimated it. India has aim to produce 175 GW energy through renewable energy power mix to reach the electricity to the all households at cheaper cost, and decentralization of smart grid-based system is needed to implement nationwide. This article represents a comprehensive study of present energy scenario in India, different energy policies which are associated with electricity generation, prospect of nationwide smart grid development. Keywords Energy demand · Renewable energy · Smart grid

14.1 Introduction The electricity market of India has witnessed a tremendous development in its energy demand, generation, transmission and distribution networks. The recent advancement technologies is deliberately deploying the advance devices and ICT infrastructure in the smart grid at the generation, distribution and transmission level. But, the historically India has been highly dependent on coal-based power plant for its energy needs. To meet the huge electricity demand of society at the reasonable cost, it is necessary to integrate the coal and renewable energy resources together to generate the electricity. New smart grid technology is an advance electricity network infrastructure which has the ability to integrate the various renewable energy sources like solar, wind, hydel, biomass, etc. with better reliability and conversion efficiency [1, 2]. This K. Chakraborty · S. Paul (B) Advanced Materials Research and Energy Application Laboratory, Department of Energy Engineering, North Eastern Hill University, Shillong, Meghalaya 793022, India e-mail: [email protected] S. Mukherjee Electrical Engineering Department, Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_14

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smart grid system can be controlled through the PLC-based automatic control system and which can be visualized through the SCADA-based technology [3, 4]. This smart grid is suitable for the household electricity generation as it has low operational cost and reduces the dependence on the fuels (mainly coal) [5]. This research article highlights the nation’s electricity demand in the various sectors and power mix, renewable energy prospect, the energy policy framework, smart grid-based pilot projects and the future outlook of Indian electricity sector.

14.2 Present Energy Scenario in India Due to its higher population index, India has the 4th rank in energy consumption in the world. Such huge demand in energy reflects in the growth of Indian economy. The energy consumption has grown approximately to about 270 times from 1950 to 2018 [6–8]. Initially, the energy demand was needed mainly by industries. But, now the power consumers are more diversified into the domestic, commercial, industry and agriculture sectors [9]. Such huge shift of demand is shown in Fig. 14.1. Despite such huge increment in the electricity demand, around 400 million people have no use to electricity in India. To fulfill the electricity demand of all, India needs to explore the renewable energy sources for the long-term sustainable development. Power mix of India’s power scenario is shown in Fig. 14.2. Total electricity generation capacity of India is 370,106 MW (370 GW) in the year of 2019–20. From Fig. 14.2, it is clear that India has been highly dependent on the thermal energy sources of around 62.8% (230.6 GW) [10, 11]. Energy production through renewable energy sources

Fig. 14.1 Nation’s electricity demand shifts from 1950 to 2018

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Fig. 14.2 Power mix of India’s power sector in the year of 2019–20

is around 23.5% (87 GW). Government of India has planned to shift the electricity generation up to 175 GW from the renewable energy sources by 2022.

14.3 Energy Policy-Making Institutional Framework in India The institutional framework of energy-related departments are controlled directly by the Prime Minister Office (PMO) and the ministry offices. Department of Atomic Energy (DAE) and NITI Aayog (official think tank of nation’s policy making) are directly run by the PMO [12, 13]. On the other side, Ministry of New & Renewable Energy (MNRE) has charge of the development of the non-conventional energy in India. The Ministry of Coal (MoC) is in charge of exploration and the development of coal/lignite-based resources in India, whereas the Ministry of Oil and Natural Gas (MoPNG) has oversight of policy building relating to oil and natural gas. An institutional framework of energy policy-makers in India is shown in Fig. 14.3

14.4 Smart Grid System Prospects in India The goal of India on the development of smart grids is to transform the power sector into an adaptive, safe, sustainable and digitally enabled ecosystem that offers a reliable and quality electricity for all the citizen of the nation with positive participation of stakeholders of the country. But, there are few challenges which exist in the

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Fig. 14.3 Institutional framework of energy policy-makers in India

implementation of the smart grid projects which include the capital costs and benefit issues within the same electricity cost to the consumer as noticed by the regulator [10, 11]. For successful implementation of smart grid projects, a continuous network connectivity is required to integrate the complete hardware system to manage the entire database system effectively. Huge demand of power with the requirement of advance technology in the grid network proposes the motive of creation of smart girds. With the development of smart grids, a two-way communication was created between the energy utility and customer requirement in generation, transmission and distribution. This research article shows latest development plans in the smart grid-based projects in India through the different agencies [12]. By realizing the importance of smart grid-based system for electrification, Ministry of Power (MoP) in 2010 has constituted India Smart Grid Task Force (ISGTF) and India Smart Grid Forum (ISGF) to handle the smart grid based pilot projects in India. In this contrast, Government of India in 2015 has started National Smart Grid Mission (NSGM) to plan and monitor the policies related to the smart grid projects. Total monetary allocation proposed under NSGM is 2400 crores till 2020, and with this fund, 12 smart grid-based pilot projects and 35 million smart meters have been implemented throughout the country. The details of this projects can be shown in Table 14.1.

14 Indian Energy Scenario and Smart Grid Development Table 14.1 List of smart grid-based projects implemented up to 2020

175

Projects

Implementing agency

Cost (USD)

Consumer base

CESC, Mysore

Enzen

5.05

21,824

UHBVN, Haryana

NEDO, Japan

5.57

11,000

HPSEB, Himachal Pradesh

Alstom

3.01

1554

APDCL, Assam

Phoenix IT

4.64

15,083

PSPCL, Punjab

Kalkitech

1.57

2737

WBSEDCL, West Bengal

Chemtrols

1.09

5265

TSECL, Tripura

Wipro

9.83

45,290

TSSPDCL, Telangana

ECU

6.48

11,906

PED, Puducherry

Dongfang

7.14

33,499

AVVNL, Ajmer

USAID PACE-DTA

–

1000

UGVCL, Gujarat

Genus and Fluentgrid

12.81

23,760

IIT-K Smart City

IIT Kanpur

1.94

20 Households

14.5 Future Outlook Indian energy sector is undergoing a radical transformation with the help of digitization, de-carbonization, democratization, role of battery. Through the digitization, smooth operation of data flows at all levels. An IoT-based smart grid system is the example digitization in power system. More and more implementation of renewable energy sources means less carbon emission and healthy environment. By using the democratization policy, roof-top PV module with microgrids is started to implement but need to accelerate the pace. An effective battery module will help to maintain a better grid management. Acknowledgements The authors are grateful to the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), India, for their financial support (EMR/2016/002430) to carry out this research work.

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References 1. V.C. Gungor, Smart grid technologies: communication technologies and standards. IEEE Trans. Ind. Inf. 7(4), 529–539 (2011) 2. N. Iksan, S.H. Supangkat, Home energy management system: a framework through context awareness, in ICT for Smart Society (ICISS) International Conference (2013), pp. 1–4 3. K. Chakraborty, M.G. Choudhury, S. Das, S. Paul, Development of PLC-SCADA based control strategy for water storage in a tank for a semi-automated plant. J. Inst. 15(4), 1–8 (2020) 4. K. Sayed, H.A. Gabbar, SCADA and smart energy grid control automation. Smart Energy Grid Eng. 481–515 (2017) 5. P. Purwanto, H. Hermawan, S. Suherman, Integration of solar energy supply on the smart home micro grid to support efficient electricity and green environment. IOP Conf. Ser.: Earth Environ. Sci. 239, 1–7 (2019) 6. G. Raina, S. Sinha, Outlook on the Indian scenario of solar energy strategies: policies and challenges. Energy Strategy Rev. 24, 331–341 (2019) 7. S.S. Chandel, R. Shrivastva, V. Sharma, P. Ramasamy, Overview of the initiatives in renewable energy sector under the national action plan on climate change in India. Renew. Sustain. Energy Rev. 54, 866–873 (2016) 8. India 2020—Energy Policy Review (NITI Aayog, 2020). https://niti.gov.in/IEA-India2020-indepth-Energypolicy_0.pdf 9. I.S. Jha, S. Sen, R. Kumar, Smart grid development in India—A case study, in National Power system Conference (2014), pp. 1–6 10. Copy of ES March 2017 (Central Electricity Authority, 2017). https://www.cea.nic.in/reports/ monthly/executivesummary/2017/exe_summary-03.pdf 11. India Smart Grid Forum (Booklet). https://www.indiasmartgrid.org//reports/Smart%20Grid% 20Vision%20and%20Roadmap%20for%20India.pdf 12. India Country Report on Smart Grids (DST). https://dst.gov.in/sites/default/files/India%20C ountry%20Report%20On%20Smart%20Grids.pdf 13. Ministry of New and Renewable Energy. https://mnre.gov.in

Chapter 15

Applications of Machine Learning in Harnessing of Renewable Energy Chris Daniel, Anoop Kumar Shukla, and Meeta Sharma

Abstract Nonrenewable sources of energy are depleting rapidly as the ratio of their consumption is higher than its reproduction as it takes millions of years to replenish, high demand, and utilization in agriculture and industries have affected the environment drastically. To fulfill the growing demand and necessity of energy for the continuous process of production and manufacturing promoted the use of renewable sources of energy, but renewable sources of energy need more development to be used with its full potential despite its massive availability. Fortunately, evolution in artificial intelligence has conferred scientists and developers to come up with several methods and improvements in the effective, efficient, and optimized usage renewable energy sources. This paper explores the areas of machine learning application for the effective harnessing of renewable energy sources such as wind energy, geothermal energy, solar energy, and wave energy. Keywords Machine learning · Renewable energy · Solar energy · Wind energy · Artificial intelligence

15.1 Introduction With time demand and consumption of energy sources are increasing, as a result, it is affecting our environment, drastically creating severe imbalances in nature, and the energy that is majorly used is of nonrenewable sources. The replenishment of these energy sources takes hundreds, and millions of years, the consumption rate is higher than its replenishment. Considering these factors gave rise to the methods to use renewable energy sources, despite its undeniable advantages and its colossal availability across the world renewable sources. Intact utilization of renewable energy to its full potential is affected due to some significant drawbacks such as discontinuous generation, atmospheric variations, geographic limitations, and expensive setups. However, advancements in technology lead to the discovery of various methods C. Daniel · A. K. Shukla (B) · M. Sharma Department of Mechanical Engineering, Amity University Uttar Pradesh, Noida 201313, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_15

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for effective and efficient utilization of renewable energy. Expansion in computational technology led to the hybrid integration of hardware and software that reduced the complexity in methods to utilize the energy resources and made more efficient and effective systems to control, monitor, and optimize the utilization of renewable sources of energy [1]. Plans for large scale implementation of renewable energy sources must incorporate the scheme for integration systems that entice efficiency and saves energy [2, 3]. Wind power systems have relatively lowest greenhouse gas emission; consumption of water is minimal and in most of the cases does not require water. It is most sustainable in comparison to others but only set back is that it requires a large geographic area and expensive setups [4]. Geothermal energy across this earth is in exuberance but only a small percentage of is utilized that is converted to electricity, and in recent years’ geothermal energy, the capacity of energy production is increased considerably due to system known as enhanced geothermal systems [5]. Solar energy is one of the most used renewable sources of energy used across the world as it does not harm the environment. It has many advantages and various methods of utilization. Nevertheless, due to its higher cost, sometimes it is not practical to use for selling solar energy commercially. Apart from these, it also contributes to the daily household power supply and other domestic purposes [6]. However, there are many attempts done to establish a solar-based power plant [7]. Shukla et al. [3, 8] studied solar-powered triple combined power cycle in which solar energy is used as the primary heat input to generate emission free power. The efficiency of solar energy generation depends upon the several factors such as direct normal irradiance (DNI), types of solar collectors used to accumulate the solar beams, the change of atmospheric conditions. Our planet’s vast area is covered with water known as oceans and ocean comprises of continuous waves and are a vast primarily uncalibrated source of energy. The utilization of energy from waves is significant, and for the conversion of the waves to energy, wave energy converter (WEC) is used [9]. There is the use of wave energy generator that helps in the production of electric power form the wave motion in a way that waves flow through one direction through a combination of flow powered motor generator [10]. As a result, the extraction of renewable energy sources is instantaneously taking pace machine learning techniques are widely used to comprehend with the issues that are related to the generation and integration of renewable sources of energy [11]. As machine learning provides a versatile and flexible and easily accessible framework and provides high accuracy and speed and coping with uncertainties handling extensive data and workflow. It also helps in creating an automated system to control the energy conversion process by analyzing the material, coupling large and small time and length scales [12]. In this study, an effort is made to explore current areas of machine learning application and contribution of machine learning technology in harnessing of renewable energy. The paper also discusses the role of artificial intelligence and machine learning for increasing the effectiveness and efficiency of various processes involved in renewable energy conversions.

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Table 15.1 Techniques used in machine learning renewable sources of energy Techniques used

Used to

ANFIS

Adaptive neuro fuzzy inference

Manage and optimize

FA

Fuzzy ARTMAP

Networking

WT

Wavelet transform

Optimize and analyze data

SCADA

Supervisory control and data acquisition

Data analysis and management

DNN

Deep neural network

Data networking and handling

K-cluster

K is the number of clusters in data set

Signal processing

SVM

Support vector machines

Data handling and analysis

KELM

Kernel extreme learning machine

Prediction

NMS

Nelder mead method

Function to find minimum and maximum of objective function

IOT

Internet of Things

To monitor and control operations

LSTM

Long short-term memory

Prediction, machine translation data handling

EA

Evolutionary algorithm

Optimizing

CMS

Condition monitoring systems

Monitor

15.2 Machine Learning Research in machine learning (ML) is now advancing at a more significant phase in multiple directions [13]. ML can handle massive data and big dimensional problems. It also reduces wastage and time cycle [14]. Using wireless sensor networks (WSN’s) in ML makes it more dynamic and convenient [15]. Deep learning algorithms, AI and ML all together transform the practice to be active and efficient [16] (Table 15.1).

15.3 Machine Learning in Wind Energy Precise assessment of wind energy potential is necessary the knowledge of wind speed and direction is very much essential [17]. Using MATLAB statistics toolbox, the regression tree model to plot simple branching paths to predict the wind turbine power output based on inputs and works effectively with unknown conditions using this model attained three times more accurate power predictions [18]. Using CMSs which helps in monitoring vibration and oil level of the turbine and using SCADA, we can obtain the turbine performance data and analysis such as temperature, pitch, and rotor data along with fault alarming for fault fee process [19]. To solve wind power issues, application of ANFIS works best also for the efficient operation of the power generation system [20]. A hybrid

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Fig. 15.1 Traditional approach of acquiring wind energy [23]

Fig. 15.2 Machine learning approach of acquiring wind energy [23]

technique to acquire wind speed that can be achieved by the ML algorithm known as (WT) and (FA) with precise prediction [21]. ANN helps in dealing with varying environmental conditions in every aspect by processing intricate behavior patterns and learn through experience [22]. Figures 15.1 and 15.2 is an example of smart wind energy production with the help of the ML model developed by the technology from Vayu and Emerson project based on California.

15.3.1 Application of ML in Wind Energy • • • • •

Wind energy potential assessment based on wind direction modeling and ML. Using ML to predict wind turbine power output. Diagnosing wind turbine faults using ML techniques applied to operational data. ML applications for load, price, and wind power prediction in power system. Wind speed forecast model for the wind farm based on a hybrid ML algorithm.

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15.4 Machine Learning in Geothermal Energy ML helps in revealing the patterns in time-dependent signals identification of any faulty process at geothermal field [24]. ML techniques help to quickly and accurately define the thermal energy that can reasonably be provided for different geometries determining the thermal load distributions [25]. We can determine and predict reservoir temperatures using DNN [26]. ML helps to know the functionality between the different components of the geothermal plant, specifically the reservoir tanks using K-cluster algorithms [27]. The ML can improve the performance in isobaric condition and prediction of single flash ecosystem just with temperature and pressure data can able to tell the power generation as geothermal smart grid is a candid application for machine learning [28].

15.4.1 Application of ML in Geothermal Field • Machine learning reveals cyclic changes in seismic source spectra in geysers geothermal field • A machine learning approach to energy pile design • Prediction of reservoir temperatures using hydrogeochemical data an ML approach. • ML for creation of generalized lumped parameter tank models of low-temperature geothermal reservoir systems • The machine learning model for improving single flash geothermal energy production Figures 15.3 and 15.4 show the workflow of ML in single flash geothermal energy production and performance of the system, and Figs. 15.5 and 15.6 show the effect of introducing the ML in the workflow that improved the performance of the system. Fig. 15.3 Flash ML model for enthalpy [28]

182 Fig. 15.4 ML model for entropy [28]

Fig. 15.5 Turbine work versus temperature [28]

Fig. 15.6 Power generated [28]

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ML determines the life, depth, enthalpies, entropies, and mass flow rate, turbine work, turbine efficiency, dryness fraction, and grid power thus lead to rich power production [28].

15.5 Machine Learning in Solar Energy As solar energy is the widely used renewable source of energy which can be obtained through photovoltaic cell or other thermal systems, support vector machine (SVM) is an ML technique used for management of energy generation as shown in Figs. 15.7 and 15.8 [29]. Fault diagnosis is vital for solar-powered platform that uses photovoltaic cells using KELM, and NMS method is used to optimize KELM model; with this, it can attain higher accuracy and reliability [30]. Smart monitoring devices (SMD) that connects the system with Internet of things (IoT) can provide mobile analytics, control of the solar farm, helps in detecting and fixing faults, can optimize power under varying conditions and helps in reducing inverter transients [31]. PV is also used to convert solar energy store energy for the long term and does not cause greenhouse gas emission during operation neither produce pollutants [32]. ANN methods can handle nonlinear systems due to its practical training, but SVM was found to the best machine learning technique to overcome the limitations of ANN thus helped in improving the output of PV cells [33].

Fig. 15.7 Structure of proposed machine learning methodologies and application [32]

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Fig. 15.8 ML application of fault diagnosis in PV cells based on Ref. [30]

15.6 Machine Learning in Wave Energy Deep learning comprising the long and short-term memory LSTM algorithm and principal component analysis helps in predicting the generation of power from a wave energy converter; these are some machine learning methods [34]. Implementation of controlling wave energy is difficult for this the artificial intelligence is used to overcome this problem by training the system with deep machine learning algorithms forecasting the short-term memory with this average energy absorption has been increased effectively [12, 23, 35]. EA successfully optimizes the controls even without pre-assumptions for real-time on the sea; this algorithm evolves after the installation as this is found to be a generic approach for WEC [36]. Nonlinear autoregressive with exogenous input network is used for the prediction of waves [37]. Machine learning is used for addressing sensitivity and uncertainties with ANNbased controller, evaluation of real-time wave elevation, and sensitivity based on wave frequency horizon length using ML [11, 38].

15.6.1 Application of ML in Wave Energy • Integrated deep learning model for predicting electrical power generation from wave energy converter. • Evolutionary algorithms for the development and optimization of control systems for wave energy converter.

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15.7 Conclusion Renewable energy sources are ecofriendly and easily available, with machine learning system became more efficient and effective extraction of energy as it is one of the most vital tools to create an optimized system and increasing systems performance. With the help of ML, planning is easy. ML has self-learning and accurate predictive ability. ML helps the system to evolve with varying conditions. It provides incredible flexibility to create powerful digital signal processing systems due to which system efficiency increases. ML enables the system to evolve with the process and improves the functionality of the entire system. In future, the advancement in ML will bring more powerful and improved systems for renewable power generation.

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16. J.M. Helm, A.M. Swiergosz, H.S. Haeberle, J.M. Karnuta, J.L. Schaffer, V.E. Krebs, A.I. Spitzer, P.N. Ramkumar. Machine learning and artificial intelligence: definitions, applications, and future directions. Curr. Rev. Musculoskel. Med. 1–8 (2020) 17. P. Krömer, S. Misak, J. Stuchly, J. Platos, Wind energy potential assessment based on wind direction modelling and machine learning. Neural Netw. World 26(6), 519 (2016) 18. A. Clifton, L. Kilcher, J.K. Lundquist, P. Fleming, Using machine learning to predict wind turbine power output. Environ. Res. Lett. 8(2), 024009 (2013) 19. K. Leahy, R.L. Hu, I.C. Konstantakopoulos, C.J. Spanos, A.M. Agogino, Diagnosing wind turbine faults using machine learning techniques applied to operational data, in 2016 IEEE International Conference on Prognostics and Health Management (ICPHM) (IEEE, 2016), pp. 1–8 20. M. Negnevitsky, P. Mandal, P., A.K. Srivastava, Machine learning applications for load, price and wind power prediction in power systems, in 2009 15th International Conference on Intelligent System Applications to Power Systems (IEEE, 2009), pp. 1–6 21. A.U. Haque, P. Mandal, J. Meng, M. Negnevitsky, Wind speed forecast model for wind farm based on a hybrid machine learning algorithm. Int. J. Sustain. Energ. 34(1), 38–51 (2015) 22. J. Ferrero Bermejo, J.F. Gómez Fernández, F. Olivencia Polo, A. Crespo Márquez, A review of the use of artificial neural network models for energy and reliability prediction. A study of the solar PV, hydraulic and wind energy sources. Appl. Sci. 9(9), 1844 (2019) 23. https://www.iotm2mcouncil.org/emerwind 24. B.K. Holtzman, A. Paté, J. Paisley, F. Waldhauser, D. Repetto, Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field. Sci. Adv. 4(5), eaao2929 (2018) 25. N. Makasis, G.A. Narsilio, A. Bidarmaghz, A machine learning approach to energy pile design. Comput. Geotech. 97, 189–203 (2018) 26. F.S.T. Haklidir, M. Haklidir, Prediction of reservoir temperatures using hydrogeochemical data, Western Anatolia geothermal systems (Turkey): a machine learning approach. Nat. Resour. Res. 1–14 (2019) 27. Y. Li, E. Júlíusson, H. Pálsson, H. Stefánsson, A. Valfells, Machine learning for creation of generalized lumped parameter tank models of low temperature geothermal reservoir systems. Geothermics 70, 62–84 (2017) 28. A.C. Muhammada, K.H. Kabirb, A.A. Allic, Machine learning model for improving single flash geothermal energy production: a case of Indonesia 29. H.A. Kazem, J.H. Yousif, M.T. Chaichan, Modeling of daily solar energy system prediction using support vector machine for Oman. Int. J. Appl. Eng. Res. 11(20), 10166–10172 (2016) 30. Z. Chen, L. Wu, S. Cheng, P. Lin, Y. Wu, W. Lin, Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and IV characteristics. Appl. Energy 204, 912–931 (2017) 31. A.S. Spanias, Solar energy management as an Internet of Things (IoT) application, in 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA) (IEEE, 2017), pp. 1–4 32. A.J. Trappey, P.P. Chen, C.V. Trappey, L. Ma, A machine learning approach for solar power technology review and patent evolution analysis. Appl. Sci. 9(7), 1478 (2019) 33. M.N. Akhter, S. Mekhilef, H. Mokhlis, N.M. Shah, Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques. IET Renew. Power Gener. 13(7), 1009–1023 (2019) 34. C. Ni, X. Ma, J. Wang, Integrated deep learning model for predicting electrical power generation from wave energy converter, in 2019 25th International Conference on Automation and Computing (ICAC) (IEEE, 2019), pp. 1–6 35. G. Ibarra-Berastegi, J. Saénz, G. Esnaola, A. Ezcurra, A. Ulazia, Short-term forecasting of the wave energy flux: Analogues, random forests, and physics-based models. Ocean Eng. 104, 530–539 (2015) 36. K. Gunn, C.J. Taylor, C. Lingwood, Evolutionary algorithms for the development and optimisation of wave energy converter control systems, in Proceedings of the 8th European Wave and Tidal Energy Conference, Uppsala, Sweden (2009)

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Chapter 16

Optimization of Tilt Angles for Solar Devices to Gain Maximum Solar Energy in Indian Climate Digvijay Singh, A. K. Singh, S. P. Singh, and Surendra Poonia

Abstract Availability of maximum solar radiation can be ensured by optimizing the tilt angles for a given location. Most of the optimization techniques are based on the available theoretical models. Keeping this in view, tilt angles were optimized for composite climate (Nagpur and Delhi) and hot and dry climate (Jodhpur), India, using actual solar radiation data of India Meteorological Department (IMD). The optimization of tilt angle is done by establishing a polynomial relation between tilt angle and solar radiation data for annual, bi-annual, seasonal, bi-monthly, and monthly tilts. The optimum tilt angles for New Delhi and Nagpur were found as  − 5° and  + 4°, respectively, while for Jodhpur it was  + 4° for south facing. The highest solar radiation was predicted for monthly tilt. However, total solar radiation for bi-annual tilt was also found very close to that of monthly optimum. According to the analysis carried out, it is recommended to have bi-annual tilt (zero tilt from April to September and 42°–49° degree tilt from October to March). Keywords Latitude () · Solar radiation · South facing · Optimum tilt angles · Composite · Hot and dry climate

16.1 Introduction The energy demand is going up with the time in agriculture, domestic, rural, and industrial sectors. It is also well known that increased use of conventional energy sources is leading to the release of a high amount of pollutants. In such a situation, solar energy can be used as an optional energy source. India has abundant solar radiation due to being located near the equator. It is a prerequisite for researchers to estimate the exact values of solar radiation on the surface with different tilt angles and also the Optimum Tilt Angle (OPTA) for a particular location. During summer, D. Singh (B) · S. P. Singh School of Energy and Environmental Studies, Devi Ahilya University, Indore 452001, India e-mail: [email protected] A. K. Singh · S. Poonia ICAR-Central Arid Zone and Research Institute, Jodhpur 342003, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_16

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about 90% of India receives 3.5–6.0 kWh m−2 day−1 of radiation, and the northern part may go up to 7.5 kWh m‘2 day−1 [1]. The mean daily global solar radiation of 5 kWh m−2 day−1 is available on about 58% part of India, having the potential for solar energy applications [2]. The Government of India launched the Jawaharlal Nehru National Solar Mission (JNNSM) targeting to harvest as much as 175,000 MW of energy by the end of 2022, in order to make India a leader in solar power [3]. When solar radiation is captured on an optimally inclined surface, it can ensure maximum radiation, which could be used for desalination, drying, cooking, and rooftop PV plants, etc. The optimum tilt can improve the performance of solar devices to a great extent. Solar Radiation (SR) is directly converted into electricity by the PV system, which can be easily used. The system is a useful choice for both rural and urban locations, including building integrated and attached applications. The SR depends on locations, orientations, and the tilt angle of the solar devices like Stand-Alone Photovoltaic (SAPV). Therefore, the Optimum Tilt Angle (OPTA) needs to be determined to ensure the gain of maximum SR [4]. The monthly average daily SR on the horizontal plane of Indian cities was calculated by the authors [5]. Thakur and Chandel [6] optimized the angle of tilt for a 190 kWp plant located at Khatkar-Kalan in India. They found that the annual, seasonal, and monthly tilts gave 25, 28, and 29% higher energy as compared to fixed tilt (25°). Yadav and Chandel [7] tried out the various models of diffuse SR to calculate OPTA for Hamirpur, Himachal Pradesh, India. It was observed that Liu and Jordan model is found the best with only a 4.5% error in predicted and observed SR values. Maximum insolation is ensured by setting the azimuthal angle between 10° and 20° and fixing the solar device at latitude. A number of thumb rules were followed as concrete information on tilt angle was not available [8–11]. Pandey and Katiyar [12, 13] tried different models for predicting diffuse radiation on the various tilted surfaces for Lucknow, India (26° 0.75 N, 80° 0.50 E). From the analysis, it was observed that Kulcher’s model gave the best prediction. Agrawal et al. [14] used the Liu and Jordan model to calculate radiation on a tilted surface and found that daily optimum tilt has only 4.5% more radiation than monthly tilt. Jamil et al. [15] optimized the tilt angle of Aligarh, India (27° 0.89 N, 78° 0.08 E) and compared it with that of New Delhi (capital of India, 28° 0.61 N, 77° 0.20 E) and found that monthly and seasonal optimum tilts provided substantial gain in radiation over annual optimum. Herrera-Romero et al. [16] estimated the optimum tilt angles requiring minimum adjustment only. It was observed that the monthly adjustment has only 0.15% energy loss as compared to daily adjustment. The authors [17] observed that the gain in annual solar energy harvest was about 4.28 7.06%, and 8.42 higher than latitude when inclined according to bi-annual, tri-monthly, and monthly tilts, respectively. In this paper, the optimization of the tilt angle for maximum SR has been estimated for New Delhi, Nagpur, and Jodhpur. For the estimation, global SR data of the Indian Meteorological Department (IMD) have been used at different tilt angle to develop a polynomial relationship between the SR and tilt angle. The obtained tilt angles are compared with those given by others. Also, the losses in SR at monthly, seasonal,

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Table 16.1 Criteria for the classification of climatic [19] Climate

Temperature (°C)

Relative humidity (%) Number of clear days

Hot and dry

>30

20

Warm and humid >30

>55.5

N, then go to 12, else go to 4 12. call the matrix from 7 13. assign the best-fit communication technology using β as per Eq. (43.3) 14. end

Desirability factor  βi j =

1 − CFi j  F  j=1 1 − CFi j



W ∗NDRi j )+(WDelayi j∗ NDelayi j ) 1 − ( DRi j W ( DRi j +WDelayi j ) = WDRi j ∗NDRi j )+(WDelayi j∗ NDelayi j ) ( F j=1 1 − (WDRi j +WDelayi j )

(43.3)

43.3.3 Flowchart The assessment approach described by algorithm presented in Table 43.1 has been depicted in flowchart form in Fig. 43.1. Considering data rate and latency as key performance indicators, cost function is formulated for each critical application. The value of cost function indicates the fitness (capability) of the communication technology to satisfy the communication requirements of the critical application. Lower the CF value, better the given communication technology satisfying the communication requirements of given critical application. Communication technologies with negative and higher CF values have been ignored as the same indicates limitation of the technology.

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Fig. 43.1 Flowchart for assessment approach

43.4 Assessment of Indian Smart Grid Pilots State-wise total 12 smart grid pilot projects have been implemented, while 5 large scales NSGM projects have also being implemented with RF (ZigBee 868 MHz) and GPRS (Cellular 2.5G) technologies. Communication requirements for implemented/selected critical applications have been evaluated. Next, capacities of all the options of wireless communication technologies to satisfy the communication requirements of critical applications have been assessed. Finally, four best-fit technologies have been recommended.

43.4.1 Disclosures and Considerations Following disclosures and considerations have been made: (i) (ii)

Data rates and latency have been selected as KPIs for this analysis. Data rate and latency range values for smart grid communication technologies have been adopted from various reports of Department of Energy (DoE),

43 Development of an Assessment Tool to Review …

(iii)

(iv) (v)

569

USA, Utilities Telecom Council (UTC) and National Institute of Standards and Technology (NIST), USA. Higher side values of bandwidth and lower side values of latency have been selected to make efficient selection of smart grid communication technology for each critical application. Rule for decision making: “Lower the CF value better fitness” however negative CF values have been ignored. SCADA and MDMS have been included as default critical applications.

43.4.2 Assessment Results For each critical application of every smart grid pilot/project, CF has been calculated and top four best-fit communication technologies have been identified and the best technology is recorded in Table 43.2. After assessment, it could be observed that LTE has emerged as the most suitable communication technology for catering communication needs of smart grid pilots/projects located in different geographic locations of India with different consumer strengths and critical applications. In case of unavailability of LTE, Wi-Fi, Satellite (LEO) and WiMAX should be preferred in the order of preference, on the basis of technical suitability and improvement envisaged.

43.4.3 Summary From architectural point of view, analyzed installations are all NANs, bridging HANs and WANs. Many a times, instead of HANs, either IANs or FANs are also existing which could also be connected to backbone WANs via NANs only. In all such cases, in general, NANs are expected to serve large bandwidth data with critical latency requirements. Higher CF values for the deployed technologies—GPRS and RF indicate their limitations to cater NANs, although they could be good choices for HANs. Substantial impact on the performance could result, when the pilot installations would be up-scaled including large number of customers to run on full scale basis with requirements to cater huge data traffic with stringent latencies. LTE, Wi-Fi, Satellite, and WiMAX have emerged as better options (in order of preference), due to their very low CF values as compared to implemented GPRS and RF with high CF values.

Utility, location, State

AVVNL, Ajmer, Rajasthan

APDCL, Guwahati, Assam

CESC, Mysore, Karnataka

HPSEB, Kala Amb, Himachal Pradesh

PED, Puducherry

TSECL, Tripura

TSSPDCL, Telangana

UHBVN, Haryana

UGVCL, Gujarat

Sr. No.

1

2

3

4

5

6

7

8

9

22,230

11,000

11,904

45,290

34,000

1251

21,824

15,083

1023

Consumers

LTE

LTE

LTE

LTE

LTE

LTE

LTE

LTE

LTE

AMI

LTE

×

LTE

LTE LTE

LTE

LTE

×

×

LTE

LTE

LTE

LTE

LTE

LTE

×

×

LTE

PLM

OM

×

×

× × ×

× × ×

LTE × LTE

×

×

×

×

×

×

×

×

×

×

×

×

×

×

DERS

×

DR

×

×

×

SAT

×

×

SAT

×

DGM

LTE

×

PQM

×

×

×

×

×

×

×

×

×

SA

×

×

×

×

×

×

×

×

×

HEM

×

×

×

×

×

×

×

×

×

DA

Critical applications and suitable suggested communication technologies

Table 43.2 Summary of assessment of smart grid pilots/projects in India

×

×

×

×

×

×

×

×

×

EVCI

×

×

×

×

×

×

×

×

×

DTM

LTE

LTE

LTE

LTE

LTE

LTE

LTE

LTE

LTE

SCADA

(continued)

LTE

LTE

LTE

LTE

LTE

LTE

LTE

LTE

LTE

MDMS

570 J. Bhatt et al.

Utility, location, State

WBSEDCL, West Bengal

IIT Kanpur, Uttar Pradesh

SKGC, Manesar, Haryana

Sub Division 5 under CED, Chandigarh

Complete City excluding Sub Division 5 under CED, Chandigarh

Ranchi City under JBVNL, Jharkhand

Rourkela City under OPTCL, Odisha

Sr. No.

10

11

12

13

14

15

16

Table 43.2 (continued)

87,000

360,000

184,000

LTE

LTE

LTE

LTE

LTE

100a

30,000

LTE

LTE

AMI

× ×

× ×

×

×

×

×

× LTE

×

×

×

×

×

×

×

×

×

LTE

×

PQM

PLM

OM

×

×

×

×

×

SAT

×

DGM

×

×

LTE

×

×

LTE

×

DR

×

×

×

×

×

SAT

×

DERS

×

×

×

LTE

×

LTE

×

SA

×

×

×

×

GSM

GSM

×

HEM

×

×

×

×

×

LTE

×

DA

Critical applications and suitable suggested communication technologies

17,000

5265

Consumers

×

×

×

×

LTE

×

×

EVCI

LTE

LTE

×

LTE

×

×

×

DTM

LTE

LTE

LTE

LTE

LTE

LTE

LTE

SCADA

(continued)

LTE

LTE

LTE

LTE

LTE

LTE

LTE

MDMS

43 Development of an Assessment Tool to Review … 571

150,000

Consumers

for analysis purpose

6 Towns under JVVNL, Rajasthan

17

a Estimated

Utility, location, State

Sr. No.

Table 43.2 (continued)

LTE

AMI

PLM ×

OM ×

×

PQM ×

DGM ×

DR ×

DERS ×

SA ×

HEM ×

DA

Critical applications and suitable suggested communication technologies ×

EVCI ×

DTM LTE

SCADA

LTE

MDMS

572 J. Bhatt et al.

43 Development of an Assessment Tool to Review …

573

43.5 Conclusions, Recommendations, and Future Directions 43.5.1 Conclusions NANs have been usually required and therefore should be designed to handle large data rates with critical latency requirements. Selected/implemented communication technologies RF (ZigBee 848 MHz) and GPRS (Cellular 2.5G) looking technically weaker, due to their inability to cater high data rates with strict latency. Communication performance might degrade drastically in the future when pilots/projects would run at full scale including all consumers and applications.

43.5.2 Recommendations Considering present and future requirements, in the order of preference, LTE, Wi-Fi, Satellite (LEO), and WiMAX are recommended on the basis of their high data rate capacities with better latency performance.

43.5.3 Future Directions The algorithm, analysis, and results presented in this work are based on two KPIs–data rate and latency. Further, critical applications selected by Indian smart grid installations have been analyzed for 11 wireless communication technologies. This work could be extended by considering more KPIs, critical applications, and communication technologies. Acknowledgements The authors express their sincere thanks to Dr. Chetan Bhatt, Prof.-IC Engineering and Principal, Government MCA College, Ahmedabad for his valuable advice and continued cooperation. The authors gratefully acknowledge the timely support and help provided by Mrs. Kumud Wadhwa, Sr. General Manager, NSGM, Ministry of Power, Government of India for her kind advice and issuing permission to access data and publish our interpretations.

Appendix–1: Nomenclatures and Abbreviations

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Nomenclature

Description

i

Critical application index

j

Communication technology index

RNi

Data rate required for ith application

M

Maximum data rate among all the selected critical applications

PbpsNETj

Proportional data rate for a certain fixed amount of BW in using jth technology

NWLATi

Maximum latency permissible for ith application

MAXNLAT

Maximum value among all feasible communication technologies

TotLatii

overall latency value = RTTj + TP, RTT = Round Trip Time, TP = Processing Time

W DRij

Normalized data rate for ith application while using jth technology = RNi /M

N DRij

Normalized band width for ith application while using jth technology = RNi /PbpsNETj

W delayij

Latency weight for ith application while using jth technology = [1 − (NWLATi /MAXNLAT )]

N delayij

Normalized latency for ith application while using jth technology = [1 − (TotLatii /NWLATi )]

CFij

Cost function formulated for assessment of fitness of jth communication technology for ith critical application

Abbreviation

Description

AM

Asset management

AMI

Advanced metering infrastructure

CRRM

Common radio resource management

DA

Distribution automation

DERS

Distributed energy resources and storage

DG

Distributed generation

DM

Distribution management

DR

Demand response

DTM

Distribution transformer monitoring

ET

Electric transportation

FAN

Field area network

HAN

Home area network

HEM

Home energy management

HetNet

Heterogeneous network

HSDPA

High speed downlink packet access

IAN

Industrial area network

ITS

Instrumentation telemetry system

KPI

Key performance indicator (continued)

43 Development of an Assessment Tool to Review …

575

(continued) Abbreviation

Description

LEO

Low earth orbit

LTE

Long-term evolution

M2M

Machine to machine

MDMS

Meter data management system

NAN

Neighborhood area network

OM

Outage management

OT

Operational telephony

OTLM

Overhead transmission line monitoring

PHEV

Plug-in hybrid electric vehicle

PLM

Peak load management

PMU

Phasor measurement unit

PQM

Power quality monitoring

RAT

Radio access technology

SA

Substation automation

SCADA

Supervisory control and data acquisition

UTC

Utilities Telecom Council

VS

Video surveillance

WAMS

Wide area management system

WAN

Wide area network

WASA

Wide area situational awareness

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Chapter 44

Simulation and Analysis of Building Integrated Photovoltaic System for Different Climate Zones in India Priyanka Rai , Archana Soni , and Rushikesh Kamble

Abstract In the past, electricity use in the construction industry has risen to satisfy the demand and innovation techniques are being built to take advantage of renewable energy sources. The solar energy is the most available green energy supply. Renewable electricity options are the cleanest and decreasing greenhouse gases. Today, the innovative technique used by building designer is building integrated photovoltaic system (BIPV). BIPV is multifunctional components, they are not only use for energy production, and they also used as a cladding, shading devices, facades, and roofing element. BIPV system efficiency often depends on environment and regional conditions, such as solar irradiation, temperature, and altitude. This analyzes the impact of these climatic and geographic influences on the BIPV system. In this paper, six cities (Ahmadabad, Bangalore, Bhopal, Kolkata, Mount Abu, and Srinagar) from six Bansal and Minke climatic zones are selected and the efficiency of the BIPV system in six Indian climatic zones is evaluated by modeling and simulation by taking different parameters like latitude, a tilt angle, an azimuth angle, an environment and solar cell level. PVsyst 7.0 software is used for yearly energy yield calculations. Keywords BIPV · Solar energy · Solar cells · Facades · PVsyst

44.1 Introduction With the population, there is also growing need for electricity. Currently, the need for energy is met essentially by coal, foreign oil, and petroleum. These are nonrenewable, and thus, the non-permanent solution to the energy catastrophe also causes pollution. There is a need to go into the sources of electricity to assure the energy demand. Solar power is an abundant and clean form of all the renewable energy sources present among all the renewable energy sources. The overall consumption of electricity has collapsed where even the energy of the sun, which is around 1.81011 MW, does not satisfy current needs. This solar energy can be used directly in two ways: by capturing the radiant heat and using it in thermal P. Rai (B) · A. Soni · R. Kamble Energy Centre, MANIT, Bhopal, Madhya Pradesh 462051, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_44

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collectors or by directly capturing and turning it with the aid of photovoltaic systems into electric power. Photovoltaic cells will benefit from the solar spectrum up to 80–90% of the solar radiation available. Yet depending on the output capacity of PV cells, just 10–15% of the drawn incident solar energy is turns into electricity and remainder of the energy is evaporated as heat [1, 2]. The electrical efficiency reduces as the cell temperature increases. To get full electrical conversion efficiency, the solar photovoltaic panel must cool down temperature. Scientists used active and passive cooling techniques to incorporate several strategies for PV cooling. Passive cooling technique which used are paraffin wax, cotton wick, organic material, etc., whereas active technique which is used for cooling are water, air, nano-fluids, etc. By increasing the thermal conductivity of base fluids, nano-fluids are used to bring down the panel temperature [3]. New solar technologies are evolving, such as concentrated solar power (CSP) and hybrid technologies. Hybrid solar photovoltaic thermal (PVT) parabolic trough collector, using a-Si thin film solar cells, is intended for the Indian market [4]. Unlike other thin film solar cells, amorphous Si solar cells are easily available on Indian market at affordable prices. Due to their availability on the market in India and their sustainability at higher working temperatures, A-Si cells are used in PV-T hybrid systems [5, 6]. Photovoltaic technology has achieved significant development over the past year, particularly in the construction of integrated photovoltaic (BIPV) and integrated photovoltaic thermal systems [7]. (a) (b)

Building Added Photovoltaic system BAPV, Building Integrated I photovoltaic system BIPV.

BAPV is a building add-on, which is not directly related to the functional aspects of the structure. BAPV systems are two subcategories [7]. (a) (b)

Standoff—They are installed above the roof, and for the pitched roof, they are set parallel to the slope. Rack-mounted arrays—These are not only mounted over flat roofs but also are fashioned to get a tilt, as well as an optimum orientation for the application.

BIPV is a practical component of the structural structure or is incorporated architecturally into the architecture of the building. This group covers projects that replace traditional roofing materials such as shingles, bricks, slate, and roofing with concrete. They can be classified into two main categories: façade and roofing systems. The façade systems include products made from curtain walls, spandrel panels, and glazing. Roofing systems include tiles, shingles, seam stands, and skylights. BIPV’s energy performance is accessed through modeling and simulation, taking into consideration several parameters such as azimuth angle, tilt angle, cell type, and climatic zones. The goal of this paper is to research BIPV variability due to Indian climatic and geographic conditions. There are 29 states and 7 union territories in India; having the optimal BIPV program in both of these locals is hard to try [8, 9]. The main purpose of this paper is to find the best adapted PV technology for six different climate zones by contrasting the ability of various PV technologies

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according to India’s geographical and climatic conditions. While the towns (Ahmadabad, Bangalore, Bhopal, Kolkata, Mount Abu, Srinagar) randomly picked from six climate regions. We tried to choose cities at various latitudes and as well as one another and also reflect the characteristics of each climate region, in order to choose cities [10]. The data regarding climatic zones and selected six cities is given in Table 44.1 with belief of PV system installed on a building facade whose energy was measured. For device standardization and configuration optimization purposes, the author introduces a simplified schema of PV systems, as shown in Figure, to explain all the components required to construct a functioning grid connected network. The device balance involves the inverter (most of the usable inverters today have built-in MPPT), circuit breakers, interfaces, DC disconnections, AC disconnections (Fig. 44.1). Energy engineers and architects required precise data on solar radiation for photovoltaic enhancement and sizing. Impacting on device efficiency is critical factor. In Table 44.1 Selected six cities indifferent climatic zone S. No.

Climatic zone

City

Altitude (m)

Latitude (N)

Longitude (E)

1

Hot and dry

Ahmadabad

65

23.07° N

72.63° E

2

Moderate

Bangalore

902

13.20° N

77.70° E

3

Warm and humid

Kolkata

12

22.66° N

88.45° E

4

Composite

Bhopal

531

23.21° N

77.41° E

5

Cold and sunny

Mount Abu

1672

24.65° N

72.77° E

6

Cold and Cloudy

Srinagar

1658

34.01° N

74.76° E

Fig. 44.1 Schematics of the PV system

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this analysis, each location’s hourly solar radiation was measured using the numerical method provided in the prior study. The Indian State Meteorological Service took details of the average sunshine period from the towns. The estimated monthly levels of diffuse and global radiation at the chosen cities’ horizontal surfaces are shown in figure. The selected cities’ average monthly ambient temperatures are given in Table 44.2 (Figs. 44.2 and 44.3). In this paper, different PV technologies which are available globally were compared for six different climatic zones in India by using the simulation tool Table 44.2 Monthwise ambient temperature in °C (Average) Month

Cities Ahmadabad

Bangalore

Kolkata

Bhopal

Mount Abu

Srinagar

January

19.8

21.6

17.7

18.1

10.7

2.6

February

22.6

23.8

22.1

21

14.2

5.1

March

27.9

26.1

27

26.5

20

10.3

April

31.4

26.8

29.8

31

24.8

14.5

May

33.1

26.3

31.1

33.5

28.4

18.5

June

31.4

24

30.2

30.3

25.6

21.7

July

29.1

23.6

29.5

26.8

21.7

23.9

August

28.1

23.2

29.2

25.6

20.5

23.1

September

28.8

23.3

28

26.3

21.2

19

October

28.3

23.1

27

25.9

20.4

13.7

November

24.2

21.7

23.3

22.1

16

7.2

December

21.1

20.8

18.9

19.3

12.4

3.6

Fig. 44.2 Global solar radiation on flat surfaces in selected six cities (monthly)

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Fig. 44.3 Diffuse solar irradiation on flat surface in selected six cities (monthly)

(PVsyst) and the best-suited PV technology for a location which is selected in each climate zones was found. Following technologies were explained [13].

44.1.1 Monocrystalline [11, 14] Monocrystalline solar panel is made of a single crystal of silicon by a process called the Czochralski process. The crystal is highly ordered, and the lattice is continuous and unbroken. The efficiency of monocrystalline solar panel lies between 20 and 25%. These are space efficient; they need less compare to other PV technology for the same output. These PV panels perform well in low-light condition.

44.1.2 Polycrystalline [14] Polycrystalline cell is produced in a different way. They are packing about 1300 I pounds of silicone rocks into a 3-foot, quartz I mold to create a square shape. It takes about 20 h to melt, and about three days to cool down. The blue mottled look is caused by the cooling and hardening of the melted silicon, which crystallizes like frost on the window. These solar panels have the efficiency between 16 and 20%. This solar panel needs more space as compared to monocrystalline PV technology for the same output. They cost less as compared to monocrystalline PV technology.

582 Table 44.3 PV module consideration in simulation

P. Rai et al. Tech

Si-mono

Si-poly

a-si

Mfg

Solar fun

Greenway

Xunlight Corporation

Model

SF 100 M6-18/100 W

GW-100A

XR12-100

Power (Wp)

100

100

100

Array loss % at STC

10.62

10.80

6.33

44.1.3 Thin film [14] Thin film solar panel has the less efficiency as compared to crystalline solar panels. These solar panels have the efficiency between 6 and 12%. They include different type of solar cell: • • • •

IAmorphous-Silicon (a-Si) ICopper-Indium-Gallium-Serenade (CIS/CIGS) ICadmium-Telluride (CdTe) IOrganic-Photovoltaic-Cells (OPC) (Table 44.3).

44.2 Methodology For the simulation and analysis, 2 KWp building integrated photovoltaic system with fixed array type is used for six selected cities (Ahmadabad, Bangalore, Bhopal, Kolkata, Mount Abu, and Srinagar) in different climate zone. We made analysis using PVsyst software tool for three different photovoltaic technologies. The methodology is explained with the help of flow diagram as shown in Fig. 44.4.

44.2.1 PVsyst Software PVsyst is a simulation program developed I by the Swiss university EPFL. It is used for complete PV system study, sizing, and data analyzes. Using this method, the annual energy output of the mounted BIPV device was computed.

44.3 Result and Discussion Six cities from six climatic zones have been chosen to test climate impact on the BIPV system. The analysis was done in PVsyst. The program asks which kind of

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Methodology of work Choose the integration strategy Choose the PV technology globally available Simulate each technology with PVsyst Compare the results and find best PVtechnolgy according to Annual energy production (KWh)

Performance ration (%)

Array efficiency (%)

Module area (m²)

Fig. 44.4 Methodology of work analysis

PV system to analyze. The user chooses the stand alone system for project design. The PV panels are reached in spatial position and orientation. User needs, that is, the user’s energy consumption to design the project for. The size and type of PV array and charge controller must then be entered into the program. You should pick machine losses. Now you are ready for the simulation by entering the data program. Report is generated by doing the simulation. The analysis defines as number of parameters, the three main parameters that define the produced energy and the failure of the unit as follows: • • • •

Module area (m2 ) Energy produced per year (KWh\year) Performance ratio (%) Array efficiency (%).

The daily demand for electric charge is 318 watts and operated for an average of 6 h per day resulting in 2008 Who/day daily energy demand. Figure 44.5 displays a PVsyst screenshot of the average load requirement that is used in the analysis shown below.

44.3.1 Monthly Energy Variation Between Six Cities Apart from the annual energy variation of the six selected cities, the seasonal variation can be seen in Figure. In accordance with their climatic characteristics, by examining the monthly energy variation between the six selected cities, it is clearly shown that the worst case month for Ahmadabad are May, June, July, the month of summer according to PVsyst data. As mentioned in Table 44.4, the average ambient tempera-

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Fig. 44.5 User needs for the simulation

ture in this month is 31 °C. Hence, PV production is lower during these months than other months. Similarly, the worst month for Bangalore, Bhopal, Kolkata, Mount Abu, and Srinagar are same as Ahmadabad (Fig. 44.6). Figures 44.7 and 44.8 show the PV losses due to the irradiance level and thermal losses of the six selected cities from different climate zones of India. It can be seen in figure that there is less PV thermal losses by using thin-film technology as compared to monocrystalline and polycrystalline PV technology. Ahmadabad is having high thermal losses because it lies in the hot and dry climate, and the mean monthly temperature of hot and dry climate is >30 °C. Srinagar is having lowest thermal losses, and it lies in cold and cloudy climate and there mean monthly temperature is 900 m as the height increases temperature decreases, that is the reason for less PV thermal losses. Figure 44.8 shows the PV losses due to irradiance level, and it can be seen from the figure that there are high PV losses due to irradiance level in system with thin-film technology as compared to monocrystalline and polycrystalline PV technology. This effect is output energy of the system, so from this, it is clear that mono-crystalline and polycrystalline technologies are the best to integrate into the façade of the building.

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Table 44.4 Comparison of PV technology Location

Global Horizontal irradiance (kWh/m2 )

Technology

Produced energy (kWh)

Performance ratio (%)

Ahmadabad

1891.8

Mono-silicon

1353

35.43

9.44

15.1

Poly-silicon

1375

35.67

9.96

15.5

a-si

1367

35.65

5.83

25.3

Mono-silicon

1407.5

34.07

9.45

17

Poly-silicon

1428.4

34.33

9.02

16.8

a-si

1428.2

34.32

5.81

28.5

Mono-silicon

1353.8

35.08

9.43

15.1

Poly-silicon

1375.4

35.33

9.96

15.4

a-si

1372.5

35.29

5.83

25.3

Mono-silicon

1217

40.29

9.86

14.6

Poly-silicon

1229

40.53

9.96

15.1

a-si

1219

40.6

5.83

25.3

Mono-silicon

1507

33.2

9.43

15.1

Poly-silicon

1527.7

33.46

9.97

15.1

a-si

1530

33.42

5.83

25.3

Mono-silicon

1104.2

44.6

9.37

10.4

Poly-silicon

1116

46.83

10.03

10.3

a-si

1105

46.81

5.79

17.4

Bangalore

Bhopal

Kolkata

Mount Abu

Srinagar

2039.5

1886.5

1719.2

2006.7

1866.6

Array efficiency (%)

Fig. 44.6 Graph of monthly variation of available energy of six selected cities

Module area (m2 )

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Fig. 44.7 Comparison of thermal losses % of six selected cities

Fig. 44.8 Comparison of PV loss due to irradiance level % for six selected cities

44.4 Conclusion The findings are drawn based on the PVsyst software simulation reports. This study’s main objective is to analyze the suitability of vertical facades integrated into building in India’s various climate zones. For this purpose literature review is conducted on the construction of an integrated photovoltaic system to identify the various strategies and technologies that can be used on the building located in India’s various climate zones. Different configuration parameters such as tilt angle, inclination, form of cell and climate impact (Hot and dry, mild, warm and humid, composite, cold and gloomy, cold and sunny) were analyzed and considered for analysis. Area of module, Performance ratio, and energy production were the criteria that are used for the selection of BIPV system.

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Hourly solar radiation on unit area of building façade in each climate zone was calculated using the PVsyst software to describe the metrological data year by year and topography of the landscape. Based on the simulation results, it is concluded that polycrystalline silicon solar cell is the greatest choice for BIPV facades in every climate zone, illustrate from the output. A comparative analysis of different technologies in the entire climate zone for the same system is carried out. The PV technologies used for the simulation are si-mono solar fun/SF 100 M6-18/100 W for monocrystalline PV technology, sipoly Greenway/GW-100A for polycrystalline PV technology, and a-si H: Triple Xunlight Corporation/XR12-100 for thin-film PV technology. Amid all the three PV technologies thin film have the lowest energy production mainly in Kolkata and Srinagar in comparison with other cities, thin film has the lowest array loss at STC in comparison with monocrystalline and polycrystalline PV technology and highperformance ratio. But the problem with thin-film is that thin-film required a large area for PV installation and also has low array efficiency. However, polycrystalline PV technologies require less area for PV installation and also have height array efficiency. In all, the PV technology preferred for the PV installation site is polycrystalline. BIPV system can produce a sufficient amount of energy while being at the same time part of the building envelops. These integrated elements can also as regular components, providing the other functions of insulation, waterproofing, and shading. Integrating the PV system into the building allows the designer to construct energyefficient and environment-friendly building, without immolating comfort, economy and offers a versatile and new building material.

References 1. G.N. Vivek Tomara, T.S. Tiwari, B.N. Bhatti, Thermal modeling and experimental evaluation of five different photovoltaic modules integrated on prototype test cells with and without water flow. Energy Conv. Manag. 165, 219–235 (2018) 2. S.S. Chandel, A. Sharma, B.M. Marwaha, Review of energy efficiency initiatives and regulations for residential buildings in India. Renew. Sustain. Energy Rev. 54, 1443–1458 (2016) 3. A.K. Suresh, S. Khurana, G. Nandan, G. Dwivedi, S. Kumar, Role on nanofluids in cooling solar photovoltaic cell to enhance overall efficiency. Mater. Today Proc. 5, 20614–20620 (2018) 4. D. Sarkar, A. Kumar, P.K. Sadhu, A survey on development and recent trends of renewable energy generation from BIPV systems. IETE Tech. Rev. ISSN: 0256-4602 (Print) 0974-5971 5. S.S. Tejra, B. Vishal, H. Udania, G.Dwivedi, Solar photovoltaic-thermal (PV-T) hybrid technology: an Indian perspective. J. Energy Res. Environ. Technol. 4. ISSN: 2394-1561; e-ISSN: 2394-157X 6. A. Padhy, B. Vishal, P. Verma, G. Dwivedi, A.K. Behura, Fabrication of parabolic trough hybrid solar PV-T collector using a-Si thin film solar cells in Indian perspective. Mater Today Proc. https://doi.org/10.1016/j.matpr.2020.05.652 7. T. Salem, E. Kinab, Analysis of building-integrated photovoltaic systems: a case study of commercial buildings under mediterranean climate. Int. Conf. Sustain. Des. Eng. Constr. Procedia Eng. 118, 538–545 (2015) 8. A.K.Shukla, K. Sudhakar, P. Baredar, A comprehensive review on design of building integrated photovoltaic system. Energy Build. 128, 99–110 (2016)

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9. A.K. Shukla, K. Sudhakar, P. Baredar, Recent advancement in BIPV product technologies: a review. Energy Build. 140, 188–195 (2017) 10. N.K. Bansal, G. Minke, Climate zones and rural housing in India. KernforschungsanlageJülich, Zentralbibliothek 11. A.K. Shukla, K. Sudhakar, P. Baredar, R. Mamet, BIPV in Southeast Asian countries—opportunities and challenges. Renew. Energy Focus 21 (2017) 12. B.B. Ekici, Variation of photovoltaic system performance due to climatic and geographical conditions in Turkey. Turkish J. Electr. Eng. Comput. Sci. 24, 4693–4706 (2016) 13. N. Rathore, N. Lal Panwar, F. Yettou, A. Gama, A comprehensive review on different types of solar photovoltaic cells and their applications. Int. J. Ambient Energy ISSN: 0143-0750 (Print) 2162-8246 14. C. Kalu, A. Ezenugu Isaac, U.M. Anthony, Comparative study of performance of three different photovoltaic technologies. Mathe. Softw. Eng. 2(1), 19–29 (2016) 15. H. Gupta, S. Sharma, S. Mathur, J. Mathur, Comparison of Different Type of Configurations for Photovoltaic Façade in Composite Climatic Zone of India (New Delhi)

Chapter 45

CFD Analysis of Temperature Profile and Pattern Factor at the Exit of Swirl Dump Combustor Yogesh Bhawarker, Prakash Katdare, Manish Kale, Hitesh Kumar, Shri Krishna Mishra, and Rahul Kumar Abstract The practical challenge in analysis within the field of turbine combustion primarily centers on a clean emission, an occasional liner wall temperature, a standardized exit temperature distribution for turbo machinery applications alongside a fuel economy of the combustion method. Dump combustor may be a form of leanburning combustor. Lean-burning combustors square measure at risk of combustion instabilities to boot because of non-uniformities within the fuel–air commixture, and within the combustion method, there usually exist hot areas within the combustor exit plane. These hot areas limit the in-operation temperature at the rotary engine body of water and so constrain performance and potency. The present work was directed toward the experimental study of temperature profile at the exit of dump combustor due to the interaction of swirling combustion air and a spray of kerosene fuel issuing from a fuel injector in the cylindrical type dump combustor. The study was done by different values of settling chamber pressure and different fuel flow rate. The effect of equivalence ratio ϕ, flow velocity, and air mass flow rate is also described. Pattern factor at different equivalence ratios is also observed. Effect of swirl on pattern factor and temperatures at the exit of combustor is described in this report. Flammable limit for the combustor is also calculated. Temperature is recorded with the help of four k-type thermocouples. Thermocouples are placed at the different radial distance from the combustor’s wall. All the testing was performed at limited pressure and limited fuel–air ratio and also for the two different fuel injector with two different positions. Keywords Dump combustor · Swirl flow · Pattern factor · Temperature profile

Y. Bhawarker (B) Sandip University, Nashik, India P. Katdare Sagar Institute of Science and Technology, Bhopal, India M. Kale · H. Kumar Technocrats Institute of Technology, Bhopal, India S. K. Mishra · R. Kumar Suresh Gyan Vihar University, Jaipur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_45

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45.1 Introduction Gas turbine power plant is main source of giving energy to all the required part of gas turbine-based engine, e.g., aircrafts and rockets. Gas turbine engines represent an area of particular interest to temperature measurement. Nowadays, gas turbine engines are very useful in many applications from traveling, transports to launching satellites. Thus, new generation gas turbine engines require continuous improvements in performance, reduction in emission levels, and improved fuel flexibility and safety. Furthermore, new generation turbine engines have to become more intelligent and be able to adapt to changes during their operating life, such as aging, component upgrades, and new environmental restrictions. Improved control systems employed at all operating levels can provide solutions to many of these requirements. In future turbine engine particularly, engine should give terribly low emission and high potency at low value whereas maintaining the responsibility and operability of current engines of these needs have resulted in advanced combustor styles that are captivated with effective fuel–air mixture and lean operation. Due to non-uniformities in the fuel–air mixing and in the combustion process, there typically exist hot areas in the combustor exit plane entering the turbine. These hot areas limit the operating temperature at the turbine inlet and thus constrain performance and efficiency. The non-uniformities in combustor exit temperature are described by a parameter called pattern factor. By measuring and sensing the pattern factor (temperature distribution) improvement in combustors with better mixing control, increased uniformity between injectors, and higher average temperatures, yielding increased engine performance. Pattern factor (PF), typically defined as in Eq. 45.1, is a measure of the peak deviation from the mean of the combustor exit temperature profile. PF =

T4 max − T4eq avg T4eq avg − T3

(45.1)

It may be considered an indicator of the maximum thermal loading of the turbine blades and vanes; a large PF value affects both the efficiency of the engine (forcing lower average combustor temperatures) and the life of the turbine vanes and blades. The active control methods for combustion instabilities, pattern factor, and emission in lean-burning combustor were explained by Herbon et al. [1]. All the above parameters had controlled by two ways active and passive methods. Passive control techniques include hardware and design modifications like modification of fuel delivery system and combustor hardware. Active control system includes time varying variables. Pattern factor was control by developing uniform fuel distribution system which is work with primary fuel system. Pattern factor and its control are also observed by Palaghita et al. [2]. It was found that laser tuning range and speed were the primary sources of errors, while the effects of combustor un-steadiness, variations in water temperature correlation, and ordinary noise can be reduced by use of modern hardware, averaging, and sensor design. Weigand et al. [3] studied swirl

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flames in gas turbine model combustor. Swirl flames are important in gas turbine engines because they enable high energy conversion in a small volume and exhibit good ignition and stabilization behavior over a wide operating range. In stationary gas turbine combustors, they are used mostly as premixed or partially premixed flames, and in aero engines, as diffusion flames. Broda et al. [4] explained study of combustion instabilities various equivalence ratios, chamber pressures, and inlet air temperatures. Combustion instabilities directly connect to exit temperature and emission. Variations in inlet air temperature had minimal effect on the strength of instabilities. The instability mechanism has been argued to stem from the strong interaction between flame dynamics and flow oscillations inside the combustor. Heitor et al. [5] studied the isothermal and a combusting flow characteristic of model cantype gas turbine. The distribution of temperature is maintained in the primary zone, and the former is reduced throughout the dilution zone. An annular high temperature region exists close to the combustor wall and later on that region was eliminated. They conclude that combustion is more controlled by physical than chemical processes in the primary zone. Martin et al. [6] described an investigation of swirl-stabilized flames, created in a combustor featuring co-annular swirling air flows, under unenclosed, enclosed, and submerged conditions. The structure of the flow is affected by the swirl configuration, but does not depend heavily on the Reynolds number. The objective of this research is to find out exit temperatures distribution and pattern factor over the dump combustor and to modify the combustor for the better performance. Yadav and Kushari [7] did experiments and analyzed flow fields inside combustor chamber with low aspect ratio. The variation in wall pressure and velocity field was measured. It was found that two recirculation zones are obtained and recirculation strength increased with increase in Reynolds number. The shear layers seen on either sides of potential core get combined neutralizing core as its x/h value comes near to 3.75. Kim et al. [8] studied the flame structure of various mixture velocities, swirl numbers, and equivalence ratios to get the better knowledge of role of recirculation zones and instability of combustion. It was concluded that organization of recirculation zones is much related to creation of combustion instabilities. Ruggles and Kelman [9] analyzed the behavior of three different flame and flow structures in an atmospheric swirl-stabilized dump combustor supplied with a lean premixed mixture of methane and air. Flow was perturbed taking frequencies of 100, 200, and 400 Hz. Using frequency of 100 Hz gave simple oscillatory motion while using frequency of 200 and 400 Hz created a second toroidal vortex ring developed near the inner shear layer. Degeneve et al. [10] analyzed the wall temperature and distribution of heat flux using methane-air and CO2 -diluted oxy flame in swirled combustor. The analysis concluded that using CO2 -diluted oxy-combustion instead of aero combustion does not make much difference in wall temperatures and heat flux through side walls. Chen and Swaminathan [11] studied the effect of fuel plenum thermo acoustic oscillations inside a swirl combustor. Large eddy simulation is performed of a lean mixture of methane and air. It was found that oscillations are amplified by six percent increase in mean Rayleigh index and fifty percent gain of flame transfer function. Kim et al. [12] did experiments on thermoacoustic self propagating instability in dual swirl combustors and studied its beating behavior. Fluctuation in sound pressure and

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release of heat was measured for this purpose. Combustor length and thermal power were varied during experiments. It was found that frequency decreased with increase in combustor length and thermal power. Ruan et al. [13] did experimental investigation of flame dynamics in multi-nozzle gas turbine model combustor using different swirl configurations.Flow structure was obtained using high-speed OH chemiluminescence imaging. The results showed that flame was anchored in recirculation zones near to swirler and maximum of heat release was obtained in recirculation zone near to flame interaction regions. Rao et al. [14] performed experiments to validate use of liquid hydrocarbon fuelrich gas generator in high-speed propulsion systems. Connect pipe mode testing by changing throat area of dump combustor for mach 4 is done. The position of swirler was changed along the length of combustor. It was found that there is sustained ignition when fuel flow rate of 0.8 kg/s was used. Ramirez et al. [15] studied the isothermal convective heat transfer in optical combustor with low emissions swirl flow nozzle. Isothermal convective non-reactive heat transfer was measured along fused silica optical can combustor liner for Reynolds numbers between 11,500 and 138,000. A high heat transfer increase of 18 was observed for fully developed turbulent pipe flow. In opposite to other researchers, they observed augmentation magnitude and distribution to be almost constant with respect to Reynolds number. To study the effects of stratification in the flame formation, Foodladgar et al. [16] conducted experiments on swirl stabilized premixed combustor. Swirled stabilized burner was built and analyzed through performing experiments, and two different partial premixed patterns were used. It was finally concluded that non-uniformity of mixture influences premixed flame structure, and also, its stability NOx emissions are also higher. Effects of adding hydrogen in partially premixed swirl combustor were numerically calculated by Nam and Yoh [17]. The LES turbulence model and multistep chemical mechanism of hydrogen methane were applied for simulation of three-dimensional swirling flame in combustor. It was finally found that percentage of hydrogen and rate of fuel flow play an eminent role in flame structure and combustor instability. As hydrogen percentage or fuel flow rate is increased, then structure of flame changes and flow field becomes stable.

45.2 Experimental Steps and Instrumentations The combustor used to carry out the present investigations is shown schematically in Fig. 45.1. An injector assembly, containing Swirler and a centrally positioned fluid atomizing nozzle, was used to feed fuel and air into the system. The diameter of main combustor annular pipe is 11.5 cm. Swirler was installed at the exits of the air annuli, through which the combustion air was fed. Atomization air and fuel streams were injected into the combustor through the atomizing nozzle. The main annular pipe is welded in an outer pipe. Graphite gaskets, rated of pressures of approximately 50 bars, were used to seal the gaps between the diffuser and outer casing of the combustor. The bolts had to be tightened with great care, and in a star pattern. A small drill has

45 CFD Analysis of Temperature Profile and Pattern Factor …

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Fig. 45.1 Pictorial view of swirl dump combustor

done for fuel supply pipeline on the top surface of outer casing. Diameter of fuel supply line was 5 mm. Pipeline is inserted in the outer casing and again welled at the drilled hole for preventing air leakage to outside. That pipeline is connected to fuel tank through fuel flowmeter and fuel rate operating valve. The fuel supplied to the burner was stored in pressurize able vessel. Figure 45.2 vessel was cylindrically in shape. Special fittings were machined to allow the vessel to be pressurized. Fuel was pressurized by using nitrogen gas which is kept in different

Fig. 45.2 Pictorial view of fuel tank

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cylinder. The fuel was then passed through a flow meter calibrated for kerosene. The fuel in this case was kerosene. The flow meter could thus be used to measure the flow rate of kerosene to within 1 cc/min. A fuel valve downstream of the flow meter was used to control the flow rate. The swirler used to impart rotation to the airflows was of particular importance. In order to obtain a symmetrical flow, swirler must be machined to within very tight tolerances. For this investigation, twisted-vane swirler was employed, as these are compact, can be inserted directly into an air duct, and can be machined from a single piece of stock, without any further assembly steps. In order to machine twisted-vane swirler, a stainless-steel blank was used. Swirler has inner diameter with internal thread, which is used to fit tightly over the fuel nozzle holder. At the outer diameter, twisted-vane was welded. Over all assembly of swirler fits the main combustor annular. The fuel atomization nozzle and its location relative to the swirl assemblies are shown in Fig. 45.1. The oil burner spray nozzle, produced by Spray Tech Mfg. Industrial spray nozzle and system, Mumbai, was commercially available. Ignition of the combustor under enclosed and pressurized conditions was accomplished via rod with cotton mount ignition system. A long 2 m, small diameter 1 cm rod was used with cotton cloth mount at the one end of rod. Compressed air from compressor was routed into the laboratory through pipe. The compressor was positioned in an exterior bay and was controlled in order to provide a system pressure 180 psi. The compressor was run unloaded until the pressure reached 180 psi. The temperature of the exhaust gases was measured with the help of thermocouples. Thermocouples holder was made by a 16.5 cm diameter ring. Thickness of that thermocouple’s holder was 5 mm and width was 4 cm. 5 mm diameter holes were drilled over the surface of holder. In order to obtained correct temperatures reading Teflon tube of 5 mm diameter inserted in those drilled holes. Again, holes were drilled in the inserted Teflon tube for inserting thermocouple. That holder was welled on the stand. Thermocouples can insert in and out from holes provided in Teflon stud. Temperature was measured radial at the exit of combustor (Fig. 45.3).

45.3 Calculations Lean blow out limit was calculated as described below:    γγ +1 −1 2 P0 A γ m=√ T0 R γ + 1 Diameter of orifice (d) = 5 mm, Inlet air temperature was considered = 300 K. Specific ratio of air = 1.4 Gas constant for air = 298 J/ kg K.

(45.2)

45 CFD Analysis of Temperature Profile and Pattern Factor …

595

Fig. 45.3 Exit view of combustor with thermocouple holder

Area of orifice = /4 × d2 = 1.96 × 10–5 m2 . After substituting all the values in above expression, m = 4.557 × 10−3 P0 Again, mass flow rate can be calculated by m = ρ Ae V

(45.3)

where ρ = density of air = 1.2256 kg/ m3 Ae = exit area of combustor pipe V = velocity at the exit. d e = diameter of exit pipe = 11.5 cm. Ae = /4 × de2 = 9.5 × 10−3 m2 Equating Eqs. 45.2 and 45.3, and substituting all required values, one more important result occurred V = 0.39157 P0

(45.4)

Exit velocity of flow was measured with Eq. 45.4 and graph plotted for different conditions. A Fig. 45.4 of mass flow rate vs pressure was plotted. That graph was also used for measuring mass flow rate directly seeing with pressure values say if pressure is

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Fig. 45.4 Mass flow rate versus pressure

4 bars, then we can say mass flow rate which is 0.01823 kg/s. 1 cc/ min = 1.369 ∗ 10−5 kg/sec

(45.5)

Pressure was fixed in the settling chamber, and flue rate was controlled by fuel valve. The value at which blow out occurred was noted down. Same kind of procedure was repeated again and again. Different blow out fuel flow rates were found and tabulated. Due to some technical problem very, high pressure was not achieved. Actual fuel–air ratios at blow out and their corresponding equivalence ratios were calculated and tabulated. fuel-air ratio = equivalence ratio =

fuel mass flow rate air mass flow rate

Actual fuel−air ratio Stichiometric fuel−air ration

(45.6) (45.7)

Stoichiometric air–fuel ratio of kerosene = 15.6: 1. Plot of blow out equivalence ratio verses velocity drawn. That plot is showing operating condition of combustor without blow out. That plot helped more to run combustor without blow out. All the time combustor has to run in the operating range. Combustion instabilities occurred if run in lean blow limit (Fig. 45.5).

45.4 Results and Discussion A.

Effects of fuel atomization nozzles:

45 CFD Analysis of Temperature Profile and Pattern Factor …

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Fig. 45.5 Blowout limit

Three different fuel nozzles were used. Nozzle 1 and Nozzle 2 were used in two different cases: (a) (b)

At the surface of swirler, tip of fuel nozzle was perfectly matching with swirler surface. Inside the swirler surface tip of fuel nozzle was 2 mm inside the swirler surface.

Nozzle No. 3 was used only with swirler surface. But it has large cone angle 60° that was disadvantage for the flame in the combustor. With 60° cone angle, flame did not stable in the combustor. Fuel spray was that high so that spray was coming out of the annular pipe. That spray caught fire and leaked out with fire. That was happened again and again with nozzle No.3. Hence, nozzles with large cone angle are not good for the combustor. Temperatures at the exit of the combustor were measured by 4 thermocouples. Each thermocouple was fixed at radial distance in thermocouple holder. Say, thermocouples fixed at radial distance of 4 cm from the center of annular pipe. A pressure was maintained in setting chamber, after that fire was put on the igniter, which was used to ignite the fuel spray coming out of fuel nozzle,. Temperatures were noted down. Same procedure was repeated for different radial location of thermocouples. Nozzle No.1 at the surface of swirler Temperature profiles at the exit of combustor were plotted at different equivalence ratios. Temperatures profiles were changing with every equivalence ratio (Fig. 45.6). Nozzle No.1 inside the surface of swirler As nozzle was fixed 2 mm inside the swirler, spray was not good; hence, it affects the whole combustion process (Fig. 45.7).

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φ =0.3728

φ =0.5385

φ =0.7475

φ =0.9528

φ =1.2428

φ =1.5846

φ =1.8642

φ =2.1438

Fig. 45.6 Temperature profiles at the exit of combustor plotted at different equivalence ratios

45 CFD Analysis of Temperature Profile and Pattern Factor …

φ =0.3728

φ =0.5385

φ =0.7475

φ =0.9528

φ =1.2428

φ =1.5846

φ =1.8642

φ =2.1438

Fig. 45.7 Temperature profiles at the exit of combustor plotted at different equivalence ratios

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Nozzle No.2 at the surface of swirler Nozzle No.2 has high flow rate compare to nozzle No.1. Fuel was control by fuel valve, but in this nozzle, fuel rate was high. As fuel rate is high, it affects the performance of the combustor accordingly. Temperature profiles obtained are shown (Fig. 45.8). Nozzle No.2 inside the surface of swirler In this case, nozzle was fixed inside 2 mm from the swirler surface. Fuel spray got disturb by swirler surface. In this case, leakage of fuel occurred as nozzle gave high fuel rate. In this case, only five equivalence ratios were taken into account. Temperature profiles obtained are shown below (Fig. 45.9). Pattern factor Pattern factor at the exit of combustor was calculated by using Formula 45.1. Two types of pattern factors are calculated. One is overall pattern factor and second is radial pattern factor. The main difference for calculating the pattern factor was the value of T max . Overall Pattern Factor: T max is that occurring anywhere in the combustor exit plane. Radial Pattern Factor: T max is the maximum of the circumferential mean temperature, from root to tip in the combustor exit plane. All the exit temperatures were measured already by thermocouples. By using the formula of pattern factor, different values of pattern factor were obtained at different equivalence ratios. Plots were drawn for radial pattern factor and overall pattern factor (Fig. 45.10). Radial pattern factor has low value as compared to overall pattern factor this is because of T max value. As mentioned, that T max is maximum temperature at the anywhere at the exit of combustor for overall pattern factor and maximum of the circumferential mean temperature for radial pattern factor. In overall pattern factor, T max is higher than radial pattern factor. These plots are drawn for constant fuel rate. When fuel rate is constant exit temperatures depend on air flow rate. As air rate is increased exit temperatures are decreased; hence, pattern factor is also decreased. Due to some fuel leakage, some peak values of temperatures may occur that effect can also be seen in graph.

45.5 Conclusions and Future Scope The experimental studies were carried out on swirl dump combustor for its performance. Blow out limit was calculated and plotted. Blow out limit makes a boundary for combustion which occurs in combustor. All the operations had done with in the operating range of combustor. For low values of velocity, blow out limitation was high (high equivalence ratio).

45 CFD Analysis of Temperature Profile and Pattern Factor …

601

φ =0.3728

φ =0.5385

φ =0.7475

φ =0.9528

φ =1.4228

φ =1.5846

φ =1.8642

φ =2.1438

Fig. 45.8 Temperature profiles at the exit of combustor plotted at different equivalence ratios

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φ =0.3728

φ =0.7475

φ =1.2428

φ =1.8642

φ =2.1438 Fig. 45.9 Temperature profiles at the exit of combustor plotted at different equivalence ratios

Blow out limit decreased as velocity of flow increases. Pressure was related to velocity and equivalence ratio, as pressure increased velocity of flow increases and equivalence ratio decreased. Pressure was also affecting the blow out limit. So, for better performance of the combustor, all the time combustor should operate in operation range of blow out limit. Temperature profiles for different equivalence ratio were plotted in contour plots. Those plots were also classified for two different positions of the fuel nozzles. Temperature profile which is suitable for turbine blades is obtained when combustor operate at equivalence ratio in between 0.7475 and 0.9528. By using Nozzle No 1 at the surface of swirler, good temperature profiles can obtain. If pressure at the time of

45 CFD Analysis of Temperature Profile and Pattern Factor …

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Fig. 45.10 Comparison of radial pattern factor and overall pattern factor

operation is very low, annular pipe of combustor become redhot in very short time. So, always pressure should be high. Pattern factor is also important parameter. Radial pattern factor having low values compares to overall pattern factor. The results obtained during the course of the present experimental investigation are revealing and significant. Further work can be done on the following lines to get better operating system to operate combustor.

References 1. J. Herbon, H. Scheugenpflug, R. Webster, Gas turbine engines. Aerosp. Am. 46(12), 74–75 (2008) 2. T.I. Palaghita, J.M. Seitzman, Pattern factor sensing and control based on diode laser absorption, in 41st AIAA/ASME/SAE/ASEE Jt. Propulsion of Conference on Exhibitions (2005), pp. 1–12 3. P. Weigand, W. Meier, X.R. Duan, W. Stricker, M. Aigner, Investigations of swirl flames in a gas turbine model combustor: I. Flow field, structures, temperature, and species distributions. Combust. Flame 144(1–2), 205–224 (2006). https://doi.org/10.1016/j.combustflame. 2005.07.010 4. J.C. Broda, S. Seo, R.J. Santoro, G. Shirhattikar, V. Yang, An experimental study of combustion dynamics of a premixed swirl injector. Symp. Combust. 27(2), 1849–1856 (1998). https://doi. org/10.1016/S0082-0784(98)80027-1 5. M.V. Heitor, J.H. Whitelaw, Velocity, temperature, and species characteristics of the flow in a gas-turbine combustor. Combust. Flame 64(1), 1–32 (1986). https://doi.org/10.1016/0010-218 0(86)90095-7 6. M.B. Linck, Spray Flame and Exhaust Jet Characteristics of a Pressurized Swirl Combustor (2006), p. 210 7. N.P. Yadav, A. Kushari, Vortex combustion in a low aspect ratio dump combustor with tapered exit. Energy Convers. Manag. 50(12), 2983–2991 (2009). https://doi.org/10.1016/j.enconman. 2009.07.017 8. M.K. Kim, J. Yoon, S. Park, M.C. Lee, Y. Yoon, Effects of unstable flame structure and recirculation zones in a swirl-stabilized dump combustor. Appl. Therm. Eng. 58(1–2), 125–135 (2013). https://doi.org/10.1016/j.applthermaleng.2013.04.019

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9. A. Ruggles, J. Kelman, Unsteady vortex breakdown in an atmospheric swirl stabilised combustor. part 1: chamber behaviour. Combust. Flame 162(2), 388–407 (2015). https://doi. org/10.1016/j.combustflame.2014.07.016 10. A. Degenève, P. Jourdaine, C. Mirat, J. Caudal, R. Vicquelin, T. Schuller, Analysis of wall temperature and heat flux distributions in a swirled combustor powered by a methane-air and a CO2-diluted oxyflame. Fuel 236, 1540–1547 (2019). https://doi.org/10.1016/j.fuel.2018. 09.012 11. Z.X. Chen, N. Swaminathan, Influence of fuel plenum on thermoacoustic oscillations inside a swirl combustor. Fuel 275, 117868 (2020). https://doi.org/10.1016/j.fuel.2020.117868 12. J. Kim, M. Jang, K. Lee, A.R. Masri, Experimental study of the beating behavior of thermoacoustic self-excited instabilities in dual swirl combustors. Exp. Therm. Fluid Sci. 105(March), 1–10 (2019). https://doi.org/10.1016/j.expthermflusci.2019.03.007 13. C. Ruan, F. Chen, T. Yu, W. Cai, X. Li, X. Lu, Experimental study on flame/flow dynamics in a multi-nozzle gas turbine model combustor under thermo-acoustically unstable condition with different swirler configurations. Aerosp. Sci. Technol. 98, 105692 (2020). https://doi.org/ 10.1016/j.ast.2020.105692 14. M. Raghavendra Rao, G. Amba Prasad Rao, A. Kumar, Experimental validation of liquid hydrocarbon based fuel rich gas generator for high speed propulsion systems. Acta Astronaut. 174, 180–188 (2020). https://doi.org/10.1016/j.actaastro.2020.05.009 15. D. Gomez-Ramirez, S. Kedukodi, S.V. Ekkad, H.K.X. Moon, Y. Kim, R. Srinivasan, Investigation of isothermal convective heat transfer in an optical combustor with a low-emissions swirl fuel nozzle. Appl. Therm. Eng. 114, 65–76 (2017). https://doi.org/10.1016/j.applthermaleng. 2016.11.154 16. E. Fooladgar, C.K. Chan, Effects of stratification on flame structure and pollutants of a swirl stabilized premixed combustor. Appl. Therm. Eng. 124, 45–61 (2017). https://doi.org/10.1016/ j.applthermaleng.2017.05.197 17. J. Nam, J.J. Yoh, A numerical investigation of the effects of hydrogen addition on combustion instability inside a partially-premixed swirl combustor. Appl. Therm. Eng. 176, 115478 (2020). https://doi.org/10.1016/j.applthermaleng.2020.115478

Chapter 46

Determining the Performance Characteristics of a White-Box Building Energy System Model and Evaluating the Energy Consumption V. S. K. V. Harish , Amit Vilas Sant, and Arun Kumar Abstract Building energy models are developed to evaluate the energy performance of a particular building design and to develop cost-estimation and energy-saving strategies that can be implemented. Present study develops a methodology where a building energy system model is developed based on the fundamentals of the building energy physics. Both sensible and latent thermal energy transfer processes are considered for development of the building energy model. Performance characteristics of the model are determined by developing a state-space building energy model and then evaluating the step response of the developed white-box model. Settling time, peak time, rising time, steady-state error and overshoot are determined for each state of the building energy system model under study. Modelling methodology adopted shall enable the developer or designer to evaluate the system’s performance before implementing any control strategy which would lead to reduced complexity, costreduction in troubleshooting and better stability. Daily, weekly and monthly energy consumed by HVAC and lighting systems of the building energy system model are 16.8, 41.9 and 139.67 kWh, respectively. Keywords Building energy system · Performance characteristics · State-space model · White box · Energy consumption

46.1 Introduction Building energy systems involve the energy transfer processes and the physical infrastructure and equipment responsible for energy usage in buildings [1]. Building energy modelling is a process of mapping the energy transfer processes occurring within a V. S. K. V. Harish (B) · A. V. Sant Department of Electrical Engineering, School of Technology, Pandit Deendayal Energy University (PDEU), Raisan, Gandhinagar 382007, Gujarat, India e-mail: [email protected] A. Kumar F.ASCE, F.IHA, Department of Hydro and Renewable Energy, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_46

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building and also with an external environment [2]. Output of a building energy model can vary but usually are energy usage with regards to a pre-desired time horizon with costs and certification being secondary outputs. Building energy models can be classified on the basis of the availability of data as white-box, black-box and grey-box models [3]. White-box models are benchmark models, in terms of accuracy where fundamentals of building energy physics are used to develop mathematical energy model [4, 5]. Black-box models, on the other hand, use experimental studies and regression equations are used to describe the behaviour of the building energy system under study [6]. Such a modelling approach is less complex, simple but lacks accuracy as the modeller is not aware of the energy transfer dynamics happening inside the building energy system [7, 8]. Grey-box models combine the merits of white and black-box models where parameter estimation studies of the developed modelling topology and structure are conducted using experimental data [9, 10]. Development of a reliable building energy model that could effectively map the hygro-thermal dynamics of a building space is a fundamental challenge. To pursue such an objective, a building energy system model developed in [3, 11] is studied to evaluate its energy performance. Thermal energy transfer equations are formulated for the elements of building energy systems like building envelope and space, PIDcontrolled HVAC system, lighting system and active occupancy schedule. Building space is modelled for both sensible and latent heat transfer processes. State-space approach is used to model the building envelope elements such as walls, doors and windows. Causal heat gains accounting for heat emitted from occupants, lights and other equipment are also modelled. Simulations are performed for a complete building space whose construction elements are chosen as per ASHRAE handbook of fundamentals, 2013 [12]. Present study involves developing a methodology to evaluate the performance characteristics in terms of settling time, peak time, rising time, steady-state error and overshoot. Parameters influencing transfer of energy through the building energy system under study are categorized as described in Table 46.1. Table 46.1 Parameters influencing energy transfer process in building energy system Parameters

Description

Environmental parameters

Outdoor air temperature and humidity, wind speed and direction, solar radiation and sol-air temperature

Building envelope parameters Thermo-physical properties of BCEs materials, orientation, planning and design specifications Occupancy factors

Causal heat gains (internal heat loads), functional use of building and occupancy schedule

Plant parameters

Plant heat rate, number of ventilated air changes per hour, mass flow rate and temperature of cooling water and valve signal

46 Determining the Performance Characteristics …

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46.2 Modelling Approach All the three modes of heat transfer processes, viz. conduction, convection and radiation have been considered [2, 13]. Sensible heat transfer is modelled as conduction, convection and radiation, whereas latent heat transfer is modelled as transfer of moisture and water vapour by in-room equipment and occupants, in and out of the building space. Physical elements of building space responsible for heat and mass transfer processes are summarized in Table 46.2. A simplified representation of the building energy system under study is shown in Fig. 46.1. Based on first principles of the building energy physics, a set of partial differential equations are formulated, depicting the heat transfer interactions within the building space under study. Parameter estimation studies have been conducted in [2] to identify the parameters of building energy system under study (Fig. 46.2). Table 46.2 Building elements and the modes of thermal energy transfer Heat and mass transfer processes

Building elements

Conduction and radiation heat transfer

External wall, roof, floor slabs, adjacent wall and doors

Conduction heat transfer and solar radiation transmission

Window glazing

Conduction and/or radiation heat transfer and moisture dissipation

Occupants, lights and other equipment

Convection heat and mass transfer

Infiltration from outside and adjacent lobby

Building Model Space model

Humidity

Temperature

Weather Data Temperature Relative Humidity Solar radiation Wind characteristics

Surface Temperatures

Plant heat rate

• • • •

Ventilation rate

Envelope model

HVAC system Ventilation sub system

Occupancy Model

Air conditioning system

PID Controller

Fig. 46.1 Overall structure of building energy system model

• •

Energy Consumption HVAC Lighting

608 CONTROLLABLE 1) QHVAC 2) Qcausal 3) Tbs 4) RHbs

UnCONTROLLABLE 1) RHout 2) Tout 3) Vwind 4) Hsolar_rad

V. S. K. V. Harish et al.

BES model

Building space air temperature (°C)

-> Building space -> HVAC plant -> Occupancy

Building space air humidity (%)

Fig. 46.2 Building energy system with inputs, operational parameters and outputs

46.3 Step Response of Building Energy System Developed building energy system model is simulated in MATLAB/Simulink with step input excitations. A step function, u(t), is represented as shown in Fig. 46.3. A step input function is defined as Eq. (46.1). 

0 ∀t < 0 u(t) = U ∀t ≥ 0

 (46.1)

Numerical values of the parameters driving the building energy system model are taken from [2, 3, 11–13]. A, B, C and D matrices of the state-space building energy model are given as Eqs. (46.2)–(46.5). Fig. 46.3 Step excitation/input to the building energy model

u (t)

U

0

t

46 Determining the Performance Characteristics …

⎤ 0 0 −6.65 × 10−3 6.32 × 10−3 ⎥ ⎢ 2.99 × 10−5 −3.23 × 10−5 2.43 × 10−6 0 ⎥ [A] = ⎢ −5 −4 −5 ⎦ ⎣ −1.9 × 10 9.89 × 10 0 9.15 × 10 0 0 2.98 × 10−6 −5.35 × 10−5 ⎡ ⎤ 2.31 × 10−5 0 3.33 × 10−4 ⎢ ⎥ 0 0 0 ⎥ [B] = ⎢ ⎣ ⎦ 0 0 0 −5 0 0 5.05 × 10

 [C] = 1 0 0 0

609

⎡

[D] = [0]

(46.2)

(46.3)

(46.4) (46.5)

System transfer function for the building energy system model is given as Eqs. (46.6)–(46.8). Tbs Q˙ 7.1 × 10−18 + 4.049 × 10−13 s + 6.378 × 10−9 s 2 + 2.31 × 10−5 s 3 = 1.791 × 10−16 + 7.091 × 10−11 s + 1.666 × 10−6 s 2 + 0.006929s 3 + s 4 (46.6)

G 1 (s) =

G 2 (s) = =

Tbs Tout

7.67 × 10−17 1.791 × 10−16 + 7.091 × 10−11 s + 1.666 × 10−6 s 2 + 0.006929s 3 + s 4 (46.7)

Tbs TVent 1.0189 × 10−16 + 5.84 × 10−12 s + 9.184 × 10−8 s 2 + 3.33 × 10−4 s 3 = 1.791 × 10−16 + 7.091 × 10−11 s + 1.666 × 10−6 s 2 + 0.006929s 3 + s 4 (46.8)

G 3 (s) =

Response of building space air temperature for a step input of outdoor air temperature (Eq. 46.7) is shown in Fig. 46.4. Building energy system model performance analysis is conducted by specifying the response characteristics of settling time, rise time, etc. [14]. The performance characteristics for the building energy system are given in Table 46.3. Building energy system model under study is excited with step excitations of outdoor air temperature (T out ), ventilated air temperature (T vent ) and HVAC plant ˙ and amount of HVAC heat rate or heating power is analysed for different heat rate ( Q) scenarios by varying the excitation values, as shown in Table 46.4.

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Fig. 46.4 Response of the building energy system model for step excitation of outdoor air temperature

Table 46.3 Performance characteristics of transfer function building energy system model

Characteristics

G1 (s)

G2 (s)

G3 (s)

Rise time (h)

1.217

1.217

1.217

Settling time (h)

2.308

2.308

2.308

Settling minimum (°C)

0.0364

0.3937

0.5256

Settling maximum (°C)

0.0401

0.4360

0.5809

Peak value (°C)

0.0411

0.4360

0.5809

Peak time (h)

3.768

3.768

3.768

Table 46.4 Different scenarios for evaluating the building energy model performance Q˙ (W/s) Scenario T out (°C) T vent (°C) T BS (°C) I

0

0

20

II

0

17

9.7

505 0

III

0

17

20

255

In order to maintain the building space air temperature (T BS ) at 20 °C, 505 W of HVAC power is required under zero outdoor and ventilated air temperature values. When there is no HVAC power, then the building space temperature is 9.7 °C with ventilated air temperature of 17 °C and zero outdoor temperature. Under zero outdoor temperature condition, 255 W of HVAC power is required to maintain the building space air temperature and ventilated air temperature at 20 and 17 °C, respectively. ˙ is calculated using energy balance equations as given in HVAC plant heat rate, ( Q) Eqs. (46.9) and (46.10). Q=



(U A2 ) × (TBS − Tout ) + VBS ρBS C pBS (TBS − Tvent )

(46.9)

46 Determining the Performance Characteristics …



(U A2 ) =

611

A2 1 hi

+

x1 kins

+

x2 kconc

+

x3 kb

+

1 ho

(46.10)

46.4 Simulation Results Under circumstances of movable louvres and blinds, solar radiation (H solar_rad ) is regarded as a partially controllable energy input to the building energy system model. However, in the present study, the influence of H solar_rad is significant and the building energy system model under study is composed of windows that can be opened or closed enabling full or no entrance of the solar radiation through the glazing area. Un-controllable parameters driving the building energy system model under study are shown as Figs. 46.5, 46.6, 46.7 and 46.8 [15]. A PID controller for the HVAC system is used to control flow rate of the conditioned air into the building space and also to track the desired building space air temperature. In the present study, thermostat set-point temperature has been set to 16.5 °C. This is a manual setting defined by occupants. Heat emitted to the building space by the HVAC system designed to supply maximum flow rate of 0.15 kg/s at the temperature of 17.8 °C is shown in Fig. 46.9. Heat emitted to building space from the HVAC plant varies between negative and positive values. Positive values indicate that HVAC plant is heating the building space (heating mode) and negative values indicate cooling mode. This is varied in accordance with the outdoor conditions and pre-defined thermostat set-point temperature. Building energy system model is simulated for one-month period and the variations in building space air temperature are shown as Fig. 46.10.

Fig. 46.5 Averaged global horizontal solar radiation (Wh/m2 )

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Fig. 46.6 Averaged global horizontal solar radiation (Wh/m2 )

Fig. 46.7 Outdoor relative humidity (%)

Building space air temperature value regulates about a large difference. This is a peculiar property of PID controller-driven HVAC system. AI-tuned PID controllers are being used nowadays for better regulation of building space air temperature. Present study focusses on the development of control strategies for optimal energy control and occupants’ comfort management in buildings. These strategies can be applied to any building irrespective of its functionality and type of HVAC control being used. Building space relative humidity for a one-month simulation is shown in Fig. 46.11. In order to calculate daily energy consumption, the building energy system is simulated for 24 h period and based on the power consumed by HVAC and lighting system, and energy consumption is calculated. Hourly variations of the un-controllable inputs to the building energy system model for a day are computed as averaged values

46 Determining the Performance Characteristics …

Fig. 46.8 Outdoor wind speed (m/s)

Fig. 46.9 Heat emission from the HVAC plant to the building space (W)

Fig. 46.10 Building space air temperature (°C)

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Fig. 46.11 Building space relative humidity (%)

obtained previously. Occupants comfort has been calculated as simple algebraic differential between the set-point values (of building space air temperature and relative humidity) and the values obtained from the simulations with PID-controlled HVAC system. Occupants’ overall comfort for a 24 h period is around 62.49% under PID-controlled HVAC system. For operating hours of 8 h per day and assuming that 2 lamps are switched on at all time of the day, daily, weekly and monthly energy consumed by the lighting system are calculated to be 652.8, 3,916.8, 15,667.2 Wh, respectively. Similarly, daily, weekly and monthly energy consumed by the HVAC system to condition the building space air at a desired set-point temperature is 16.15, 38 and 123 kWh, respectively.

46.5 Conclusion A building energy system model is developed and simulated in MATLAB/Simulink environment. Elements of the building energy system that have been developed include building space or zone, building envelope with windows, HVAC and lighting system. Energy transfer processes of conductive, convective and radiative heat balance for each surface of BCEs and a convective heat balance for the building space are modelled. Building space zone is modelled for both sensible and latent thermal energy transfer. In order to linearize the nonlinear energy dynamics of building energy transfer, state-space approach is used to develop the building energy system model. A simple PID-controlled HVAC system model has been developed. Daily, weekly and monthly energy consumed by HVAC and lighting systems of the building energy system model are 16.8, 41.9 and 139.67 kWh, respectively. Step response analysis of building energy system model yields rise and settling times of around 225 and 400 h. For a peak value for building space temperature of 0.5 °C and humidity of 78%, building energy system model is taking a large amount of time to settle to a steady-state value. In order to develop energy control and comfort

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management strategies, an order of the developed building energy system model is reduced by using a nonlinear time invariant optimization technique.

References 1. V.S.K.V. Harish, A. Kumar, A review on modeling and simulation of building energy systems. Renew. Sustain. Energy Rev. 56, 1272–1292 (2016) 2. V.S.K.V. Harish, A. Kumar, Reduced order modeling and parameter identification of a building energy system model through an optimization routine. Appl. Energy 162, 1010–1023 (2016) 3. V.S.K.V. Harish, A. Kumar, Smart energy control and comfort management in buildings. Green Innovation, Sustainable Development, and Circular Economy, 1st edn. ed. by N.K. Singh, S. Pandey, H. Sharma, S. Goel (CRC Press, Boca Raton, 2020). https://doi.org/10.1201/978100 3011255 4. S. Martínez, P. Eguía, E. Granada, A. Moazami, M. Hamdy, A performance comparison of multi-objective optimization-based approaches for calibrating white-box building energy models. Energy Build. 216, 109942 (2020) 5. G. Kalogeras, S. Rastegarpour, C. Koulamas, A.P. Kalogeras, J. Casillas, L. Ferrarini, Predictive capability testing and sensitivity analysis of a model for building energy efficiency. Build. Simul. 13(1), 33–50 (2020) 6. G. Ciulla, A. D’Amico, Building energy performance forecasting: A multiple linear regression approach. Appl. Energy 253, 113500 (2019) 7. C. Fan, F. Xiao, C. Yan, C. Liu, Z. Li, J. Wang, A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning. Appl. Energy 235, 1551–1560 (2019) 8. C. Cui, X. Zhang, W. Cai, An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model. Appl. Energy 264, 114734 (2020) 9. M.H. Shamsi, U. Ali, E. Mangina, J. O’Donnell, A framework for uncertainty quantification in building heat demand simulations using reduced-order grey-box energy models. Appl. Energy 275, 115141 (2020) 10. J. Arroyo, F. Spiessens, L. Helsen, Identification of multi-zone grey-box building models for use in model predictive control. J. Build. Perform. Simul. 13, 472–486 (2020) 11. V.S.K.V. Harish, A. Kumar, Modeling and simulation of a simple building energy system, in 2016 International Conference on Microelectronics, Computing and Communications 2016, vol. 1, pp. 1–6. IEEE 12. ASHRAE, ASHRAE Handbook of Fundamentals (American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Atlanta, USA, 2013) 13. V.S.K.V. Harish, A. Kumar, Modeling and simulation of a simple building energy system. In 2016 International Conference on Microelectronics, Computing and Communications 2016, IEEE, vol. 1, pp. 1–6 14. K. Ogata, Modern Control Systems (Prentice Hall, Upper Saddle River, 1997) 15. C. Underwood, F. Yik, Modelling Methods for Energy in Buildings (Wiley, Hoboken, 2008)

Chapter 47

Battery Management System with Wireless Parameter Estimation in EV K. Vishnu, Amit Ojha, and R. K. Nema

Abstract In order to achieve better performance for a battery unit, an advanced battery management system must be required. Battery management system of an electric vehicles, one of the challenges facing is estimation of battery parameters and transferring the estimated data to BMS unit. This paper introduces wireless technology in battery management system. Using this wireless technology, the sensed data is collected and transferred to the battery management system main controller. Hardware implementation is done for a 48 V battery source and battery parameters such as voltage, current and temperature are sensed with the automotive-grade IC. Data communication executed by using Bluetooth technology. Keywords BMS · Electric vehicle · Bluetooth module · CAN

47.1 Introduction The future of the automotive industry is mainly depended on renewable energy [1] because of the lack of fossil fuel. Due to the lack of fossil fuel and pollution arises due to fossil fuel [2], it introduces a new path for the automotive world. The electric-powered vehicle is one of them. The electric vehicle came into the automotive industry around 200 years ago. Automotive world is updating day by day, and battery for electric vehicle is also a part of this process. At the beginning of the twenty-first century, the automotive industry starts using a lithium-ion battery for power supply. The Roadster was the first successful lithium-ion powered electric car introduced by Tesla in 2008. It can K. Vishnu Electrical Drives, National Institute of Technology, Bhopal 462003, India e-mail: [email protected] A. Ojha (B) · R. K. Nema Department of Electrical Engineering, MANIT, Bhopal, India e-mail: [email protected] R. K. Nema e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_47

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travel around 320 km (200 miles) in a single charge. In automotive world, lithium-ion battery is the commonly used one. Properties of a lithium-ion battery [3] are very higher than other batteries so that it should be controlled and maintained properly to achieve its maximum performance. In the electric vehicle to achieve maximum performance, battery management system [4] is used. The effective performance of the battery management system improves the output of battery in terms of size and weight. The major reason for growth on battery pack energy storage shows that can give maximum performance, safe operation and optimal lifespan at different environmental conditions under diverse charge-discharge cycle.

47.2 Battery Management System Battery management system for an electric vehicle is an unavoidable system. For a battery system temperature monitoring, cell balancing, overvoltage, over current, fault detection, parameter estimation, coolant control, etc., all these functions are done by the battery management system [5]. A BMS unit block diagram is shown below in Fig. 47.1. Whole BMS and battery pack are mounted on a single system. Considering a 24 kWh battery pack having 50 battery modules, each module having 4 cells connected 2 in series and 2 in parallel. The cell nominal voltage is 3.8 V and the

Fig. 47.1 Block diagram for wired BMS

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Fig. 47.2 Battery module 2 in series and 2 in parallel

rated capacity is 32 Ah. From each module temperature, voltage and current reading to be sent to the BMS unit via wired manner. A simple battery module is shown in Fig. 47.2. For a 50 module, a large number of wires are connected to the BMS unit directly or indirectly manner. This wired communication is complex, difficult to separate, not easy to maintain, a large quantity of isolation, connectors, any short circuit due to overheating of a cell will affect the whole system.

47.2.1 CAN Communication Controlled area network (CAN) is most using wired communication in automotive industry. It is developed by Robert Bosch in 1983. CAN communication is a two-wire communication [6]. It became popular due to its advantage over other communication such as support multimaster and multicast property, CAN work at different electrical environments, 40 meters is the maximum length, half duplex communication and bidirectional, and it provides auto retransmission of lost messages and 1 Mbps data rate speed also. Due to all these criteria discussed above, wireless battery management system is effective and all mentioned problems can be avoided easily.

47.3 Wireless Battery Management System Data from the battery are sending to the BMS unit via a wireless manner. Battery parameters such as voltage, current and temperature from each battery unit via wireless communication by this method increase reliability, save weight, space, reduce cost and reduce cabling needed for the large battery pack [7]. In addition to this, wireless connection allows flexible placement of battery modules and installation of a sensor according to safety concern (Fig. 47.3). The main purpose is to separate the BMS unit from the battery pack, and by this method, we can reduce the large and complex wiring that can be avoided. After

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Fig. 47.3 Wireless battery management system block diagram

separating the BMS unit form the battery pack, we can place BMS unit anywhere of the according to the manufacturer. Normally, BMS unit will place near to user interface display.

47.4 Hardware Implementation A wireless battery management system with 48 V has been tested, and data transfer using Bluetooth communication is effectively implemented. The main block diagram used for the hardware part is shown Fig. 47.4.

Fig. 47.4 Block diagram representation of the hardware unit

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Battery parameter such as voltage, temperature and current are collected using a battery monitoring unit. The battery monitoring unit and Bluetooth module [8] are connected using a controller. The data is transmitted via Bluetooth and the controller attached with the BMS unit will receive the battery parameter; this battery parameter is used for further calculations such as the state of charge, state of health, etc. The main building blocks are explained in the next part.

47.4.1 Battery Monitoring Module The Texas Instruments IC BQ76PL455A-Q1 is used for battery monitoring purposes. This is the latest automotive-grade IC that can monitor 6 to16 cells and cell balancing can also achieved. It is highly accurate, up to 16 similar IC can connect in daisy chain using twisted pair, and it having internal overvoltage (OV) and under-voltage protection. Communication with the host device is done by using a universal asynchronous receiver/transmitter (UART) interface. The host device must request the BQ76PL455A-Q1 module [9] for each command accordingly battery module will respond. Both command and response are in the hexadecimal code. To find each cell voltage, the command must send from the host device according to the command the battery monitoring module will give a response. Let command given to the controller be 81 01 02 20 7944, and this command is for voltage. The battery nodule gives a response like 0B 7429 7519 8214, etc., and each 16 bit indicates cell voltage. After getting the response, we need to calculate cell voltage by using the equation given 1 and 2. The hex value needs to convert to decimal value for further calculation. Like this calculation for temperature, current and pack voltage by giving command separately to the battery monitoring module. To build a 48 V battery pack having each cell voltage of 3 V, a regulating DC power supply is used, having it generate 48 V by using resistor dividing network, and we generate a cell voltage of 3 V. These 16 pins are connected to the BQ76PL455A-Q1 module and shown in Fig. 47.5.

47.4.2 Controller and Bluetooth Module A controller is required for programming Bluetooth module and interfacing with a BQ76PL455A-Q1, if we are considering automotive-grade CANBUS Mod MCP2551 and MCP2515 series controller are using it is CAN-based communication. For a 48 V battery management system, Arduino UNO is used. The main advantages are easy to program, user-friendly, low cost and reliable. The wireless communication is done by using Bluetooth module and a low power Bluetooth module HC-05 is used for this purpose having a speed of 1Mbps synchronous speed. The battery

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Fig. 47.5 16 cells generated using DC regulated supply

monitoring module with Arduino UNO communication is done via UART. The Bluetooth module is directly connected to the Arduino and programmed by using Arduino software.

47.4.3 BMS Controller and Display Unit The controller used for the BMS unit must have a high specification. Some of them are TI Hercules, NXP5743, etc. This project has used Arduino UNO and LabVIEW [10] software for the calculation of the battery parameter and displays it on the monitor. The data collected by the Bluetooth module connected at the BMS side send these to the Arduino board this Arduino process value and send data to the LabVIEW. The voltage of each cell, total voltage, temperature and current are displayed. While considering program, three units need to be coded such as controller, Bluetooth module, BMS controller or display unit (such as LabVIEW). Bluetooth module is programed for setting baud rate and password setup (Figs. 47.6 and 47.7). The flowchart for the controller at the BMS side is shown. Here, all the commands are sending via Bluetooth module. The user interface is developed by using LabVIEW. It shows 16 cell voltage, pack voltage, alert indicator, temperature and battery current. 14th cell voltage drops to low value due to the resistor problem; the exact value is measured using a wireless method and compared value with a multi-meter. If the voltage and temperature excided maximum value, an alert indicator will indicate. Temperature measured by the system in room temperature for an actual electric vehicle thermistor will put inside a battery pack of 2–6 cell, it will measure the temperature inside the pack, and corresponding

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Fig. 47.6 Flowchart used in the controller

voltage will measure by the battery monitoring module. Generally, 8-16 thermistor unit will be there in an electric vehicle. Battery pack current is measured by using a shunt current sensor. The sensor produces a voltage corresponding to the current. Only one unit of the current sensor is required and it is connected in series with the battery pack. Current sensor has a low-value and high-precision resistor (Fig. 47.8).

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Fig. 47.7 Hardware setup

Fig. 47.8 LabVIEW front panel

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47 Battery Management System with Wireless … Table 47.1 Comparison of results between wireless and multi-meter data reading

625

Hex value of cell voltage

Wireless data read (V) Multi-meter reading (V)

947A

2.89

2.95

9806

2.96

2.98

9855

2.97

2.99

94AA

2.90

2.95

921A

2.85

2.91

981B

2.97

3.01

93AE

2.88

3.91

9540

2.91

2.96

9910

2.98

3.02

9855

2.97

3.00

930E

2.87

2.91

95FF

2.92

2.97

98EA

2.98

2.97

9610

1.22

1.45

9545

2.91

2.94

9855

2.97

2.95

47.5 Experimental Results The experiment is conducted for a 48 V the battery system, and a parameter such as voltage, current and the temperature is accurately measured. The voltages of 16cells are measured using the wireless manner and have an error of ±(1–9%) while comparing with multi-meter. The temperature and current measured have a negligible error. The total system parameter is shown by using LabVIEW software to interact with the user (Table 47.1). The main advantage of this wireless system is to get data at the various platform that is Bluetooth can pair with more than one device so that we get battery information accordingly. The programming part for the system is done by using Arduino and LabVIEW software.

47.6 Conclusion Traditional method of placing battery pack and BMS unit at the same place is modified by using this technology. In this, BMS can be placed anywhere in the vehicle. Wireless battery parameter estimation is proposed and tested for a 48 V, 16 cell battery unit. Parameter such as cell voltage, current and temperature are accurately measured and the measured data are displayed in LabVIEW software. CAN-based

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communication is replaced via wireless Bluetooth technology. Additional finding such as wireless BMS increases reliability, reduces costs, saves weight and reduces cabling requirements for large multi-cell battery packs. Acknowledgements This work has been carried out partly at KPIT technology under power train department for a time period of 1 year.

References 1. John A. Turner, A realizable renewable energy future. Science 285(5428), 687–689 (1999) 2. Jane Dignon, NOx and SOx emissions from fossil fuels: A global distribution. Atmos. Environ. Part A. Gen. Top. 26(6), 1157–1163 (1992) 3. X. Yuan, H. Liu, J. Zhang (eds.), Lithium-ion Batteries: Advanced Materials and Technologies (CRC Press, Boca Raton, 2011) 4. P.T. Krein, Battery management for maximum performance in plug-in electric and hybrid vehicles, in 2007 IEEE Vehicle Power and Propulsion Conference, pp. 2–5. IEEE (2007) 5. S. Li, C. Zhang, Study on battery management system and lithium-ion battery, in 2009 International Conference on Computer and Automation Engineering, pp. 218–222. IEEE (2009) 6. Bosh, CAN Specification. Version 2.0 (Robert Bosh GmbH, Germany, 1991), pp. 5–72 7. M. Lee, J. Lee, I. Lee, J. Lee, A. Chon, Wireless battery management system, in 2013 World Electric Vehicle Symposium and Exhibition (EVS27), pp. 1–5. IEEE (2013) 8. C. Shell, J. Henderson, H. Verra, J. Dyer, Implementation of a wireless battery management system (WBMS), in 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, pp. 1954–1959. IEEE (2015) 9. Zaw Aungkyaw, Lithium-Ion Battery Management System for FSAE Electric Vehicles. PhD Dissertation (University of Manitoba, Winnipeg, 2016) 10. S. Buller, E. Karden, A. Lohner, R.W. De Doncker, LabView-based universal battery monitoring and management system, in INTELEC-Twentieth International Telecommunications Energy Conference (Cat. No. 98CH36263), pp. 630–635. IEEE (1998)

Chapter 48

A Novel Cascaded ‘H’ Bridge-Based Multilevel Inverter with Reduced Losses and Minimum THD Madhusudhan Pamujula, Amit Ohja, Pankaj Swarnkar, R. D. Kulkarni, and Arvind Mittal Abstract An industrial application requires a wide range of voltage and power levels. Inverters based on multilevel structure are most preferable in medium voltage and high power applications nowadays. Power quality defines the life of the equipment; hence, major attention is given for reducing harmonic component in an output. Also, constraints of space in industry demand compactness among the devices. This work mainly focused to generate output with minimal harmonic content and minimum power losses with reduced number of switches with level shifted (alternate opposition and disposition) control technique at lower switching frequency. Twelve switching devices are used to generate twenty-seven level output. These twelve switches are used in such a way that two opposite switches are complimentary in nature. This will reduce the requirement of driver circuits. Only six drivers are enough to drive twelve switches making proposed inverter compact. The simulation has been performed in MATLAB/Simulink software platform for testing performance of proposed inverter. Keywords Multilevel inverters · Total harmonic distortion · Trinary · Binary · Alternate phase opposition disposition

48.1 Introduction Rapid industrialization in twentieth century demanded more power which led to the establishment of large number of coal-based power plants. Fuel gases from these plants caused a huge environmental pollution and adding to global warming. These factors made the world to search for alternative ways of power production. One of M. Pamujula (B) · A. Ohja · P. Swarnkar Department of Electrical Engineering, MANIT, Bhopal, India e-mail: [email protected] R. D. Kulkarni Scientific Officer ‘H’, BARC, Mumbai, India A. Mittal Energy Centre, MANIT, Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_48

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the ways is power from the renewable sources like wind, solar, tidal, etc. Once the distributed generation evolves, conventional vertically integrated power system will be ruled out [1]. Also, several problems like under voltage, over voltage, instability of the system and transmission losses can be solved by this distributed generation. It is said that “economic growth and development of a country is increasingly centred on reliable power system. Recently, Australia experienced an issue with reliability of renewable sources. Though solar and wind power can be pre-estimated; however, these sources have been all the time inconsistent and power drip could be unexpected. Because of deficit creation in wind energy, black out occurred in state of South Australia [2]. As a solution to this problem, a large number of grid connected battery banks are installed. As a conversion system between AC operating grid and DC operated battery bank, inverters must be installed. Multilevel inverters offer voltage with reduced THD, less common mode potential reducing stress on motor bearings, reduced dv/dt and reduced voltage stress on switch when compared with conventional two-level inverter. Multilevel are generally utilized in medium voltage higher power implementation like HVDC, FACTs system, higher power drives, integration of renewable sources, etc. Multilevel inverters also suffer from disadvantages like increased number of switches when more levels are required in the output, complex control algorithm, circuitry, etc. [3–5]. Traditional methodologies of MLI can be classified into three groups. They are • Diode clamped or neutral point clamped MLI • Flying capacitor MLI • Cascaded H-bridge MLI. These classical topologies have attracted huge attention both from academia and industry, but no topology is absolutely advantageous as MLI are specifically designed according to applications and cost considerations. Neutral point clamped topology suffers from neutral point unbalance, and this result in unequal thermal distribution among switching devices and poor power quality. Flying capacitors inverters also suffer due to issues of capacitor voltage variance that leading to bad power quality and different blocking voltages between switching devices. Also, this method requires pre-charged huge capacitors bank. So it needs added charging facilities. By rising no. of levels in output, no. of diodes and capacitors required for these topologies, respectively. CHB inverter requires more no. of switches including isolated DC power sources [6–8]. In recent years, an intensive research has been carried out on CHB inverters because of its advantages like circuit modularity, balanced DC voltage sources and output voltage with more levels and least number of switching devices of less rating each. There are two types of CHB inverters, they are symmetrical and unsymmetrical inverters. In symmetrical configuration inverters, all the DC power sources utilized will be having identical value and these are implemented for building fault withstanding systems. In unsymmetrical inverters, DC sources used are unequal. There have been various configurations of unsymmetrical inverter, however, most wellknown types are binary, trinary as well as quasi-linear schematics. In binary, DC power sources have been chosen as 20 , 21 , 22 …, in trinary circuits, and voltage

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sources have been chosen as 30 , 31 , 32 …, where as in quasi-linear, it is 1, 2, 6… [9]. These inverters are generally used to build systems to obtain more no. of levels in output. Source circuit schematic has been chosen based on topology as well as the no. of output levels desired in output. As research intensified on the topologies of CHB inverter, a large number of new topologies have been introduced in the recent past [10, 11]. In [12], topologies cannot generate all feasible arithmetic (additive and subtractive) combinations of input DC power sources. A proposed MLI in [13, 14] can able to generate all additive combinations but not subtractive. Based on the observations, a new 27 level inverter topology that uses DC sources in trinary sequence and can generate entire additive and subtractive combinations of input DC power sources in output is proposed in this paper. An organization of paper is given below. In Sect. 48.2, design and functioning of proposed MLI are described. Section 48.3 discusses simulation results with R and RL loads. Finally, conclusion has been presented in Sect. 48.4.

48.2 Novel CHB-Based MLI 48.2.1 Design and Working Asymmetrical multilevel inverters can produce higher levels in the output when compared to symmetrical multilevel inverter with equal number of switches and sources. All the semiconductor switches are unidirectional blocking and bidirectional conducting in nature because of which number of IGBTs and driver circuits are equal in number. No additional capacitors and diodes are required. Since the switches used are complimentary, only six-driver circuits are required which makes proposed inverter compact. A structure of proposed inverter has been shown in Fig. 48.1. D1

S2

D2

S1

S3

D4

D3

VDC S7

D7

S6

D5

S4

S5

S10

S11

3VDC D8

D9

S8

S9

D10 V0

Fig. 48.1 Novel CHB-based MLI

D11

D6

9VDC D12

S12

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The voltage sources used here are in the trinary configuration. The algorithm used to calculate sources is as follows. The basic voltage is denoted as VDC which will be selected based on value of output voltage required. A remaining voltage sources are calculated by using (1). V = 3(k−1) ∗ VDC

(1)

where ‘k’ is no. of sources used. Total no. of levels that can be developed by using the proposed topology and the maximum output value is calculated using (2) and (3), respectively. Nlevel = 3n

(2)

Vo,max = 3n ∗ VDC

(3)

where ‘n’ represents total no. of DC power sources used. A total no. of switches required for given methodology can be calculated using (4). Nswitch = 4n

(4)

For example, to develop 27 levels in the output, 3 DC sources with magnitude VDC , 3 VDC , 9 VDC are chosen. The proposed topology consists of 12 switches denoted as S1 –S12 . The switches S1 , S2 , S3 , S4 , S5 , S6 are complimentary to switches S7 , S8 , S9 , S10 , S11 , S12 , respectively. The switching sequences for 27 levels have been shown in Table 48.1, whereas different working modes of proposed inverter have been highlighted in Fig. 48.2.

48.2.2 Modulation Technique There have been several modulation methods for control of multilevel inverters as shown in Fig. 48.3 [15–23]. Multicarrier pulse width modulation (PWM) has been incorporated having carrier frequency as 50 Hz as carrier frequency. Level shifted PWM leading less distorted line voltages because all carriers have been in phase compared with phase shifted PWM. A switching frequency of converter will be decided by carrier frequency. It also decides the harmonic content of the output. Sinusoidal PWM topology has been incorporated with level shifted carriers. Frequency of the reference wave will decide the output voltage frequency; here, it is 50 Hz. Carriers are arranged in alternate disposition and opposition (APOD) fashion. In APOD, each carrier is out of phase with its neighbouring carrier by 180°. A reference wave shape has the amplitude Am and frequency f m. The reference wave and carrier signals are compared constantly with each other. If reference signal has been more than carrier signal, the corresponding switch will be on otherwise off. The sample arrangement

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Table 48.1 Switching sequence for 27 levels Mode No.

ON state switches and diodes

Output voltage (volt)

Mode no.

ON state switches and diodes

Output voltage (volt)

1

D7 , S8 , S9 , D10 , D11 , S12

0 VDC

14

S1 , S1 , S9 , S4 , 13 VDC S5 , S12

2

S1 , S8 , D3 , S4 , 1 VDC S5 , D6

15

D7 , D2 , D3 , S4 , S5 , D6

−1 VDC

3

D7 , D2 , S9 , S4 , S5 , D6

2 VDC

16

S1 , S1 , D3 , D10 , D11 , S12

−2 VDC

4

D7 , S8 , S9 , S4 , 3 VDC S5 , D6

17

S1 , D2 , D3 , D10 , D11 , S12

−3 VDC

5

S1 , S1 , S9 , S4 , 4 VDC S5 , D6

18

D7 , D2 , D3 , D10 , D11 , S12

−4 VDC

6

D7 , D2 , D3 , D10 , S5 , S12

5 VDC

19

S1 , S1 , S9 , S4 , −5 VDC D11 , D6

7

S1 , D2 , D3 , D10 , S5 , S12

6 VDC

20

D7 , S1 , S9 , S4 , −6 VDC D11 , D6

8

S1 , S1 , D3 , D10 , S5 , S12

7 VDC

21

D7 , D2 , D3 , S4 , D11 , D6

−7 VDC

9

D7 , D2 , D3 , S4 , S5 , S12

8 VDC

22

S1 , S1 , S9 , D10 , D11 , D6

−8 VDC

10

S1 , D2 , D3 , S4 , S5 , S12

9 VDC

23

D1 , S1 , S9 , D10 , D11 , D6

−9 VDC

11

S1 , S1 , D3 , S4 , 10 VDC S5 , S12

24

D7 , D2 , D3 , S4 , D11 , D6

−10 VDC

12

D7 , D2 , S9 , S4 , S5 , S12

11 VDC

25

S1 , S1 , S9 , D10 , D11 , D6

−11 VDC

13

D7 , S1 , S9 , S4 , 12 VDC S5 , S12

26

S1 , D2 , D3 , D10 , D11 , D6

−12 VDC

27

D7 , D2 , D3 , D10 , D11 , D6

−13 VDC

of carriers in APOD scheme is shown in Fig. 48.4. Ma = Amplitude Modulation Index = Am /(m − 1)Ac

48.2.3 Switching Losses The switching power losses in multilevel inverter are due to the presence of nonidealities in switching devices. Due to non-idealities, when switch varies its state

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D1

S2

S1

D2

S3

D5

D3

S6

S4

VDC

S5

D6

S11

9VDC D12

3VDC D8

S7

D4

D9

S10

S8

D7

D10

S9

D1

S2

D2

S1

S7

S12

D11

S3

D4

D5

D3

VDC D8

S6

S4

D9

S10

S8

D7

S11

D10

S9

S2

D6

S5

3VDC

D1

D2

S1

9VDC D12

D11

S12

D4

D5

D3

VDC D8

S7

S3

S6

S4

D6

S5

3VDC D9

S8

D7

S10

D10

S9

9VDC D12

S11

D11

S12

V0 = 2VDC

V0 = VDC

V0 = 0

Mode 2

Mode 1 D1

S3

S2

D2

S1

S7

D4

D5

D3

VDC D8

S6

S4

S10

S8

D7

S11

D10

S9

S2

D6

S5

3VDC D9

D1

D2

S1

9VDC D12

D11

S7

S12

S3

VDC D8

D2

S1

S10

S8

S7

D5

S6

S4

S5

D6

S11

9VDC D12

3VDC S10

S8

D10

S9

D1

S2

D2

S1

D2

S1

S7

S3

S7

S12

D11

D7

D5

D9

D9

S10

D7

S9

S5

D6

S11

9VDC D12

D10

S2

D2

S1

S1

D2

S3

S12

D9

S8

S9

D7

S1

D1

S6

S4

S5

D6

S10

S11

9VDC D12

D10

D2

S2

S1

D5

D9

S8

S3

D2

S6

S4

S5

S10

S11

D10

S9

S1

D2

S3

S7

S8

D1

D6

S9

S2

S1

D7

S7

S12

S3

D2

D6

S11

9VDC D12

D3

S9

S6

S5

D6

S11

9VDC D12

S10

S9

D10

V0 = -5VDC

Mode 19

D11

D1

S2

S1

S8

D7

D2

S7

S12

D7

D2

S10

S11

D12

S12

D11

S3

S3

S4

S5

S11

9VDC D12

D5

S6

S5

D6

S11

9VDC D12

S4

3VDC D9

S8

D7

D1

S10

S9

S2

S1

D10

D2

S3

S12

D11

D4

D5

S6

S4

S5

D6

S10

S11

9VDC D12

D3

VDC

3VDC D8

S7

S12

D11

D9

S8

D7

S9

D10

S12

D11

Mode 15 D1

S6

S4

S5

S10

S11

D6

S2

S1

D2

S3

D9

S8

D7

S12

S10

S11

S9

D6

9VDC D12

S12

D11

D10

Mode 18

S10

D10

S6

S5

V0 = -4VDC

D5

S4

D9

D5

S4

3VDC D8

S7

D4

D3

VDC

9VDC D12

D11

D4

S9

D4

D3

D8

S7

S10

S6

S5

D6

S11

9VDC D12

3VDC

S8

9VDC

D10

VDC

S6

D5

D3

D8

D6

S5

S4

S9

S2

S1

D6

D10

VDC

S6

Mode 12

D4

D9

S8

D5

3VDC

D1

S12

3VDC

D7

S12

V0 = -1VDC

D3

D8

D4

D9

V0 = -3VDC

D5

S4

D9

S8

S5

D5

D10

S3

VDC

9VDC D12

D11

3VDC D8

D2

VDC D8

Mode 17

D4

D3

VDC

S2

S7

S6

3VDC

Mode 16 S2

D1

S1

S12

D11

D4

D9

D7

S11

D11

V0 = 11VDC

D3

D8

V0 = -2VDC

D1

S10

D10

S9

9VDC D12

V0 = 5VDC

Mode 14

D4

3VDC

D7

D10

VDC S7

S12

D11

D3

D8

S8

D7

V0 = 13VDC

S3

VDC S7

S11

D5

S10

S9

D6

S5

Mode 9

D4

D9

Mode 13 S2

D6

9VDC D12

D11

S4

V0 = 12VDC

D1

D9

Mode 11 D5

3VDC

D7

S4

V0 = 10VDC

D3

D8

S5

3VDC

S8

S6

V0 = 8VDC

D3

D8

S7

D11

D4

VDC S7

D10

S3

VDC

Mode 10 S2

S6

S10

S8

V0 = 9VDC

D1

D5

3VDC

D1

S6

S4

S9

S12

D5

3VDC

Mode 8

D4

3VDC

S8

S7

D4

D3

VDC D8

V0 = 7VDC

D3

VDC D8

9VDC D12

D11

S4

D3

VDC D8

Mode 7 S2

D2

S1

S3

Mode 6

D4

S3

V0 = 6VDC

D1

D6

Mode 5

D4

S3

D9

D7

D10

S9

S11

S2

V0 = 4VDC

D3

VDC D8

S5

3VDC

D7

D1

S6

S4

D9

Mode 4 S2

D5

D3

V0 = 3VDC

D1

Mode 3

D4

D11

D1

S2

S1

D2

S3

D3

S12

V0 = -6VDC

Mode 20

Fig. 48.2 Different modes of operation of proposed 27 levels MLI

D7

D5

S4

VDC S7

D4

S6

S5

D6

S11

9VDC D12

3VDC D8

D9

S8

S10

S9

D10

V0 = -7VDC

Mode 21

D11

S12

48 A Novel Cascaded ‘H’ Bridge-Based Multilevel … D1

S2

S1

D2

S3

D3

D5

S4

VDC S7

D4

S5

D6

S11

9VDC D12

3VDC D8

D9

S8

D7

S10

S9

D10

D1

S6

S1

D2

S4

D7

D9

S8

S10

S9

S1

D2

S3

D7

D5

S6

S4

S5

D6

S10

S11

9VDC D12

3VDC D8

D9

S8

S9

D6

S11

9VDC D12

S3

D10 V0 = -11VDC

Mode 25

D11

D1

S2

S1

D2

S3

S4

VDC S7

S12

D7

D9

S8

S9

D10

S5

D6

S11

9VDC D12

D9

S8

D7

S10

S9

D10

S12

D11

Mode 24 D5

S6

S4

S5

D6

S10

S11

9VDC D12

3VDC D8

S6

V0 = -10VDC

D4

D3

D5

3VDC D8

S7

D4

D3

VDC

S12

D11

D2

S1

Mode 23

D4

D3

VDC S7

S5

S2

V0 = -9VDC

Mode 22

S2

D10

D1

S6

3VDC

V0 = -8VDC

D1

D5

D3

D8

S7

D4

S3

VDC

S12

D11

S2

633

D11

D1

S2

S1

D2

S3

D3

VDC S7

S12

V0 = -12VDC

Mode 26

D7

D4

D5

S6

S4

S5

D6

S10

S11

9VDC D12

3VDC D8

D9

S8

S9

D10

D11

S12

V0 = -13VDC

Mode 27

Fig. 48.2 (continued)

Fig. 48.3 Categorization of multilevel modulation topologies

from ON to OFF or vice versa, it requires small interval of time called switching time (T SW ). Variation of voltage and current across the switch results in switching loss. If this variation is linear, then the switching losses are given by Eq. (5). E sw = 1/6 ∗ Vblk ∗ Isw ∗ Tsw

(5)

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Fig. 48.4 Carrier arrangement in APOD PWM technique

where V blk denotes the blocking voltage of the switch, I sw denotes the switch current. Switching losses over a cycle are given by Eq. (6) E = n ∗ E sw

(6)

where n is given by the number of switching transitions over a cycle. This shows that switching losses depends on the carrier wave frequency. E α fc

(7)

Hence, in order to have less switching losses, the carrier wave frequency should be minimum. Switching losses in multilevel inverter will be Mf times the lesser than switching losses in two-level inverter where mf is given by (8) which is ratio of carrier frequency to modulation frequency [23]. Mf = f c / f m

(8)

Apart from the switching losses, conduction losses will also take place. Conduction losses will depend on no. of switching devices in the conduction path. Hence, overall power losses are addition of switching as well as conduction losses.

48 A Novel Cascaded ‘H’ Bridge-Based Multilevel …

635

48.3 Simulation Results Simulation of the proposed novel CHB-based MLI has been performed in MATLAB/Simulink software platform. A magnitude of Vdc is selected as 18 V, hence, all three sources are 18, 54, 162 V, respectively. So the maximum voltage that can be obtained has been sum of all three voltages, i.e. 234 V. Switching has been performed as per the switching as shown in Table 48.1. Simulation has been performed for R and RL loads with R = 9 Ohms and L = 530 mH. In order to select the carrier frequency such that switching losses are minimum, the proposed topology is simulated for different frequencies by varying modulation index and results are tabulated in Table 48.2. Figures 48.5 and 48.6 indicate output voltage waveform along with its THD, load current and its THD for both R and RL loads at M a = 1.02 for carrier frequencies F c = 50 and 900 Hz, respectively. From Table 48.2, it can be concluded that THD is below 5 as per power quality standard IEEE-519 for different frequencies tested but it is minimum for 900 Hz at M a = 1.02. In order to keep switching losses minimum, carrier frequency needs to be minimum. Hence, a compromise has been in selection of carrier frequency as 50 Hz to decrease losses. Power loss when the conduction through diode is less than the power loss during conduction through the IGBT. To obtain each level in the output, six switches need to be conducted as shown in Fig. 48.2. Hence, in order to arrest the conduction losses to minimum, switching is done in such a way that conduction is through 3 IGBTs and 3 diodes in maximum cases. Because of very high inductive nature of the load, DC offset is present in the current waveform when RL load is connected. Table 48.2 Variation of THD with carrier frequency and modulation index Frequency (Hz) Modulation index

% THD 50

600

900

1800

2400

5100

10.8 k

0.95

4.63

4.71

4.70

5.41

4.65

4.81

4.68

0.99

4.45

4.45

4.06

4.83

4.59

4.39

4.51

1.00

4.35

4.31

3.77

4.56

4.24

4.24

4.32

1.01

4.29

4.20

3.73

4.45

4.01

4.22

4.20

1.02

4.27

4.13

3.73

4.41

4.00

4.21

4.12

1.03

4.29

4.09

3.77

4.14

4.21

4.19

4.08

1.04

4.36

3.91

3.85

4.16

4.26

4.25

4.15

1.05

4.46

4.19

3.94

4.32

4.16

4.30

4.21

1.06

4.60

4.53

4.07

4.35

4.28

4.31

4.32

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Fig. 48.5 a Output voltage waveform, b voltage THD, c current waveform for RL load, d current THD for RL load, e current waveform for R load, f current THD for R load (M a = 1.02 and switching frequency 50 Hz)

48.4 Conclusion The sources used in the proposed topology are in trinary configuration; hence, different levels in output can be obtained even by subtracting voltage levels, and the circuit is simple and modular, only unidirectional switching devices are used are the merits of the proposed topology. In the open loop operation, a reference wave of fixed amplitude is selected. This reference amplitude is predetermined. Since reference wave has been fixed, the dynamic response of proposed topology has been poor. Also, the requirement of several isolated DC sources is the limitations of the proposed MLI. The main intension of the proposed method is limiting harmonic content in output which is determined by THD and to decrease the losses which are achieved by reducing no. of devices in conduction path which reduces conduction losses and by selecting carrier frequency as 50 Hz which is fundamental frequency,

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Fig. 48.6 a Output voltage waveform, b voltage THD, c current waveform for RL load, d current THD for RL load, e current waveform for R load, f current THD for R load (M a = 1.02 and switching frequency 900 Hz)

and switching losses are decreased. Simulation is done using MATLAB/Simulink, a 27 level output with THD of 4.27%. Isolated DC sources can be replaced by solar PV panels. In addition to this, if MPPT technique is used with the PV panels, better results can be obtained. Acknowledgements This work is ostensibly supported by SERB-DST, Government of India and authors are thankful to SERB-DST for funding the project titled ”Performance Investigation of Grid Connected Micro Multilevel Inverter Based Solar Photovoltaic System” SERB sanction no. EMR/2017/004604. Also, authors are extremely thankful to Director, Maulana Azad National Institute of Technology, Bhopal for extending all supports for the implementation of the SERB Project.

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References 1. Y. Cheng, C. Qian, M.L. Crow, S. Pekarek, Atcitty, A comparision of diode-clamped and cascaded multilevel inverter for a STATCOM with energy storage. IEEE Trans. Electron. 53(5), 1512–1521 (2006) 2. A.S. Mohamad, M.A.M. Radzi, N.F. Mailah, M.L. Othman, The effects of number of conducting switches in a cascaded multilevel inverter output, in IEEE 10th Control and System Graduate Research Colloquium (ICSGRC) (2019) 3. Y. Hoon, M.A.M. Radzi, M.K. Hassan, N.F. Mailah, Operation of three-level inverter-based shunt active power filter under non ideal grid voltage conditions with dual fundamental component extraction. IEEE Trans. Power Electron. 33(9), 7558–7570 (2018) 4. J. Lyu, X. Cai, M. Molinas, Optimal design of controller parameters for improving the stability of MMC-HVDC for wind farm integration. IEEE J. Emerg. Select. Top. Power Electr. 6(1), 40–53 (2018) 5. S. Du, B. Wu, N.R. Zargari, Common-mode voltage elimination for variable-speed motor drive based on flying-capacitor modular multilevel converter. IEEE Trans. Power Electron. 33(7), 5621–5628 (2018) 6. S. Sajedi, M. Basu, M. Farrell, New grid-tied cascaded multilevel inverter topology with reduced number of switches, in European Union (2017) 7. H. Choi, W. Zhao, M. Ciobotaru, V.G. Agelidis, Large-scale PV system based on the multiphase isolated dc/dc converter, in Proceedings of 3rd IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), pp. 801–807 (2012) 8. A. Nami, F. Zare, A. Ghosh, F. Blaabjerg, A hybrid cascade converter topology with seriesconnected symmetrical and asymmetrical diode-clamped H-bridge cells. IEEE Trans. Power Electron. 26, 51–65 (2011) 9. M. Pamujula, A. Ohja, R.D. Kulkarni, P. Swarnkar, Cascaded ‘H’ bridge based multilevel Inverter Topologies: a review, in 2020 International Conference for Emerging Technology (INCET) (2020) 10. K. Janardhan, A. Mittal, A. Ojha, A symmetrical multilevel inverter topology with minimal switch count and total harmonic distortion. J. Circuit. Syst. Comput. https://doi.org/10.1142/ s0218126620501741 11. C. Luciano, K.A. Aganah, Mandoye, B. Oni, in New switched-multi-source inverter topology with optimum number of used switches, in IEEE PES/IAS Power Africa (2018), pp. 414–419 12. R.S. Alishah, D. Nazarpour, S. Hosseini, M. Sabahi, Design of new single-phase multilevel voltage source inverter. Int. J. Power Electron. Drive Syst. (IJPEDS) 5(1), 45–55 (2014) 13. D. Mudadla, N. Sandeep, G. Rama Rao, Novel asymmetrical multilevel inverter topology with reduced number of switches for photovoltaic applications, in International Conference on Computation of Power, Energy, Information and Communication (2015), pp. 123–128 14. A. Khan, M. Ahmad, M. Ahmed Bhatti, M. Adeel Ijaz, S. Ullah, A comparative study of multilevel inverter topologies with reduced switches, in International Conference on Emerging Technologies (ICEET) (2019) 15. V. Anil Kumar, M. Arounassalame, Comparision of CHB multilevel inverters using level shifted modulation techniques with closed loop PI control, in 4th International Conference on Electrical Energy Systems (ICEES) (2018), pp. 168–172 16. O. Amit, P.K. Chaturvedi, A. Mittal, S. Jain, Carrier based common mode voltage reduction techniques in neutral point clamped inverter based AC-DC-AC drive system. J. Power Electr. 16(1), 142–152 (2016) 17. O. Amit, P.K. Chaturvedi, A. Mittal, S. Jain, Neutral point potential control for three phase 3level neutral point clamped active front end converter. Int. J. Electr. Eng. Inform. 9(2), 342–363 (2017) 18. T. Jitendra, A. Ojha, S. Jain, Five-level cascaded H-bridge MLC-based shunt active power filter for active harmonics mitigation in distributed network. J. Circuit. Syst. Comput. 28(2) (2019)

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19. P. Chaturvedi, A. Ojha, S. Jain, A. Mittal, Unity power factor controller for neutral point clamped active front end converter with DC voltage balancing, in 2016 IEEE International Conference on Industrial Technology (ICIT), Taipei, 2016, pp. 384–389 20. J. Tandekar, A. Ojha, S. Jain, Real time implementation of multilevel converter based shunt active power filter for harmonic compensation in distribution system, in 7th India International Conference on Power Electronics (IICPE), Patiala (2016), pp. 1–5 21. J. Tandekar, A. Ojha, S. Jain, SEIG-based renewable generation for MVDC ship power system with improved power quality. Electr. Power Compon. Syst. 47, 1–2, 27–42 (2019). doi: https:// doi.org/10.1080/15325008.2019.1570394 22. J.K. Tandekar, A. Ojha, S. Das, P. Swarnkar, S. Jain, SEIG-based renewable power generation and compensation in MVDC ship power system, 29(4), (2019) 23. K.K. Gupta, P. Bhatnagar, Multilevel Inverters Conventional and Emerging Topologies and Their Control (Academic Press, Cambridge)

Chapter 49

Assessing Factors Influencing Supply Chain 4.0: A Case of Smart City Development Hritika Sharma, Saket Shanker, and Akhilesh Barve

Abstract Smart cities are emerging as the future of urban development. By integrating technology and conventional city framework, smart cities intend to improve quality of life, ensure the security of citizens, and maintain the sustainability of the environment. The escalating growth in the present-day infrastructure of cities manifests that the day is not far when all the cities globally will transform into smart cities. The prominent driving factor of this development is Industry 4.0. The integrated framework of Industry 4.0 and smart city drives the digitalization of supply chains, and thus form the basis of Supply Chain 4.0. With the introduction of Supply Chain 4.0, the supply chains functioning worldwide are continuously incorporating significant technological advancements, which are assisting enterprises in gaining a remarkable competitive strength in the business market. This research work intends to develop a framework for understanding Supply Chain 4.0 with the perspective of smart city development. Keywords Supply chain management (SCM) · Information and communications technology (ICT) · Internet of things (IoT) · Artificial intelligence (AI)

49.1 Introduction In the present-day world, technology is advancing ceaselessly at a rapid pace [1]. This escalating growth of technology has led to various unprecedented developments [2]. Technological advancements are an inseparable part of society, possessing a long history. The evolution of industry has recognized four remarkable revolutions in the period of history. Starting from Britain in late eighteenth century, when steam H. Sharma · S. Shanker (B) · A. Barve Maulana Azad National Institute of Technology, Bhopal 462003, MP, India e-mail: [email protected] H. Sharma e-mail: [email protected] A. Barve e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_49

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engines were first introduced, and water was identified as the source of power, and the entire world encountered the First Industrial Revolution or Industry 1.0 [3]. The Second Industrial Revolution or Industry 2.0 marks its significance in the nineteenth century with the discovery of electricity and introduction of assembly line production [4]. Third Industrial Revolution or Industry 3.0 began in twentieth century with an introduction to partial automation and computers. The current revolution corresponds to Industry 4.0 which incorporates the implementation of information and communications technology to industry [5]. Industry 4.0 predominantly expands the idea of Industry 4.0 and focuses on production automation. Industry 4.0 comes up with various noteworthy technological innovations based on smart technologies [6]. These smart technologies incorporate advance robotics, artificial intelligence, blockchain, machine-to-machine communication, Internet of things, cloud-stored data, and autonomous vehicles [7]. These smart technologies, when clubbed with the infrastructure of cities, form the smart city framework. Smart cities emphasize on improving the quality of life and ensuring the sustainability of the environment, so that the world can be transformed into a better version having more reliability, enhanced security, and improved efficiency [8]. The integrated framework of Industry 4.0 and smart city development leads to one of the most prominent transformations for the conventional world, the digitalization of supply chains [9]. Supply chain functioning at all levels in the contemporary market is continuously developing and enhancing with the help of technology into a new, unprecedented form, known as Supply Chain 4.0 [10]. While the literature is already present for smart city and Supply Chain 4.0 individually [11, 12], there has not been any research work yet which links the smart city development and Supply Chain 4.0. Hence, this study attempts to fill this research gap by providing an assessment of Supply Chain 4.0 as an outcome of smart city development, and furthermore analyzing the factors which drive the digitalization of the supply chain. The research attempts to answer the following questions: (a) (b) (c) (d)

What is Industry 4.0? What is smart city development? What is Supply Chain 4.0, and how is it related to smart city development? What are the different factors which drive the digitalization of supply chains?

The research work attempts to answer these questions with the help of the following research objectives: • Explaining the terms ‘Industry 4.0’ and ‘smart city’ in detail. • Explaining ‘Supply Chain 4.0’ and recognizing the role of smart city development in digitalization of supply chains. • Identifying all the factors which drive the evolution of Supply Chain 4.0. The paper is divided into three sections. Section 49.1 being an introduction, Sect. 49.2 comprises of the relevant literature related to Industry 4.0, smart city, and Supply Chain 4.0. Section 49.3 comprises of the concluding remarks.

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Table 49.1 Technologies associated with Industry 4.0 Technology

Description

References

Internet of things

IoT refers to the interconnection of various devices which can share and transfer information within them. It is the most prominent technology used to operate advanced devices over a wireless connection

[15, 16]

Artificial intelligence

AI refers to the imitation/simulation of human thinking capability and intelligence in machines that are specially programmed for this

[17, 18]

Robotics

Robotics corresponds to the specific development of work-specialized machines, which can be used as a replacement of humans, known as robots

[19, 20]

49.2 Literature Review This section consists of literature associated with Industry 4.0, smart city, and Supply Chain 4.0.

49.2.1 Industry 4.0 Industry 4.0 corresponds to the Fourth Industrial Revolution. Industry 4.0 consists of various technologies which collectively transform the conventional way of working [13]. These advanced technologies incorporate Internet of things, advance robotics, artificial intelligence, blockchain, cyber-physical systems, cloud-stored data, machine-to-machine communication, advanced sensors, and autonomous vehicles [14]. Industry 4.0 leads the revolution of smart cities and when deployed, possess the power to transform the entire city. Table 49.1 consists of some prominent technologies in Industry 4.0.

49.2.2 Smart City Smart cities intend to build a better environment where citizens can be more secure, and the existing quality of life may be improved [21]. This aim is achieved with the help of technology. Smart cities are primarily the integration of technology and the infrastructure of the city, which make them advanced enough to deal with all the existing, realistic problems in an innovative way [22]. Smart city strategy is fundamentally based on five sub-factors, namely smart governance, smart living, smart economy, smart people, and smart environment. Thus, by improving all the crucial sectors of society, the smart city comes up as an integrated framework of

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Table 49.2 Elements of smart city development Elements of smart city Description

References

Smart governance

The smart government makes the use of digital technology so as to make better analysis and improvised decisions so that the transparency increases

[23, 24]

Smart economy

Smart economy utilizes technology to create the business environments that are more healthier, flexible, and efficient than the conventional ones

[25, 26]

Smart living

Smart living corresponds to the enhanced living standards [27, 28] with increased productivity and a more secure environment, which are developed with the help of technology

Smart people

Smart people who will be more connected to the technology and the Internet and will be more aware regarding the updates of their surroundings

[29, 30]

Smart environment

The smart environment which is sustainable with the comparatively mitigated amount of carbon emissions in the atmosphere

[31, 32]

efficiency, reliability, and faster operation. Table 49.2 includes all the factors related to the smart city.

49.2.3 Supply Chain 4.0 The combined endeavor of Industry 4.0 and smart city leads to the digitalization of supply chains, which is known as Supply Chain 4.0 [33]. Supply Chain 4.0 comes up with innovative solutions to mitigate and eliminate the prevailing problems with supply chain management, such as idle time lapsed, demand management, and environmental concerns [34]. Thus, Supply Chain 4.0 emerges as an unprecedented strategy to deal with all the challenges, by offering more advanced, digitalized, and flexible supply chains which are more efficient, consume less time, offers desired variety in products, and maintains sustainability of environment. Supply Chain 4.0 is primarily based on the practical implementation of digital technologies such as AI, IoT, and robotics in different sectors of supply chain, such as manufacturing sector, transportation, and warehousing. With the assistance of latest technological advancements, supply chains can be reconstructed into a more refined form, incorporating enhanced accuracy levels and more variety at the minimum cost. Inventory can be controlled and managed more efficiently, as the introduction of IoT in inventory management will lead to the access of real-time data. Thus, the existing inventory levels may be analyzed more accurately, and the records produced will be error-free, which will eliminate all the extra costs spent in maintaining the excess inventory.

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Fig. 49.1 Combined framework of Industry 4.0, smart city, and Supply Chain 4.0

Figure 49.1 explains the combined framework of Industry 4.0, smart city, and Supply Chain 4.0. Factors Driving Digitalization of Supply Chain Table 49.3 summarizes all the factors which drive the digitalization of supply chains.

49.3 Conclusion With the concept of the smart city becoming popular, the implementation of Industry 4.0 is escalating. The combined impact due to the integration of smart city framework and Industry 4.0 lays the foundation for the digitalization of supply chains. The concept of the digital supply chain recognized as Supply Chain 4.0 comes up with various associated driving factors. This study seeks its significance in providing an analysis of the impact of smart city development on supply chain management. This research work can be furthermore utilized in assessing the driving factors associated with Supply Chain 4.0 with the help of graph theory matrix approach (GTMA).

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Table 49.3 Factors driving supply chain digitalization Factor

Description

References

Institutional pressures

Institutional pressures include all types of pressures exerted by institutions on the other organizations/individuals by means of certain restrictions or business norms. These can be normative, coercive, or mimetic

[35]

Competitive advantage Due to digitalization, there is an overall increase in production accuracy and an overall decrease in effective time consumed to deliver a certain product. Thus, these factors lead to a competitive advantage for the supply chain over others

[36, 37]

Product variety

With the deployment of Industry 4.0 in business, the [38] capability of manufacturing a different variety of products according to the requirement of market increases considerably

Sustainable concerns

Supply Chain 4.0 emerge as an inventive idea to deal with [39] ecological problems such as reducing carbon emissions, and thus Supply Chain 4.0 comes up as an environment-friendly strategy

Customer satisfaction

Customized products and faster response service along with a well-managed reverse logistics supply chain are some of the key features of Supply Chain 4.0 which ensures the customer satisfaction

Cost efficiency

Deployment of Industry 4.0 in supply chains leads to [41] comparatively more cost-effective systems, cutting-off the extra costs which used to be present earlier due to inaccurate measurements and forecasts

[40]

Enhanced transparency Supply Chain 4.0 introduces new terms of accuracy and data management, including real-time data access and precise inventory forecasts. This increases the overall transparency in the supply chain

[42]

Decentralization

Industry 4.0 is fundamentally conceptualized on the concept of decentralization

[43]

Sustainable concerns

Supply Chain 4.0 emerge as an inventive idea to deal with [39] ecological problems such as reducing carbon emissions, and thus Supply Chain 4.0 comes up as an environment-friendly strategy

References 1. F.P. Appio, M. Lima, S. Paroutis, Understanding smart cities: innovation ecosystems, technological advancements, and societal challenges. Technol. Forecast. Soc. Chang. 142, 1–14 (2019) 2. M. Sadriddinov et al., Assessment of technological development and economic sustainability of domestic industry in modern conditions, in IOP Conference Series: Materials Science and Engineering. IOP Publishing (2020)

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3. N. Crafts, Explaining the first industrial revolution: two views. Eur. Rev. Econ. Hist. 15(1), 153–168 (2011) 4. H.S. Jevons, The second industrial revolution. Econ. J. 41(161), 1–18 (1931) 5. Y. Liu, D.B. Grusky, The payoff to skill in the third industrial revolution. Am. J. Sociol. 118(5), 1330–1374 (2013) 6. G. Li, Y. Hou, A. Wu, Fourth Industrial Revolution: technological drivers, impacts and coping methods. Chin. Geogra. Sci. 27(4), 626–637 (2017) 7. H. Lasi et al., Industry 4.0. Bus. Inform. Syst. Eng. 6(4), 239–242 (2014) 8. K. Su, J. Li, H. Fu. Smart city and the applications, in 2011 International Conference on Electronics, Communications and Control (ICECC). IEEE (2011) 9. B. Tjahjono et al., What does industry 4.0 mean to supply chain? Procedia Manuf. 13, 1175– 1182 (2017) 10. D. Makris, Z.N.L. Hansen, O. Khan, Adapting to supply chain 4.0: an explorative study of multinational companies. Supply Chain Forum: Int. J. (2019) 11. A. Caragliu, C.F. Del Bo, Smart innovative cities: the impact of Smart City policies on urban innovation. Technol. Forecast. Soc. Chang. 142, 373–383 (2019) 12. G.J. Hahn, Industry 4.0: a supply chain innovation perspective. Int. J. Product. Res. 58(5), 1425–1441 (2020) 13. P.K. Muhuri, A.K. Shukla, A. Abraham, Industry 4.0: a bibliometric analysis and detailed overview. Eng. Appl. Artif. Intell. 78, 218–235 14. E. Oztemel, S. Gursev, Literature review of Industry 4.0 and related technologies. J. Intell. Manuf. 31(1), 127–182 (2020) 15. A. Nauman et al., Multimedia internet of things: a comprehensive survey. IEEE Access 8, 8202–8250 (2020) 16. A. Tewari, B. Gupta, Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework. Fut. Gener. Comput. Syst. 108, 909–920 (2020) 17. U. Paschen, C. Pitt, J. Kietzmann, Artificial intelligence: building blocks and an innovation typology. Bus. Horiz. 63(2), 147–155 (2020) 18. S.K. Singh, S. Rathore, J.H. Park, Blockiotintelligence: a blockchain-enabled intelligent IoT architecture with artificial intelligence. Fut. Gener. Comput. Syst. 110, 721–743 (2020) 19. C. Webster, S. Ivanov, Robotics, artificial intelligence, and the evolving nature of work, in Digital Transformation in Business and Society. Springer (2020), pp 127–143 20. J. Seo, J. Paik, M. Yim, Modular reconfigurable robotics. Ann Rev Control Robot Auton Syst 2, 63–88 (2019) 21. N. Komninos et al., Smart city ontologies: improving the effectiveness of smart city applications. J. Smart Cities 1(1), 31–46 (2019) 22. A. Camero, E. Alba, Smart City and information technology: a review. Cities 93, 84–94 (2019) 23. S. Barns, Smart cities and urban data platforms: designing interfaces for smart governance. City Cult. Soc. 12, 5–12 (2018) 24. H.J. Scholl, M.C. Scholl, Smart governance: a roadmap for research and practice, in Conference 2014 Proceedings (2014) 25. T.V. Kumar, B. Dahiya, Smart economy in smart cities, in Smart Economy in Smart Cities. Springer (2017), pp 3–76 26. A. Arroub, et al., A literature review on Smart Cities: paradigms, opportunities and open problems, in 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM). IEEE (2016) 27. C.-T. Lin et al., Brain computer interface-based smart living environmental auto-adjustment control system in UPnP home networking. IEEE Syst. J. 8(2), 363–370 (2012) 28. A. Hosseinian-Far, M. Ramachandran, C.L. Slack, Emerging trends in cloud computing, big data, fog computing, in IoT and smart living, in Technology for Smart Futures. Springer (2018), pp 29–40 29. S.A. Barab, J.A. Plucker, Smart people or smart contexts? Cognition, ability, and talent development in an age of situated approaches to knowing and learning. Educ. Psychol. 37(3), 165–182 (2002)

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30. V.A. Thompson et al., Do smart people have better intuitions? J. Exp. Psychol. Gener. 147(7), 945 (2018) 31. T.V. Kumar, Smart environment for smart cities, in Smart Environment for Smart Cities. Springer (2020), pp 1–53 32. S.L. Ullo, G. Sinha, Advances in smart environment monitoring systems using IoT and sensors. Sensors 20(11), 3113 (2020) 33. S. Luthra, S.K. Mangla, Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Saf. Environ. Protect. 117, 168–179 (2018) 34. V.L. da Silva, J.L. Kovaleski, R.N. Pagani, Technology transfer in the supply chain oriented to industry 4.0: a literature review. Technol. Anal. Strateg. Manage. 31(5), 546–562 (2019) 35. I. Okhmatovskiy, R.J. David, Setting your own standards: Internal corporate governance codes as a response to institutional pressure. Organ. Sci. 23(1), 155–176 (2012) 36. J.B. Barney, Looking inside for competitive advantage. Acad. Manag. Perspect. 9(4), 49–61 (1995) 37. J. Barney, Firm resources and sustained competitive advantage. J. Manag. 17(1), 99–120 (1991) 38. H. ElMaraghy et al., Product variety management. CIRP Ann. 62(2), 629–652 (2013) 39. J. Sathaye, P. Shukla, N. Ravindranath, Climate change, sustainable development and India: global and national concerns. Curr. Sci. 314–325 (2006) 40. R.T. Rust, A.J. Zahorik, Customer satisfaction, customer retention, and market share. J. Retail. 69(2), 193–215 (1993) 41. M. Daneshvar et al., Effective factors of implementing efficient supply chain strategy on supply chain performance. Technol. Econ. Dev. Econ. 26(4), 947–969 (2020) 42. C. Higgins, S. Tang, W. Stubbs, On managing hypocrisy: the transparency of sustainability reports. J. Bus. Res. 114, 395–407 (2020) 43. F.S. Oliveira, A causal map analysis of supply chain decentralization. J. Comput. Inform. Syst. 1–11 (2020)

Chapter 50

Electrical Equivalent Model for Proton Exchange Membrane Fuel Cell Useful in On-Board Applications Sujit Sopan Barhate

and Rohini Mudhalwadkar

Abstract Proton exchange membrane (PEM) fuel cell is a complex device with the integration of multi-physics domains. Fuel cell performance is modeled by many researchers in the literature. Many models are constructed by integrating models of the multi-physics phenomenon in the fuel cell. Hence, the final fuel cell model becomes complex and computationally expensive. Whereas, this paper presents a simplified electrical equivalent model for PEM fuel cell. The proposed model has prediction and correction algorithms and it estimates fuel cell output and the potential error in the prediction algorithm. The estimated error is compensated in the estimated fuel cell output in the correction algorithm. The proposed model has fewer parameters to estimate and lesser fuel cell system-related inputs needed as compared to the models in the literature. Moreover, the proposed model is simple and computationally inexpensive. It is validated experimentally with three different fuel cells. Mean square error between the experimental observations and the model output is less than 0.00004. The model is suitable not only for off-board but on-board applications like fuel cell monitoring and diagnostics. Keywords Electrical equivalent model · Fuel cell monitoring · Fuel cell diagnostics · Fuel cell experiment

50.1 Introduction Fuel cell is an electrochemical device which generates electricity. There are several types of fuel cells, like PEM, direct methanol, alkaline, phosphorous acid, molten carbonic and solid oxide. Each fuel cell type has different fuel and operating temperature conditions. The PEM fuel cell has excellent power density, turndown dynamics, scalability and robustness. Its start-up time is faster and better lifetime among other types [1]. Furthermore, it is simple in construction, light in weight, low cost, S. S. Barhate (B) Department of Technology, Savitribai Phule Pune University, Pune, India R. Mudhalwadkar Instrumentation and Control Department, College of Engineering Pune, Pune, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_50

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better transient response, high efficiency, low operating temperature and absence of corrosive electrolyte [2]. These characteristics of the PEM fuel cell make it most appropriate power generator for automotive applications [3, 4]. Fuel cell is a complex system and it requires high degree of competence in knowledge areas such as electrochemistry, thermodynamics, fluid mechanics, materials and others. It also requires integrated multi-physics phenomenon to understand fuel cell system behavior. Mathematical modeling helps simplifying the fuel cell system. It allows understanding of the fuel cell system and it reduces time and cost in experimentation and analysis. Numerous researchers have developed mathematical models for PEM fuel cell. Mathematical models. Fuel cell physical models have been developed [5, 6]. Models are developed using physical and electrochemical details [7, 8], and a nonlinear fuel cell system model is developed to investigate behavior in load conditions [9]. The physical models are simulated using MATLAB/Simulink [10]. Mathematical models [11–13] describe fuel cell performance in static condition. Fuel cell dynamic models developed to analyze fuel cell performance in dynamic conditions [14, 15]. Moreover, the list of formulae used in fuel cell models are consolidated [16], and most of these models are proven experimentally. Models have developed using physical parameters and internal phases [5, 13]. These models are accurate in producing correct behavior of fuel cell in given condition or configuration. However, these models need information related to fuel cell construction, such as cell size, effective area, membrane thickness and others. They need details of operating parameters, such as reactant gas pressure, temperature and humidification temperature. They assume standard conditions like appropriate humidification of reactant gases, heat and water management and no cell degradation, often it needs huge processing power to execute these models on computer, and this makes these models ineffective for system approach. Hence, these have limitations in using on-board applications like automobiles. Recently, researchers have tried to simplify fuel cell models by using semiempirical technique like electrical equivalent circuits [17–19]. Researchers [20] have adopted models in system applications. Electrical equivalent circuit models were developed for activation polarization, ohmic polarization, double layer capacitance and mass transport effects of PEM fuel cell [21]. It needs information of physical parameters to estimate values of resistors and capacitors. Simplified electrical equivalent circuits for fuel cell are proposed [11, 15]. MATLAB® was used in the analysis of the models. Activation, ohmic and concentration polarization are modeled in electrical equivalent circuit model proposed by Dicks-Larminie [22]. Randles used Warburg element along with double layer capacitance and electrolyte resistance. Dicks-Larminie and Randles models were validated using simulation and experiments [14]. RC equivalent fuel cell temperature model was developed and validated along with error analysis [4]. Error between experimental temperature readings and model output is calculated as root mean square error. Fuel cell electrical equivalent circuit and parameter estimation using least square polyfit approximation were proposed [20]. Electrical equivalent circuits for fuel cell components were developed with analogy of pneumatics and fluidics for fuel cell sub-system models [23, 24]. Low and high fuel cell current equivalent circuit models for 1.2 KW fuel cell stack

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was developed and analyzed [25]. The results were analyzed using electrochemical impedance spectroscopy (EIS). Most of the models are validated by comparing polarization curve (V–I curve) computed by the model with polarization of actual fuel cell obtained by experimentation [22]. If V–I curve computed by the model is within acceptable error limit to the actual fuel cell V–I curve, then the model is considered to be reliable [8]. Battery, which has similar electrical equivalent model is studied for its correctness in V–I curve [26]. Electrical equivalent circuit models for fuel cell can be termed as a black box model because inner working of multi-physics is not required. The black box model parameters are tuned using data-driven approach. Measured data is used to predict the parameters [27] and these models are computationally inexpensive. Most of the researchers have predicted fuel cell out but did not provide the error information. So, accuracy and reliability of model are unknown. Model’s accuracy represents its closeness to the actual fuel cell output. Consistent accuracy in repeated experiments prove the reliability. A model shall be accurate and reliable for system applications such as on-board cell monitoring and diagnostics. Keeping limitations and complexities of existing models, the focus of this study is to develop a simple but accurate, reliable and computationally inexpensive PEM fuel cell model. The rest of the paper is organized as follows: Sect. 50.2 explains the proposed electrical equivalent model and the prediction-correction algorithm. Section 50.3 describes PEM fuel cell experimental setup. Section 50.4 validates simulation with the experimental observations and Sect. 50.5 concludes the paper.

50.2 Electrical Equivalent Model for PEM Fuel Cell A simplified electrical equivalent circuit for fuel cell which is based on Thevenin cell model is proposed in Fig. 50.1. It is a black box model which does not need cell construction details and multi-physical parameters to predict the output and it consists of resistors and capacitor network. Resistor R1 represents activation and concentration loss. Capacitor C 1 is a double layer charge and it delays electronic charge dissipation at electrolyte and electrodes. Resistor R0 is resistance to flow of

Fig. 50.1 Electrical equivalent model for PEM fuel cell

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Fig. 50.2 Prediction and correction algorithm

electrons. V FC (z(t)) is a fuel cell potential at no load condition and v(t) is the terminal voltage. Current i(t) is sum of currents flowing through resistor R1 , iR1 (t) and current through capacitor C 1 , iC1 (t). Prediction and correction algorithms are shown in Fig. 50.2. The fuel cell potential is predicted through steps 1a–1c. Predicted fuel cell potential is corrected by compensating estimated error through correction steps 2a–2c.

50.2.1 Prediction Steps Prediction steps help estimating fuel cell output voltage for the load current. Values of resistors R0 , R1 and capacitor C 1 depend on the fuel cell under test. Values of resistors R0 and R1 shall be small and capacitor C 1 value high for new fuel cell. a.

Step 1a: Initialize

Initialize i R1 [0] and i[0] to a small initial current, let us say 0.01 A. Fuel cell potential at small i[0] shall be near to theoretical fuel cell potential, let us say 0.9 V. b.

Step 1b: Assignment

Previous measured fuel cell potential is assigned to current fuel cell potential as shown in Eq. (1).

50 Electrical Equivalent Model for Proton Exchange …

VFC (z[k]) = v[k − 1],

653

(1)

where v[k − 1] is measured values from the fuel cell. c.

Step 1c: Predict fuel cell output

Fuel cell current i(t) can be derived as: i(t) = i R1 (t) + i C1 (t),

(2)

i C1 = C1 v˙C1 ,

(3)

i(t) = i R1 (t) + C1 v˙C1 (t),

(4)

vC1 (t) = R1 i R1 (t).

(5)

where

therefore,

Substituting, vC1 (t) in Eq. (4) i(t) = i R1 (t) + R1 C1

di R1 (t) . dt

(6)

Rearranging the Eq. (6), di R1 (t) 1 1 = i(t) − i R (t). dt R1 C 1 R1 C 1 1

(7)

Cell terminal voltage V (t) can be obtained by V (t) = VFC (z(t)) − vC1 (t) − i(t)R0 ,

(8)

substituting Eq. (3) in Eq. (8), V (t) = VFC (z(t)) − R1 i R1 (t) − i(t)R0 ,

(9)

VFC (z(t)) = v(t − 1).

(10)

where

Equations (7) and (9) are expressed in continuous time as ordinary differential equations (ODE). The equations need to be converted into to discrete ODEs to program them in on-board system.

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Convert Eq. (7) in discrete ODE. Let, τ1 = R1 C1 . Substituting τ1 , in Eq. (7), 1 di R1 (t) 1 = i(t) − i R1 (t), dt τ1 τ1

(11)

x(t) ˙ = ax(t) + bu(t).

(12)

let us refer generic ODE

Convert Eq. (12) to discrete ODE x[k + 1] = eat x[k] +

 1  at e − 1 bu[k], a

(13)

where a = − τ11 , b = τ11 , x[k] = i R1 [k] and u[k] = i[k]. As conversion of Eq. (12) is Eq. (13), similarly, Eq. (11) can be converted into discrete ODE Eq. (14), (−t/ τ ) 1 iR

i R1 [k + 1] = e

  (−t/ τ ) 1 i[k], + 1 − e [k] 1

(14)

Conversion of Eq. (8) into discrete ODE is as Eq. (15) v[k] ˆ = VFC (z[k]) − R1 i R1 [k] − R0 i[k],

(15)

where v[k] ˆ is predicted fuel cell potential at load current i[k].

50.2.2 Correction Steps An error is estimated in the predicted fuel cell potential in step 1c. The fuel cell output potential is corrected for the estimated error. a.

Step 2a: Error coefficients

The error coefficients A and B are depending on the resistors and capacitance values used in the equivalent circuit.

b.

Step 2b: Estimate error

A = (R0 + R1 ) × C1 ,

(16)

 B = A 2.

(17)

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Estimated error in the fuel cell potential prediction can be calculated by Eq. (18). ErrorEst [k] = Ai[k] − B,

(18)

where i[k] is the current flowing through the fuel cell, A and B are error coefficients as calculated in Eqs. (16) and (17). c.

Step 2c: Fuel cell potential

Prediction of fuel cell potential is corrected by adding the estimated error with predicted fuel cell potential v[k] ˆ as shown in Eq. (19). ˆ + ErrorEst [k], vcFC [k] = v[k]

(19)

where vcFC [k] is error compensated fuel cell potential. The models we have proposed in Fig. 50.1 and algorithm as shown in Fig. 50.2 need to be validated with the experimental data

50.3 Experimental Setup Description Fuel cell testers, FCT-50S used for the validating model on actual fuel cells. The FCT-50S is a compact fuel cell test equipment developed by Bio-Logic Science Instruments and PaxiTech. It is a computer controller test station for electrochemical testing of single cell of PEM fuel cell. FCT-50S has connections for hydrogen, oxygen, nitrogen, cell current collectors, cell voltage sense, cell temperature probe and cell heater. Electronic load inside the tester to control either cell voltage or current passing through the cell. The tester is capable of simultaneous measurement of current and voltage of the cell. The tester is built with humidifier, heater, condenser, back pressure sensors and mass flow meters. Figure 50.3 shows a typical experimental setup using FCT-50S fuel cell tester. A PEM fuel cell under test is connected to the tester along with connection of hydrogen, oxygen and nitrogen cylinders. FCT50S has a software interface, FC-Lab® . The software offers functions for the cell, gas lines and electronic load control. It offers controls for temperature, pressure, flow and water. Control parameters and fuel cell output are monitored and logged. Logged data is also presented in graphical format in the FC-Lab® . The FCT-50S is connected to computer through ethernet. It makes FCT-50S easy to operate and use in experimentation. The software is capable of performing electrochemical tests; fuel cell internal voltage (VFC), voltage pulse, current pulse, power pulse, load pulse, voltage scan, current scan and electrochemical impedance spectroscopy (EIS). Compressed reactant gases, hydrogen and oxygen, are used in the test setup. Ultrapure hydrogen (99.99%) is used as fuel on anode region. Medical grade oxygen is used as oxidant on cathode region. Hydrogen is stored in high pressure cylinder of pressure 200–690 kPa. Nitrogen is used as purge gas. Reactants and the purge gas cylinders are part of the test setup.

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Fig. 50.3 Experimental setup using FCT-50S

50.4 Experimental Results and Analysis The proposed model in Figs. 50.1 and 50.2 needs to validated experimentally to prove its accuracy and reliability. So, the model is validated on three single PEM fuel cells by conducting cyclic voltammetry experiment on each cell. One of the cells is assembled with 36 cm2 MEA. Two other cells were assembled with 25 cm2 MEA. One of the MEA is new and another is used for more than 500 h. Whereas, the 36 cm2 cell was assembled with unused MEA. The cells were connected to the fuel cell test setup as shown in Fig. 50.3 one by one. The test environment is configured for the experimentation. Nitrogen gas is used to purge the cell and it ensures removing any water droplets in the cell. Nitrogen is used as it is an inert gas and it does not take action in reaction. Then, the voltage scan test is configured in FC-Lab® . The test was executed on all the fuel cells, 25 cm2 cell with new MEA, 25 cm2 with old MEA and 36 cm2 fuel cells. Current and voltage values were logged of the experimentation. Cell current and voltage values were logged in all the three experiments. Log files from these experiments are in FCT-50S specific format; with ‘elt’ extension. The log file can be opened in Microsoft Excel® for further analysis. The proposed electrical equivalent model for fuel cell has two discrete ODEs; (14) and (15). The model is simulated for the three fuel cells under test. These ODEs are solved empirically in the Microsoft Excel® 2019. Resistors R0 , R1 and capacitor C 1 values assigned in the calculation are R0 = 0.50 m , R1 = 0.10 m  and C 1 = 10 F. Predicted fuel cell terminal voltages are obtained by solving the equations. Error is estimated in each experimental point and corrected as per Eqs. (18) and (19).

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Figures 50.4a, 50.5a and 50.6a, respectively, show the results for 36 cm2 , 25 cm2 (new MEA) and 25 cm2 (used MEA) PEM fuel cell single cells. Fuel cell current (i[k]) is plotted on X-axis and fuel cell potential v[k] on Y-axis. Solid black line shows the experimental fuel cell potential (v[k]), whereas dash line shows model predicted fuel cell potential (v[k]). ˆ Dotted line shows the error (Error[k]) between measured fuel cell potential and the predicted fuel cell potential as shown in Eq. (20). Error shares X-axis and has secondary Y-axis. The error is observed increasing as the current increases. Figures 50.4b, 50.5b and 50.6b, respectively, show the results compared with error compensated fuel cell potential (vcFC [k]). New error is calculated between the experimental fuel cell potential (v[k]) and error compensated fuel cell terminal (vcFC [k]) as shown in Eq. (21). Error[k] = v[k] − v[k], ˆ

(20)

ErrorNew [k] = v[k] − vcFC [k].

(21)

Moreover, mean square error (MSE) is calculated to measure the accuracy of the model. MSE of the error is the average of the squares of the errors between the experimental fuel cell potential and model predicted fuel cell potential as shown in Eq. (22). Similarly, MSE of the new error is the average of the squares of the errors between the experimental fuel cell potential and error compensated fuel cell potential

Fig. 50.4 a 36 cm2 PEM fuel cell polarization before error compensation, b 36 cm2 PEM fuel cell polarization after error compensation

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as shown in Eq. (23). MSE of Error =

n 1 (Error[k])2 , n k=1

(22)

Fig. 50.5 a 25 cm2 PEM fuel cell (new MEA) polarization before error compensation. b 25 cm2 PEM fuel cell (new MEA) polarization after error compensation

Fig. 50.6 a 25 cm2 PEM fuel cell (used MEA) polarization before error compensation, b 25 cm2 PEM fuel cell (used MEA) polarization after error compensation

50 Electrical Equivalent Model for Proton Exchange … Table 50.1 MSE in model simulation

PEM fuel cell under test 36

cm2

25

cm2

659 MSE of error

MSE of ErrorNew

with new MEA

5.64 × 10−5

9.93 × 10−6

with new MEA

3.8 × 10−5

1.6 × 10−7

3.27 × 10−6

1.81 × 10−8

25 cm2 with used MEA

MSE of ErrorNew =

n 1 (ErrorNew [k])2 , n k=1

(23)

where n is number of data points. The smaller the MSE, the simulation is closer the experimental data. Near-to-zero MSE is observed from Table 50.1 for the error compensated model output.

50.5 Conclusion A simplified electrical equivalent circuit model for the PEM fuel cell is proposed in the paper. The model is experimentally validated with three different PEM fuel cells. Experimental results are compared with the model predicted results and found matching with near-to-zero MSE. Absolute error for the measurements is less than 1% of the actual fuel cell potential for a given load current. Hence, the model is able to predict fuel cell output potential very close to the actual fuel cell and it proves the model to be accurate and reliable. The model has only three parameters to estimate, two discrete ODEs and one linear equation to solve. Hence, it needs low computational power to estimate fuel cell output. This makes the model useable in onboard applications for stationary as well as mobile applications. The model predicted fuel cell performance can be compared with the actual fuel cell performance to ensure desired functioning on the fuel cell. The study can be further extended to verify the model performance in fault conditions. The model can be used in fuel cell diagnostics and monitoring functions.

References 1. M. Perez-page, V. Perez-Herranz, Effect of an operation and humidification temperatures on the performance of a pem fuel cell stack on dead-end mode. Int. J. Electrochem. Sci. 6, 492–505 (2011). https://doi.org/10.1149/1.3210625 2. F. Migliardini, P. Corbo, CV and EIS study of hydrogen fuel cell durability in automotive applications. Int. J. Electrochem. Sci. 8, 11033–11047 (2013) 3. S.D. Gaikwad, P.C. Ghosh, Sizing of fuel cell electric vehicle: A pinch analysis-based approach. Int. J. Hydrog. Energy 45(15), 8985–8993 (2020). https://doi.org/10.1016/j.ijhydene.2020. 01.116

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Chapter 51

Predicting Waste to Energy Potential and Estimating Number of Transfer Station Based on Indore Waste Management Model: A Case of Indian Smart Cities Ankit Tiwari and Pritee Sharma Abstract For Indian cities monitoring, planning, and designing strategies of waste from its production to disposal is a very essential agenda in Smart City Mission (SCM). Therefore, many measures are planned and executed by the Indian government both at the central and local level recently. One of important phase needed for successful planning is matching of prediction for generated waste inflows and treated waste outflow. This balance between inflow and outflow of waste should be sustainable in the long run to match the pace on the global scale of smart city development. The sustainable planning needs coupling of parameters forecasted with the policy perspectives which are to be addressed in smart city mission. Our analyzes through this paper try to highlight the projected values of biogas, bio-CNG, and waste to electricity potentials from municipal solid waste for the year 2031. These investigations and estimation will help cities to plan the number of transfer station, processing facilities with bio-CNG, waste to electricity and biogas plants, hence, preparing themselves for broader agenda of sustainable development goals (SDGs), 2030. The estimated number of transfer stations based on Indore waste management model, which will provide a more situated solution to decision making for waste infrastructural transformation for these cities eyeing became a smart city. The study also suggests some of funding option available for implementation of projects. For the study, 13 cities are taken from different states which are part of the Smart City Mission (SCM) of India. Keywords Smart city mission (SCM) · Sustainable planning · Bio-CNG · Waste to energy · Municipal solid waste management (MSWM)

A. Tiwari (B) · P. Sharma Indian Institute of Technology Indore, Indore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_51

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51.1 Introduction One of the most urgent issues is the achievement of sustainably sound solid waste management practices as per agreement by 193 countries in sustainable development goals (SDGs) in September 2015 [1]. The issue of urgency can be easily understood as for the agenda 2030 for sustainable development, 12 out of 17 SDGs have a direct connection with waste management [2]. For sustainable development, municipal solid waste (MSW) is considered an important issue since all three areas sustainability namely society, environment, and economy are associated with it [3, 4]. Increase quantity of MSW amounts is very closely coupled with the pace of urbanization and population growth [5]. Therefore, for sustainable development, solid waste management cannot be neglected and needs urgent attention for future planning. With the similar urgency of addressing solid waste management, the Indian government has introduced Municipal Waste Management Rules, 2016 and it is a priority issue in the Smart City Mission (SCM) of India also. Many measures and applied models are introduced and suggested under the guidelines of the central government under different ministries to support decision-makers of cities. The planning processes of the waste management system are very complex, due to different geographic condition, lifestyles, climate, population count, level of income, and financial status of the cities in India. Any decision on waste processing, the establishment of the transfer station and waste to energy plant will be based on the balance of legal, technical, financial, sociocultural, and environmental variables. A successful decision making for smart city missions related to waste management should be data-driven-based evaluation of technologies of waste to energy at varying scales and performance indices. The smart city project is an opportunity to grab for cities and decisions on waste management infrastructure transformation without environmental and economic long-term consequences will be a huge loss for city planning. The smart city waste management planning and measures should eye on the broader goal of climate change mitigation and global investments. These wastes infrastructural transformation should employ technologies that can extract more energy and recover material from waste. These transformations should be based on city-specific requirements. For achieving effective waste management, the planners and policymakers need reliable data. The data concerning biogas, bio-CNG, and waste to energy will help them to make an attractive proposal for cities. The government’s recent policy emphasis on 100 percent collection, segregation of waste in dry and wet waste, development of waste to energy plants and transfer station. The segregation at source is a huge plus for biogas, bio-CNG, and waste to energy technologies to be implemented. This paper will add to the waste management decision matrix by evaluating the projected values of biogas, bio-CNG, and waste to electrical potentials from municipal solid waste. The uniqueness of the study is that it is using a reference model of Indore, which is the cleanest city as a various survey by the Government of India. This paper is attempted to aid planners and policymakers in planning transfer stations, processing facilities, and intermediate waste storage plants by forecasting data required for their analysis. This paper highlights the new

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responsibility of ministries and some funding options given by them for new facility installation. The paper explores the scope of waste as a source of energy which can be utilized as alternative fuel or can be used for electricity generation, which will be a step toward meeting agenda 2030 for sustainable development.

51.2 Literature Review The global need for energy is rising due to socio-economic development, technological transformation, changes in the demographic structure, rapid industrialization, and urbanization [6]. The major portion of energy demand is fulfilled by coal and oil in India [7, 8]. As India imports, these non-renewable resources, hence, pose a financial constraint and these options are also non-ecofriendly [7]. Due to its finite nature, dependency on fossil fuel is unsustainable [9]. The smart city project is an opportunity for Indian cities to reduce their dependence on fossil fuels. The Indian cities are also in pressure to tackle their increasing energy demand, which forced them to search for options of non-conventional resources. One such potential resource is solid waste and can be used for electricity generation [10–12]. Production of energy from waste and reduction in its amount simultaneously is a valuable outcome. To convert waste into a valuable outcome, it needs effective municipal solid waste management (MSWM). Many multidisciplinary activities like generation, collection, storage, transportation, disposal, treatment, and waste forecasting combinedly can be called as municipal solid waste management (MSWM) [13]. For successful waste management planning in India, assessment of the characteristics and quantity of solid waste and future waste generation forecasting is necessary [27]. The proper storage of waste in integrated waste management planning at remote places in rural and urban areas is important for optimal energy recovery utilization from waste [14]. The number of the transfer station and its optimal location is required for an efficient and economic collection system [15]. The community solid waste generation point is linked by processing plant or disposal facility by the transfer station. The overall efficiency of the MSWM system is improved as this transfer station reduces pollution and the cost of the system [16]. In the Indian city’s context, 85 percent of total spending is on collection [17]. A large of spending in the collection services can be reduced by improvement and changes in the functional system [18]. The planning and designing of the transfer station based on the required context and composition of the household can be a possible solution. This transfer station aids in traffic reduction, volume reduction (due to the compaction process), and transportation fund reduction [19]. For planning and designing effective systems of waste collection and disposal, data on generation and quantity variation is necessary. A significant amount of funds will be an investment in waste to energy as more cities come into the smart city race in India. The biogas, bio-CNG, electricity, and heat generation through waste to energy technologies can become an important resource for cities that are part of a smart city by addressing their energy consumption needs. Indian cities with a similar

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aim, but in different context tried schemes for the development of biogas plantbased infrastructures. The programs and initiatives to support biogas infrastructure are off-grid biogas power generation programs and National Biogas and Manure Management Program (NBMMP) [30]. The first biogas plant was developed in 1946 by the Indian Agricultural Research Institute (IARI) with the capacity of producing 5.7 m3 of biogas per day [20]. In 1981, for biogas plant development, first national program was launched as National Program for Biogas development (NPBD) [33]. The capital subsidy was provided under this scheme for installing up small plants. To overcome the failure of the scheme, it was renamed National Biogas and Manure Management Program in the year 2005, under the part of the 11th Five Year Plan (2007–2012) by the government [21]. Regardless of these efforts by government certain constraints, like institutional and regulatory barriers [21], infrastructural and technical barriers [28], and economic and financial barriers [32], are hindering the implementation of biogas-based plants. To further improve and eliminate barriers for biogas-based plants, the government revised waste handling rules 2000, after sixteen years. The role and responsibilities of ministries as per guidelines [22] are 1. 2. 3. 4. 5.

6.

Ministry of Power: Mechanism development for the compulsory purchase of power and deciding charges or tariff of waste to energy plants. Ministry of Agriculture (MoA): the quality test of compost by setting up of laboratories and issuing guidelines for quality management of compost. The Ministry of New and Renewable Energy: providing appropriate incentives or subsidy and enabling the waste to energy infrastructure creation. Ministry of Chemicals and Fertilizers: scaling up production and consumption, providing fixed Rs. 1500 per tonne amount as market assistance of city compost. Ministry of Urban Development (MoUD): promoting R&D of solid waste management and circulate data to local bodies and state governments. The ministry will also be responsible for waste management policy formulation with a consultation with stakeholders including waste to energy policies. The ministry will provide training to the local bodies. Ministry of Environment, Forest and Climate Change: monitoring of the implementation of waste management rules 2016 in the country.

Apart from the roles of urban local bodies is important, as per the State Municipal Laws, the SWM Rules, 2016, and the 74th CAA, it is the responsible body for providing municipal waste management services. As per guidelines, the role of experts and decision-makers is also enhanced in planning and deciding new infrastructural implementation for cities. Therefore, any waste to energy infrastructural transformation needs the support of decision making, which can help in implementing the optimal long-term waste to energy system for cities. Any decision of planners and experts is based on primary data and secondary which is often obtained by computation. The primary data of waste to energy potentials which will ease the deployment and operation of upcoming infrastructural transformation in Indian cities are potential biogas, waste generation projection, bio-CNG, and energy value of methane and other related factors. This study will provide primary data that can aid planners in planning new facilities like the location of transfer stations for ensuring increased frequency

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and maximum collection. The calculated per unit capacity, establishment cost, operation, and maintenance cost combinedly required for transfer station functioning [23].

51.3 Methodology The study in paper is divided into two stages, estimation of waste to energy potential (bio-CNG and biogas) and number of possible transfer station for the year 2031. The proposed methodology for addressing these two sections of paper is shown in Fig. 51.1 with all the outlined steps.

51.3.1 Stage 1: Estimation of Waste to Energy Potential (Bio-CNG and Biogas) To determine the future potential of recoverable bio-CNG, waste to electricity and biogas, the work started with study area identification and characterization of cities. For thirteen cities namely Prayagraj (Uttar Pradesh), Vadodara (Gujarat), Dehradun (Uttarakhand), Rajkot (Gujarat), Meerut (Uttar Pradesh), Agartala (Tripura), Amritsar (Punjab), Madurai (Tamil Nadu), Agra (Uttar Pradesh), Nagpur (Maharashtra), Varanasi (Uttar Pradesh), Bhubaneswar (Orissa), and Ludhiana (Punjab)

Methodology

Section 1 1. Review of Existing Situation (Waste generation Rate, Population, Waste composition, other information related with city) 2. Projection of values for target year 2031(waste per capita, population, waste generation) 3. Waste to energy Potential values for target Year (Methane, Biogas and Bio-CNG)

Fig. 51.1 Proposed methodology

Section 2 Finding Number of Transfer station by comparing with Reference model (Based on section data)

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are selected which belongs to Smart City Mission project, data from various like census of India, National Solid Waste Association of India (NSWAI), the Web page of Smart City Mission, Central Pollution Control Board (CPCB), National Environmental Engineering Research Institute (NEERI), Web page of Swachh Bharat Mission(SBM), Web page of Swachh Survekshan, solid waste literature reviews of India, document and report of Municipal Solid Waste Management and Handling, and Atal Mission for Rejuvenation and Urban Transformation (AMRUT) are searched. Apart from these municipal reports, documents of smart city development were also searched. This stage of the study was useful in finding data of different city population and waste per capita generation. The calculation of projected values for estimation future trends of energy potentials is based on previous data or records of populations and waste per capita of cities selected. The population data for the cities selected are shown in Table 51.1 are required for population projection for the year 2031. These selections of data are based on the requirement of desired output (longterm planning) and input data available in literature and documents. Based on these visible past trends, incremental increase extrapolation method is for the population forecasting. Here, the year 1961 is used as the initial population (restricting the study and setting the boundary for mathematical analysis), and the year 2011 as launch year population and year is target population (Fig. 51.2). With all the available data, the equation used for project population is as follows: Pm = Pl + m × α + β × {m(m + 1)/2}

(1)

Here, Pm is population after mth decade, in our case, it is target year 2031 (Pt), and Pl is launch year population (year 2011), α is average increase, and β incremental Table 51.1 Population data (Source Census of India) Year

1961

1971

Prayagraj

411,955

490,622

616,051

792,858

975,393

1,112,544

Vadodara

309,716

467,487

734,473

1,031,346

1,338,244

1,670,806

Dehradun

126,918

166,073

211,416

283,537

426,674

569,578

Rajkot

194,145

300,612

445,076

640,462

967,476

1,286,678

Meerut

219,519

297,691

448,788

753,778

1,068,772

1,305,429

Agartala

54,878

100,264

132,186

198,320

269,492

400,004

Amritsar

390,055

454,805

594,844

708,835

979,801

1,132,383

Ludhiana

24,416

34,820

53,761

71,990

103,099

128,137

Madurai

458,981

633,989

820,891

940,989

928,869

1,017,865

Agra

462,020

591,917

694,191

891,790

1,275,134

1,585,704

Nagpur

643,659

866,076

1,219,461

1,624,752

2,052,066

2,405,665

Varanasi

485,083

598,020

728,511

943,907

1,103,952

1,198,491

38,211

105,491

219,211

411,542

648,032

840,834

Bhubaneswar

1981

1991

2001

2011

51 Predicting Waste to Energy Potential and Estimating …

Po= Year 1961

669

Pl= Year 2011

Base Period

Pt=Year 2031

Projection Period

Fig. 51.2 Setting the boundary for mathematical analysis

increase. The waste per capita and percentage of biodegradable/compostable waste of the selected cities are shown in Table 51.2. The waste per capita data then forecasted, which is used for calculating the waste generation for target year 2031. Equation 2 used to estimate the waste generation per day for target year which is as follows: SGt = Pt × δ × 0.001

(2)

Here, SGt = solid waste generated of target year in MT/day Pt = population of target year δ = waste per capita (kg/capita/day) of target year. Table 51.2 Waste generation per capita and composition (Source CPCB 2005) [31]

City

Waste per capita Biodegradable/compostable (%)

Prayagraj

0.52

35.49

Vadodara

0.27

47.43

Dehradun

0.31

51.37

Rajkot

0.21

41.5

Meerut

0.46

54.54

Agartala

0.4

58.57

Amritsar

0.45

65.02

Ludhiana

0.53

49.8

Madurai

0.3

55.32

Agra

0.51

46.38

Nagpur

0.25

47.41

Varanasi

0.39

45.18

Bhubaneswar

0.36

49.81

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A. Tiwari and P. Sharma

This value of SGt for the cities obtain is now used for further calculation of energy potential. The biogas production estimation for target year is done with the help of Buswell and Mueller [29], a theoretical stoichiometric method. Ca Hb Oc Nd Se + X 1 H2 O → X 2 CO2 + X 3 CH4 + X 4 H2 S + X 5 NH3

(3)

where c 3d e b − + + 4 2 4 2 b c 3d e − + + + 8 4 8 4 b c 3d e + − − − 8 4 8 4

X1 → a − a 2 a X3 → 2 X4 → d

X2 →

X 5 → e are constants The obtained potential value of biogas then used to find out the bio-CNG potential.

51.3.2 Stage 2 Estimation of Number of Transfer Station Based on all calculated values, quantity of waste, population, waste per capita, biogas and bio-CNG potential for target year, it becomes necessary to predict number of transfer station. For the cities, which are part of Smart City Mission, it is important to utilize all the available funds for achieving maximum efficiency for improving living standard and stand out frontrunner in development aspect. The cities should also eye on capital subsidy from Rs 2 to 10 crore from the Ministry of New and Renewable Energy (MNRE) for energy recovery from urban wastes. The planning and establishment of transfer station is very important step for utilizing all the funds from different ministry. For finding the number of transfer station for these cities, there are compared with Indore City (Cleanest City of India, Swachh Bharat Abhiyan surveys). The Indore presently generates 1115 MT per day with eight ultra-modern transfer station. Figure 51.3 shows the method for calculating number of transfer station for target smart city for year 2031.

51.4 Result and Discussions The mathematical analysis for projecting population of year 2031 was carried out according to methodology discussed above. The population for target year was forecasted by using Eq. 1 using the data of Table 51.1 and shown in Table 51.3.

51 Predicting Waste to Energy Potential and Estimating … Indore (Real World Scenario)

Mathematical Planning for city (Year 2031)

SGt MT/day ? Transfer station

1115 MT/day 8 Transfer station

Real World Solution

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Interpret solution/Compared with Indore Model

Fig. 51.3 Method for calculating number of transfer station

Table 51.3 Projected population (calculated)

City

Year 2031

Prayagraj

1,436,643

Vadodara

2,346,335

Dehradun

824,454

Rajkot

1,883,242

Meerut

1,858,657

Agartala

601,899

Amritsar

1,495,188

Ludhiana Madurai

180,601 1,176,910

Agra

2,170,682

Nagpur

3,208,854

Varanasi

1,470,056

Bhubaneswar

1,256,025

The above data is obtained by extrapolation of the data incorporating borrowed fluctuation from historical trends of all the cities from the census data of India. This projection of population for cities is associated with uncertainties which is always present in any study using forecasting techniques, but in tune with the goal of planning sustainable future and build scenario for waste to energy, it is necessary to neglect few uncertainties with limited assumptions, as in our case. The estimated increment rate (%) in population of Prayagraj, Vadodara, Dehradun, Rajkot, Meerut, Agartala, Amritsar, Ludhiana, Madurai, Agra, Nagpur, Varanasi, and Bhubaneswar from Pl are 1.46, 2.02, 2.24, 2.32, 2.12, 2.52, 1.60, 2.05, 0.78, 1.84, 1.67, 1.13, and 2.47, respectively, on yearly basis. These summary statistics of population are one parameter for estimating SGt and other is projected value of per capita waste generation (δ).

672 Table 51.4 Projected MSW generations and waste per capita (calculated)

A. Tiwari and P. Sharma City

Waste per capita (δ)

Waste generated (SGt ) MT/day

Prayagraj

0.62

891

Vadodara

0.32

751

Dehradun

0.37

305

Rajkot

0.25

471

Meerut

0.55

1022

Agartala

0.48

289

Amritsar

0.54

807

Ludhiana

0.63

114

Madurai

0.36

424

Agra

0.61

1324

Nagpur

0.3

963

Varanasi

0.47

691

Bhubaneswar

0.43

540

As per Guidelines report of Ministry of housing and Urban Affair for selection of technologies for final disposal and processing of MSW, per capita waste generation is increase is 1.3% annually and 3 to 3.5% increase in urban population is also reported. But, recent programs like Swachh Bharat Abhiyan and other similar campaigns by Indian government impacted citizen mindsets. Therefore, in our study, we have assumed half of 1.3% per annum of increment in per capita waste generation. Both projected values of population and waste per capita for target year used for calculating waste generation (SGt ) by using equation. Table 51.4 shows the projected values for waste per capita and waste generation for all the cities selected. The projected values SGt for cities are used for the biogas, bio-CNG, and other waste to energy-related variables. The calculated values of biogas will always less than measured values in real-life condition due many factors like suboptimal conditions, nutrients, and the presence of inhibitors. But this estimated values by mathematical analysis help in speedily developing strategies for presenting a scenario and extracting recommendation which can be incorporated by governmental agencies for sustainable outcomes. The projected values annually can be calculated by equation already discussed in methodology section and is shown in Table 51.5. The above table highlights the statistics of estimates of biogas, methane, and bio-CNG potential which can be possible waste to energy decision matrix for decision maker for transformation of infrastructure for utilization of alternative fuel. Here, we have assumed 50% of carbon content in organic matter of which 60% is biodegradable. Apart from these assumptions, the 45% and 55% are CO2 and CH 4 from Buswell equation. The biogas is considered as alternative energy option for cooking. For five people in Indian context estimated daily cooking energy requirement is 1.7–2.1 m3 , i.e., 0.34–0.42 m3 of biogas [24]. The amount forecasted can meet of cooking demands for 0.35, 0.39, 0.17, 0.21, 0.62, 0.19, 0.58, 0.06, 0.26,

51 Predicting Waste to Energy Potential and Estimating …

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Table 51.5 Projected waste to energy potential (calculated) City

(m3 * 1000) of methane

Potential of biogas in (m3 * 1000)

Bio-CNG (kg * 1000)

Prayagraj

35,548.99148

43,481.41

19,566.63

Vadodara

40,043.92531

48,979.35

22,040.71

Dehradun

17,613.79697

21,544.15

Rajkot

21,974.1753

26,877.5

12,094.88

Meerut

62,662.7731

76,645.38

34,490.42

Agartala

19,029.02987

23,275.18

10,473.83

Amritsar

58,988.05559

72,150.68

32,467.81

Ludhiana

6382.30824

7806.46

26,368.87546

32,252.84

Madurai

9694.867

3512.907 14,513.78

Agra

69,033.8843

84,438.14

37,997.16

Nagpur

51,326.28409

62,779.26

28,250.67

Varanasi

35,096.827

42,928.35

19,317.76

Bhubaneswar

30,238.05708

36,985.39

16,643.43

0.68, 0.51, 0.35, 0.3 million people annually for Prayagraj, Vadodara, Dehradun, Rajkot, Meerut, Agartala, Amritsar, Ludhiana, Madurai, Agra, Nagpur, Varanasi, and Bhubaneswar, respectively. The potential obtained highlighted one more option of raw material for cities to meet their ever-growing energy demands. The values highlighted the potential of waste to energy from waste, but there is certain market barrier [34] for implementation of the projects, which are as follows:1. 2. 3.

Low-priced electricity from natural gas-fired and coal power plants. High cost of maintenance and operation of biogas in comparison with thermal power plants. With few supports of government like renewable power commitments and feed in tariff renewable option like wind, solar, and hydro-delivers electricity cheaper than biogas plants.

The Indian government present initiatives try to overcome these barriers by redefining guidelines and subsidies. To utilize this raw material special attention is need on planning of transfer station which necessary for establishment of distribution network for alternative fuels, which is also essential step for infrastructural transformation as per guidelines by government agency. This study already pointed out few options for cities to utilize opportunities under umbrella of smart city mission, Swachh Bharat Abhiyan (SBA). The transfer station is fruitful for optimal utilization of these raw material for cities and follow section estimated probable number of transfer station by comparing with model of Indore, which is awarded many times in Swachh Survekshan (Survey) as cleanliest city of India. This survey was launched under umbrella of Swachh Bharat Abhiyan in 2016, with aim of ranking cities based on cleanliness and sanitation. It is conducted on yearly basis. In first survey, 73 Urban

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Local Bodies (ULBs) participated, 434 in second, and in the year 2019, 4237 ULBs participated [26]. The Swachh Survekshan 2020, 4242 cities participated in survey. The Indore city was ranked 25th in 2016, now since then ranked number one in the survey [25, 26]. This transformation and consistent performance in the survey is the reason of taking Indore city as for the comparison for the cities. The estimation of transfer station numbers is a multi-stage decision-making problem. The optimal transfer station planning is important organ of waste management services planning. With rapid urbanization and pressure of developing cities for investment ready for foreign investor, cities need appropriate technology and infrastructure for every service offered by local urban bodies. The transfer station infrastructural transformation can also open the opportunities for private partner as government is offering incentivization for waste to energy. As in our we are only concern about finding number of transfer station and not interested in finding location of these facility. Finding the optimal location and capacity for transfer is another stage of decision making and it is depended on this stage of study. In association with the methodology discussed, the number of transfer station for cities is shown in Table 51.6. The matrix of number of transfer station will promote and anchor decision-makers toward transition of new waste management facilities which may include refusederived fuels, biogas, bio-CNG, and waste to energy plants. The financial aid for these transfer station developments can be chosen from available option as shown below [22]: 1.

Central Government Grants • Jawaharlal Nehru National Urban Renewal Mission (JNNURM). • Swachh Bharat Abhiyan (SBA). • Smart City Mission.

Table 51.6 Number of transfer station (Calculated)

S. No. City

Number of transfer station (calculated)

1

Prayagraj

6

2

Vadodara

5

3

Dehradun

2

4.

Rajkot

3

5.

Meerut

7

6

Agartala

2

7.

Amritsar

6

8.

Madurai

3

9.

Agra

9

10.

Nagpur

7

11.

Varanasi

5

12.

Bhubaneswar 4

13

Ludhiana

1

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Subsidies • Rs. 15 million–Rs. 30 million per MW for waste to energy projects like biogas, bio-methanation and refuse-derived fuel (RDF) by the Ministry of New and Renewable Energy (MNRE) to both public and private sector investors and entrepreneurs. • Subsidy of 50% on the capital cost of compost plant by the Ministry of Agriculture (MoA) and the Ministry of Environment and Forests and Climate Change (MoEFCC). • Interest reduction subsidy for a commercial project, with a 7.5% capitalized interest rate. • Incentive on power at Rs. 5.00 lakh per MW to state nodal agencies for monitoring, promoting, and coordination.

3.

State Government Grants • To support municipal service delivery, governance, and administration of local government, state government once in five years give funds.

Apart from these loan from financial institute like Indian Renewable Energy Development Agency, Housing and Urban Development Corporation (HUDCO), Industrial Finance Corporation of India (IFCI) and Infrastructure Leasing and Financial Services (IL&FS) can be used for funding for SWM Projects. The municipal government can also generate funds by user fees for services, sale of products derived from waste processing, land monetization, and floating tax-free municipal bonds like Indore city. By utilizing all the possible funding option, the effective result can be achieved especially in context of future waste management. These measures will promote toward effective waste management and financial sustainability in terms of energy needs for cities selected. The purpose of selecting Indore as reference model is based on its performance in recent years in smart city project development measures specially in developing modern transfer station facility and these cities will have local essence in planning for the problem. For limiting uncertainty in real-life situation for these cities, Indore fits best as reference model. Though separate studies are required for capacity planning (few parameters are already estimated in this study related to waste to energy potentials), location search, network flow, and allocation of private partners for transfer station for these studies and can be considered for extended research problem of the study.

51.5 Conclusion For any country and city, its growth and development mainly depend on energy. The biggest challenges for cities in this era to become smart city is meeting their growing energy demand in sustainable way. The use of non-ecofriendly and conventional energy source is not sustainable in longer run; hence, the balance of non-renewable and renewable energy is necessary for fulfilling the demand. The search is alternate

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fuel is always in research and development in every planning agenda locally or globally. It is also in 2030 Agenda for Sustainable Development. The hidden potential of MSW for energy generation gained attention of planners and researchers worldwide. The estimate of infrastructural requirement for future planning can be decided based on many parameters, few of them is highlighted in this study. The decision making for long-term planning associated with waste management is always complex and risky task due to involvement of multidimensional and multilevel complexity. A balance of trade-off between the social, environment, and economic dimension is need for uncertain development for future of cities. A study should touch maximum parameters and possible options for this uncertain study of waste management for sustainable development. This paper with few computations highlighted the waste to energy potential values for the target year in a limited and restricted environment of assumptions due to complexity of problem. The study reported the number of transfer station with some of finding option available for the selected cities which are part of smart city development project. This step used a reference model of Indore, which is functioning at par from these cities. It was found based on the analysis that round 4.67 million people cooking demand annually can be meet, if this utilized with proper supporting infrastructure. The studies also estimated that, there is need of 60 modern transfer station needed as per forecast made in this study for utilizing the potential of waste. The study also suggested some funding option in form incentives, subsidies, and grants from central and state government, which can be utilized for preparedness for future waste management planning. By doing this, we try to open option of creating the competitive waste market for private partner specially for waste to energy. This also promotes planning of construction new processing facilities for cities selected for the study. We have presented one way of solution for this issue of planning and opens the multi-stage decision making for additional future research studies.

References 1. T. Hosono, K. Aoyagi, Effectiveness of interventions to induce waste segregation by households: evidence from a randomized controlled trial in Mozambique. J. Mater. Cycles Waste Manage. 20(2), 1143–1153 (2018) 2. L. Rodi´c, D.C. Wilson, Resolving governance issues to achieve priority sustainable development goals related to solid waste management in developing countries. Sustainability 9(3), 404 (2017) 3. R. Heidari, R. Yazdanparast, A. Jabbarzadeh, Sustainable design of a municipal solid waste management system considering waste separators: a real-world application. Sustain. Cities Soc. 47, 101457 (2019) 4. A. Pires, G. Martinho, S. Rodrigues, M.I. Gomes, in Sustainable solid waste collection and management (Springer International Publishing, Switzerland, 2019), pp. 349–360 5. Z. Minghua, F. Xiumin, A. Rovetta, H. Qichang, F. Vicentini, L. Bingkai et al., Municipal solid waste management in Pudong new area, China. Waste Manage. 29(3), 1227–1233 (2009) 6. A. Soni, A. Mittal, M. Kapshe, Energy Intensity analysis of Indian manufacturing industries. Resour. Eff. Technol. 3(3), 353–357 (2017)

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7. A. Kumar, N. Kumar, P. Baredar, A. Shukla, A review on biomass energy resources, potential, conversion and policy in India. Renew. Sustain. Energy Rev. 45, 530–539 (2015) 8. M.A. Majid, Renewable energy for sustainable development in India: current status, future prospects, challenges, employment, and investment opportunities. Energy Sustain. Soc. 10(1), 2 (2020) 9. S.M. Mobin, F. Alam, A review of microalgal biofuels, challenges and future directions, in Application of Thermo-fluid Processes in Energy Systems (Springer, Singapore, 2018), pp. 83– 108 10. T. Anuradha, S. Anushree, V. Saurav, V. ManujaShrey, Electricity from waste–bibliographic survey. Sustain. Energy 2(3), 108–15 (2014) 11. A. Can, The statistical modeling of potential biogas production capacity from solid waste disposal sites in Turkey. J. Clean. Prod. 243, 118501 (2020) 12. G. Dwivedi, M.P. Sharma, M. Kumar, Status and policy of biodiesel development in India. Int. J. Renew. Energy Res. (IJRER) 4(2), 246–254 (2014) 13. V.C. Hemmelmayr, K.F. Doerner, R.F. Hartl, D. Vigo, Models and algorithms for the integrated planning of bin allocation and vehicle routing in solid waste management. Transport. Sci. 48(1), 103–120 (2014) 14. H.N. Chanakya, T.V. Ramachandra, M. Vijayachamundeeswari, Resource recovery potential from secondary components of segregated municipal solid wastes. Environ. Monit. Assess. 135(1–3), 119–127 (2007) 15. C. Bosompem, E. Stemn, B. Fei-Baffoe, Multi-criteria GIS-based siting of transfer station for municipal solid waste: the case of Kumasi Metropolitan Area, Ghana. Waste Manage. Res. 34(10), 1054–1063 (2016) 16. N.B. Chang, Y.T. Lin, An analysis of recycling impacts on solid waste generation by time series intervention modeling. Resour. Conserv. Recycl. 19(3), 165–186 (1997) 17. M.K. Ghose, A.K. Dikshit, S.K. Sharma, A GIS based transportation model for solid waste disposal—a case study on Asansol municipality. Waste Manag. 26(11), 1287–1293 (2006) 18. G. Greco, M. Allegrini, C. Del Lungo, P.G. Savellini, L. Gabellini, Drivers of solid waste collection costs. Empirical evidence from Italy. J. Clean. Prod. 106, 364–371 (2015) 19. L. Cui, L.R. Chen, Y.P. Li, G.H. Huang, W. Li, Y.L. Xie, An interval-based regret-analysis method for identifying long-term municipal solid waste management policy under uncertainty. J. Environ. Manage. 92(6), 1484–1494 (2011) 20. V.P. Kharbanda, M.A. Qureshi, Biogas development in India and the PRC. Energy J. 6(3) (1985) 21. D. Raha, P. Mahanta, M.L. Clarke, The implementation of decentralised biogas plants in Assam, NE India: the impact and effectiveness of the National Biogas and Manure Management Programme. Energy Policy 68, 80–91 (2014) 22. S.B. Mission, Municipal solid waste management manual, in Part II: The manual. Central Public Health and Environmental Engineering Organisation (CPHEEO) Ministry of Urban Development (2016) 23. T.V. Ramachandra, in Management of Municipal Solid Waste (The Energy and Resources Institute (TERI), 2006) 24. K.J. Singh, S.S. Sooch, Comparative study of economics of different models of family size biogas plants for state of Punjab, India. Energy Convers. Manage. 45(9–10), 1329–1341 (2004) 25. MoUD, in Swachh Survekshan 2020. Ministry of Urban Development (2020). https://swachh survekshan2020.org/Images/SS_2018_Report.pdf 26. MoUD, in Swachh Survekshan 2020. Ministry of Urban Development (2020). https://www. swachhsurvekshan2020.org/Images/SS_2019_Report.pdf 27. R.P. Singh, D. Yadav, S. Ayub, A.A. Siddiqui, in Status and Challenges in Solid Waste Management: A Case Study of Aligarh City (Civil Engineering and Environmental Technology, 2014), pp. 20–24 28. G.V. Rupf, P.A. Bahri, K. de Boer, M.P. McHenry, Barriers and opportunities of biogas dissemination in Sub-Saharan Africa and lessons learned from Rwanda, Tanzania, China, India, and Nepal. Renew. Sustain. Energy Rev. 52, 468–476 (2015)

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29. A.M. Buswell, H.F. Mueller, Mechanism of methane fermentation. Ind. Eng. Chem. 44(3), 550–552 (1952) 30. Milieu en Natuur Planbureau, P.R. Shukla, Biomass energy strategies for aligning development and climate goals in India. MNP (2007) 31. Central Pollution Control Board, in Management of Municipal Solid Waste. Ministry of Environment and Forests, Delhi, India (2020). https://cpcb.nic.in/uploads/MSW/Waste_generation_C omposition.pdf. Accessed 30 Jan 2020 32. M. Bansal, R.P. Saini, D.K. Khatod, Development of cooking sector in rural areas—in India—a review. Renew. Sustain. Energy Rev. 17, 44–53 (2013) 33. S.K. Lohan, J. Dixit, R. Kumar, Y. Pandey, J. Khan, M. Ishaq et al., Biogas: a boon for sustainable energy development in India’s cold climate. Renew. Sustain. Energy Rev. 43, 95–101 (2015) 34. S. Mittal, E.O. Ahlgren, P.R. Shukla, Barriers to biogas dissemination in India: a review. Energy Policy, 112, 361–370 (2018)

Chapter 52

Analysis of Thermal Energy Storage Mediums for Solar Thermal Energy Applications Shivansh Aggarwal, Rahul Khatri, and Shlok Goswami

Abstract Energy storage mediums are highly popular in solar applications due to their ability to store heat and release it during any time period of the day. This study provides a classification of different thermal energy storage (TES) mediums in various solar energy systems with their feasibility and future applications. The concept of TES and the various studies on the application of TES in solar thermal applications have been presented. Recent advances and the performance of common solar thermal systems with and without TES have also been presented. Working conditions, economical aspects, suitability, and selection criteria of TES materials have also been discussed based on their application. This paper also uncovers the future aspects that possibly will improve the use of TES and lead to the performance optimization of solar thermal systems. Keywords Charging and discharging · Energy storage materials · Latent heat · Sensible heat · Solar energy

52.1 Introduction Solar energy has employed in various applications in the current scenario of renewable energy technologies. There is abundance of sunlight available during daytime while during night solar energy is not available. The fluctuations in the supply of energy while using solar energy as primary energy source have been a common concern. Thermal energy storage (TES) systems are employed to overcome this by storing the excess energy and utilizing it during different time periods of the day. TES systems are able to provide a greater evenness in the energy utilization received throughout the day and night which can be used for various industrial or domestic applications. TES systems work on the concept of predominantly storing heat as S. Aggarwal (B) · R. Khatri · S. Goswami Manipal University Jaipur, Jaipur 303007, Rajasthan, India e-mail: [email protected] R. Khatri e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_52

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Fig. 52.1 Working cycle of TES system Charging (Daytime)

Discharging (Night)

Storing (Thermal Energy)

sensible heat in the substance which can be then followed by the change of phase of the material utilizing latent heat to store energy. Phase change material (PCM) is able to store a larger amount of thermal energy per unit volume than sensible heat storage materials, using the process of melting and freezing, PCMs store and release the thermal energy, these PCMs have a higher energy storage density and have various benefits in particular applications [1, 2].

52.1.1 Working Cycle of TES Systems Utilizing TES has three major working steps: charging, storing, and discharging, which forms its working cycle as shown in Fig. 52.1. During the daytime, when solar energy is available, the TES system charge themselves and store this energy for future use. This energy is stored until it is actually required during night when sunlight is not available and provides the device with energy continuously which improves efficiency of the system.

52.1.2 Classification of TES Systems TES systems can be broadly classified under the following criteria as shown in Fig. 52.2 based on the type of technology, storage material, application, and enduser type. This classification of TES systems helps to distinguish between the types, technical, and user-based application of energy storage materials.

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Sensible By Technology

Latent Thermochemical

Molten Salt By Storage Material

PCM Water/Ice

Thermal Energy Storage District Heating By Application

Process Heating and Cooling Power Generation

Industrial By End User

Residential and Commercial Utilities

Fig. 52.2 Classification of thermal energy storage systems [3]

52.1.3 Work Methodology

Analyzing Concept

Working and Classification

Determination of Selection criteria

Future Developments

Application Comparison

Discussion of Applications

This paper compares the experimental work and methodologies that various authors have utilized to incorporate thermal energy storage. These methods have shown effective means of improving the performance and working time of the energy storing devices. Thus, this paper would provide information about the various TES systems and the extent to which they help in optimizing the performance of these devices. Thermal energy can be stored in the form of sensible heat, latent heat, and thermochemical energy. Although these mediums have different impacts on the performance of the TES devices, this paper focuses more on the latent heat storage methods.

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52.2 General Selection Criteria for a TES System The appropriate selection of a particular TES system according to a specific application is based on few aspects that govern its use and utility for that application. For optimizing its energy usage, it is very important to choose a TES system according to its application characteristics. JA Duffie and WA Beckman [3] listed these major characteristics as: • • • • • • • •

Operating range of temperature Storing capacity per unit volume Heat removal or addition means and the related difference in temperature Storage unit temperature stratification Requirements of power for heat removal or addition Equipment’s and devices related to the system of storage Thermal losses control from the system of storage Involved cost.

Thus, before selecting any TES system, its feasibility has to be ensured. This can be done by taking into consideration the above-stated factors. Once a TES system is appropriately chosen, it can yield the optimized results. The most commonly used TES systems, which are nowadays gaining the attention of researchers are PCMs that use the phenomenon of phase transition to store thermal energy.

52.2.1 Phase Change PCMs are highly used due to their difference in the heat storing abilities from sensible heat storage systems. This can be understood by looking at their phase transition change profile as shown in Fig. 52.3. It can be seen form Fig. 52.3 that there is a huge gap of energy during the change of phase, through which thermal energy can be stored and used whenever required. This enhances the system efficiency. This type of energy storage has been utilized by various authors whose works have been presented in this paper.

52.2.2 Encapsulation of PCMs Encapsulating means the packing or covering of the PCM inside a shell of material to prevent it from leaking when in liquid phase. This packing has to be chosen very appropriately for efficient use. Encapsulation of PCMs has numerous advantages: • Separation from the outside surroundings which improves compatibility with the material • Ensures that the PCM does not mix with the fluid being transferred

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Fig. 52.3 Phase transition profile of phase change materials [4]

• • • • •

The possible reaction of chemicals is prevented Flexibility in the process of phase change Reduces changes in the outside volume Production improvement in handling of materials Surface sufficiency leading to improved rate of heat transfer [5].

The shape of the container for encapsulation can be rectangular, cylindrical, tubular, or spherical. The structure of encapsulated PCMs and the working principle has been shown in Fig. 52.4. Kinga Pielichowska and Kryzysztof Pielichowska [4] worked out on the improvements in the various encapsulation techniques.

Fig. 52.4 Working principle and structure of encapsulated phase change material [6]

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52.3 Applications of TES in Solar Thermal Systems The use of TES in overcoming the drawback of discontinuous supply of solar energy can be very useful. The thermal energy storage mediums have been used in many applications like solar water heater, solar air heater, solar still, and solar cooking. The thermal performance of these systems can be improved with continuous use even during non-sunshine hours with the use TES mediums. Some of the storage mediums along with their specific use in solar thermal systems has been presented in this section.

52.3.1 Solar Air Heater (SAH) Solar air heaters have been used for a long time, and research is constantly under process for improving their efficiency. Solar-powered air heaters have shown a decent increase in efficiency with the use of TES systems. Various authors have worked upon integrating TES into SAH which in turn increases the working efficiency of the system. Different types of TES systems have been used by various authors, and their output/efficiencies comparison has been presented in Table 52.1. Thus, numerous studies have been done for improving the overall output of SAHs, and different methods involving sensible and latent heat storage have shown great results. The outcomes of various studies in which SAH with TES systems were used to confirm that it is very effective and can also lead to some major improvement in the near future.

52.3.2 Solar Water Heater (SWH) Solar water heaters are very useful and have been used since ages. Regular research is done to improve its performance. Hasan et al. [16–18] worked on some fatty acids which can be used as phase change materials for SWH and concluded that palmitic acid, myristic acid, and stearic acid, having melting point temperatures between 50 and 70 °C are the most suitable PCMs for domestic water heating. Xue [19] in his experimental study found that as the ratio of tank volume to collector area increases, its energy efficiency consequently increases. It is also discussed that the SWH efficiency depends mainly on the following two factors, i.e., type of PCM and design of the tank. The author also compared the results in two test conditions: one in exposure and the other one with a constant flow rate, and it was noticed that the setup with a constant flow rate proved to be more thermally efficient. This is because the excess energy which is there can be actually stored in the PCM and is then used to heat the water making it a better performer than the traditional one.

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Table 52.1 Performance comparison of TES systems References

TES system

Inferences

Enibe [7]

PCM-Paraffin

Improvement in thermal efficiency from 7.5 to 18%

Naphon [8]

Porous medium

Increase in the thermal efficiency by upto 25.9%

Alkilani et al. [6]

PCM-Paraffin wax and Aluminum powder

Enhancement in thermal efficiency from 10 to 20%

Tyagi et al. [9]

PCM-paraffin wax and hytherm Efficiency of the system improved oil from 20 to 53%

Aissa et al. [10]

Granite stone

Outlet temperature of the SAH was 10–25 °C higher than the outside ambient temperature

Yadav et al. [11]

Dessert sand

Enhancement in thermal efficiency from 47 to 69%

Saxena et al. [12]

Carbon Powder (Granular)

Efficiency improvement from 43 to 73%

Karthikeyan et al. [13]

Packed bed of Paraffin wax

With growth of the transfer surface area of heat by using small balls, there was a higher difference in temperature be- tween the phase change temperature of the PCM and the HTF inlet; higher rate of mass flow of HTF had a crucial consequence on the SAH’s charging time

Bouadila et al. [14]

AC27 Packed bed

Temperature at the outlet was at all times during the night more than the inlet temperature by almost 70 °C

Wadhawan [15]

Lauric acid integrated with the TES device

Mean increment in the temperature of output air was 86.47%

Canbazo˘glu et al. [20] used sodium thiosulfate pentahydrate as PCM for their experiment which yielded an average temperature value of 6 °C higher than the conventional SWH with no PCM. It was also found that with the combination of other few salt hydrates with Glauber’s salt, the mass of hot water produced along with the storage time of heated water and the cumulative heat in the tank was around 2.59–3.45 times than that of traditional system. de Gracia et al. [21] in their analysis of a domestic electrical hot water cylinder showed that with the use of phase change materials, hot water discharge capacity had shown an increase from 40 to 55%. Various researchers have worked on different PCMs as TES mediums incorporated in the system’s water storage tank. Mazman et al. [22] used paraffin and stearic acid weighing 3 kg in a tank of 150 L kept the temperature of water near the melting range of the PCM for an average of 6–12 h more compared to without PCM. Al-Hinti et al. [23] used paraffin

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wax to maintain the water temperature at 30 °C for around 11 h more than without PCM. Kousksou et al. [24] in their simulation incorporated approximately 57 L of NaOAc.3H2O in a tank of 150 L capacity to keep the temperature of water at 50 °C for 6 h more than without PCM. Murray et al. [25] used lauric acid as TES medium, the result of their simulation showed that the water temperature was maintained at 43 °C for 3 h. Bouadila et al. [26] used 49.4 L of paraffin as TES system in their study which showed a uniform source of heat even during 5 h after the sun was not available. Fazilati and Alemrajabi [27] worked with paraffin wax PCM of about 5.2 L in a 9.5 L tank as TES medium, which extended the supply time of hot water by up to around 25%. Naghavi et al. [28] in their study used 0.703 kg paraffin wax which maintained the water temperature higher than 55 °C for 2 h when there was low solar radiation and 4 h with regular solar radiations. Khalifa et al. [29] used 42.4 kg paraffin wax in the a 1.248 m2 collector to maintain the temperature of the collector plate higher than 40 °C for 4 h after the solar radiations starts decreasing. Al-Kayiem and Lin [30] worked on using 28 kg paraffin wax noncomposite 1 w.t.% nano-Cu particles on a 1 m2 collector and observed that it keeps the temperature of water above 50 °C for extra 1 h than without using the PCM. Xue [19] in his experimental investigation used 14.2 L of Ba(OH)2 .8H2O as PCM in a 1.272 m2 collector that kept the tank water temperature higher for 2 h in the afternoon. Papadimitatos et al. [31] incorporated dual PCM using 4.2 kg Erythritol and 6 kg Tritriacontane on the collector plate of 0.947 m2 which resulted in maintaining the temperature of water above 40 °C for 2 h more than without the use of PCM. Thus, by analyzing the difference in SWHs with and without TES system, it is evident that TES systems play a crucial role in enhancing the working performance of SWHs. The results imply that the usage of TES systems has improved the utilization of solar thermal energy available for use. Research in the field of finding new PCMs and improving the existing ones is being done to optimize the performance of SWH. New composites are also being worked upon to be added to the existing PCMs which could provide better results.

52.3.3 Solar Dryers Solar dryers are devices that use solar energy for drying substances. A solar dryer is another application of solar thermal energy, which is immensely used in the food and agriculture industry. Present industrialization has created a need for drying products at controlled rates which has led the researchers to find methods that can work according to the requirement. Butler and Troeger [32] experimentally evaluated a collector-cum-rockbed storage system for drying peanuts. The drying time varied from 22 to 25 h and reduced the content of moisture of the peanuts from around 20% to a range suitable for its safe storage.

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Nadukwu et al. [33] in their work used glycerol as TES medium and observed that drying in the solar dryer integrated with a wind air generator and without thermal storage material yielded a relative humidity in range from 7 to 47%. On the other hand, drying integrated with glycerol had a relative humidity of 7–32%. The author also presented results of the potato sample reaching its equilibrium moisture content value of 10.3 ± 1.3% w.b in 20 h without TES and 8–12 h with the TES. The heat transfer coefficient to dry the potato slice was also compared which was in the range from 13.02 to 18.62 W/m°C and 16.2 to 17.07 W/m°C for without and with the use of TES, respectively. The exergy efficiency compared was ranging from 23.8 to 67.5% and 26.1 to 92.7% for without and with the use of TES, respectively [33]. Alimohammadi et al. [34] worked on a solar dryer with a parabolic trough solar collector by using four fluids. These fluid types were engine oil (10W40), nano-fluid (Al2 O3 , 4%), glycerin, and water. The overall thermal efficiency of the dryer was improved by about 20.2, 9.7, and 12.4 with respect to water. The overall input of thermal energy was 18.46, 17.36, 16.80, and 17.76 MJ for oil, nano-fluid, water, and glycerin, respectively. Reyes et al. [35] worked with paraffin wax and electrical resistances as phase change material for drying mushrooms using a hybrid solar dryer. Its thermal efficiency was observed to change between 22 and 62%. Atalay [36] evaluated that the solar dryer without any thermal storage used 28.76 MJ of waste heat energy and 61.36 MJ solar energy for removing 9.027 kg of water from 10 kg of oranges. On the other hand, using a packed bed TES, the system used 23.38 MJ of waste heat energy, 64.2 MJ of stored thermal energy, and 0.815 MJ of thermochemical energy to remove 9.012 kg of water in about the same drying time. Thus, the emerging need of the world requiring efficient solar dryers for drying products can be met by the utilization of TES mediums which eventually increase the performance of the device and lower the drying time that would have been required otherwise.

52.3.4 Other Emerging Applications Apart from the above-mentioned solar thermal devices and setups, solar energy is also well utilized in the below-mentioned applications where its use is gaining popularity and is producing efficient results for future needs to be catered through renewable energy. These applications can be termed as follows: • • • • •

Distillation Heating of buildings Pumping Agricultural and animal products drying Furnaces

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Cooking Generation of electric power Production of thermal power production Green houses.

In solar distillation, PCMs are able to improve the performance and distillate output. Various researches show that by using PCMs, the solar distillation working time of the system can be increased by 3–4 h and the distillate output can also be increased from 50 to 160%. This percentage change is dependent on the type of PCM utilized [37]. In the past decade, it has been seen that building operations account for more than 30% emission of greenhouse gases that involve no use of energy storage. TES systems not only improve the working efficiency for heating buildings but also play a crucial role in reducing these emissions to a great extent [38]. Saxena et al. [39] tested various PCMs for cooking various food items and concluded that stearic acid in the solar cooker achieved appropriate temperature range to cook types of eatables like beans, rice, fish, and pulses. Thermoelectric generators (TEG) can convert the difference of temperature between two junctions into electrical energy. PCM is embedded near the hot junction of the generator which stores thermal energy from the heat source. This method of installation of PCM as a TES system for generating power, resulted in improving the performance of the TEG to a great extent [40]. Similarly, for other applications, TES systems shown positive result outputs. The recent advances in science have made the use of different TES systems for solar applications possible, which has resulted in the enhanced quality and output of these systems.

52.4 Discussion and Future Development Scope This paper discusses the working concept of thermal energy storage systems, their classification, advantages, and various mediums that are used to produce improvement in the results. It can be concluded that by using TES mediums, there is high improvement in the working efficiency of the solar energy integrated systems. There are various TES systems used for SAHs like PCMs, granite stone, desert sand, and carbon powder which have shown great effects. The choice of a particular TES system for a specific application is very important as it defines the quality. SWHs differ in their outputs majorly due to their tank design and the type of PCM if it is used as TES. The amount of TES used, and the area of the collector plate also plays a vital role in governing its thermal efficiency. Solar dryers have also shown significant improvement in performance by employing thermal storage and also reduced the waste heat energy produced. The moisture content was reduced much faster, and the total working cost is also lowered by the use of TES systems.

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Their use has shown various benefits but a constructive research with categorization and optimization in this field is highly essential. The present scenario of using TES systems shows the improvement in performance of solar energy-based devices and extends the scope of their applications. The enhancement in performance is based on the effective use of TES mediums. Various studies have been presented with different TES systems used for the thermal performance improvement of solar energy devices. There is still a large scope for the improvements that can be done to optimize the output of these devices. Future research work in this field can include: • The development of novel composites for PCMs and other TES systems which can enhance the working performance of solar thermal equipments. • The use of hybrid solar equipments with TES can show even higher efficiencies • The improvement in thermal conductivity and heat transfer rate by using extended surfaces or some integrated techniques. • The improvement in PCM storage tanks and system design optimization can be done to ameliorate the performance of the solar devices. • Ways to prevent energy losses during its transmission. • More advanced equipments can be used to properly analyze the appropriateness of TES systems and improve the reliability. • Light absorption enhancement of PCMs can improve their efficiency. TES systems pragmatically push us toward the sustainable dwelling in the environment. Thus for the benefit of nature and improved energy utilization, constant research in this field is very important.

References 1. M. Conti, C. Charach, Thermodynamics of heat storage in a PCM shell-and-tube heat exchanger in parallel or in series with a heat engine. Solar Energy, 59–68. 2. T. Kerslake, M. Ibrahim, Analysis of thermal energy storage material with change of phase volumetric effects. ASME J. Solar Energy Eng. 22–31 3. J. Duffie, W. Beckman, Solar Engineering of Thermal Processes (Wiley, Hoboken, 2013). 4. K. Pielichowska, K. Pielichowska, Phase change materials for thermal energy storage. Prog. Mater Sci. 65, 67–123 (2014) 5. G. Kokogiannakis, J. Darkwa, W. Su, Review of solid–liquid phase change materials and their encapsulation technologies. Renew. Sustain. Energy Rev. 48, 373–391 (2015) 6. K. Sopian, M. Alkilani, S. Mat, Fabrication and experimental investigation of PCM capsules integrated in solar air heater. Am. J. Environ. Sci. 7, 542–546 (2011) 7. S. Enible, Thermal analysis of a natural circulation solar air heater with phase change material energy storage. Renew. Energy 28, 2269–2299 (2003) 8. P. Naphon, Effect of porous media on the performance of the double-pass flat plate solar air heater. Int. Com. Heat Mass Transf. 32, 140–150 (2005) 9. V. Tygai, A. Pandey, S. Kaushik, S. Tyagi, Thermal performance evaluation of a solar air heater with and without thermal energy storage. J. Therm. Anal. Calorim. 1–8 (2011) 10. W. Aissa, M.E. Sallak, A. Elhakem, An experimental investigation of forced convection flat plate solar air heater with thermal storage material. Therm. Sci. 1105–1116 (2012)

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11. H. Yadav, A. Saxena, N.K. Sharma, Thermal performance evaluation of a design, and cost optimized solar air heater, in Int Cong Renew Energy (ICORE-2012) Grid Power from Renewables organized by Solar Energy Society of India (SESI), pp. 345–353 (2012) 12. A. Saxena, N. Agarwal, G. Srivastava, Design and performance of a solar air heater with long term heat storage. Int. J. Heat Mass Transf. 60, 8–16 (2013) 13. S. Karthikeyan, G. Solomon, V. Kumaresan, R. Velraj, Parametric studies on packed bed storage unit filled with PCM encapsulated spherical containers for low temperature solar air heating applications. Energy Convers. Manage. 78, 74–80 (2014) 14. S. Bouadila, S. Kooli, S. Slouri, M. Lazaar, A. Farhat, Improvement of the greenhouse climate using a solar air heater with latent storage energy. Energy 64, 663–672 (2014) 15. A. Wadhawan, A.S. Dhoble, V.B. Gawande, Analysis of the effects of use of thermal energy storage device (TESD) in solar air heater. Alexandria Eng. J. 57(3), 1173–1183 (2018) 16. A. Hasan, Thermal energy storage system with stearic acid as phase change material. Energy Conserv. Manage. 35(10), 843–856 (1994) 17. A. Hasan, Phase change material energy storage system employing palmitic acid. Sol Energy 35(10), 143–154 (1994) 18. A. Sayigh, A. Hasan, Some fatty acids as phase change thermal energy storage materials. Renew. Energy 4(1), 69–76 (1994) 19. H. Xue, Experimental investigation of a domestic solar water heater with solar collector coupled phase-change energy storage. Renew. Energy 86, 257–261 (2016) 20. S. Canbazo˘glu, A. Sahinaslan, ¸ A. Ekmekyapar, Ý.G. Aksoy, F. Akarsu, Enhancement of solar thermal energy storage performance using sodium thiosulfate pentahydrate of a conventional solar water-heating system. Energy Build. 37(3) (2005) 21. d.A. Gracia, E. Oró, M. Farid, L.F. Cabeza, Thermal analysis of including phase change material in a domestic. Appl. Therm. Eng. 3938–3945 (2011) 22. M. Mazman, L. Cabeza, H. Mehling, M. Nogues, H. Evliya, H. Paksoy, Utilization of phase change materials in solar domestic hot water systems. Renew. Energy 34, 1639–1643 (2009) 23. I. Al-Hinti, A. Al-Ghandoor, A. Maaly, I.A. Naqeera, Z. Al-Khateeb, O. Al-Sheikh, Experimental investigation on the use of water-phase change material storage in conventional solar water heating systems. Energy Convers. Manag 51, 1735–1740 (2010) 24. T. Kousksou, P. Bruel, G. Cherreau, V. Leoussoff, T.E. Rhafiki, PCM storage for solar DHW: From an unfulfilled promise to a real benefit. Sol. Energy 85, 2033–2040 (2011) 25. R. Murray, L. Desgrosseilliers, J. Stewart, N. Osbourne, G. Marin, A. Safatli, D. Groulx, M. White, in Design of a latent heat energy storage system coupled with a domestic hot water solar thermal system (2011) 26. S. Bouadila, M. Fteïti, M. Oueslati, A. Guizani, A. Farhat, Enhancement of latent heat storage in a rectangular cavity: solar water heater case study. Energy Convers. Manage. 78, 904–912 (2014) 27. A. Alemrajabi, M. Fazilati, Phase change material for enhancing solar water heater, an experimental approach. Energy Convers. Manag 71, 138–145 (2013) 28. M. Naghavi, K. Ong, I. Badruddin, M. Mehrali, H. Metselaar, Thermal performance of a compact design heat pipe solar collector with latent heat storage in charging/discharging modes. Energy 127, 101–115 (2017) 29. A. Khalifa, K. Suffer, M. Mahmoud, A storage domestic solar hot water system with a back layer of phase change material. Exp. Therm. Fluid Sci. 44, 174–181 (2013) 30. H. Al-Kayiem, S. Lin, Performance evaluation of a solar water heater integrated with a PCM nanocomposite TES at various inclinations. Sol. Energy. 109, 82–92 (2014) 31. A. Papadimitratos, S. Sobhansarbandi, V. Pozdin, A. Zakhidov, F. Hassanipour, Evacuated tube solar collectors integrated with phase change materials. Sol. Energy 129, 10–19 (2016) 32. J. Butler, J. Troeger, Drying peanuts using solar energy stored in a rockbed. Agric. Energy Solar Energy 1 (1980) 33. M. Ndukwu, D. Onyenwigwe, F. Abam, A. Eke, Development of a low-cost wind-powered active solar dryer integrated with glycerol as thermal storage. Renew. Energy (2020)

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34. Z. Alimohammadi, H.S. Akhijahani, P. Salami, Thermal analysis of a solar dryer equipped with PTSC and PCM using experimental and numerical methods. Sol. Energy 201, 157–177 (2020) 35. A. Reyes, F. Vásquez, A. Mahn, Mushrooms dehydration in a hybrid-solar dryer, using a phase change material. Energy Convers. Manage. 83, 241–248 (2014) 36. H. Atalay, Performance analysis of a solar dryer integrated with the packed bed. Energy 172, 1037–1052 (2019) 37. R. Grewal, H. Manchanda, M. Kumar, A review on applications of phase change materials in solar distillation, in 2nd International Conference on Emerging Trends in Science, Engineering & Technology, Pune (2018). 38. UCLA, in Phase Change Composite Materials for Energy Efficient Building Envelopes, San Diego. 39. A. Saxena, S. Lath, T. Vineet, Solar cooking by using PCM as a thermal heat storage. Int. J. Mechan. Eng. 3(2), 91–95 (2013) 40. S.E. Jo, M.S. Kim, M.K. Kim, J.Y. Kim, Power generation of a thermoelectric generator with phase change materials. Smart Mater. Struct. 22 (2013).

Chapter 53

Application of Concrete Filled Steel Tubes in Solar Module Mounting Structure Jitendra Pratap Singh and Ajay Kumar

Abstract Solar energy is a renewable form of energy. Sustainable development requires the promotion of renewable sources of energy as much as possible. Solar energy can be used to generate the electric energy by using a photovoltaic system, which could convert sunlight into electricity. Solar panels are arranged in a solar module mounting structure made of steel. The tracking of the solar panel is facilitated by the linear actuators. The solar module mounting structure is subjected to various different types of loading. Wind loading is a major concern for the structural integrity and stability of the module mounting structure. The solar module mounting structure is analyzed for various loads using the STAAD PRO structural analysis software, and then the results are used by ABAQUS finite element software to compare the behavior of hollow steel torque tube and concrete filled steel torque tubes under flexural and torsional stresses. Keywords Concrete filled steel tubes · Flexural and torsional stresses in torque tubes · Single axis tracker · Solar energy · Solar module mounting structure

53.1 Introduction Every nation is striving for sustainable development. The dependence on the nonrenewable sources of energy like coal and fossil fuel [1] is a matter of concern, since these resources are not present in unlimited quantity. Massive demand for energy production due to industrial and infrastructural development causes huge exploitation of these non-renewable natural resources [2]. Once they get exhausted, it will take a very long time for their recreation. Hence for the sustainable development and growth of the nation, we have to promote the use of renewable sources of energy like hydel, wind, and solar energy [3]. J. P. Singh (B) Kamla Nehru Institute of Technology, Sultanpur 228118, India e-mail: [email protected] A. Kumar National Institute of Technology Patna, Patna 800005, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_53

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Solar Energy is one of the most popular renewable sources of energy [4]. Solar energy can be used for the generation of electricity by using the photovoltaic system and solar water heating system. A photovoltaic system [5] will generate the electricity from solar radiation directly by using photovoltaics (certain types of semi-conductors which can convert sunlight into electricity). However, the efficiency of the conversion of solar energy to electric energy is very low. Hence, there is a requirement of large amount of area for the generation of useful power. Since solar energy generation happens only on sunny days during the daytime, there is a need for supplementing it with other sources of electricity generation. The solar electricity generated by the solar grids needs is readily available for transmission because storage of this energy is not economical. Solar panels are arranged in a grid structure made of steel. Steel is used for the solar module mounting structure because steel members can be prefabricated in the factories. The connection between steel members and installation of grids is quick. The steel members are lightweight, strong, and durable [6]. The scrap value of the steel is also very high so it can be recycled profitably for reuse. The solar photovoltaic (PV) module used for the analysis is the 465-watt monocrystalline Vikram Solar module [7]. There are 40 modules are arranged in a single row. They are connected by the linear motorized actuator [8] in the middle of the row. The actuator is used for the tracking operation, i.e., to change the angle of the panel with respect to the angular position of the sun so that the sun ray falls perpendicularly on the surface of the solar panel. This will result in maximum electricity generation. Initial infrastructure investment in the solar module mounting structure is very high. Normally such a structure needs to have a minimum lifespan of twenty years so that the initial investment can be profitably recovered from the electricity generation. The solar module mounting structure is subjected to various kinds of loads. There is the dead load of the panel, structure members, connections, and wirings. There is wind and earthquake load. Of all these loads, wind load is the major cause of concern regarding the stability of the structure [9]. This is because the solar module mounting structure is usually built in an open field, without any natural barriers like plants which could decrease the velocity of the winds. The solar module mounting structures are very lightweight so that the dead load of the structure could be minimized, and hence, the forces generated by the pressure of the wind can cause significant damage to the structure. The tracking mechanism assisted by the linear actuator will bring the solar panel to stow position (horizontal position) within five to ten minutes so that the winds with higher velocity could cause less harm to the structure. However, at the stow position, the structure must sustain the effect of wind loading. The structural analysis is performed by the STAAD PRO software [10], which stands for Structural Analysis and Design Software, for dead load and wind loads. Two positions are considered: one is an inclined position and the other is a horizontal position. The magnitude of the torsional moment which comes at the center of the torque tube is taken for the finite element analysis in ABAQUS software [11]. The analysis is done for the hollow steel torque tube, and then the analysis of the torque tube filled with concrete is performed. The comparison of the hollow steel tube and

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Table 53.1 List of abbreviations and their meaning Abbreviation

Description

ρ

Density

A

Area

p

Pressure of the wind

V

Speed of wind

K1

Risk coefficient (which depends on wind velocity and design life)

K2

Terrain category (which depends on the height of the structure)

K3

Topography factor (which depends on the slope of the area)

K4

Importance factor

Ka

Area averaging factor

Kd

Wind directionality factor

Kc

Combination factor

Cp+

Downward pressure coefficient

Cp−

Uplift pressure coefficient

f ck

Characteristic compressive strength of the concrete

Fy

Characteristic yield strength of the structural steel

E

Modulus of elasticity

W

Uniformly distributed load kN/m

the concrete filled steel tube for various grade of the concrete and for various kind of loading is being done which is made to draw the conclusion. For the computation of the various loads on the structure, Table 53.1 provides the list of various abbreviations and their corresponding descriptions.

53.1.1 Tracking Mechanism The solar energy which reaches the earth’s surface may vary from 1025 W/m2 in a clear sky to 550 W/m2 in a cloudy sky [12]. The sun rises from the east and sets in the west direction during the day, which implies that the sun ray does not fall vertically on the surface all the time. Hence, there is a need for changing the orientation of the surface of the panel throughout the day so that the surface of the panel remains perpendicular to the solar radiation. This is done with the aid of a linear actuator. Tracking mechanism consists of the linear motorized actuator, which changes the angle of orientation of the panel from stow position (horizontal position) to the maximum angle which is allowable by the actuator for tracking as shown in Fig. 53.1. Stow position provides the least obstruction to the wind and hence induces least wind generated stresses on the structure.

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Fig. 53.1 PV Solar Module mounting Structure in stow position and inclined position

53.1.2 Solar Module Mounting Structure The capacity of the linear actuator is limited. It can create a certain magnitude of torque. Hence, the whole solar plant is divided into smaller grids arranged over a single torque tube at the center. This torque tube is connected to the actuator. Figure 53.2 shows a 40 PV module single axis tracking system. The PV solar module assembly is supported by a long steel torque tube at the center with the help of hat sections and connections. The steel torque tube is supported by a set of seven columns. At the top of each column, there is a nylon bearing which facilitates the rotation of the torque tube. There is a lever arm, which is connected to the linear actuator and when the actuator displaces linearly, the linear displacement is converted into the rotation of the torque tube with the help of this lever arm to change the orientation of the torque tube from the stow position to the inclined position.

Fig. 53.2 A 40 PV Module mounting single axis tracking system

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53.1.3 Loads on the Solar Module Mounting Structure Steel is the most suitable material for the construction of the solar module mounting structure. Steel is lightweight, and its strength-to-weight ratio is very high. It allows modular construction, prefabrication and quick assembling and installation. Since the structure is lightweight, the stresses induced by the dead load of the structure are small. However, there is significant stress induced by the wind load. Under normal circumstances, the structure can tolerate a certain wind velocity. When the wind velocity rises, the stresses induced in the structure due to wind load increases. When the sensors from the anemometers sense the wind velocity greater than the tolerable velocity, the actuator will bring the structure to stow (horizontal position). The structure in stow position will experience minimal wind induced stress because the panel will experience very little drag force. However, the wake effect of the wind, gust effect of the wind and turbulence will result in some twisting moment in the structure. This twisting moment will result in the development of the torsional stresses in the structure.

53.1.4 Concrete Filled Steel Torque Tubes Hollow square steel tubes are used for the torque tube. Square and the rectangular cross section are preferred over the circular cross section of the torque tube because they facilitate the proper sitting of the hat section and PV solar module on the torque tube as shown in Fig. 53.3a. The hollow tube can be filled with the concrete as shown in Fig. 53.3b so as to achieve additional torsional and flexural resistance. The additional weight of the concrete will also decrease the stresses in the structure due to uplift pressure of the wind. The concrete can be made by using the nominal mixes described in Table 53.2, based on the availability of the materials on the site.

Fig. 53.3 Hollow Steel Torque tube and Concrete filled Steel Torque tube

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Table 53.2 Nominal mix proportion for various grades of the concrete Serial Number

Grade of the concrete

Mix Proportion

Cement (per 100 kg)

Sand (Per 100 kg)

Aggregates (per 100 kg)

1 2

M5

(1:5:10)

6.25

31.25

62.5

M7.5

(1:4:8)

7.69

30.76

61.58

3

M10

(1:3:6)

10

30

60

4

M15

(1:2:4)

14.28

28.57

57.14

5

M20

(1:1.5:3)

18.18

27.27

54.54

6

M25

(1:1:2)

25

25

50

53.2 Methodology 53.2.1 Computation of the Load The dead load is calculated as per the IS 875: Part 1 [13] and the wind load is calculated as per the IS 875: Part 3 [14] The weight on the torque consists of the weight of the PV Solar panel and weight of the hat, fasteners and self-weight of the torque tube. The dimensions of the PV panel are shown in Fig. 53.4. From the PV module datasheet, we can calculate the weight and area of the panel. Calculation of Wind Pressure Basic wind velocity Design wind velocity, V d Wind pressure

Vb k1 * k2 * k3 * k4 * V b 0.6 * V 2d

Fig. 53.4 Dimension of the 465-Watt Monocrystalline PV Solar Panel

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Fig. 53.5 STAAD PRO Model of the PV Solar Module Mounting Structure in inclined and stow position

Design wind Pressure, Pd Center of pressure Wind force

Cp * p 0.3 * Length of the panel Pd * A

53.2.2 Structural Analysis Using STAAD PRO STAAD PRO v8i software is used for the analysis and design of the structure. The structure is modeled in the STAAD PRO using the nodes distances as shown in Fig. 53.5. The geometries are different for the stow position of the panel and the inclined position of the panel. It uses the inbuilt steel design code IS 800 [15]. Panel and hat section are replaced by their corresponding loads as point loads on the torque tube. The moment due to wind load is also applied near the point where the panel is sitting on the torque tube. Figure 53.6 shows the loading of the structure. The bending moment and the twisting moment diagram of the structure are shown in Fig. 53.7. These bending moments and the twisting moments are considered for the analysis in the ABAQUS software.

53.2.3 Finite Element Analysis For the finite element analysis of the hollow steel torque tube and steel torque tube filled with the concrete, ABAQUS 6.14 CAE software is used. The cross section given by the STAAD PRO software is used for the analysis. The assembly of the steel tube filled with concrete is shown in Fig. 53.8. The density of the concrete and steel, modulus of elasticity of concrete and steel are taken as follows. Density of steel Density of concrete Modulus of elasticity of steel

7850 kg/m3 2400 kg/m3 200 GPa

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Fig. 53.6 Vertical and horizontal and moment due to the wind load, connection load, bearing load

Fig. 53.7 Twisting moment and Bending moment Diagram of the Structure

Fig. 53.8 ABAQUS model of the concrete filled steel tube

53 Application of Concrete Filled Steel Tubes …

Modulus of elasticity of concrete

701

√ 5000 fck MPa

The binding moment and the twisting moment are applied on the section. The reference point is used for the point loading, and the reference point is coupled with the cross section of the torque tube. The other end of the torque tube is made fixed as shown in Fig. 53.9. Messing is done with initial start as 50% of the smallest element thickness, and then the mesh is refined so that the results converge. The stresses for the flexure and shear as shown in Fig. 53.10 are considered for the comparison study.

Fig. 53.9 Support and the loading of the concrete filled steel Torque tube in ABAQUS model

Fig. 53.10 Flexural stresses of the concrete filled steel torque tube

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Table 53.3 STAAD PRO analysis results for the Hollow square torque tube Wind speed (m/s)

Cross section of Utility factor the torque tube (mm)

Maximum deflection (mm)

Maximum bending moment (kNm)

Maximum twisting moment (kNm)

30

91.5 × 91.5 × 4.5 (SHS)

0.69

4.81

2.15

9.01

35

91. 5 × 91.5 × 0.93 4.5 (SHS)

7.16

2.93

12.26

40

91.5 × 91.5 × 5.4 (SHS)

0.83

7.60

3.83

16.02

45

91.5 × 91.5 × 5.4 (SHS)

0.92

9.28

4.85

20.27

50

100 × 100 × 6.0 (SHS)

0.88

7.67

5.99

25.03

55

100 × 100 × 6.0 (SHS)

0.97

9.19

7.25

30.29

53.3 Results Since the base speed is taken as 15 m/s, which the structure can tolerate and above which, the structure is turned to stow position, we will consider the wind speeds only for the stow position. Weight of the panel Weight of the Hat section

25 kg 3 kg

Wind speed for the stow position is 30, 35, 40, 45, 50, 55 m/s. STAAD PRO Analysis and design results are in Table 53.3. These cross sections are used for the finite element analysis, and the results are as shown in Table 53.4. Maximum flexural stresses of the hollow steel tube and the concrete filled steel torque tube and maximum torsional stresses of the hollow steel tube and concrete filled steel tube are compared. The grades of the concrete taken for the comparison are M5, M10, M15, M20, and M25, respectively. The STAAD PRO model is refined by the additional weight of the concrete filled steel torque tube. The effect of the additional concrete weight is considered in the change of the utility factor and deflection by comparing the results with the previous values obtained in hollow steel torque tubes.

53.4 Conclusion The wind load of the given panel is calculated, and the analysis is done in the STAAD PRO software for various wind speeds in the stow position of the solar

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Table 53.4 Finite element analysis of the concrete filled steel torque tube Cross section of the Grade of the torque tube (mm) concrete

Maximum flexural stress in hollow steel tube (MPa)

Maximum flexural stress in concrete filled tube MPa % decrease

Maximum torsional stress in hollow steel tube (MPa)

Maximum torsional stress in concrete filled steel tube MPa % decrease

(91.5 * 91.5 * 4.5)

M5

55.2

49.6 10.14

150.2

134.2 10.65

M10

55.2

47.6 13.76

150.2

128.2 14.64

M15

55.2

46.2 16.31

150.2

124.1 17.37

M20

55.2

45.2 18.11

150.2

120.8 19.57

M25

55.2

44.1 20.11

150.2

116.7 22.31

M5

74.7

68.5 8.29

193.4

177.5 8.22

M10

74.7

66.2 11.37

193.4

170.9 11.63

M15

74.7

64.5 13.65

193.4

166.2 14.06

M20

74.7

63.2 15.39

193.4

162.5 15.97

M25

74.7

62.1 16.86

193.4

157.5 18.56

M5

92.7

84.4 8.95

228.5

206.8 9.49

M10

92.7

81.4 12.18

228.5

199.1 12.86

M15

92.7

79.3 14.45

228.5

193.3 15.40

M20

92.7

77.5 16.39

228.5

188.8 17.37

M25

92.7

76.1 17.90

228.5

185.1 18.99

(91.5 * 91.5 * 5.4)

(100 * 100 * 6.0)

module mounting structure. The designed section is considered for the finite element analysis in ABAQUS software, with the maximum bending moment taken from the STAAD PRO analysis. The concrete filled steel section is also analyzed, and the comparison is made between the hollow and the concrete filled steel torque tube section. It has been found that the concrete filled steel torque tube is reducing the torsional and flexural stresses as shown in Table 53.4. The additional weight of the concrete

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Table 53.5 STAAD PRO analysis results for the concrete filled steel torque tube Wind speed (m/s)

Cross section of Utility factor the torque tube (mm)

Maximum deflection (mm)

Percentage change in the utility factor

Percentage change in the maximum deflection

30

91.5 × 91.5 × 4.5 (SHS)

0.64

3.99

7.24

17.05

35

91. 5 × 91.5 × 0.89 4.5 (SHS)

6.15

4.30

14.58

40

91.5 × 91.5 × 5.4 (SHS)

0.73

6.09

5.19

19.87

45

91.5 × 91.5 × 5.4 (SHS)

0.89

7.75

3.26

16.49

50

100 × 100 × 5.4 (SHS)

0.83

6.59

5.40

14.08

55

100 × 100 × 5.4 (SHS)

0.91

8.08

5.09

12.08

Table 53.6 Change in the mass moment of inertia due to concrete filled steel torque tube Total in kg m2

Inertia of the Total in kg m2 concrete in the torque tube in kg m2

Percentage change

91.5 × 91.5 × 371.7 33.7 1.1 4.5 (SHS)

406.5

1.2

407.7

0.29

91.5 × 91.5 × 371.7 33.7 1.4 5.4 (SHS)

406.8

1.2

408.0

0.29

100 × 100 × 5.4 (SHS)

407.2

1.7

408.9

0.41

Cross section of the torque tube (mm)

Inertia of the assembly in kg m2 Panel hat tube

371.7 33.7 1.8

will work against the uplift pressure by the wind, and hence, the deflection and the utility factor are also reduced as shown in Table 53.5. However, since the additional concrete ads up to the cost of the structure, further economic analysis of the structure needs to be done to weigh the benefits of additional structural safety with additional structural cost. Additional weight will also increase the tracking torque because of the friction in the Nylon bearing. However, the increase in the torque is insignificant because the radius of the gyration of the additional mass of concrete lies near the centroidal axis of rotation as shown in Table 53.6. Further, seasonal tracking can be a viable alternative for such a scenario.

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References 1. J.B. Ang, CO2 emissions, energy consumption, and output in France. Energy Policy (2007) 2. https://www.resilience.org/stories/2007-03-31/what-are-our-alternatives-if-fossil-fuels-areproblem/ 3. G. Xydis, Comparison study between a renewable energy supply system and a supergrid for achieving 100% from renewable energy sources in Islands. Int. J. Electr. Power Energy Syst. 46, 198–210 (2013) 4. A.B. Meinel, M.P. Meinel, Applied Solar Energy—An Introduction (STIA, 1977). ui.adsabs.harvard.edu 5. https://en.wikipedia.org/wiki/Photovoltaic_system 6. T.V. Galambos, M.K. Ravindra, Properties of steel for use in LRFD. J. Struct. Div. (1978). cedb.asce.org 7. https://35bjjk3fzaio4epare24j5l9-wpengine.netdna-ssl.com/wp-content/uploads/2020/07/ Somera-grand-ultima-plus-MBB-E-2020.pdf 8. https://en.wikipedia.org/wiki/Linear_actuator 9. A.M. Aly, G. Bitsuamlak, Wind-induced pressures on solar panels mounted on residential homes. J. Architect. Eng. (2014). ascelibrary.org 10. https://www.bentley.com/en/products/product-line/structural-analysis-software/staadpro 11. https://www.3ds.com/products-services/simulia 12. https://simple.wikipedia.org/wiki/Solar_energy 13. IS 875: Part 1 1987 Rev 3 Code of Practice for Design Loads (other than earthquake loads) for Building and Structures Part 1 Dead Loads 14. IS 875: Part 3 2015 Rev 5 Code of Practice for Design Loads (other than earthquake loads) for Building and Structures Part 3 Wind Loads 15. IS 800: 2007 Code of Practice for General Construction in Steel

Chapter 54

Reduction of Over Current and Over Voltage Under Fault Condition Using an Active SFCL with DG Units G. Sasi Kumar, G. Radhika, and D. Ravi Kumar

Abstract Distributed generation resources are progressively found great advantages in distribution systems. In a distribution system where multiple distributed generation (DG) units are connected, over current and induced overvoltage that occur under fault conditions should be considered seriously to improve system reliability. Connecting active superconducting fault current limiters (SFCLs) in the distribution system is one of the best method among other conventional methods to reduce the over current and surge voltage that occur during fault condition. An active SFCL is made of PWM converter and an air-core transformer. This paper gives a detailed study of an active SFCL that suppress the fault current and surge voltage that may occur due to different types of fault at different location of DG units so connected into the network at different fault positions. The simulation results reveals that, by connecting an active SFCL in the power system can suppress effectively the over current and overvoltage that occurs during fault and hence the power system’s consistency and security be improved. Keywords Active superconducting fault current limiter (SFCL) · Over voltage · Distributed generation (DG) · Fault current · Distribution system

54.1 Introduction Electricity is one of the most basic requirements of a developing country. The higher the demand of energy determines the standard of people living in that country. Day by day the consumption of natural resources like coal, gas, and oil is increasing and their cost are also increasing. In addition loss I2 R is more in a distribution network G. Sasi Kumar (B) · G. Radhika · D. Ravi Kumar EEE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India e-mail: [email protected] G. Radhika e-mail: [email protected] D. Ravi Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_54

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compared to transmission network in a power system network. Therefore, to minimize the above problems and also to give emergency power backup, distributed generation (DG) is becoming more popular and acts as a main constituent of distribution system to supply electrical loads presented in [1–3]. But the introduction of DG in a distribution system decreases the Thevenin’s impedance seen from the faulty point; as a new impedance is connected in parallel to the distribution system, as a result, it will increase the level of fault current. Also the existence of DG in a distribution network may change the current direction, the way it flows from source to destination consumer, in some lines. The presence of DG in distribution network can cause various problems such as miss-coordination between the protective equipment, false tripping of the protecting equipment, etc., in [4]. Fault currents are said to be transient currents which flows through the power system whenever short circuit occurs. When there is a LG fault, in a neutral isolated distribution system,over current be induced in that particular phase and over voltage also be induced into two healthy phases. Considering the connection of DG multiple units in distribution system, the effects of the induced over voltage should be taken seriously for the purpose of distribution network’s operation safety and insulation stability. The equipments that are installed at power generating station and distribution system station are very expensive and costly. Therefore, it is necessary to protect these equipments from fault current that occurs during abnormal conditions. Considering both economic and technical points of view, connecting active type of superconducting fault current limiter (SFCL) is one of the good solutions to protect sensitive devices and loads from fault current and over voltages that occurs in a power system during abnormal condition. A lot of work been carried out [5–13] by connecting different types of SFCL with DG units into the distribution network, by focusing mainly in current-limitation aspects and their improvements in protection coordination [14–17]. Using a SFCL for suppressing the induced overvoltage in another two phases for 1 − f, LG fault is relatively less. This paper deals with active SFCL as an assessment element and its impact on limiting the over voltage and fault current in a distribution network containing number of DG units at different locations with different DG injection capacities with different types of fault and fault positions.

54.2 Theoretical Analysis of Active SFCL 54.2.1 Structure of Active SFCL Figure 54.1a represents the basic circuit of the 1 − f voltage compensation-type active SFCL, which is made up of a voltage-type PWM converter and an air-core superconducting transformer, where Ls1 , Ls2, and Ms represent the self-inductances, mutual inductance, and Z1 and Z2 are the circuit impedance and load impedance, respectively. The higher order harmonics caused by the converter are filtered by

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Fig. 54.1 1 − f Active SFCL. a Basic circuit of active SFCL. b Active SFCL equivalent circuit

L d and C d . The capability of voltage-type converters is to control power by regulating the AC side voltage, assuming converter as controlled source of voltage Up. Figure 54.1b shows the active SFCL’s equivalent circuit without taking the losses of the transformer.

54.2.2 Operating Principle of Active SFCL No fault condition is nothing but normal condition where injected current (I 2 ) that flows through transformer secondary winding should be kept in control to retain a assured value, while the air-core magnetic field could be compensated to zero, such that there will be no effect of active SFCL on the main circuit of the distribution system. Whenever a fault is noticed, injected current should be adjusted in time either by amplitude or by phase angle by controlling the primary voltage of superconducting transformer’s connected in series with the main circuit of the distribution system

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and additional the fault current be suppressed. Below shows the so suggested exact regulating mode of active SFCL’s. Under normal condition, the equations were as follows: Us = I1 (Z 1 + Z 2 ) + jωL s1 I1 − jωMs I2

(54.1)

U p = jωMs I1 − jωL s2 I2

(54.2)

When I 2 is controlled to make jωL s1 I1 − jωMs I2 =0, then U 1 —primary voltage be controlled to zero. Thus, the equivalent limiting impedance √ Z SFCL (Z SFCL = U 1 /I 1 ) becomes zero, then I 2 could be regulated as I 2 = U s L √ s1 /L s2 / (Z1 + Z2) k, a coupling coefficient which could be found from k = M s / L s1 L s2 . Under faulty scenario, the load impedance Z2 has been shorted. During this time, the current in the distribution network I 1 will be up surge to current I 1f , and primary voltage be increased from U 1 to U 1f . I1 f =

(Us + jωMs I2 ) (Z 1 + jωL s1 )

U1 f = jωL s1 I1 f − jωMs I2 Us ( jωL s1 ) − I2 Z 1 ( jωMs ) = Z 1 + jωL s1

(54.3)

(54.4)

The current suppressing impedance ZSFCL could be controlled by: Z S FC L =

U1 f jωMs I2 (Z 1 + jωL s1 ) = jωL s1 − I1 f Us + jωMs I2

(54.5)

Three different modes of operation are there to regulate the current I 2 : (1)

By keeping I2 to remain in its original state, and by regulating the impedance, Z SFCL−1 = Z 2 ( jω L s1 )/(Z 1 + Z 2 + jω L s1 )

(2) (3)

In this, mode current I2 is kept zero, and ZSFCL−2 = jωLs1 . By changing I2 phase angle, to make 180° angle difference between Us and jωMs I2 . By keeping jωMs I2 = −cU s, and Z SFCL−3 = [cZ 1 /(1 − c) ] + [ jωL s1 /(1 − c)]

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Fig. 54.2 Distribution system with DG units—Application of sctive SFCL

54.3 Distribution System with DG Units Using Active SFCL A distribution system when connected to number of DG units where active SFCL is incorporated in the system as shown in Fig. 54.2, where buses B–E were chosen to be the most feasible locations of DG units to be installed. When a 1 − f LG fault occurs on phase A at K1 point, the active SFCL will be initiate automatically and by this the fault current that raises in that particular phase can be controlled well-timed. During this type of fault, there is an overvoltage in other two phases. The method so used to calculate the induced over voltage in remaining two phases is symmetrical component method and complex sequence networks. Coefficient of √ grounding denoted by G, be formulated as G = − 1.5 m/(2 + m) ± j 3/2, under this fault condition, whereas m = X 0 /X 1 . While X 1 and X 0 are the positive-sequence and zero-sequence reactance, respectively. The over voltage magnitude in the Bth & Cth phases can be measured as (Table 54.1): UBO = UCO

 √ √  G 2 + G + 1  = 3 UAN   G+2

(54.6)

54.4 Simulink Results 54.4.1 Simulation Results During Normal Condition (Without Fault) Figure 54.3 shows the 3 − f voltage waveforms during normal condition and Fig. 54.4 shows the 3 − f current waveforms during normal condition.

712 Table 54.1 Simulation model parameters of the system

G. Sasi Kumar et al. Active SFCL parameters Primary inductance

50 mH

Secondary inductance

30 mH

Mutual inductance

32.9 mH

Parameters of distribution transformer Rated capacity

500 KVA

Transformation ratio

35 kv/10.5 kv

Feeder lines parameter Length of the line

L AF = 6km, L AB = 4km, L BC = 4km, L CD = 10km, L DE = 16km

Line parameter

(0.259 + j0.093) /km

Power load parameters Load 1

100 

Load 2

(20 + j13) 

Fig. 54.3 3 − f Voltage waveforms under normal condition

54.4.2 Reducing of Over Voltage Characteristics of Active SFCL During Fault Condition Assuming that, DG’s injection capacity around 100% of load 1. When 1 − f LG fault occurs at k1 point, then immediately phase A gets shorted. The fault time is taken from 0.2 s to 0.3 s. In this model, the simulation is done by changing the installation of DG2 in C, D, and E buses also by terming them as case I, case II, and case III, respectively. Figure 54.5a shows the overvoltage in two phases when 1 − f LG fault occurred on

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Fig. 54.4 3 − f Current waveforms under normal condition

Fig. 54.5 a During 1 − fLG fault at load-1, DG units voltage characteristics under different locations. b Voltage characteristics at various locations of DG units with active SFCL when single phase to ground

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Table 54.2 Normal peak voltage: −10.5 kV Injection capacity of DG

DG location

Peak voltage during fault without active SFCL (kV)

Peak Vvoltage during fault with active SFCL (kV)

100%

Case I

12

10.45

Case II

12.8

10.5

90%

50%

Case III

13.6

10.6

Case I

11.4

10.45

Case II

12.4

10.4

Case III

13

10.52

Case I

10.6

10.48

Case II

10.9

10.48

Case III

12.45

10.4

the phase A. Figure 54.5b waveforms shows the active SFCL overvoltage suppressing characteristics when it is connected before transformer. Throughout the simulation, a study has been carried out how DG’s injection capacity effects the magnitude of induced overvoltage. To a larger extent, each DG unit’s injection capacity adjustable range will be about 50%–100% of load capacity of load-1 and 2 DG’s were located in all the above three cases with fault conditions keeping unchanged. Table 54.2 shows the peak voltage during normal and fault conditions with and without SFCL for three cases with different DG’s injection capacity. Fault Type: 1 − f, LG fault near load-1 at k1 point. Fault occurs near Load 1. Case I:- DGs are connected with Bus B and C. Case II:- DGs are connected with Bus B and D. Case III:- DGs are connected with Bus B and E.

54.4.3 Active SFCL, Over Current Suppressing Characteristic For getting active SFCL current limiting characteristics, initially, each DG injection capacity to be taken as 100–50% of the capacity of load-1. To the buses B and C, two DG units were connected separately. For a 1 − f, LG fault at K1 point, active SFCL current limiting characteristics has been observed for a time of 0.2 s to 0.3 s. Fault Type: 1 − f, LG fault in phase A near load-1 at point k1 .

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Figure 54.6 shows current waveforms when 1 − f, LG fault occurs near load-1. Figure 54.7 shows current waveforms with SFCL when 1 − f, LG fault occurs near load-1. Table 54.3 also shows the peak current during normal and faulty condition with and without active SFCL. Fault Type: Three phase to ground fault near load 1 at k1 point. Figure 54.8 shows current waveforms with and without SFCL when 3 − f ground fault occurs near load-1. Table 54.4 shows the peak current during normal and faulty conditions with and without SFCL. Fault Type: 3 − f ground fault near load-2 at k2 point. Figure 54.9 shows current waveforms with and without SFCL when 3 – f ground fault occurs near load-2. Table 54.5 shows the peak current during normal and faulty conditions with and without SFCL.

Fig. 54.6 Current characteristics without active SFCL when 1 − f fault occurs near load-1

Fig. 54.7 Current characteristics with active SFCL when 1 − f fault occurs near load-1

Table 54.3 Normal peak current—700 Amp DG injection capacity

Peak current during fault without active SFCL

Peak current during fault with active SFCL

100%

850Amp

730Amp

90%

907Amp

700Amp

50%

1120Amp

705Amp

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Fig. 54.8 Current characteristics with and without SFCL For 3 − f fault occurs near Load-1 Table 54.4 Peak current with and without active SFCL Normal peak current

Peak current during fault without active SFCL

Peak current during fault with active SFCL

700 Amp

1175 Amp

680 Amp

Fig. 54.9 Current characteristics with and without SFCL For 3 − f fault occurs near load-2 Table 54.5 Peak current with and without active SFCL Normal peak current

Peak current during fault without active SFCL

Peak current during fault with active SFCL

700 Amp

975 Amp

720 Amp

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Fault Type: 3 − f to ground fault near source side at k3 point. Figure 54.10 shows current waveforms without SFCL and Fig. 54.11 shows current waveform with SFCL when 3 − f ground fault occurred near source side. Table 54.6 gives the peak current during normal and faulty conditions with and without active SFCL.

Fig. 54.10 Current characteristics without SFCL for 3 − f ground fault occurs source side

Fig. 54.11 Current characteristics with SFCL For 3 − f fault occurs source side

Table 54.6 Peak current with and without active SFCL Normal peak current

Peak current during fault without active SFCL

Peak current during fault with active SFCL

700 Amp

875 Amp

723 Amp

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54.5 Conclusion This paper reveals that whenever 1 − f LG fault occurs there will definitely be an overvoltage in the other two phases. Thus by means of an active SFCL with DG units in power distribution network, the active SFCL could help in reducing magnitude of overvoltage as well as in avoiding the so connected relevant equipment damage. Thus, active SFCL improves safety of power system as well as reliability by avoiding distribution equipment damage and also suppress the magnitude of the over current occurred by different faults effectively. In recent years, various energy sources such as solar power and wind power are connected into distribution systems. Experiments were carried out with active SFCL with conventional transformer for limiting over voltage and over current. Thus, experimental results were in line with MATLAB simulations shown in this paper. In the future, study over a coordinated control method for various renewable energy sources by using active SFCL would become very effective with real system modelling.

References 1. S. Conti, Analysis of distribution network protection issues in presence of dispersed generation. Elect. Power Syst. Res. 79(1), 49–56 (2009) 2. A.S. Emhemed, R.M. Tumilty, N.K. Singh, G.M. Burt, J.R. McDonald, Analysis of transient stability enhancement of LV connected induction micro generators by using resistive type fault current limiters. IEEE Trans. Power Syst. 25(2), 885–893 (2010) 3. S.Y. Kim, J.-O. Kim, Reliability evaluation of distribution network with DG considering the reliability of protective devices affected by SFCL. IEEE Trans. Appl. Supercond. 21(5), 3561– 3569, (2011) 4. S. Hemmati, J. Sadeh, Applying super conductive fault current limiter to minimize the impacts of distributed generation on the distribution protection systems, in Proceedings Under International Conference on Environment and Electrical Engineering, Venice, Italy, pp. 808–813 (2012) 5. L. Chen, Y.J. Tang, J. Shi, L. Ren, M. Song, S.J. Cheng, Y. Hu, X.S. Chen, Effects of a voltage compensation type active superconducting fault current limiter on distance relay protection. Phys. C 470(20), 1662–1665 (2010) 6. J. Wang, L. Zhou, J. Shi, Y. Tang, Experimental investigation of an active superconducting current controller. IEEE Trans. Appl. Super Cond. 21(3), 1258–1262 (2011) 7. H. Yamaguchi, T. Kataoka, Stability analysis of air-core superconducting power transformer. IEEE Trans. Appl. Supercond. 7(2), 1013–1016 (1997) 8. H. Yamaguchi, T. Kataoka, H. Matsuoka, T. Mouri, S. Nishikata, Y. Sato, Magnetic field and electromagnetic force analysis of 3-phase air core superconducting power transformer. IEEE Trans. Appl. Supercond. 11(1), 1490–1493 (2001) 9. M. Song, Y. Tang, N. Chen, Z. Li, Y. Zhou, Theoretical analysis and experiment research of high temperature superconducting air core transformer, in Proc. Int. Conf. Electr. Mach. Syst., Wuhan, China, pp. 4394–4397, Oct. (2008) 10. R. Wu, Y. Wang, Z. Yan, W. Luo, Z. Gui, Design and experimental realization of a new pulsed power supply based on the energy transfer between two capacitors and an HTS air-core pulsed transformer. IEEE Trans. Plasma Sci. 41(4), 993–998 (2013)

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11. R. Wu, Y. Wang, Z. Yan, Z. He, L. Wang, Simulation and experimental investigation of an inductive pulsed power supply based on the head-to-tail series model of an HTS air-core pulsed transformer. IEEE Trans. Appl. Supercond. 23(4), 5701305 (2013) 12. S. Chen, W. Wang, P. Yang, Effects of current-limiting inductor on power frequency over voltages in transmission line. Power Syst. Technol. 34(3), 193–196 (2010) 13. L. Chen, Y.J. Tang, J. Shi, N. Chen, M. Song, S.J. Cheng, Y. Hu, X.S. Chen, Influence of a voltage compensation type active superconducting fault current limiter on the transient stability of power system. Phys. C 469(15–20), 1760–1764 (2009) 14. S.A.A. Shahriari, A. Yazdian, M.R. Haghifam, Fault current limiter allocation and sizing in distribution system in presence of distributed generation, in Proceedings of IEEE Power & Energy Society General Meeting, Calgary, AB, Canada, pp. 1–6 (2009) 15. S.H. Lim, J.-S. Kim, M.-H. Kim, J.-C. Kim, Improvement of protection coordination of protective devices through application of a SFCL in a power distribution system with a dispersed generation. IEEE Trans. Appl. Supercond. 22(3), 560–1004 (2012) 16. L. Chen, Y. Tang, J. Shi, Z. Sun, Simulations and experimental analyses of the active super conducting fault current limiter. Phys. C. 459(1/2), 27–32 (2007) 17. L. Chen, Y. Tang, J. Shi, Z. Li, L. Ren, S. Cheng, Control strategy for three-phase four-wire PWM converter of integrated voltage compensation type active SFCL. Phys. C 470(3), 231–235 (2010)

Chapter 55

Mathematical Modeling of Air Heating Solar Collectors with Fuzzy Parameters Purnima Pandit , Prani R. Mistry , and Payal P. Singh

Abstract Limited fuel and fossil energy have compelled the world to look forward for other renewable energy sources. Solar energy is one such vital energy resources that finds the application in various industrial as well as residential processes. Heat from this source is useful for increasing the temperature of air used for blow drying processes. The mathematical model for such phenomenon with imprecise parameter and/or initial condition leads to a fuzzy nonlinear dynamical model. In this paper, we propose Fuzzy Adomian Decomposition Method to obtain solution of this system. This solution obtained is compared at core. Keywords Heat transfer · Solar air collector · Fuzzy parameters · Fuzzy Adomian Decomposition Method MSC 34A07

Nomenclatures δx η τ α ε Gt

An element in x direction (m) Thermal efficiency Transmittance Absorbance Emissivity Solar radiation (W/m2 )

P. Pandit · P. R. Mistry (B) Department of Applied Mathematics, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India P. Pandit e-mail: [email protected] P. P. Singh Department of Applied Science and Humanities, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_55

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Cp Ta Tb F Re D ρ V μ m˙ Rg Qu As Ac W T L t a b k Tp Ts S s hc hr Ut Ul Ub Ue h c, p−a h c, b−a h r, p−a h r, p−b

P. Pandit et al.

Specific heat (J/kg K) Ambient temperature (K) Back plate temperature (K) Collector efficiency factor Reynolds number Hydraulic diameter (m) Density (kg/m3 ) Flow velocity (m/s2 ) Dynamic viscosity (m2 /s) Air mass flow rate (kg/s) Gas constant (J/kg K) Useful collected heat Area of surface (edge) (m2 ) Area of collector (m2 ) Width of collector (m) Temperature (K) Length of the collector (m) Time (s) Ambient Back plate Conductivity of the fin (W/m K) Absorber plate temperature (K) Initial temperature (K) Absorbed solar radiation per unit area (W/m2 ) Depth of air flow section (or channel height) (m) Convective heat exchange coefficient (W/m2 K) Radiative heat exchange coefficient (W/m2 K) Top heat loss coefficient (Absorber plate) (W/m2 K) Overall heat loss coefficient (W/m2 K) Bottom heat loss coefficient (Back plate) (W/m2 K) Heat loss coefficient of collector edges (W/m2 K) Convective heat transfer coefficient between absorber plate and ambient (W/m2 K) Convective heat transfer coefficient between back plate and ambient (W/m2 K) Radiative heat transfer coefficient between absorber plate and ambient. (W/m2 K) Radiative heat transfer coefficient between absorber plate and back plate (W/m2 K)

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55.1 Introduction In various field, like engineering, medical, finance, physics, chemistry, real-life problems can be modeled as linear or nonlinear, partial or ordinary differential equations. These mathematical models give logical and valid insight of real world problems. For formulating mathematical equations, we require precise parameters but due to various reasons viz. measurement, counting, we may not be able to obtain them as desired. A promising branch of mathematics, i.e., fuzzy set theory, allows to handle such imprecision occurring in the model, in very realistic and practical manner. Nowadays, solar energy is very growing and demanding concept which is used in many areas like electricity generation, blow drying process, heating of water for many industrial applications, etc. Solar collectors are used to convert solar radiation into solar energy. In solar collectors, air heating plate is used to collect this energy and hence various physical factors like Reynolds number, emission, thermal absorption, efficiency of collector, etc., play vital role in mathematical formulation for its working. This mathematical model is represented as nonlinear differential equation. At times, exact solution of nonlinear differential equation by analytical method is not possible, so we use numerical, semi-analytical techniques to obtain approximate solution for such equations. Many semi-analytical techniques are popular like Adomian decomposition method (ADM) [1], homotopy analysis method (HAM) [2], variational iteration method (VIM) [3], and transformation technique [4]. Adomian decomposition method was developed by George Adomian to solve nonlinear differential equation almost 1970 after. In this method, the nonlinear term is decomposed into the series of Adomian polynomials that converges and approximate solution is obtained. In this article, we propose fuzzy Adomian decomposition method in parametric form to solve nonlinear differential equation of heat transfer in the air heating flat plate solar collectors with fuzzy parameters. The solution is obtained by using a newly defined Modified Hukuhara derivative, as in [5]. This article contains two major sections after introduction, the first one is mathematical description of problem and proposed method for its solution. This section also contains the main result and the relevant lemmas for it; section after it contains application which is followed by conclusion and references.

55.2 Mathematical Model of Solar Air Heater Initially, we list out the concepts of fuzzy sets required in our description followed by the mathematical model of the solar air heater.

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55.2.1 Basic Concepts Let E n = {u˜ : R n → [0 1] such that u˜ satisfies following properties} • u˜ is normal, i.e., there exist an x0 ∈ R n  u(x0 ) = 1. • u˜ is a fuzzy convex, that is for 0 ≤ λ ≤ 1, u(λx1 + (1 − λ)x2 ) ≥ min[u(x1 ), u(x2 )]. • u˜ is upper semi-continuous. • supp(u) ˜ = {x ∈ R n /u(x) ˜ ≥ 0} is compact.   E n is set of fuzzy numbers. Let t˜α ∈ E n then t˜α =  t˜1α , t˜2α , t˜3α , . . . , t˜nα is called the α − cut, such that for each i = 1, 2, . . . , n, t˜iα = ti , ti . The fuzzy operation, ∗ : E n × E n → E n is binary fuzzy operation defined, as in [5], is given as,      u˜ α ∗ v˜ α = min u ∗ v, u ∗ v, u ∗ v, u ∗ v , max u ∗ v, u ∗ v, u ∗ v, u ∗ v where ∗ stands  for fuzzy  multiplication   and division. Scalar multiplication is given by k u˜ α = min ku, ku , max ku, ku , where, k is any scalar. In this article, parameters are taken to be fuzzy triangular numbers which are represented as (a, b, c) with a ≤ b ≤ c and each a, b, c ∈ R. Algebraic form of triangular fuzzy number is given as, ⎧ x−a ⎪ ⎨ b−a ; a < x ≤ b u(x) ˜ = c−x ;b 0 sufficiently small ∃ f˜(x0 + h) f˜(x0 ), f˜(x0 ) f˜(x0 − h) should exist and the limits, lim

h→0+

f˜(x0 + h) f˜(x0 ) f˜(x0 ) f˜(x0 − h) = lim = f˙˜(x0 ) h→0− h h

The equivalent parametric form,

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55.2.2 Mathematical Model Figure 55.1 shows the typical setup of solar air heater. As shown in Fig. 55.1, solar radiation reaches to absorber plate through glass cover. This makes the air in back plate area hot. When such hot air travels in back plate area, it leads to the temperature gradient. The mathematical model of this phenomenon is as given in [6].

m˙ dT m˙ dT a + b T(x) + FUl T (x) = F(S + Ta Ul ) with T (0) = Ts W dx W dx

(Refer nomenclature) As shown by the equation above, temperature and efficiency of solar air collector are affected by several parameters. In this work, we have proposed the mathematical model of solar air collector involving fuzzy parameters and given results pertaining to solution of this model with the fuzzy initial temperature and fuzzy rate of mass of air flow. When we consider mass of air flow as a fuzzy, collector’s efficiency factor F becomes fuzzy because it directly depends on mass of air flow, leading to temperature Fig. 55.1 Energy balance of absorber plate, back plate, and air flow in the solar air collector

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being fuzzy. Thus, the above mathematical equation gets transformed into fuzzy form as,            d T˜ α d T˜ α m˜˙ α m˜˙ α α ⊗ ⊕ b ⊗ ⊗ T˜ (x) ⊕ a W dx W dx   Ul F˜ α ⊗ T˜ α (x) = F˜ α (S + Ta Ul ) (55.1)     with the fuzzy initial condition T˜ α (0) = T (0), T (0) = T s , T s . Since the air mass flow rate is considered to be fuzzy, Reynolds number and collector efficiency factor will also be fuzzy. We obtain the parametric form ˜α by substituting  α  the parametric form of the stated entities, m˙ = of Eq. (55.1), α m, ˙ m¯˙ , T˜ = T , T , F˜ = F, F , as follows      m˙ m¯˙      m˙ m¯˙ a , ⊗ T , T , ⊗ T , T ⊗ T , T ⊕ b W W W W        ⊕ Ul F, F ⊗ T , T = F, F (S + Ta Ul )

(55.2)

    with initial condition, T (0), T (0) = T s , T s . Dividing (55.2) by

m˙ W

,

m¯˙ W

, on both the sides, we get

        ⊕ b T , T ⊗ T , T a T , T           W W ⊗ F, F (S + Ta Ul ) Ul F, F ⊗ T , T , = m˙ m¯˙ Define, L as first-order differential operator taken in sense of mH-differentiability, we can write the above equation as         a LT , LT ⊕ b T , T ⊗ LT , LT           W W ⊗ F, F (S + Ta Ul ) Ul F, F ⊗ T , T , = ¯ m˙ m˙ Let L −1 be fuzzy integral operator corresponding to L, such that, L −1 = Taking L −1 on both side of Eq. (55.3), we get,

(55.3) x 0

        L −1 a L T , L T ⊕ b T , T ⊗ L T , L T  

        W W ⊗ F, F (S + Ta Ul ) Ul F, F ⊗ T , T , = L −1 m˙ m¯˙ That is,

(·)dx.

55 Mathematical Modeling of Air Heating Solar …

727

        L −1 L T , L T ⊕ b T , T + a T , T = a T (0), T (0)  

        W W −1 , ⊗ F, F (S + Ta Ul ) Ul F, F ⊗ T , T +L (55.4) m˙ m˙ In Adomian decomposition method, we obtain series solution of Eq. (55.2). Let the series solution be, ∞  ∞     α ˜ T n, Tn T = T,T = n=0

n=0

    this is because we decompose the nonlinear term, L T , L T ⊗ T , T occurring in  ∞  Eq. (55.4) into series of Adomian polynomials, i.e., n=0 An , An . The fuzzy multiplication of nonlinear term is defined as,    LT , LT ⊗ T , T      = min L T T , L T T , L T T , L T T , max L T T , L T T , L T T , L T T



In the above expression, since temperature always occurs in as positive value, the fuzzy multiplication reduces to 

     LT , LT ⊗ T , T = LT T , LT T

Also, assume that the above nonlinear term can be represented in the series form as 



LT T , LT T =

∞  

An , An



n=0

Putting this value in Eq. (55.4), we get bL

−1

∞ 

An , An

 

n=0

+L

−1



W W , m˙ m˙¯

 ⊕a 

 ⊗

∞ 

T n,

n=0



∞ 

  T n = a T (0), T (0)

n=0



n=0









F, F (S + Ta Ul ) Ul F, F ⊗

That is, ∞  ∞     a T n, T n = a T (0), T (0) ⊕ n=0



∞  n=0

T n,

∞  n=0

 Tn

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∞     ∞        W W ⊗ F, F (S + Ta Ul ) Ul F, F ⊗ L , T n, Tn m˙ m¯˙ n=0 n=0  ∞   bL −1 An , An −1



n=0

On simplification it becomes,     a T 0 + T 1 + · · · , T 0 + T 1 + · · · = a T (0), T (0)      W W ⊗ F, F (S + Ta Ul ) , ⊕ L −1 m˙ m¯˙      Ul F, F ⊗ T 0 + T 1 + · · · , T 0 + T 1 + · · ·  ∞  −1 A0 + A1 + · · · , A0 + A1 + · · · bL n=0

Dividing by a on both side and arranging the terms, we obtain, 

    T 0, T 0 ⊕ T 1, T 1 ⊕ · · ·  

     −1 1 W W ⊗ F, F (S + Ta Ul ) , = T (0), T (0) ⊕ L a m˙ m¯˙  

       −1 1 W W ⊗ Ul F, F ⊗ T 0 , T 0 ⊕ T 1 , T 1 ⊕ · · · , L a m˙ m¯˙  ∞     b −1  L A0 , A0 ⊕ A1 , A1 ⊕ · · · a n=0

Comparing term wise form both the sides, we get 

       1 W W T 0 , T 0 = T (0), T (0) ⊕ L −1 ⊗ F, F (S + Ta UL ) , a m˙ m¯˙

Applying operator L −1 , we get, 

   T 0 , T 0 = T (0), T (0) ⊕

 

  1 W W ⊗ F, F (S + Ta UL )x , a m˙ m¯˙

Next terms onwards, we get as 

T 1, T 1



1 = a

x  0



   W W   Ul F, F ⊗ T 0 , T 0 dx , m˙ m˙¯

55 Mathematical Modeling of Air Heating Solar …



x b  a

729

A0 , A0 dx 

0



T 2, T 2



1 = a

x  0





   W W   Ul F, F ⊗ T 1 , T 1 dx , m˙ m¯˙

x b  a

 A1 , A1 dx

0

And in general, 

T n, T n



1 = a

x  0



   W W   Ul F, F ⊗ T n−1 , T n−1 dx , m˙ m˙¯

x  b  An−1 , An−1 dx a 0

By using decomposition theorem as in KLIR [7], we can write above recurrence relation in following form, T˜0 = T˜ (0) ⊕ 1 T˜1 = a

x  0

1 T˜2 = a

(55.5)

 x  W W  ˜ b ˜ Ul F ⊗ T˜0 dx , A0 dx m˙ m˙¯ a

(55.6)

 x  W W  ˜ b ˜ Ul F ⊗ T˜1 dx , A1 dx m˙ m˙¯ a

(55.7)

0

x  0

 

1 W W ˜ ⊗ F(S + Ta UL )x , a m˙ m¯˙

0

.. . 1 T˜n = a

x  0

 x  W W  ˜ b ˜ ˜ Ul F ⊗ Tn−1 dx , An dx ¯ m˙ m˙ a

(55.8)

0

    To derive A˜ i , we expand the nonlinear term N˜ T˜ = L T˜ ⊗ T˜ by using the approximations of T˜ up to the i th term. That is A˜ i is determined as function   of T˜0 , T˜1 , · · · T˜i . This can be derived by using the series approximation of N˜ T˜ ,

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shown as below,  N˜ (T˜ )        0 N˜ T˜ = N˜ (T˜0 ) ⊕ N˜ (T˜0 ) ⊗ T˜ T˜0 ⊕ ⊗ T˜ T˜0 ⊗ T˜ T˜0 2      N˜ (T˜0 )  ˜ ⊗ T T˜0 ⊗ T˜ T˜0 ⊗ T˜ T˜0 ⊕ · · · ⊕ 3! Now put, T˜ = T˜0 ⊕ T˜1 ⊕ T˜2 ⊕ T˜3 . . . in R.H.S of above equation,     N˜ T˜ = N˜ (T˜0 ) ⊕ N˜ (T˜0 ) ⊗ T˜0 ⊕ T˜1 ⊕ T˜2 T˜0  N˜ (T˜0 )  ˜ ⊗ T0 ⊕ T˜1 ⊕ T˜2 T˜0 2  N˜ (T˜ )    0 ⊗ T˜0 ⊕ T˜1 ⊕ T˜2 T˜0 ⊗ T˜0 ⊕ T˜1 ⊕ T˜2 T˜0 ⊕   3!   ˜ ˜ ˜ ˜ (55.9) ⊗ T0 ⊕ T1 ⊕ T2 T0 ⊗ T˜0 ⊕ T˜1 ⊕ T˜2 T˜0 ⊕ · · ·

⊕

To construct A˜ n polynomials, we need to determine the order of each term of Eq. (55.9) in following manner, A˜ 0 contains polynomial of order 0, A˜ 1 contains polynomial of order 1, and so on. Here, T˜1 is considered as order 1, T˜nk has order nk. So, some fuzzy Adomian polynomials can be obtained as,   A˜ 0 = N˜ (T˜0 ) = T˜0 ⊗ T˜0 = T 0 T 0 , T 0 T 0

(55.10)

  A˜ 1 = N˜ (T˜0 ) ⊗ T˜

  = (T˜0 ⊗ T0 ⊕ T˜0 ⊗ T˜0 ) ⊗ T˜1

(55.11)

N˜ (T˜0 ) A˜ 2 = N˜ (T˜0 ) ⊗ T˜2 ⊕ ⊗ T˜1 ⊗ T˜1 2!  N˜ (T˜ ) N˜ (T˜0 )  ˜ 0 ⊗ 2T1 ⊗ T˜2 ⊕ ⊗ T˜1 ⊗ T˜1 ⊗ T˜1 A˜ 3 = N˜ (T˜0 ) ⊗ T˜3 ⊕ 2 3! Similarly, we can obtain the general term, (55.12)   Thus, we can conclude from Eq. (55.12) that A˜ n = f T˜0 , T˜1 , . . . T˜n . Now, we substitute A˜ i s and obtain simplified expression for, T˜i s. Putting A˜ 0 from Eq. (55.10) and T˜0 from (55.5) in Eq. (55.6), we get

55 Mathematical Modeling of Air Heating Solar …

 

   W W   1 Ul F, F ⊗ T 0 , T 0 , x T˜1 = a m˙ m¯˙

x   b 1 W W ˜ ˜ , T (0) ⊕ ⊗ F(S + Ta UL )x a a m˙ m¯˙ 0 

 1 W W ˜ + Ta UL ) dx ⊗ F(S , a m˙ m¯˙

731

(55.13)

Similarly, we can obtain T˜2 , T˜3 , . . . Thus, the series solution of Eq. (55.2) can be given as, T˜ = T˜0 ⊕ T˜1 ⊕ T˜2 ⊕ T˜3 . . . 

 1 W W ˜ ˜ ˜ ⊗ F(S + Ta UL )x , T = T (0) ⊕ a m˙ m¯˙  

   W W   1 Ul F, F ⊗ T 0 , T 0 , x ⊕ a m˙ m¯˙ x 

 b 1 W W ˜ + Ta UL )x ⊗ F(S , T˜ (0) ⊕ a a m˙ m¯˙ 0

  1 W W ˜ ⊗ F(S + Ta UL ) dx ⊕ · · · , a m˙ m¯˙

(55.14)

Equation (55.14), gives the required solution. Also, its representation in the parametric form can be given as ˜α





T = T,T =



∞  n=0

T n,

∞ 



      T n = T 0, T 0 ⊕ T 1, T 1 ⊕ T 2, T 2

(55.15)

n=0

That is,  

      1 W W T˜ α = T , T = T (0), T (0) ⊕ ⊗ F, F (S + Ta UL )x , a m˙ m¯˙   !     1 W W ⊗ Ul F, F ⊗ T (0), T (0) x , a m˙ m¯˙  !        W W W W x2 1 ⊗ ⊗ Ul F, F ⊗ F, F ⊗ (S + Ta UL ) , , 2 a m˙ m¯˙ m˙ m¯˙ 2 x    b T 0 T 0 , T 0 T 0 dx ⊕ · · · (55.16) a 0

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55.2.3 Theorem Equation (55.6) is a fuzzy solution of Eq. (55.2) if, it is satisfying following conditions, (1) T n ≤ T n ∀n = 0, 1, 2, 3 . . . .. β

(2) α < β, T α ≤ T β ≤ T ≤ T

β

  Proof As defined in Sect. 2.2, T 0 , T 0 forms proper interval because it depends on these inequalities T (0) ≤ T (0) and W F T (0) ≤ W F T (0) which are true. m˙       m¯˙ Similarly, T 1 , T 1 , T 2 , T 2 . . . . T n , T n are properly formed. By the definition of interval, T n ≤ T n ∀n = 0, 1, 2, 3 . . . . Convergence of series given in Eq. (55.6) depends on nature of nonlinear term. Since the nonlinear term is Lipschitz continuous, the series given in Eq. (55.6) is convergent.   Therefore, by first decomposition theorem, as given in Klir [7], T˜ α = T , T is fuzzy solution of Eq. (55.2) as it satisfies both conditions given in the theorem.

55.3 Result and Discussion We derived the results for solution by applying fuzzy Adomian decomposition method to solve heat transfer problem in the air heating solar flat plate collectors for the analytical solution. Mathematical model is given by Eq. (55.1) formulated on the basis of Fig. 55.1. Crisp data for computation is taken from [8] as: a = 980.54, b = 0.083, μ = 2.05×10−5 , W = 1.2, L = 4, Ul = 6.5, k = 0.029, τ α = 0.90, s = 0.015, G t = 890, Ta = 293 K , h r, p−b = 7.395. Also, for the proposed fuzzy model, the fuzzy data fuzzy  is taken as triangular   numbers, m˜˙ = 0.03, 0.06, 0.08 and T˜ (0) = T˜s = 320, 323, 325 . To calculate   ˜ we compute the values of R˜ e , h˜ which depends on m˜˙ and find that F˜ α = F, F = F,   0.31841, 1.52773 . Table 55.1 shows the effect on temperature in a fuzzy setup as air flow pass through the collector; temperature of air changes with fuzzy air mass flow and fuzzy initial temperature. Changes in air mass flow and initial temperature also affect the efficiency of collector. Temperature of air flow decreases as air mass flow rate increases but if we change both initial temperature and air mass make fuzzy scenario which effects the temperature and efficiency. Figures 55.2, 55.3, and 55.4 show effect of fuzzy initial temperature and fuzzy air mass flow by three-dimensional triangular graph. The temperature of air flow decreases as air mass flow increases but increases as initial temperature increases. The efficiency of collector increases as air mass

m = 0.03

320

325.7179

331.0962

336.1551

340.9135

345.3893

349.5992

353.5591

357.2838

360.7873

364.0828

367.1825

X

0

0.36

0.72

1.08

1.44

1.8

2.16

2.52

2.88

3.24

3.6

3.96

T

368.7121

365.7090

362.5163

359.1220

355.5133

351.6768

347.5981

343.2618

338.6517

333.7505

328.5397

323

369.7319

366.7932

363.6689

360.3474

356.8161

353.0619

349.0706

344.8273

340.3160

335.5200

330.4300

325

350.9635

348.6185

346.1892

343.6727

341.0659

338.3654

335.5680

332.6701

329.6681

326.5585

323.3371

320

m = 0.06

352.9986

350.7266

348.3731

345.9350

343.4094

340.7931

338.0829

335.2753

332.3669

329.3541

326.2331

323

354.3553

352.1321

349.8290

347.4432

344.9718

342.4116

339.7594

337.0121

334.1660

331.2178

328.1638

325

345.3031

343.3083

341.2573

339.1487

336.9808

334.7520

332.4605

330.1046

327.6825

325.1923

322.6321

320

m = 0.08

347.5146

345.5819

343.5949

341.5520

339.4516

337.2923

335.0722

332.7897

330.4431

328.0305

325.5501

323

348.9890

347.0977

345.1533

343.1541

341.0989

338.9858

336.8134

334.5799

332.2835

329.9227

327.4955

325

Table 55.1 Effect on temperature at different distances for fuzzy air mass flow rate (0.03, 0.06, 0.08) and fuzzy initial temperature (320, 323, 325)

55 Mathematical Modeling of Air Heating Solar … 733

734

Fig. 55.2 Effect of air mass flow rate at fuzzy initial temperature

Fig. 55.3 Effect of air mass flow rate at fuzzy initial temperature

P. Pandit et al.

55 Mathematical Modeling of Air Heating Solar …

735

Fig. 55.4 Effect of air mass flow rate at fuzzy initial temperature

increases but decreases as the initial temperature increases. Also, it is observed that our results at core matches with those obtained in [6].

55.4 Conclusion In this article, we have discussed about how thermal behavior of solar air collector evolves when fuzzy parameters are used. The proposed FADM is very effective to find approximate analytical solution of problem. It is can be seen from the graphs, how the efficiency of collector behaves with fuzzy parameters. Following observations are made: increase in air mass flow improves the efficiency of the collector but increase in length and width of the collector decreases the efficiency of the collector. With fixed initial temperature, support of temperature values increases as air mass flow increases. If the volume of mass in air flow increases, it results into decrease in temperature at the end of the collector.

References 1. G. Adomian, Solving Frontier Problems of Physics: The Decomposition Method. (Kluwer Academic Publishers, Berlin, 1994) 2. S.J. Liao, The Proposed Homotopy Analysis Technique for the Solution of Nonlinear Problems, PhD thesis, Shanghai Jiao Tong University (1992)

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3. J.H. He, Some asymptotic methods for strongly nonlinear equations International. J. Modern Phys. B 20(10), 1141–1199 (2006) 4. S. Charkrit, On the solutions of first and second order nonlinear initial value problems. In: Proceedings of the World Congress on Engineering (I), WCE 2013, July 3–5, London, U.K. (2013) 5. P. Pandit, P. Singh, Fully fuzzy semi-linear dynamical system solved by fuzzy laplace transform under modified Hukuhara derivative. Soft Comput. Problem Solv. AISC 1, 155–179 (2019) 6. S. Kalogirou, Solar energy engineering-processes and systems (Kalo Elsevier, USA, 2009) 7. G.J. Klir, B. Yuan, Fuzzy sets and fuzzy logic: theory and applications (Prentice Hall, Englewood Cliffs NJ, 1995) 8. S.E. Ghasemi, M. Hatami, D.D. Ganji, Analytical thermal analysis of air-heating solar collectors. J. Mechan. Sci. Technol. 27(11) (2013)

Chapter 56

Performance of Machine Learning Approaches for Malicious Traffic Intrusion Detection in Network Madhavi Dhingra , S. C. Jain , and Rakesh Singh Jadon

Abstract Intrusion detection has always been the major research area in the field of network security. From the past few years, intelligent mechanisms like artificial intelligence and machine learning have played a key role in developing some remarkable mechanisms for intrusion detection systems. The identity of the node is very important in the network and are categorised in terms of normal and malicious nodes. Identification of malicious node is equally essential as identification of attack in the network. Thus, intelligent machine learning algorithms are also used for identification of malicious nodes in the network. But still, the node behaviour is dynamic in nature and require detailed study. The aim of this paper is to apply various techniques of machine learning on the recent dataset and to observe the effectiveness of results in terms of detection of malicious attack traffic. The CIDDS-001 dataset has been used containing different category of attacks. The dataset is preprocessed and transformed using ensemble feature selection method. The reduced dataset containing the relevant and essential 12 features are trained and tested with the classification algorithms and outcome is achieved in terms of detecting the attacks with 99.6% accuracy. Keywords Information security · Intrusion detection system · Malicious attack · Machine learning · Malicious intrusion detection · Network attack

M. Dhingra (B) · S. C. Jain Amity University Madhya Pradesh, Maharajpura Dang, Gwalior 474005, MP, India e-mail: [email protected] S. C. Jain e-mail: [email protected] R. S. Jadon MITS, Gwalior, MP, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_56

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56.1 Introduction The network traffic faces many security challenges during the transmission along the network. These challenges are related with the network attacks that are done either on the hosts or on the data travelling across the network. To prevent the attack, such type of intrusions must be detected. Intrusion detection mechanism helps in identifying such hosts or data traffic that may impact the network negatively. Such intrusions are termed as malicious traffic attack. A node is called as malicious when it behaves abnormally in the network either intentionally or deliberately. Such nodes may act as victim or normal node while being in the network. Malicious nodes must be detected so as to prevent further damages in the network. When any node becomes malicious, it violates the security principles triad, availability, confidentiality, integrity and nonrepudiation. An attacker can take advantages of these security breaches and further breaches the security information of the network. The intruder can perform varies of attacks like manipulation of data, destruction of data, deleting the data and also can prevent the authentic user from accessing the services. The effects of malicious node include the following [1]: • • • • •

Packet dropping False routing Reduces network connectivity Isolation of nodes Reduces network performance.

Artificial intelligence techniques have provided many intelligent mechanisms to deal with variety of attacks. Intelligent systems have also influenced the category of malicious nodes and many intelligent algorithms have been developed for detection of malicious intrusions. This paper has first reviewed regarding the work done for malicious intrusion detection systems. In the third section, a recent dataset is taken and feature selection methods and classification methods are applied on the dataset to determine the attack. The results were analysed in the fourth section and concluded at last.

56.2 Related Work Network traffic may fall in normal or malicious category of attacks. Attacks are of various kinds, each of which has its own impact on the network performance. Intrusion detection systems (IDS) dealing with all kinds of attacks falls into two types namely signature-based IDS and anomaly-based IDS [2]. Many researchers have focused on the malicious category of attacks while using standard datasets and used certain machine learning classification and regression techniques for the identification process of such kind of attacks. Artificial intelligence techniques have been giving a major contribution in the development of more secure

56 Performance of Machine Learning Approaches …

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intrusion detection systems. The techniques involve machine learning classifications, clustering and several logistic regression methods. Several techniques have been utilised by researchers giving different benefits and limitations. The study of malicious attacks on several datasets has been summarised in Table 56.1. In addition to this, several intelligent machine learning algorithms have worked in the field of identification of attacking nodes in the network. These approaches have some limitations in their process of implementation, like some are time-consuming, some are identifying only a specific category of attack while rest have worked with the old dataset and classified into four categories of attacks. Thus, a faster and efficient approach is required that can detect variety of attacks. The research work done for development of such approach is explained in the next section. Table 56.1 Limitations of the research work done on malicious attacks Author

Approach used

Limitations

Tyugu et al. [3]

Intelligent methods

It suggests that in the areas of decision support, situation awareness and knowledge management, there is a need of application of intelligent methods. Empirical research is missing

Catania et al. [4]

Support vector machines algorithms for network traffic anomaly detection on DARPA

Classified into attack and non-attack, two categories

Shin et al. [5]

Used probabilistic approach for network intrusion forecasting and detection

DDoS attack can be determined

Chinthanai Chelvan et al. [6]

Enhanced adaptive acknowledgment IDS

Extra computation is required in application of digital signature

Srimuang and Intarasothonchun [7, 8]

Classification model of network intrusion using weighted extreme learning machine approach by using KDD-99 dataset

4 Categories of attacks probing, DoS, R2L, U2R are identified

Hadri et al. [9]

Intrusion detection system using PCA and fuzzy PCA techniques

4 Categories of attacks probing, DoS, R2L, U2R are identified

Thirumalai et al. [10]

Neuro-fuzzy classifier

Computational time is more

Vidhya et al. [11]

Collaborative contact-based watchdog technique

Working process becomes slow at the time of network congestion

Fabrice et al. [12]

Behavioural trust detection and prevention

Worked only for nodes affected by wormhole attack

Hodo et al. [13]

Machine learning approach for nonTor traffic is covered detection of nonTor traffic

Li et al. [14]

Polynomial feature correlation Only DoS attack is identified

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Fig. 56.1 Methodology of experimental work

56.3 Experimental Work and Results The latest real-time dataset of network traffic CIDDS-001 (Coburg Intrusion Detection Datasets) has been taken as input [15, 16]. The experiments were done on Weka simulator [17]. This dataset has total 14 features including class which categorises class into five several labels (Fig. 56.1). The implementation work involves four major steps: • Feature Reduction—The dataset has total 14 features, out of which some features may be less prominent in identification of attack traffic. The attribute selection methods help in determining the most effective features in the dataset. In this work, the ensemble feature selection [18] is performed on the dataset by using Gainratio, Infogain, CorrelationAttribute Evaluator and OneR attribute selection. The resulting output dataset has 12 features including the class feature as shown in Fig. 56.2. • The resulting dataset is trained using classification machine learning algorithms that include NaiveBayes, LazyIBK, RandomTree, DecisionTable and AdaBoost. • The trained model is evaluated on the testing dataset and the results are presented in Table 56.2. • The results are analysed by using performance parameters to obtain the best classifier.

56.4 Result Analysis The achieved results are analysed by observing the performance parameters of the classifier [19]. The parameters used are described below:

56 Performance of Machine Learning Approaches …

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Fig. 56.2 CIDDS-001 modified dataset in Weka

Table 56.2 Summary of results on the modified dataset in Weka S. No.

Name of the classifier

1

NaiveBayes

2 3 4

DecisionTable

5

AdaBoost

Time to train (seconds)

Time to test (seconds)

Root mean squared error (RMSE)

Accuracy rate (%)

0.16

1.22

0.0816

98.2

LazyIBK

0.02

234.11

0.0438

99.51

RandomTree

2.38

0.07

0.0397

99.6

329.91

1.27

0.0987

99.4

3.48

0.08

0.1747

90.3

• True Positive Rate (TP Rate)—True positive rate calculates the correct positive predictions (Fig. 56.3). • False Positive Rate (FP Rate)—False positive rate calculates the incorrect positive predictions (Fig. 56.4). • ROC Area—Receiver operating characteristics (ROC) curve depends on true positive rate and false positive rate. It plots FP rate on x-axis and TP rate on y-axis. An area of 1 represents a perfect result (Fig. 56.5). • The precision-recall (PRC) area is based on recall and precision of the classifier. It takes recall as the x-axis and the precision as y-axis (Fig. 56.6). The different performance parameters are used to determine the best classifier. The RandomTree classifier is giving maximum true positive rate and PRC area. The false positive rate is also very less for this classifier. The ROC area is 0.996 which is approximately 1, i.e. the highest value. The accuracy rate is also highest (99.6%)

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Fig. 56.3 True positive rate

Fig. 56.4 False positive rate

Fig. 56.5 ROC area

for the mentioned classifier. Thus, after performing the ensemble feature selection, the classifiers are able to detect the various kinds of attacks present in the dataset.

56 Performance of Machine Learning Approaches …

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Fig. 56.6 PRC area

56.5 Conclusion The network traffic is the major focus point of the attackers while attacking and observing its details for further use. The machine learning has supported information security field in identification and detection of attacks in the network. The research paper has implemented machine learning classification and feature selection algorithms for classification of malicious traffic and normal traffic in the wireless network. The intelligent real-time dataset has been taken to identify the malicious traffic by training and testing the dataset. The dataset has been reduced by using ensemble feature selection and tested by using classification machine learning algorithms. The results have shown that RandomTree classifier has given the best results in terms of performance parameters with the testing dataset. The results are analysed on the basis of the performance measurements of the classifier. The work can be used for more recent datasets for identification of multiple category of malicious traffic attacks in the network.

References 1. S. Radhika, K.M. Saini, Defining malicious behavior of a node and its defensive methods in ad hoc network. Int. J. Comput. Appl. 20(4), 18–21 (2011) 2. S. Axelsson, Research in Intrusion-Detection Systems: A Survey, Technical Report TR 98–17, Goteborg (Department of Computer Engineering, Chalmers University of Technology, Sweden, 1999). 3. E. Tyugu, Artificial intelligence in cyber defense, in 3rd International Conference on Cyber Conflict (2011) 4. A. Carlos, Catania: an autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection. Expert Syst. Appl. 39(2), 1822–1829 (2012) 5. S. Shin, Advanced probabilistic approach for network intrusion forecasting and detection. Exp. Syst. Appl. 40(1), 315–322 (2013) 6. K. Chinthanai Chelvan, T. Sangeetha, V. Prabakaran, D. Saravanan, EAACK—a secure intrusion detection system for MANET. Int. J. Innov. Res. Comput. Commun. Eng. 2(4), 3860–3866 (2014)

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7. W. Srimuang, S. Intarasothonchun, Classification model of network intrusion using weighted extreme learning machine, in 12th International Joint Conference on Computer Science and Software Engineering (JCSSE), Songkhla, pp. 90–194 (2015) 8. B.G. Atli, Y. Miche, A. Jung, Network intrusion detection using flow statistics, in 2018 IEEE Statistical Signal Processing Workshop (SSP), Freiburg, pp. 70–74 (2018) 9. A. Hadri, K. Chougdali, R. Touahni, Intrusion detection system using PCA and fuzzy PCA techniques, in 2016 International Conference on Advanced Communication Systems and Information Security (ACOSIS), Marrakesh, pp. 1–7 (2016) 10. T.V. Selvi, E.J. Thomson Fredrick, Secure on demand distributed protocol for spontaneous wireless ad hoc network. Int. J. Comput. Sci. Inform. Technol. Secur. 6(6), 21–24 (2016) 11. K. Vidhya, U. Sundhar, B. Anantharaj, Detection of node activity and selfish & malicious behavioural patterns using watch Dog-Chord algorithm. Int. J. Emerg. Technol. Comput. Sci. Electron. 23(1), 22–30 (2016) 12. S. Fabrice, E.J. Thomson Fredrik, Detection and prevention of malicious node based on node behaviour in MANET. Int. J. Adv. Res. Comput. Sci. 8(9), 774–777 (2017) 13. G. Hill, X. Bellekens, CryptoKnight: generating and modelling compiled cryptographic primitives. Information 9(9), 231 (2018) 14. Q. Li, Z. Tan, A. Jamdagni, P. Nanda, X. He, W. Han, An intrusion detection system based on polynomial feature correlation analysis, in 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, pp. 978–983 (2017) 15. M. Ring, S. Wunderlich, D. Gruedl, D. Landes, A. Hotho, Flow-based benchmark data sets for intrusion detection, Proceedings of the 16th European Conference on Cyber Warfare and Security (ECCWS), pp. 361–369 (2017) 16. M. Ring, S. Wunderlich, D. Gruedl, D. Landes, A. Hotho, Creation of flow-based data sets for intrusion detection. J. Inform. Warfare (JIW) 16(4), 40–53 (2017) 17. S.K. Kalmegh, Analysis of WEKA data mining algorithm REPTree, simple cart and randomtree for classification of indian news. IJISET—Int. J. Innov. Sci. Eng. Technol. 2(2), 438–446a (2015) 18. S. Mukkamala, A.H. Sung, A. Abraham, Intrusion detection using an ensemble of intelligent paradigms. J. Network Comput. Appl. 28(2), 167–182 (2005) 19. Y. Liu, A strategy on selecting performance metrics for classifier evaluation. IJMCMC 6(4), 20–35 (2020)

Chapter 57

Applications of Synchrophasors Technology in Smart Grid Marwan Ahmed Abdullah Sufyan, Mohd Zuhaib, and Mohd Rihan

Abstract Synchrophasor technology is now widely accepted throughout the world. The driving force is the increasing complexity of the modern power system, which has caused numerous power outages around the world. It enables efficient resolution to substantially improve transmission system planning maintenance, operation, and energy trading. Their efficiency is examined on diverse applications all around the globe in the area of transmission and distribution system. This paper explains various applications offered by synchrophasors technology in the modern power system. Keywords Phasor measurement unit (PMU) · Wide area measurement system (WAMS) · Phasor data concentrator (PDC) · Wide area control (WAC)

57.1 Introduction The nature of the large interconnected modern power system is becoming complex day by day due to the introduction of large dynamic loads, integration of renewable energy resources like solar, wind, etc. [1]. Several severe blackouts have been occurred throughout the world the driving force of which remain unnoticed to the power system operators to prevent their occurrences. These outages necessitates the precise monitoring of critical grid parameters such as frequency deviation, power flows, voltage, angle, etc. Therefore, it is necessary for the utility to develop such a system that can monitor, control, and protect its element from generation to distribution [2, 3]. In the past few years, the power system mainly depends on traditional supervisory control and data acquisition system (SCADA) for processes and control. These M. A. A. Sufyan · M. Zuhaib (B) · M. Rihan Z.H.C.E.T Collage, Aligarh Muslim University (AMU), Aligarh, India e-mail: [email protected] M. A. A. Sufyan e-mail: [email protected] M. Rihan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_57

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746 Table 57.1 Comparison between PMU and SCADA

M. A. A. Sufyan et al. Parameters

SCADA

PMU

Measurement

Analog

Digital

Resolution

2–4 samples per cycle

Upto 60 samples per cycle

observability

Steady state

Transient

Monitoring

Local

Wide area

Phase angle

NO

Yes

measurements are not time-stamped and are unable to capture the completed dynamic of the grid. They have very slow data scanning rate of about once in 4 s. This can lead to insufficient capturing of actual dynamics of the system due to slow scanning rate, unsynchronized measurements, etc. [4]. Toward this direction of achieving the total grid observability, synchrophasors technology is widely accepted across the world. This technology involves installation of PMU across the grid that has very high sampling rate of about 25–60 samples per second and makes it a highly suitable device to capture the accurate dynamics of the system. Another advantage of PMU is its ability to provide synchronized measurements that enables the grid operator to analyze the stability of the large interconnected system effectively. They have proved its potential worldwide in enhancing the situational awareness of the grid operator to ensure safe and reliable grid operation. A summary of performance comparison of SCADA- and PMU-based system is given in Table 57.1 [5, 6]. This paper discusses various applications that the synchrophasor-based wide area monitoring system offer to modern power system that includes real-time monitoring and control, modal analysis, dynamic state estimation, contingency analysis, system planning, etc. The rest of the paper is divided in to the following sections. Section 57.2 provides the basic concept of synchrophasors technology. Section 57.3 gives an overview of synchrophasor-based wide area monitoring system. Section 57.4 discusses potential synchrophasors applications in smart grid. Section 57.5 presents a case study of an application of synchrophasors in Indian power grid under fog condition. The conclusion is drawn in Sect. 57.6.

57.2 Synchrophasors Technology The phasor synchrophasor considers the sinusoidal signal represented as a pure sinusoidal waveform [6–8]. X (t) = X m cos(ωt + ϕ)

(57.1)

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Fig. 57.1 Common reference for the phasor calculations at different locations

where X is the amplitude signal,ω is the frequency in rad/s, F is the phase angle in radian. The rms value of X is. Xm . The phasor of sinusoidal signal is given by X = √ 2 Xm Xm X = X r + j X i = √ e jϕ = √ (cos ϕ + j sin ϕ) 2 2

(57.2)

From Eqs. 57.1 and 57.2, the sinusoidal signal and its phasor representation are shown in Fig. 57.1. The positive angles are estimated in an opposite clockwise direction from the actual center. Even if the frequency of a sine wave was applied in the Xm . phasor determination, the phase variation is (0); hence, the phasor becomes = √ 2

57.3 Synchrophasors-Based Wide Area Monitoring System PMU-based wide area measurement system plays an important role in safe and reliable gird operation. Whenever fault appears in the system, the wide area security system uses the online measurement data to discover the faulty buses and, consequently, the faulted line to isolate it from rest of the system. They are playing a significant role in modern studies related to energy system transient stability [9, 10]. A simplified architecture of PMU-based wide area monitoring system is shown in Fig. 57.2. Its key components are [1, 11–13].

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PEAK-RC/ WECC/ External Data

Secure Non- EMS Alarms Environment

Phasor Analysis Applications

Visualization To a common Visualization load

Monitoring & Control Applications

Planning and Engineering Applications

Real Time (withen 100msec

Super PDC (Non-EMS)

To a common Visualization load

Secure EMS Environment

EMS-RAS (Automatic Arming) And VSA,TSA SCADA

Contingency Analysis

EMS Data Archives

Sub- PDC

PMU* AC

PMU* BD

Application Tier Functions • EMS &State Estimation • Remedial Action Schemes(RAS) • Dynamic Stability Indication • Contingency Analysis • Situational Awareness • post-Disturbance Event Analysis • Fault Location Application Interface Tier Function • Aggregate and concentrate data • Provide archival function • Provide interface for data exchange with external entities

Super PDC (tied to EMS)

Phasor Data Storge

Sub- PDC

Other Synchrophasor functions

Once per 4 second

State Recommended Estimator Actions

Data Archives (External to EMS)

Phasorbased Dynamic Stability Analysis

Substation Data Management And Applications

Data Storage

* The PMU function may be provided by dedicated devices and /or multifunction IEDs(e.g relays,recorders,meters,..)

Data Aggregation Tier Function • Aggregate and concentrate data • Local storage (e.g.2 months Phasor Data Collection Tier Function • Collect accurate high resolution multi-purpose Synchrophasor data

Fig. 57.2 Conceptual architecture of synchrophasors based wide area monitoring system

57.3.1 Phasor Measurement Units PMUs are an electronic system to use digital signal conversion technique to measure AC waveforms and transfer them into phasors using global time synchronization signal from global positioning system (GPS).Internal architecture of PMU is shown in Fig. 57.3. The main components of PMU include [14, 15].

PMU model Voltage Current

Frontend anti-aliasing filter

Re A/D converter

Im sin

GPS signal

GPS clock

Fig. 57.3 PMU architecture

Backend Performance Class filter (P-class/ M-class

Phasors Frequency ROCOF Real/Reactive power

cos

Quadrature oscillator

Communication interface

Phasors message

57 Applications of Synchrophasors Technology in Smart Grid

i.

ii.

iii.

iv.

749

Sensor module: It comprises of current transformers (CTs) and voltage transformer (PTs).The three-phase current and voltage signals are applied to analogto-digital converter through CT and PT modules, which convert their values in a range suitable for processing the input voltage and current. PMU module: All the electronic circuitry and processing unit comes under PMU module. It estimates the phasor value from the sampled data at the input. The sampling rate of the input data is quite high and varies from 24 to 64 samples per cycle. The selection of algorithm for phasor estimation is user defined which could be discrete Fourier transform (DFT), Kalman filtering, etc. Usually, DFT is used in commercially available PMUs. The phasor reported by the PMU module varies from 25 to 60 frames per second. GPS receiver: It generates 1 pulse per second (pps) signal with time tag and contains the time information of the local area at UTC where PMU is to be installed. Standard protocol: The latest PMU protocol is IEEE C37.118 of 2011, which replaces the IEEE C37.118 protocol of 2005. The first PMU protocol was IEEE-1344 of 1998. The first PDC protocol was IEEE C37.242, introduced in 2013. These protocols define standards for data to phasor conversion, data synchronization, input/output timing formats, etc.

57.3.2 Sub/Local Phasor Data Concentrator They align the phasor data achieving from multiple locations installed with PMUs to capture the coherent picture of the system [15]. They store the PMU data locally and send it to super PDC installed at control center.

57.3.3 Communication Network It is required to transfer huge data from PMUs to the PDC/control centers. The performance of WAMS containing huge amount of data heavily rely on fast, reliable and secure communication system. It help in developing suitable control action to mitigate the faulty conditions. The various options available for efficient data transfer are microwave, telephone lines, satellite, power line communication, optical fiber, etc. Fiber optic cable is widely accepted for this purpose due to its wide bandwidth, fast propagation speed, and immune to electromagnetic interference [16].

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57.3.4 Super PDC Super PDC is used for time aligning huge dataset aggregating from PMUs and local PDCs. It can simultaneously store and send PMU data to multiple locations. By using multiple PDCs, customer can create multiple layers of concentration, within a synchrophasors data system. It is located at central location of the power grid such as transmission system control center [15].

57.3.5 Synchrophasors Based Application or Tools Once the coherent picture of the entire grid is obtained at the central PDC, some application tools are required to perform certain functions like power flow analysis, load scheduling, load forecasting, fault location, contingency analysis, etc.

57.4 Synchrophasors Applications Synchrophasors technology enables the advantage of efficient resolution to substantially improve transmission system planning maintenance, operation, and energy trading. Synchrophasors can give precise grid operating states. They can give enhanced computation of power flows, allowing higher power transfer and reduced crowding payments [6, 17, 18]. Need of synchrophasors includes. (1) (2) (3) (4) (5)

To obtain high resolution data. The data from various locations using SCADA system are not apprehending at the same instant time. Support system operators to be more aware about grid operating states. Achieve power quality. Frequency changes, MW, MVAR-measurement. These applications are classified as:

57.4.1 Real-Time Control and Monitoring: Real-time control and monitoring assistance enable the power grid to remain under safety margins in the event of fault, while operating close to its limits. A power system equipped with PMUs enable it to operate at higher capacity, best economics, and enhanced accuracy. Various monitoring and control applications that a WAMS offers in the modern power system are summarized in Table 57.2 [19].

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Table 57.2 Real-time and off—line description WAMS application

Description

Real-time (situational awareness improvement)

Visualization/ contorting of magnitude as well as the angle of all three current/voltage phasors, angular, frequency, the separation between pair

Off-line (low, frequency oscillations)

Finding of amplitude, duration, time, type, nature, frequency

Off-line (post-dispatch analysis of network operation)

Validation of generator grouping based on slow coherency, steady-state dynamic state detail model, detection of islanded network synchronization, and substation disturbances

Off-line (post-mortem analysis of faults)

Finding of all types of errors, fault clearing time and location, nature and time of errors, successful and unsuccessful system reclosing, voltage recovery post-fault clearance

57.4.1.1

Power System Rebuilding

In case of partial or complete power system failure of a supply region, the synchrophasors estimates can help in a fast reconnection. They give the significant data for the reclosure of the circuit breakers by reporting values of the voltage, current, frequency, and phase angle. It has been experienced, after India’s most massive blackout on July 30, 2012, a PMUs based WAMS can play a major role in ensuring reliable power system operation.

57.4.1.2

Enhanced State Estimation

Synchrophasors can participate in enhancing quality of SE by feeding in previous estimates. It can enhance the speed and exactness of the state estimations. Another favorable impact is that by using PMU measurements for state estimation, less estimates centers are required because of the time synchronization of measurements [20, 21].

57.4.1.3

Voltage and Angular Stabilization Control

Voltage stabilization control is one of the applications of PMU. In the power system, this application monitors a load of a line, utilizing PMU measurement at the two ends. In any case, observing the variation of the voltages gives a good overview of system responses.

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Real-Time Synchrophaser Measurements

Online Event Detection

Near Real-Time Event Replay

Location of Disturbances

Operators

Early Warning of Grid Problems

Wide-Area Visualization & Reliability Monitoring

Fig. 57.4 Situational awareness system

57.4.1.4

Post-Mortem Analysis of Disturbances and Fault

The requirement of analysis after a fault is quite important to study the effects of faults on the system. With these fault recorder data, the root causes of power system events can be determined. It enables the grid operator to discover the reason for the aggravation of fault. This application can be also useful for distribution system also [22].

57.4.2 Situational Awareness Coordination 57.4.2.1

Situational Awareness

This application has been developed to give wide-area systems deep insight of power system behavior and give early warning to the potential power system events and hence raise the situational awareness of the grid operator. The flowchart for such a system is shown in Fig. 57.4 [23].

57.4.2.2

Frequency and Angle Monitoring

Utilizing PMU measurements for frequency and angle estimation is a general feature of such a system to improve the observability of electric grid. It can also be useful

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753

in distribution systems in light of the fact that the functions they have to perform are much more complex compared to transmission system. This is because of the presence of large intermittent renewable energy resource. These measurements are quite helpful in developing plenty of application for proper functioning of smart grid [24, 25].

57.4.3 Analysis/Estimate Planners 57.4.3.1

Baselining Power System

Using this application with significant grid state data can provide several key indicator for performance of the grid. (1)

Computing system performance index • Oscillation detection damping ratio, oscillation frequency, its duration, classification, and source of location • frequency index performance (e.g., the size of generation before blackout and its frequency, steep frequency, solution, time of the blackout) • power-angle stability • voltage stability indicator

(2)

Power system measurements that better sign system stress • • • • •

57.4.3.2

power flow on key lines total generation correlation reactive power reserves generator group phase angles Post-Event Analysis

The huge amount of PMU data stored in the phasor data concentrator can be used to conduct analysis of events that introduces instability in the power system. Every user can choose any of these events to conduct the post-event examination by exchanging the related event information from the event database to the clients’ computer so that client can begin the event analysis when the main segment of the event information is accessible. This is one of the most important aspect of WAMS since it enables the grid operator to understand several potentially dangerous grid events [26, 27].

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Table 57.3 Time requirement-based synchrophasors application

Time

Application

Fast (0–5 min)

– Stability of frequency – Out of step confirmation – Automatically arming of the fixed work program – Low-term passing stability

Short-term (5 min-1 h) – – – – Long-term (1 h)

57.4.3.3

Short signal stability control Voltage stability control/estimates Model efficiency State estimation

– Achievement controlling and trending – Designing abnormality alarming – Baseline natural phase angle direction – Phasor network achievement test and data type

Modal Analysis

Modal analysis is performed on synchrophasors data to determine angular between several generator groups to distinguish prevailing between various oscillations modes present in the system. It includes determination of frequencies, damping rate, and mode shapes (showing whether two generate swing together or against each other) [28].

57.4.3.4

State Estimation

SE performs many power system application such as power flow analysis, security assessment load forecasting SE can achieve, etc. This solves that requires past, present, and available redundant measurements. The accuracy of SE depends on the assumed system model and hence the estimation of the states highly rely on the robustness of the system model [16, 29, 30]. The various performance indicator on which the robustness of SE rely are accuracy, robustness against measurement and modelling noise, speed of computation, and scalability. Of all the applications discussed, they are further classified based on the time required by them to complete the task provided. Hence, based on time required, these applications are classified as fast, short-term, and long-term applications and are given in Table 57.3.

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57.5 Case Study NR of Indian Power Grid Using Synchrophasors Data For analyzing application of synchrophasors in the power system, a case study of the event during fog condition of PMUs at Meerut and Hissar is caused out. The PMUs data helps in monitoring significant tripping in NR that enabled the grid operators to have best situational awareness about the grid operating states. This helps in identifying more cases of multiple tripping, blackout due to flashover under heavy fog condition through many previous years. This problem is increased in recent years due to increase in pollution level and may result in black out in large parts of the grid for many hours. These problems usually occur mid-night and early morning while the atmospheric temperature is low and relative humidity is high, and the situation appropriates for fog formation. In this analysis, PMU plots were taken on an hourly basis at Meerut and Hissar. These plots were time stamped with different tripping instants mentioned with the screenshots, which enhance situational awareness of the power system grid operator. Figure 57.5 describes tripping for the times (20:50– 21:50 h), (01:00–02:00 h) and (02:15–03:15 h). This happen at Muzaffarnagar and Muradnagar, Kaithal, Panki substations, and Roorkee. Several instances of Auto Recloses were also captured in the events. It can be tackled by regulating the generator load. In Fig. 57.6, positive sequence voltage between Meerut and Bassi shows failure in auto reclosure attempt of 400 kV line, between Meerut and Muzaffarnagar. In Fig. 57.7, PMU captures positive sequence voltage between Moga and Bassi and has successful auto reclosure attempt of 765KV line between Moga and Bhiwani [31, 32].

2050-2150 hrs

Meerut PMU

400KV Kaithal-Meerut-II tripped again at 2054hrs

15 KV

400KV Kaithal-Meerut-II tripped again at 2140hrs

Hissar PMU

Fig. 57.5 Positive sequence voltage plots of Meerut and Hissar PMU (20:50–21:50 h)

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Meerut PMU

Autoreclose attempts that failed

23 KV

400 KV Meerut-Muzaffarnagar finally tripped at 0736hrs

Fig. 57.6 Positive sequence voltage plots of Meerut and Bassi PMU (failed Auto reclosure of 400 kV Meerut-Muzaffarnagar)

Moga PMU

30 kv

Successful Auto Reclose attempts by 765KV MogaBhiwani 765KV Moga-Bhiwani tripped at 0006hrs

Bassi PMU

Fig. 57.7 Positive sequence voltage plots of Moga and Bassi PMU (successful Auto reclosure of 765 kV Moga-Bhiwani)

57.6 Conclusion This paper highlights the importance of synchrophasors based wide area monitoring system in improving the situational awareness of grid operators of modern power system. Various applications that the synchrophasors based WAMS offer to the grid are discussed. These includes real-time monitoring and control, fault detection and isolation, modal analysis, load scheduling, state estimation, etc. A case study is also presented highlighting the utility of such a system in detecting and isolating the fault under worse operating conditions.

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21. M. Gol, A. Abur, A robust PMU based three-phase stateestimator using modal decoupling. IEEE Trans. Power. Syst. 29(5), 2292–2299 (2014) 22. P. Ashton, G. Taylor, M. Irving et al., Novel application of detrended fluctuation analysis for state estimation using synchrophasormeasurements. IEEE Trans. Power Syst. 28(2), 1930–1938 (2013) 23. A. Obushevs, A. Mutule, Application of synchrophasor measurements for improving situational awareness of the power system. Latv. J. Phys. Tech. Sci. 55(2), 3–10 (2018) 24. L.T.M. Trang, I. Uhlíˇr,The frequency stability assessment of the transmission system using phasor measurement unit data, in 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, 2018, pp. 1–5. doi: https://doi.org/10.1109/ ISGT.2018.8403322 25. P. Prabhakar, A. Kumar, Voltage stability assessment using phasor measurement technology, in 2014 IEEE 6th India International Conference on Power Electronics (IICPE), Kurukshetra, 2014, pp. 1–6, doi: https://doi.org/10.1109/IICPE.2014.7115747 26. M. Cui, J. Wang, J. Tan, A.R. Florita, Y. Zhang, A novel event detection method using PMU data with high precision. IEEE Trans. Power Syst. 34(1), 454–466 (2019). https://doi.org/10. 1109/TPWRS.2018.2859323 27. Y. Ge, A.J. Flueck, D. Kim, J. Ahn, J. Lee, D. Kwon, Power system real-time event detection and associated data archival reduction based on synchrophasors. IEEE Trans. Smart Grid 6(4), 2088–2097 (2015). https://doi.org/10.1109/TSG.2014.2383693 28. J. Seppänen, S. Au, J. Turunen, L. Haarla, Bayesian approach in the modal analysis of electromechanical oscillations. IEEE Trans. Power Syst. 32(1), 316–325 (2017). https://doi.org/ 10.1109/TPWRS.2016.2561020 29. N. Mobin, M. Rihan, M. Zuhaib, Selection of an efficient linear state estimator for unified real time dynamic state estimation: a case study for indian smart grid. Int. J. Emerg. Electr. Power Syst. IJEEPS 20(4), (2019) 30. A. Khair, M.A.A. Sufyan, M. Zuhaib, M. Rihan, PMU assisted state estimation in distribution system with PV penetration, in 2019 International Conference on Electrical, Electronics and Computer Engineering (UPCON), Aligarh, India, (2019), pp. 1–5. doi: https://doi.org/10.1109/ UPCON47278.2019.8980207 31. Government of India, Report on Grid Disturbance on July 30th and 31st July 2012, 8 Aug 2012 32. Synchrophasor Initiative in India, Annual Report of Power System Operation Corporation Limited (2013)

Chapter 58

Numerical Analysis of Performance Parameters and Exhaust Gas Emission of the Engine with Regular Air Intake System and with Insulated Air Intake System Sanjay Chhalotre, Prem Kumar Chaurasiya , Upendra Rajak , Rashmi Dwivedi, R. V. Choudri, and Prashant Baredar Abstract In this study, an efficient method is proposed to enhance the performance of the spark-ignition engine at low speed by insulating the air intake assembly. The efficiency of the proposed insulated air intake congregation has been numerically analyzed. The variation in engine performance parameters has been recorded by the using engine scanner tool LAUNCH C Reader VI and Diesel—RK engine simulation software. The engine speed varies from 800 to 1500 revolution per minute at part open throttle and the intake air is allowed through the non-insulated and insulated air intake system, respectively. The Ansys version 14.5 has been used for the numerical analysis. The numerical simulation has been performed to evaluate the engine performance at part open throttle and variation in exhaust gas emission level due to variation in intake air temperature at the actual driving conditions. Keywords Internal combustion engine · Combustion · Simulation · CFD

58.1 Introduction Combustion of the air–fuel mixture inside the combustion chamber of the engine cylinder is one of the processes that generates and manages the engine power. The SI engine combustion, improved with increased air induction by using turbocharger and gasoline direct injection system in which fuel is directly injected into the combustion S. Chhalotre · P. K. Chaurasiya (B) · R. Dwivedi · R. V. Choudri Sagar Institute of Science and Technology, Gandhi Nagar, Bhopal, India e-mail: [email protected] U. Rajak Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, Andhra Pradesh, India P. Baredar Maulana Azad National Institute of Technology, Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_58

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chamber during suction stroke gives enough time to prepare perfect homogeneous air–fuel mixture [1, 2]. The performance and emission characteristics of a given engine can be deducted theoretically but it is necessary to compare the theoretical result by test result under actual working conditions. Comparison of actual results with the theoretical ones and thereafter the analysis of the causes of deviation leads to improvement in design [3, 4]. In Internal Combustion engines, the liquid and gaseous fuels are used to produce energy. The liquid hydrocarbons such as gasoline, kerosene and diesel fuel are the common fuels. The gasoline fuel is a mixture of many hydrocarbons. Gaseous hydrocarbon fuels are also a mixture of the various constituent of hydrocarbons. The change of chemical energy into thermal energy is important for producing power in IC engines [5]. It is therefore essential to understand the combustion phenomenon. There are two types of chemical reactions. One is exothermic, in which heat energy is liberated and the other is endothermic, in which heat energy is absorbed. The combustion of fuel in internal combustion engines is a fast-exothermic reaction in the gaseous phase where oxygen obtained from the air is usually one of the reactants [6]. Air contains many elements. The volumetric composition of air is around 21% of oxygen, 78% of nitrogen, and argon of 1%. In IC engines, the combustion of fuel takes place in the presence of air and not pure oxygen. Nitrogen and argon are impartial gases [7]. For every 0.21 mol of oxygen, there are also 0.79 mol of nitrogen; for every mole of oxygen needed for combustion, a total of 4.76 mol of air must be required for combustion that is oxygen one mole and nitrogen 3.76 mol. The general formula for the fuel used in gasoline engines can be taken as (Cm Hn Op ) where m, n, and p represent the number of moles of carbon, hydrogen, and oxygen atoms in a mole of fuel [8]. Cm Hn Op + Ycc O2 + 3.76 Ycc N2 → mCO2 + H2 O + 3.76 Ycc N2

(58.1)

58.1.1 Chemistry of Combustion in SI Engine In the SI engine, the mixture of gasoline vapors and air ignited by spark plugs just before end of compression blow. The combustion is an exothermic process that releases heat and energy. The initiation of flame after ignition and spread of the flame front in the combustion chamber depends upon the chain reaction, turbulence, whirl, and squish inside the cylinder [9]. The entire combustion process is divided into three stages: ignition and flame development stage, flame propagation stage, flame termination.

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58.1.2 Heat Transfer and Cooling of SI Engine The heat transfer is the concern of the loss of energy from the combustion chamber through burn gases, which reduces the amount of piston work and reducing the life of engine components due to high thermal stresses [10, 11]. The lubricating oil loses its lubricating property due to higher temperature and increases the friction losses. To prevent from overheating of the engine, the efficient cooling system removes the excess heat about one-third of the heat produced in the combustion chamber by the burning of the air–fuel mixture. The main purposes of the cooling system are keep the engine at its most efficient temperature at all speeds and operating conditions [12]. Burning of fuel in the presence of air in the combustion chamber produces the heat, and some of this heat must be taken away before it damages the engine parts [13]. A lot of research has been done on the subject of improving the engine performance by adopting the supercharger or turbocharger, variable valve timing mechanism, design of combustion chamber and air intake system, using alternative fuels and additives to increase the air capacity of the engine so that volumetric and combustion efficiency maximize during the running of engine in all different driving circumstances. Yang et al. [14] determined the effect of performance and exhaust gas pollution of the naturally aspirated gasoline engine by changing the length of air intake pipe. A GT—power software is used to study the harmonic effect of the air intake system on engine performance. The results show that the engine performance is improved with a shorter length of intake manifold pipe about 7% of torque. Gosai et al. [15] investigated the performance of the adiabatic engine. The experimental engine piston, cylinder head, and liners is coated by ceramic Zirconia material to achieve low heat rejection engine or adiabatic condition. The specific fuel consumption and thermal efficiency in the adiabatic engine are found to be 5–8% and 10%, respectively, better than that of no-coated engines. But the volumetric efficiency of the adiabatic engine is found 3–7% lower than the normal engine. Because of low heat rejection, the intake air heated very rapidly. Roberts et al. [11] presented a review of various researches based on vehicle thermal management during the cold-starting of the SI engine. The vehicle exerted a large number of emissions and increased the thermal losses during the engine cold start until the engine not reached up to optimum steady-state temperature. The review study identified three major problems with engine cold start that is to increase the cylinder temperature as early as possible to achieve complete combustion, minimize the exhaust gas pollutant level, and maintaining the lubricant film. The review of the literature has clarified how different techniques can help to achieve a reduction in fuel consumption between 0.5 and 7%, HC emissions between 25 and 40%, and CO emissions between 25 and 40%. Kilicarslan et al. [16] have performed experimental work on an eight-cylinder V-type gasoline engine to investigate effects on exhaust gas pollutant levels concerning variation in gasoline engine speed. The results show that the exhaust gas temperature raised to 1820 °C when the engine speed is around 3751 rpm and the exhaust gas pollutant NOx and CO is low at 2133 engine rpm. O. Lim et al. [17] experimentally investigate the combustion parameters

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of gasoline compression ignition engines fuelled with biodiesel gasoline blends up to the range of 5 to 20%. The result calculated by fuel flow rate, injection pressure, and injection period varies by using seven-hole injectors and Bosch common rail diesel injection system research. Chen et al. [18] numerically studied the heat dissipation rate and the knocking characteristics of a gasoline downsized engine. The parameters taking into consideration are variation in spark-ignition timing and intensity of turbulence of air–fuel mixture in the cylinder. The results showed that due to the low heat transfer rate, the tendency of knock increased because of the early auto-ignition of the fuel mixture. Duan et al. [19] developed a one-dimensional simulation model of the actual spark-ignition engine, fuelled by blend fuel of hydrogen and natural gas to evaluate the performance of the engine when the supply of exhaust gas through exhaust gas recirculation valve in the combustion chamber at different pressure. The result showed that increasing the exhaust gas ratio the combustion pressure inside the cylinder is decreased but the level of exhaust gas NOx emission goes down. Verma et al. [20] experimentally investigated the impacts of various blends of Karanja biodiesel with a chain of fewer alcohols (ethanol, 2-propanol, methanol, 1-butanol, and 1-pentanol) to identify the potential of higher alcohols in the production of biodiesel and application to the diesel engine. Gaurav Dwivedi et al. [21] presented a detailed discussion on the work done in the area of biodiesel and also the impact analysis of biodiesel on the engine performance. Puneet Verma et al. [22] studied the impact of higher alcohols, use of different raw materials for biodiesel preparation, and effect of their combustion on oxidation stability and cold flow properties. Dwivedi et al. [23] focused on cold flow property of biodiesel and its impact on engine performance and also provide several remedial measures for improving the cold flow properties of biodiesel. Chhabra et al. [24] provide the detail of optimization process of biodiesel yield using Box Behnken Design technique and used twenty nine set of experiment for the optimization process. Chaurasiya et al. [25] investigated raw oil (Jatropha, soyabean and waste cooking oil to prove its suitability as an alternative fuel for compression ignition (CI) engines. The objective of this research work is to enhance the performance of the SI engine at low engine velocity by maintaining the intake air temperature. This research also focuses to improve the naturally aspirated SI engine, output power and torque at low engine speed and also to minimize the unburnt hydrocarbon pollution caused by engine thermal losses. This study also includes the CFD simulation to compare the effectiveness of thermal insulation material.

58.2 Methodology This research has based on numerical simulation to evaluate the engine performance at part open throttle and variation in exhaust gas emission level due to variation in intake air temperature at the actual driving conditions. The following methodology has been used to complete the research work:

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1.

2.

763

Comparing the numerically calculated engine performance data when the intake air allowed for combustion through non-insulated and insulated intake manifold, respectively. Validation of modified experimental setup by computational flow dynamics model for non-insulated and with insulated intake manifold, respectively, for the variation in intake air temperature. The physical properties and chemical composition of insulation material have been discussed by Chhalotre et al. [26]. The reason behind the selection of wool ceramic glass fiber blanket comparing to other insulation material is based on easily availability, low price, good insulation properties, better chemical and water-resistant properties, soft and easily foldable, safe and reliable.

58.2.1 Computational Fluid Dynamics Computational fluid dynamics (CFD) is a computer-based simulation method for analyzing fluid flow, heat transfer, and related phenomena such as chemical reactions. This project uses CFD for analysis of flow and heat transfer. The flow domain is divided into a numerous finite amount. The governing equations are divided by means techniques: finite difference, finite volume or finite element.

58.2.1.1

Governing Equation

CFD is a mathematical tool to compute fluid flow and heat transfer coefficient based on the standard equation code of conservation of mass, momentum, and energy equation. ∂(ρu i ) = 0.1 ∂ xi

(58.2)

The rate of change of momentum is written as.    ∂u j ∂p ∂ ∂  μ − ρu i u j = ∂ xi ∂ xi ∂ xi ∂x j

(58.3)

The total rate of change in energy is given by  ∂ k ∂u j ∂  ρu i u j T = ( ) ∂ xi ∂ xi C p ∂ xi

(58.4)

Liquid conduct can be described as far as the liquid properties speed vector u, weight p, thickness ρ, consistency μ, the heat conductivity k, and temperature T. The adjustments in these liquid properties can happen over space and time. Utilizing

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CFD, these progressions are figured for minor components of the liquid, after the preservation laws of material science recorded previously [27].

58.3 Modeling The CAD model, mesh generation and postprocessing for viewing and interpretation of results are showing in the figure under two stages. The first stage and second stage analyses have been carried out on regular air intake manifold (without insulation) and with insulation intake manifold, respectively. The selected insulating material is a ceramic fiber wool blanket of 15 and 20 mm thickness. The variable parameters are engine speed, ranging from 800 to 1500 rpm and the temperature range of intake air vary from 0 to 30 °C. The objective of this CFD analysis is to validate the experimental result (Figs. 58.1, 58.2, 58.3 and 58.4).

Fig. 58.1 CAD and mesh model of air intake system at 800 rpm without

Fig. 58.2 CAD and mesh modeling of air intake system at 1500 rpm without insulation

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Fig. 58.3 CAD and mesh modeling of air intake system at 800 rpm with insulation

Fig. 58.4 CAD and mesh modeling of air intake system at 1500 rpm with insulation

Generation of the 3D CAD model of ACC using Unigraphics. Then the CAD model has been imported into Ansys Design modeler in Parasolid format (.xtl or .x_t). Mesh: Generation of mesh of ACC in the Ansys Mesh software. Mesh Type: Tetrahedral Element edge length: 2.5 mm, Number of nodes: 198,006, Number of element: 184,068, Problem type: 3D, Steady State, Type of solver: Pressure based, Material property: Fluid–Air, PA66, Insulation, Physical model: K-e turbulence model and energy model (Table 58.1).

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Solution method

Air inlet type: pressure inlet

Pressure velocity coupling: simple

Air inlet temperature: 303.15 K

Momentum: second order

Intake air density: 1.164 kg/m3 Energy: second order Atmospheric pressure: 101.325 kPa

Turbulent kinetic energy: first order

Turbulent intensity: 5%

Turbulent dissipation rate: first order

Hydraulic dia: 0.06 Heat–Engine bonnet: Constant temperature = 362.15 k

58.4 Result and Discussion The CFD analysis has been performed to study the variation in air intake temperature between inlet and outlet of the regular non-insulated intake manifold and the insulated intake manifold. Figure 58.1 shows the CAD modeling of the air intake system. The CFD modeling carried out in two stages where the variations in temperature, pressure, and velocity profile of the air are analyzed at different engine speeds. At partial open throttle, simulation calculations showed a 10% change in combustion chamber heat losses which results in a change of 2 and 5% in brake specific fuel consumption, and usual fuel utilization change of about one-third the extent of the heat transfer change. At wide-open throttle, the outcome on mean effective pressure is comparable, and a 10% enhancement in heat transfer results in about a 3% change in bmep [27, 28].

58.4.1 Effect of Engine Thermal Losses on Intake Air Temperature Using Non-insulated or Regular Air Intake System The result of CFD analysis at different engine speeds from 800 to 1500 rpm has shown the effect of heat transfer from the engine compartment to the air intake system. With variation in engine speeds cause of throttle valve percentages opening and rapid closing of the inlet valve a sudden back pressure developed in the plenum that affects the pressure and velocity field developed inside the air intake system. Also by the engine thermal losses, the temperature of the intake air gradually increases from the atmospheric temperature. Figures 58.5, 58.6, 58.7, and 58.8 show the different contours of pressure distribution, variation in intake air temperature from inlet to output, intake air velocity magnitude and vectors inside the air intake system respectively when airflow through the non-insulated air intake system.

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Fig. 58.5 Pressure contour at different engine speed from 800 and 1500 rpm without insulation

Fig. 58.6 Temperature contour at different engine speed from 800 to 1500 rpm without insulation

58.4.2 Effect of Engine Thermal Losses on Intake Air Temperature Using Insulated Air Intake System The result of CFD modeling with an insulated air intake system has shown in Figs. 58.9, 58.10, 58.11, 58.12, 58.13, 58.14 and 58.15. This analysis has been done by choosing the insulation thickness of ceramic fiber wool from 15 to 20 mm to protect the air intake system from the engine thermal losses. After using an insulated air intake system with 25 mm thickness, the intake air temperature which has supplied between 27 and 30 °C is found throughout constant in the air intake system at different engine speeds. The heat exerted by the engine does not heat the intake air coming from atmospheric temperature and maintaining

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Fig. 58.7 Velocity contour at different engine speed from 800 to 1500 rpm without insulation

Fig. 58.8 Velocity vector contour at different engine speeds from 800 to 1500 rpm without insulation

the density of air throughout the air intake system which improved the volumetric and combustion efficiency at part open throttle (Table 58.2). The CFD modeling has also shown the variation in insulation thickness causes variation in intake air temperature from inlet to the outlet point of the air intake system. It has been observed that the mass flow rate of the intake air varies with insulation thickness at the temperature of 303.15 K. The engine takes time to reach a steady-state temperature up to 363 K, and during this period, the coolant is not circulated from the engine block to the radiator. Due to thermal losses, the temperature of the engine room goes up to 362.15 K and heated the entire air intake system up to 338.012 K through convection. The intake air temperature also increases when it passed through the warm intake system. This pre-heated intake air increases the

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Fig. 58.9 Pressure contour at different engine speeds from 800 to 1500 rpm with an insulated air intake system using insulation thickness t = 15 mm

Fig. 58.10 Temperature contour at different engine speeds from 800 to 1500 rpm with an insulated air intake system using insulation thickness t = 15 mm

pre-cyclic temperature of the engine which leads to a lowering of volumetric and combustion efficiency and tends to increase the tendency of knocking at the part open throttle of the engine. To prevent the air intake system from engine heat losses, different thickness of the glass fiber insulation blanket has been used. With a 25 mm thickness insulation blanket, the air intake system almost prevents by engine thermal losses. Although, the thickness of 15 mm and 20 mm insulation blanket also helps to protect the air intake system from the engine thermal losses at part open or full throttle engine speed.

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Fig. 58.11 Velocity contour at different engine speeds from 800 to 1500 rpm with an insulated air intake system using insulation thickness t = 15 mm

Fig. 58.12 Pressure contour at different engine speed from 800 to 1500 rpm with an insulation air intake system using insulation thickness t = 20 mm

58.5 Conclusion The result of this study suggest that the low throttle response of the SI engine is significantly improved due to the allowing intake air for combustion between the temperature range of 27–30 °C. Following are the conclusion from the present study: 1.

The fuel consumption is improved with a decrease in air intake temperature at a low engine speed of about 1.57%. The ignition delay period is reduced with lower intake air temperature due to higher oxygen molecules availability. The results show that the bsfc decreases as the air intake temperature decreases.

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Fig. 58.13 Temperature contour at different engine speeds from 800 to 1500 rpm with an insulated air intake system using insulation thickness t = 20 mm

Fig. 58.14 Velocity contour at different engine speeds from 800 to 2500 rpm with an insulated air intake system using insulation thickness t = 20 mm

2.

3.

The engine thermal losses are controlled by 2.6% with the insulated air intake system which also helps to prevent engine components damaged by thermal stresses. For the emission analysis of the exhaust gas of SI engine with insulated air intake system, the result shows that percentage of the carbon monoxide, unburned hydrocarbons, and parts per million (ppm) of oxides of nitrogen decreased with the decrease of air intake temperature because in the cold air, the oxygen moles denser which increase the mass flow rate of air and occupy more cylinder space thus achieved the complete combustion with minimum thermal losses.

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Fig. 58.15 Velocity vector contour at different engine speeds from 800 to 2500 rpm with an insulated air intake system using insulation thickness t = 20 mm

With insulation t = 20

5.86

9.1

1500

9.4

1500

800

6.0

800

9.6

1500

With insulation t = 15

6.2

800

Without insulation t=0

Throttle valve opening %

Engine speed (RPM)

Insulation thickness

– 29

– 32

– 29

– 32

– 29

– 32

Suction pressure manifold MAP (kPa)

0.218

0.235

0.218

0.235

0.232

0.235

Mass flow rate of intake air (kg/s)

303.1

303.1

303.1

303.1

303.1

303.1

Air temp. at the inlet (K)

306.69

306.54

307.52

307.33

336.94

336.38

Air temp. at the outlet (K)

305.198

305.084

305.772

305.631

328.024

327.350

Air temp. inside the air intake system (K)

362.1

362.1

362.1

362.1

362.1

362.1

Engine room temp. (K)

Table 58.2 CFD analysis results in controlling the heat flow rate by using different thicknesses of glass fiber insulation material

780.4049

806.9214

961.3562

992.4110

7432.747

7888.624

Heat flow rate (q) W

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23. G. Dwivedi, M.P. Sharma, Impact of cold flow properties of biodiesel on engine performance. Renew. Sustain. Energy Rev. 1(31), 650–656 (2014) 24. M. Chhabra, G. Dwivedi, P. Baredar, A.K. Shukla, A. Garg, S. Jain , Production & optimization of biodiesel from rubber oil using BBD technique, in Materials Today: Proceedings (2020) 25. P.K. Chaurasiya, S.K. Singh, R. Dwivedi, R.V. Choudri, Combustion and emission characteristics of diesel fuel blended with raw Jatropha, soybean and waste cooking oils. Heliyon 5(5), e01564 (2019) 26. S. Chhalotre, P. Baredar, S. Soni, Experimental investigation of the effects of insulated air intake system on the performance of naturally aspirated MPSEFI spark ignition engine, in AIP Conference Proceedings, p. 2039 (2018) 27. E. Akbarian, B. Najafi, M. Jafari, S. Faizollahzadeh Ardabili, S. Shamshirband, K.W. Chau, Experimental and computational fluid dynamics-based numerical simulation of using natural gas in a dual-fueled diesel engine. Eng. Appl. Comput. Fluid Mechan. 12(1), 517–34 (2018) 28. M.M. Pandian, K. Anand, Experimental optimization of reactivity-controlled compression ignition combustion in a light-duty diesel engine. Appl. Therm. Eng. 138, 48–61 (2018)

Chapter 59

Investigation of AI Based MC-UPFC for Real Power Flow Control C. Boopalan, V. Saravanan, and T. A. Raghavendiran

Abstract Unified power flow controller (UPFC) is one among the FACTS devices which is dealt in this work to improve the power transferring ability of the system. In this paper, we developed the matrix-converter-based UPFC (MC-UPFC), whereas classical UPFC is made up of two standard converters back to back connected through DC link, the converters are static synchronous shunt converter (STATCOM) and static synchronous series converter (SSSC). In our work, a MC-UPFC was implemented and tested in the IEEE-standard 14 bus system. The control of the MC-UPFC is done with the artificial intelligence system called fuzzy logic controller (FLC). UPFC can control both basic parameters of transmission system called real power [P] and reactive power [Q]. FLC gives the switching control states, and accordingly MCUPFC will inject control voltage [Vc] at a control angle [α]. At a same time, MCUPFC can control both real power flow and reactive power flow in the transmission system. With appropriate switching states in the MC-UPFC, the FLC gives the control signal to maintain both real and reactive power independently. The control signal was derived based on the proportional sliding surface switching states given as lookup tables in the FLC. The FLC will take decision, so that the magnitude and angle of the control voltage to be injected will fed to the power system by the MC-UPFC. FLC based MC-UPFC maintains the preset standards of required real and reactive power at a given condition. The result shows FLC give good performance. The work was designed in the MATLAB Simulink platform which gives very accurate results with user friendly. The tool gives access to store and study various performance analysis parameters like total harmonic distortion index. Keywords Matrix converter · Unified power flow controller · Fuzzy logic controller C. Boopalan (B) Anna University, Chennai, India e-mail: [email protected] V. Saravanan Arunai Engineering College, Tiruvannamalai, India T. A. Raghavendiran Srivenakateswara Engineering College for Women, Tirupathi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_59

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59.1 Introduction In the modern world, the usage of electric power is keep on increasing day by day. The quality of the power also becomes more concern for the modern industrial needs which has electronic control units. These electronic controllers are highly sensitive to the variation or fluctuation in the power. So, it is highly important to maintain the quality of the power as important as generating power. Though the power generated in good quality, due to transmission loss, the quality at the distribution side may be reduced due to the losses in the transmission system. To ensure the effective and quality power transmission in the transmission system, flexible AC transmission system (FACTS) are implemented [1–5]. The control schemes available for the MC-UPFC is developed, and performance analysis is studied for the conventional methods called proportional-integralderivative (PID) controller, space vector modulation (SVM) based controller using direct power controller (DPC) through sliding mode control techniques. To validate the study, the 2-bus, 7-bus, and 14-bus power systems were developed in the simulation platform, the FLC control techniques are implemented, and the performance was analyzed. The parameters taken for the analysis are the injected voltage and the angle at which voltage was injected to the bus. The voltage is injected through the series transformer connected in the bus, the current, and voltage angle. This paper comprises of MC-UPFC switching control in Sect. 59.2, and modeling of the system is shown in Sect. 59.3. In Sect. 59.4, fuzzy logic controller was designed, and in Sect. 59.5, the simulation results were highlighted, and conclusion and future enhancement were discussed.

59.2 MC-UPFC Switching Control The power flow diagram of the simplified power system connected with the MCUPFC can be represented in Fig. 59.1. To model the system, the basic parameters such as coupling. Fig. 59.1 Equivalent circuit MC-UPFC for 2-bus system

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Transformer, and generators are taken as ideal. Further to simplify the control region, the matrix converter switches are also taken as ideal. V1 are the bus-1 voltages, V2 are the bus-2 voltages, and Vc represents the control voltages injected by the MC-UPFC.

59.3 Modeling of MC-UPFC in Power System The basic diagram of the MC-UPFC is shown in Fig. 59.2, which includes two transformers to connect the input and output of the MC-UPFC with the power system. The input side MC-UPFC is connected through the shunt transformer, whereas the output side of the MC-UPFC is connected through the series transformer in which the primary winding of the series transformer is connected in parallel with the output of the MC-UPFC. The secondary winding of the transformer is connected in series to inject the current into the power system. The voltage controlled by the matrix converter and fed to the bus further was mentioned as Vc with the change of phase angle δ from the bus voltage. The power system taken here is a symmetrical system where the load is three-phase balanced. The current flow in three phases is same at normal condition. The matrix converter is arranged and switched in a sequence, so that each one phase of the input supply will be connected to the output side at a time. A three-phase inductor capacitor-based LC filter is corporate to ensure the smooth input currents to the MC-UPFC adopted from Monteiro et al. (2011).

Fig. 59.2 Schematic diagram of MC-UPFC

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The following factor to be considered while understanding the control of the line parameters. 1. 2. 3. 4.

The shunt voltage input to MC-UPFC Series injected line current Least line voltage input Least output line voltage.

The shunt and series voltage are the two factors used to control the real and reactive power flow from the MC-UPFC. S1 = P1 + j Q 1  S1 = V1 ∗

V1 − V2 Zl

(59.1)  (59.2)

where S1 is apparent power available in the bus one, P1 and Q 1 are the real and reactive power with respect to bus-1 and bus-2, estimated by Liu et al. (2017). The above data is used for the 2-bus system simulation study. The line current injecting limit can be obtained by the following relationship I =

V1 + Vi − V2 Zl

(59.3)

If the line current falls due to load, the injecting current can be calculated based on the ratio between the bus voltage and the load impedance. Stated in the below relationship (Predrag et al. 2002). For an N-bus system, the power and the voltage parameters can be calculated as follows. Real power P(t) =

∞ 

Vn ∗ In cosθn

(59.4)

Vn ∗ In sinθn

(59.5)

n=1

Reactive power Q(t) =

∞  n=1

I (t) =

∞  √ n=1

Average active power

2Vn sin(nωt)

(59.6)

59 Investigation of AI Based MC-UPFC for Real Power Flow Control

P(t) =

∞  √

2Vn cos(nωt + ∅n )

781

(59.7)

n=1

where Vn and In are RMS value of voltage and current of nth harmonics and ∅n is phase difference at nth harmonics of voltage and current. The power flow of a n number of bus power system are calculated by can by Predraget al. (2002) Cai et al. (2002), Kannan et al. (2002), and Morcoset al. (2013). For the estimation of kth bus power parameters, the following equations can be used, ∞     |Vk |V j Yk j cos(δk − δ j − θk j ) Pk = PU +

(59.8)

n=1

Qk = QU +

∞ 

   |Vk |V j Yk j sin(δk − δ j − θk j )

(59.9)

n=1

The power equation is given for the UPFC connected n-bus system is given by,  2 PU = G U + V j  − |Vk ||EU ||YU |cos(δk − δ j − θk j )

(59.10)

 2 Q U = G U + V j  − |Vk ||EU ||YU |sin(δk − δ j − θk j )

(59.11)

where PU and Q U are respectively the real and reactive power injected to the system by UPFC.

59.3.1 MC-UPFC in Two Bus System The PI-based controller implemented MC-UPFC is simulated and tested in with two cases based on the proportional and integral constant of the real and reactive power and the time response. The schematic arrangement of the power system simulation is shown in the above Fig. 59.3. Two generators were connected to the power system one in the sending end, which is named as sending end generator. The second generator is connected in the receiving end, and both the generators will feed the load through the transmission line. Power transformers with ideal features are connected in the input and output side of the transmission system. The schematic of MC-UPFC consists of two transformers one has both primary and secondary connected in parallel with the power system bus and to the matrix converter. The second transformer has a constructional difference—the secondary of the transformer is connected to the power system bus in series, whereas the primary winding is connected in parallel with the output of the matrix converter.

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Fig. 59.3 Block diagram of sliding mode SVM MC-UPFC

To ensure smooth switching, an LC filter is connected in front of the matrix converter. The switching scheme of the matrix converter is controlled by the gate control circuit. The control signal given to the gate circuit is controlled by the space vector modulation circuit. For the functioning of the system, three reference signals are provided. The input reactive power reference, output real, and reactive power references were fed to the system as preset values. The actual reactive input power, output real, and reactive power were measured using the measuring device and compared with the reference values. In addition, the sign and location of the voltage input and current output waveforms were determined to function the space vector locator. The space vector function is further converted into the direct and quadrature axis component, and to reduce the complexity, the control data flow was converted into alpha, beta coordinates. Based on the error and reference values, the control signal for the real power control and reactive power control were converted into the switching signal lookup table. Based on the switching signal lookup table data, gate circuit will convert into gate triggering pulse. The switches which are turned on will transfer the supply voltage received as parallel and injected in series to the power system to improve the power quality parameters.

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59.4 Fuzzy Logic Controller (FLC) In this section, we discuss the design and control of fuzzy logic to enhance the performance of the MC-UPFC to improve the power flow in the power system. From the study of Venkatesh et al. (2004), Aihong et al. (2008), a multi-layer FLC technique is developed to control the system. The computational algorithm development and performance test are given below [6–9]. The FLC design is made based on the knowledge on the following. • Power system information • MC-UPFC control parameters • System output. The control block diagram of the FLC controller with PID controlling technique shown in Fig. 59.4 helps to understand the function of the controller. The proportional control is designed by incremental FLC. The controller will give the control voltage and angle of the voltage. The flowchart shown in Fig. 59.5 explains the control data flow with the reference input and the controlled signal given to the switching circuit. The real and reactive power available in the bus is taken as measured value, and it was compared with the reference real and reactive power. Based on the PID control using FLC inference, the switching scheme is given as output with reference to the fuzzy rule base, which was listed in Table 59.1.

59.4.1 Implementation of PID Based FLC The simulation of the power system with FLC with 2-bus, 9-bus, and 14-bus systems was designed using Simulink, MATLAB. The FLC controller with proportional and integral control parameters and the samples are derivate sample values which are compared and fed to the FLC system. The controlled switching signal is fed to the matrix converter, which fed the power supply based on the input control signal [10–15].

Fig. 59.4 Block diagram of FLC for MC-UPFC

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Fig. 59.5 Flowchart of fuzzy logic controller

Table 59.1 Fuzzy rule base V1

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

I1

S1

S2

S4

S6

S8

S10

S12

S14

S16

S18

S1

S3

I2

S3

S1

S2

S6

S8

S10

S10

S14

S16

S18

S1

S3

I3

S5

S2

S1

S2

S6

S8

S10

S10

S14

S16

S18

S3

I4

S7

S7

S2

S1

S2

S6

S8

S10

S14

S16

S18

S1

I6

S9

S9

S7

S2

S1

S2

S6

S8

S10

S14

S16

S18

I7

S11

S11

S9

S7

S2

S1

S2

S6

S8

S10

S14

S16

I8

S13

S13

S11

S9

S7

S2

S1

S2

S6

S8

S10

S14

I9

S15

S15

S13

S11

S9

S7

S2

S1

S2

S6

S8

S10

I10

S17

S17

S15

S13

S11

S9

S7

S2

S1

S2

S6

S8

I11

S2

S2

S17

S15

S13

S11

S9

S7

S2

S1

S2

S2

I12

S4

S4

S2

S17

S15

S13

S11

S9

S7

S2

S1

S1

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Table 59.2 FLC performance P

Q

Time response

THD in %

2 Bus

0.6478

0.2

0.0055

8.26

9 Bus

0.645

0.2

0.0063

8.22

14 Bus

0.66

0.2

0.007

8.1

Fig. 59.6 Output member function of FLC

Figure 59.6 shows the design diagram of the fuzzy inference system block where fuzzy input and output system is connected to the UPFC system. The FLC was designed with the Mamdani system with centroid functions as shown in Fig. 59.6.

59.4.2 Test Case-IEEE 14-Bus System with FLC Controller The IEEE 14-bus is taken for simulation and checking the loading capability of FLC based controller for MC-UPFC. The power system designed in MATLAB is shown in Fig. 59.7. The power system assumption are highlighted in the appendix Table 59.3.

59.5 Simulation and Results The IEEE 14-bus system is taken into the study with five generators. The parameters of the system are declared in the appendix which was controlled with conventional control and the performance verified, the power flow ratings were shown in Fig. 59.8, and the voltage variations were shown in Fig. 59.9. The power flow is increased 0.66 p.u. with reference to the value of 0.7 p.u. The performance of the FLC based control for MC-UPFC is checked for 2-, 9-, and 14-bus systems. The real power control in

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Fig. 59.7 Bus system simulation in MATLAB

different buses with their time responses and THD of the control circuit is mentioned in the following Table 59.3, Fig. 59.10 shows the THD analysis of the 14-bus system with FLC through MC-UPFC, and the analysis of each case is presented in Figs. 59.11 and 59.12. With the analysis charts shown in Figs. 59.11 and 12, we could observe a better performance and reduce THD in 14-bus system with high power improvement in the bus up to 0.66 pu, which is 95% of the reference value. The THD value is maintained from 8.1 to 8.25%. The research work on the optimal power flow using fuzzy logic controller based algorithm was developed and tested in the power system with the case studies 4-bus system, 9-bus system, and IEEE-14 bus system. The analysis results are obtained from

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Fig. 59.8 P-control response using ANN in 14-bus

Fig. 59.9 Bus voltage and MC-UPFC control voltage in ANN 14-bus system

the comparison with the existing techniques is shown in Table 59.2. The performance based on the voltage injected and the angle injected is measured and plotted. The bus voltage, real power, and reactive power were controlled by FLC. The response of the FLC algorithm based on the response time was compared. The study shows that the FLC gives better performance when compared with the classic PID controller technique. With the analysis charts shown in Figs. 59.13, 59.14, and 59.15, we could observe a better performance and reduced THD in 14-bus system with high power improvement in the bus up to 0.66 pu, which is 95% of the reference value. The THD value is maintained from 8.1 to 8.25%.

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Fig. 59.10 THD of MC-UPFC under ANN for 14-bus system

Fig. 59.11 Comparison based on real power control

Real power Response 0.665 0.66 0.655 PID

0.65

FLC

0.645

ANN

0.64 0.635 0.63 2 Bus

Fig. 59.12 Comparison based on time response (settling time)

9 Bus

14 Bus

Time response 0.01 0.009 0.008 0.007 0.006

PID

0.005

FLC

0.004

ANN

0.003 0.002 0.001 0 2 Bus

9 Bus

14 Bus

59 Investigation of AI Based MC-UPFC for Real Power Flow Control Fig. 59.13 Real power control FLC analysis

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Real Power control FLC 0.665 0.66 0.655 0.65

P

0.645 0.64 0.635 2 Bus

Fig. 59.14 Time response FLC analysis

9 Bus

14 Bus

TIME Response 0.008 0.006 0.004

TIME Response

0.002 0 2 Bus

Fig. 59.15 Total harmonic distortion FLC analysis

9 Bus

14 Bus

THD in % 8.3 8.2 THD in %

8.1 8 2 Bus

9 Bus

14 Bus

59.6 Conclusion From the experiments conducted in the simulation platform using MATLAB/Simulink, it is concluded that the MC-UPFC with artificial intelligence based switching algorithm acts as optimum control for the power flow in the power system. The designed control algorithms for MC-UPFC in this research work presented show that the loss is reduced and increasing power utilization. The AI based FLC and ANN algorithms for MC-UPFC identified as suitable for the faster voltage control technique. Modeling of the MC-UPFC based power system with voltage injection control parameter gives a good response in control strategies input error identification. In general, the application of the more MC-UPFC in the power system gives good power flow and improved efficiency by reducing loss.

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Table 59.3 Line resistance and reactance between buses Bus number

Voltage (kV)

Load impedance

Line impedance

Resistance ()

Inductance (mH)

Resistance ()

Inductance ( mH)

Bus 1

11

5

15

5

15

Bus 2

11

65

150

5

15

Bus 3

11

–

–

5

15

Bus 4

11

–

–

5

15

Bus 5

–

50

150

5

15

Bus 6

–

100

300

5

15

Bus 7

–

–

–

5

15

Bus 8

11

–

–

5

15

Bus 9

11

65

500

5

15

Bus 10

11

5

15

5

15

Bus 11

11

65

150

5

15

Bus 12

11

–

–

5

15

Bus 13

11

–

–

5

15

Bus 14

–

50

150

5

15

59.7 Future Work As a continuation of this work, other methods of artificial intelligence can be developed, and performance can be analyzed. MC-UPFC can be implemented for the microgrid power systems. MC based device can be modified and implemented in the hybrid power systems. Interline power flow controller (IPFC) can be implemented with the ANN and FLC algorithms.

Appendix See Appendix Table 59.3.

References 1. E. Sheeba Percise, A. Nalini, S.T. Rama, S. Bhuvaneswari, J. Jayarajan, T. Jenish, Reactive power compensation using fuzzy logic controlled UPFC in a hybrid microgrid, in International Conference on Advanced Computational and Communication paradigms (2019) 2. A. Siddique, X.U. Yonghai, A. Waseem, A. Rehman, M. Kaleem Aslam, Performance of fuzzy logic based controller for UPFC on a power quality issues in transmission network, in International Conference on Power System Technology (November 2018)

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3. A.M. Pourshotorbani, A. Ajami, S.G. Zadeh, M.P. Aghababa, B. Mahboubi, IET Gener. Transm. Distrib. 8(10), 1712–1723 (2014) 4. J. Yuan, L. Liu, W. Fei, L. Chen, Baichao, Hybrid electromagnetic unified power flow controller: a novel flexible and effective approach to control power flow. IEEE Trans. Power Deliv. 5. C. Udayashankar, R. Thottungal, M. Sundaram, Matrix converter based UPFC for transient stability enhancement using fuzzy logic control, in International Conference on Power System (November 2014) 6. M. Ali, A. Iqbal, M. Rizwan Khan, M. Ayyub, M. Anas Anees, Generalized theory and analysis of scalar modulation techniques for a mxn matrix converter. Trans. Power Electron. (2016) 7. A. Dasgupta, P. Tripathy, P.S. Sensarma, in Matrix Converter as UPFC for Transmission Line Compensation, EXCO, Daegu, Korea, 22 October 2007 8. J. Monterio, J. Fernando Silva, S.F. Pinto, J. Palma, Matrix Converter-Based Unified Power Flow Controllers: Advanced Direct Power Control Method. IEEE Trans. Power Deliv. 26(1) (2011) 9. F. Villarroel, JR. Espinoza, C.A. Rojas, J. Rodriguez, M. Rivera, D. Sabrbaro, Multi objective switching state selector for finite-states model predictive control based on fuzzy decision making in a matrix converter. IEEE Trans. Industr. Electron. 60(2) (2013) 10. R. Ravishankar, K. Srinivasan, Fuzzy logic controller based UPFC for reactive power compensation in transmission line. J. Xi’on Univ. Architect. Technol. 11. M.E.A. Farrag, G.A. Putrus, L. Ran, in Design of Fuzzy Based-Rules Control System for the Unified Power Flow Controller. IEEE (2002) 12. A. Tang, Y. Yuan, S. Cheng, in The Study of Fuzzy-Logic Self-Adaptive Controller for UPFC. IEEE (2008) 13. C. Eswar Prasad, S. Vadhera, in Damping of Sub Synchronous Reasonance Using Fuzzy Based PI Controlled UPFC. IEEE (2015) 14. R. Kumar, M. Kumar, in Improvement Power System Stability Using Unified Power Flow Controller Based on Hybrid Fuzzy Logic-PID Tuning in SMIB System. IEEE (2015) 15. F.M. Albatsh, S. Mekhelef, S. Ahmad, H. Mokhils, in Fuzzy Logic Based UPfc and Laboratory Prototype Validation for Dynamic Power Flow Control in Transmission Lines. IEEE Transactions. On Industrial Electronics

Chapter 60

Sizing and Performance Investigation of Grid-Connected Solar Photovoltaic System: A Case Study of MANIT Bhopal Arvind Mittal, Radhey Shyam, and Kavali Janardhan

Abstract In this paper, sizing and performance investigation of grid-connected solar photovoltaic system on the basis of load demand of MANIT, Bhopal (23.2599° N, 77.4126° E), Madhya Pradesh, India, is presented. All the aspects associated with grid-connected solar photovoltaic system are measured for fiscal viability of photovoltaic system for the proposed location. 750 kWP is proposed for satisfying the MANIT campus load. Proposed system can generate 1144 MWh annual average energy with 17.4% capacity factor. Per unit cost of the proposed system estimated as 3.129 INR per kWh and this proposed system has present value approximately 3.825 cr. INR. This study helps to appreciate design of grid-connected solar photovoltaic system in Bhopal and nearby region. Keywords Photovoltaic (PV) · Grid-connected system · System designing · Payback period · Energy demand analysis

60.1 Introduction Energy demand is increasing in India because of population growing day by day. This energy requirement can be fulfilled by energy generation from non-conventional energy resources like solar, wind, geothermal, tidal waves, etc. As solar energy available everywhere in India and electrical energy generation from photovoltaic system provides several advantages like renewable and the environment friendly due to these advantages Popularity of solar energy utilization in India is increasing day by day in all sectors [1–3].There are two types of solar photovoltaic system A. Mittal · R. Shyam · K. Janardhan (B) Energy Centre, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh 462003, India e-mail: [email protected] A. Mittal e-mail: [email protected] R. Shyam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_60

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named as off grid and grid-connected system. Stand alone or off grid systems are used at remote areas where electricity is not available but enough solar irradiance. In standalone SPV system, batteries are used as energy storing device for energy backup [4, 5]. The PV system with grid associated contained PV panels for providing the greater part of the needed power over daytime and is associated with the nearest electrical grid to get the power after sunset time or absence in sun. The PV panel, some of the time, produces more power than the demand load during daytime and consequently, the excess power is sustained to the nearest grid. On the other hands, when these photovoltaic panels do not generate enough energy to meet the demand load, the shortage control is repaid by the local grid [6, 7]. Roof top grid-connected photovoltaic systems (GCPVS) are more prevalent nowadays because these kinds of systems can install on unoccupied roof of any organization which wants to install. This paper presents seasonal load pattern, sizing, designing, lifecycle evolution, CO2 emission, and performance investigation of grid-connected solar PV system for Maulana Azad National Institute of Technology (MANIT), Bhopal, Madhya Pradesh state in India. There are three main feeders in MANIT substation names as college feeder, hostel feeder, and residential feeder. In this paper College feeder was under study which has four sub-feeders. Sizing of this proposed SPV system was done by taking readings from MANIT substation for a year. PVsyst 6.78 simulation software was used to investigate energy injected into grid, performance ratio (PR) and CO2 emission reduction by SPV system during its tenure which considered as 25 years [8, 9].Performance analysis plays vital role for investors because it provides essential information about energy payback period, per Watt cost, per unit cost, and present value of the system to be install.

60.2 Energy Demand Energy demand was determined after collection of load readings from MANIT Substation for a Year. Seasonal load curves are shown from Figs. 60.1, 60.2 and 60.3. Addition of hourly load gives energy required for a day. Thus, total average energy required per day is 3309.65 kWh.

60.3 Sizing of Solar PV System Sizing of solar photovoltaic system depends on various factors like daily solar irradiance at site location, panel generation factor, daily energy requirement etc. Schematic block diagram of grid-associated SPV system [10] and solar irradiance available in Bhopal is shown in Figs. 60.4 and 60.5, respectively. Solar irradiance variation shows that months June to September have less solar irradiance than winter months because of cloudy weather. Average daily solar irradiance measured in Bhopal is 5.60 kWh/m2 [11].

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Load in kW--------------->

350 300 250 200 150 100 50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour--------------------------------> Fig. 60.1 Average hourly load curve during summer season

Load in kW------------->

300 250 200 150 100 50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour--------------------------> Fig. 60.2 Average hourly load curve during rainy season

60.3.1 Panel Generation Factor Panel generation factor plays key role in designing of grid-connected solar PV system and it defines as the ratio of daily solar irradiance to standard test conditions irradiance for PV panels. Panel generation factor varies with location of project. Thus, Panel generation factor =

Daily solar irradiance Standard test conditions irradiance

Panel generation factor =

5.6 ∗ 1000 = 5.6 1000

(60.1) (60.2)

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Load in kW-------------->

160 140 120 100 80 60 40 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour --------------------> Fig. 60.3 Average hourly load curve during winter season

10

Monthly avg. solar irradiance

5 0 Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb

Solar irradiance (kWh/m2/day) ---------->

Fig. 60.4 Block diagram of grid-connected solar PV system

Yearly avg. solar irradiance

Fig. 60.5 Plot of available solar irradiance in Bhopal

60.3.2 Energy Generation from Solar PV Modules The energy generation from PV modules should be enough to fulfill the daily energy demand of the college and additional demand of system losses. Mostly system losses are taken as 30% of energy demand for MANIT [12] Consequently,

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total energy generation from PV modules = (Perday Energy Demand ∗ System Losses Compensation Factor) = 3309.65 ∗ 1.3 = 4302.5 kWh/day

(60.3) (60.4)

60.3.3 Watt Peak Rating for PV Modules Watt peak rating for PV modules define as the ratio of energy required from PV Modules to the panel generation factor. Watt peak rating = =

Energy Required from PV modules Panel generation factor

(60.5)

4302.5 ≈ 750 kWp 5.6

(60.6)

60.4 System Designing Designing of grid-connected solar PV system consist of module size, inverter size, module circuit, etc. Designing of solar photovoltaic system requires geographical and climate details of selected site.

60.4.1 Number of Solar PV Modules Required Number of solar PV modules can be determined by dividing total peak power rating to peak-rated output of a module. Cost effective Vikram Solar and Somera VSM.72.370.05 are selected for this proposed system which having specifications as mono crystalline,370 W, 19.07% efficient,48 V open circuit voltage, 38.4 V as Vmpp, and 9.63 A as maximum current. Panels are selected in this study on the basis of easy availability and optimum cost and efficiency in Bhopal. Number of modules required =

Total Watt peak rating PV module peak rated output

(60.7)

Number of modules required =

750 ∗ 1000 = 2028 modules 370

(60.8)

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60.4.2 Number of Inverters Required Inverter is an electronic device which is used for DC-to-AC conversion. Transformer less(TL) grid-connected PV inverters are more prevalent in present days since they present the merit of high efficiency and density to perform renewable energy generation and power conversion [13]. Number of inverters required for solar PV plant depends on total watt peak rating of plant and power rating of selected inverters. highly efficient and cost-effective Kaco new energy, blueplanet 125 TL3-INT string inverters are selected for proposed system which having specifications, three phase, 125 kWac as nominal AC power, 875–1300 V as Vmpp range,1500 V as maximum open-circuit voltage, maximum input current, and maximum short-circuit current as 160 A and 300 A, respectively. Number of inverters =

Total Watt peak rating of plant rated power of an inverter

(60.9)

750 = 6 Inverters 125

(60.10)

Number of inverters =

60.4.3 Solar PV Modules Arrangement Module network presents numbers of PV modules connected in series and parallel in SPV system and peak voltage input to inverters. After simulation of proposed system, we have found that 26 modules will be connect in series and 13 in parallel for each inverter. Number of modules required for an inverter =

Number of modules required for an inverter =

Power rating of an inverter Power rating of a module (60.11)

125000 = 338 modules (60.12) 370

Max. Power point Voltage input to Inverter = (Vmpp of a module ∗ No. of modules in a string) (60.13) = 38.4 ∗ 26 = 1000 V

(60.14)

799

Jan

Feb

Dec

Oct

Nov

Sep

Jul

Aug

Jun

Apr

Energy injected into grid during summer season Energy injected into grid during rainy season

May

140 120 100 80 60 40 20 0

Mar

Energy injected into grid (MWh) ------->

60 Sizing and Performance Investigation of Grid-Connected …

Energy injected into grid during winter season

Fig. 60.6 Plot of energy injected into grid

60.5 Performance Investigation This proposed system was simulated by PVsyst 6.78 version to check performance of system such as energy injected into grid, performance ratio, and CO2 emissions reduction.

60.5.1 Energy Injected Into Grid Energy feed from solar PV plant into grid for supply is called energy injected into grid. In this study, 1% drops in energy generation per year is considered for this proposed system. Seasonal deviation in energy injected into grid is shown in Fig. 60.6.

60.5.2 Performance Ratio (PR) The performance ratio represents the relation between actual energy generation and the reference energy generation for a specific period. It depends on the various energy losses in the system [14]. Seasonal variation in performance ratio is shown in Fig. 60.7. Average performance ratio during summer, rainy, and winter season found 0.80, 0.84, and 0.85.

60.5.3 CO2 Emission Reduction Solar photovoltaic system is an environment friendly source of electricity generation and it does not emit CO2 as fossil fuel-based power plants. System Lifecycle Emissions details are shown in Table 60.1 which provides information about total replace

A. Mittal et al. 0.9

PR during summer season

0.85

PR during rainy season

0.8

PR during winter season

0.75 Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb

Performance ratio ------------>

800

Fig. 60.7 Plot of performance ratio

Table 60.1 Lifecycle emissions by components

Item

Modules

Supports

LCE (life cycle emisssion) 1713 kgCO2 /kWp 6.24 kgCO2 /kg Quantity

750 kW

20,280 kg

Subtotal [KgCO2 ]

1,284,750

126,547

emissions, i.e., reduced CO2 emissions which would emit if whole electric units will be generate by coal-based power plants. Total produced emissions = 1284.75 + 126.547 ≈1412 tCO2 . Average system production = 1144 MWh/yr. Lifetime = 25 years. Grid lifecycle emissions = 936 gmCO2 /kWh. Total replace emissions = Grid Lifecycle emissions* average system emissions* lifetime. = 26,770 tCO2 . Total CO2 emission balance = 26,770–1412. = 25,358 tCO2 .

60.6 Life Cycle Evaluation of PV System The lifecycle evaluation of grid-connected solar photovoltaic plant includes the total energy taken by the system components for their materials built-up and transportation of material to be precise embodied energy of the system, total energy generated by the plant, its energy payback period, its useful life time, and capacity factor.

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60.6.1 Energy Payback Period Energy payback period indicates time taken by the system to generate same amount of energy which is used to build it. Energy payback period can be determined as, Energy payback period =

(Em + Emf + Et + Ei + Emg) Eg

(60.15)

where Em = Prime energy required to produce materials comprise PV system. Emf = Prime energy required to fabricate PV system. Et = Prime energy to transport materials used during its tenure. Ei = Prime energy required to install the SPV system. Emp = Prime energy required for end-of-life management. Eg = Annual electricity generation in primary energy terms. Em + Emf + Et + Ei + Emg = 1516.59 kWh/m2 of PV modules [11]. Area required for PV modules = Area of a Panel ∗ no of panels = 1.94*2028 = 3935 m2

(60.16)

Total embodied energy is = 3935 ∗ 1516.59 = 5968 MWh

(60.17)

Average annual energy generation (Eg ) = 1144 MWh. Energy payback period = =

Total Embodied Energy Annual energy genration

5968 = 5.3 years 1144

(60.18) (60.19)

60.6.2 Capacity Factor Capacity factor is defined as “the ratio of the Annul energy generated in kWh per kWp by grid connected solar PV system to the yearly period”. It should be close to unity [15]. Energy generation by solar PV system depends on solar irradiance and clear sunny days at site location. Average yearly energy generation and annual

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average energy generation per kWp are calculated as 1144 MWh and 1525 kWh/kWp, respectively. Thus, Capacity factor =

Annual Energy Generation per kWp 24 ∗ 365

Capacity factor =

1525 = 0.174 8760

(60.20) (60.21)

60.7 Economic Analysis Economic analysis of solar photovoltaic system includes net present value, capital recovery factor, annual uniform cost, per watt generation cost, and per unit generation cost. Present value of proposed system is shown in Table 60.2. For this, system capital recovery factor will be 0.0936 at 8% discount rate for its tenure. Annual uniform cost plays vital role in the calculation of per unit cost. Annual uniform cost can be determined by the product of net present value and capital recovery factor. Cost per unit =

Annual Uniform cost of plant Annual Energy Generated

(60.22)

3580200 = 3.129 INR 1144000

(60.23)

Total Investment Installed Capacity

(60.24)

Cost per unit =

Cost per watt = =

38250000 = 51.00 INR 750000

(60.25)

Table 60.2 Cost of various component used in SPV system Item

Cost per watt (INR)

Total cost (INR)

Modules

30

22,500,000

Inverters

6

4,500,000

Support and Integration

5

3,750,000

Setting wiring cost

5

3,750,000

Miscellaneous

5

3,750,000

Total cost of solar PV plant = 38,250,000 INR

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60.8 Conclusion In this paper, a methodology for optimal sizing, designing, and performance analysis of grid-connected solar photovoltaic system for MANIT has been presented. Investigations of the relevant study can be concluded as the 750 kWp SPV system requires 2028 number of modules and these modules occupy area 3935 m2 . This planned system requires six inverter of 125 kWac each. Energy payback period investigated 5.3 years with capacity factor 0.174. The study of this grid-associated SPV system presents approx. 83% performance ratio and 1140 MWh average yearly energy generation. Fiscal estimation on present market prices of components provides cost per watt, cost per unit and present value of system as 51.0 INR, 3.129 INR and approx 3.825 cr. INR, respectively. This projected system reduces CO2 emission near 25,358 tonnes during its tenure of 25 year.

References 1. K. Atluri, S. M. Hananya, B. Navothna, Performance of rooftop solar PV system with crystalline solar cells, National Power Engineering Conference, Madurai, pp. 1–4 (2018) 2. G. Velasco-Quesada, F. Guinjoan-Gispert, R. Pique-Lopez, M. Roman-Lumbreras, A. ConesaRoca, Electrical PV array reconfiguration strategy for energy extraction improvement in gridconnected PV systems. IEEE Trans. Industr. Electron. 56(11), 4319–4331 (2009) 3. K. Janardhan, T. Srivastava, G. Satpathy, K. Sudhakar, Hybrid solar PV and biomass system for rural electrification. Int. J. ChemTech Res. 05(02), 802–810 (2013) 4. A. Kornelakis, E. Koutroulis, Methodology for the design optimization and the economic analysis of grid-connected photovoltaic systems. IET Renew. Power Gener. 3(4), 476–492 (2009) 5. M. Kolhe, Techno-economic optimum sizing of a stand-alone solar photovoltaic system. IEEE Trans. Energy Convers. 24(2), 511–519 (2009) 6. W. Libo, Z. Zhengming, L. Jianzheng, A single-stage three-phase grid-connected photovoltaic system with modified MPPT method and reactive power Compensation. IEEE Trans. Energy Convers. 22(4), 881–886 (2007) 7. K. Janardhan, A. Mittal, Analysis of various control schemes for minimal total harmonic distortion in cascaded H-bridge multilevel inverter. J. Electr. Syst. Inf. Technol. 03(03), 428–441 (2016) 8. B. Karanayil, V.G. Agelidis, J. Pou, Performance evaluation of three-phase grid-connected photovoltaic inverters using electrolytic or polypropylene film capacitors. IEEE Transactions on Sustainable Energy 5(4), 1297–1306 (2014) 9. R. Sharma, L. Gidwani, Grid connected solar PV system design and calculation by using PV∗SOL premium simulation tool for campus hostels of RTU Kota. International Conference on Circuit, Power and Computing Technologies, Kollam, pp. 1–5 (2017) 10. K. Janardhan, A. Mittal, A. Ojha, “Performance investigation of stand-alone solar photovoltaic system with single phase micro multilevel inverter. Energy Reports 06, 2044–2055 (2020) 11. R. Khatri, Design and assessment of solar PV plant for girls hostel (GARGI) of MNIT University, Jaipur city: A case study. Energy Reports, vol. 2, pp. 89–98 (2016) 12. E. Serban, F. Paz, M. Ordonez, Improved PV inverter operating range using a miniboost. IEEE Trans. Power Electron. 32(11), 8470–8485 (2017) 13. A. Ghouari, Ch. Hamouda, A. Chaghi, M. Chahdi, Data monitoring and performance analysis of a 1.6 kWp grid connected PV system in Algeria. Int. J. Renew. Energy Res. 6(1) (2016)

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14. V. P. Singh, B. Ravindra, V. Vijay, M. S. Bhatt, A comparative performance analysis of C-Si and A-Si PV based rooftop grid tied solar photovoltaic systems in Jodhpur. Int. Conf. Renew. Energy Res. Appl. Milwaukee, 250–255 (2014) 15. K. Janardhan, T. Srivastava, K. Sudhakar, Matlab modelling and simulation of solar photovoltaic panel, LAP LAMBERT Academic Publishing (2013)

Chapter 61

Comparative Study and Trend Analysis of Regional Climate Models and Reanalysis Wind Speeds at Rameshwaram B. Abhinaya Srinivas, Garlapati Nagababu, Hardik Jani, and Surendra Singh Kachhwaha Abstract Climate change may affect wind patterns. It will impact wind energy generation. Climate models will help to assess how wind speed is affected by climate change. Climate models have different boundary conditions, and the forcing variables lead to the uncertainty of data. The present study provides a comparison of six regional climate models (RCMs) with reanalysis data (ERA-Interim) and validated with measured data at Rameshwaram. Further quantile mapping technique has been used for the removal of bias from RCM models. Results show that all six RCMs have lesser correlation (~0.50), high bias (~1.4 m/s) with measured data before quantile mapping. However, after the quantile mapping with reanalysis data, the RCMs achieved a higher correlation (~0.63) and less bias (~0.45). Further, the trends of wind speeds for all RCMs have been analysed and checked the significance of trends with the t-test. Results show that wind speed trends are increasing with 0.03 m/s/decade at Rameshwaram. Keywords RCM · Wind speed · Quantile mapping · ERA-Interim

Nomenclature CCCR CORDEX ECMWF ERA GCM ICTP IITM NIWE

Centre for Climate Change Research. Coordinated Regional Climate Downscaling Experiment European Centre for Medium-Range Weather Forecasts ECMWF Reanalysis General circulation model India with the help of the Abdus Salam International Centre for Theoretical Physics Indian Institute of Tropical Meteorology National Institute of Wind Energy

B. Abhinaya Srinivas (B) · G. Nagababu · H. Jani · S. Singh Kachhwaha School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_61

805

806

RCM RCP RCAO QM SD

B. Abhinaya Srinivas et al.

Regional climate model Representative concentration pathways Rossby Centre coupled regional climate model Quantile mapping Standard deviation

Mathematical symbols Fs Fo − 1 M S Qm (t) Qs (t) Rg t

The cumulative distribution function (CDF) The inverse CDF function Measured wind speeds RCM model wind speeds Simulated data from the RCM Bias-corrected data Regression function of wind speed time series Year from 1979 to 2005

61.1 Introduction Globally, energy demand is increasing. Renewable energy sources are playing a crucial role in maintaining the demand and supply of energy. At the same time, renewable energy sources are helpful in the mitigation of climate change but can be affected by different aspects of regional climate change [1]. Wind speed is one of the climate parameters. Therefore, climate change may affect atmospheric dynamics and impact wind profiles, which ultimately leads to a significant impact on wind energy potential [2]. Here, wind power is related to the cube of wind speed. Even a small change in the future wind circulation patterns leads to differentiate the projected wind energy production for the future in a higher range. Future changes of other characteristics of wind flow such as extreme wind patterns, inter and intra-annual wind variability can strongly affect the capability to harness the available wind energy potential [3] fully. It is necessary to consider future climate change while projecting future wind energy production. General circulation models (GCMs) are the mathematical models involved by earth climate system physics which can reproduce future climate by considering different emission scenarios as representative concentration pathways (RCPs). GCMs provide wind speeds output at a global level with coarser spatial resolution. The regional climate models are dynamically downscaled models from GCMs and developed to provide the wind speeds at the regional level with fine spatial resolution. The dynamic downscaling method further adds the good temporal chronology, long-term temporal homogeneity and physical consistency to all atmospheric variables [4].

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In most of the past studies, researchers used RCMs for identifying climate impact on wind resources. Hagen et al. [5] used RCM wind speed data with RCP 2.6 and RCP 8.5 for analysis of wind potential for Germany. According to Hagen, wind power production has been reduced by 2% by the year 2050. Daniel et al. used the EURO CORDEX dataset and ERA-Interim reanalysis data and identified that 51% of locations are showing an increment in wind speed trend [6]. Tobin et al. identified a variation of 5% in wind energy potential for Europe [7]. Janes et al. have developed three RCMs from three parental GCMs with RCP8.5 scenario for the South Asia region and identified that the magnitude of wind speeds has positive and negative change concerning the parental GCM for different locations [8]. Gao et al. have identified that wind power is decreasing in trend at wind power abundant region in China under various RCMs with RCP4.5 and RCP8.5 scenarios [9]. Soares et al. have predicted a small increment in the annual wind energy density at the Iberian northwestern coast by using EURO CORDEX multi-model ensemble [10]. A finer resolution of RCMs may not reduce the model bias [11]. The studies of the efficiency of RCM winds in wind resource assessment are limited in the past. Studies conducted using RCM along the Bay of Bengal and the Arabian Sea have overestimated the wind speeds in comparison with the reanalysis data [12]. Climate simulations developed by Rossby Centre with an identical scale as that of future change were assessed for wind energy resources stability [13]. Kulkarni [4] has identified that wind characteristics simulated by RCM are not giving any value addition to the parent GCM. Therefore, it is necessary to remove the bias from the RCMs wind speeds for wind resource assessment. In the present study, wind speed data has been collected from six RCMs of CORDEX–SA for the period from 1951 to 2005 at Rameshwaram. Further, analysed statistically and compared with measured data (from January 1989 to November 1993) provided by the National Institute of Wind Energy (NIWE). To improve the quality of wind speeds, Quantile mapping (QM) technique is used to remove bias from the RCMs. The bias of RCMs has corrected from 1979 to 2005 with the help of ERA-Interim wind speeds due to high correlation and less bias for ERA-Interim with measured wind speeds. Further, the trend analysis and cumulative change in the wind speeds have been analysed with the help of first-degree polynomial regression.

61.2 Data and Methods 61.2.1 Data In this study, available monthly averaged wind speeds have been considered from six RCMs. The details of RCM and the related parental GCMs are given in Table 61.1. The fine resolution RCM wind speed data is generated by the Centre for Climate Change Research (CCCR), Indian Institute of Tropical Meteorology (IITM), India with the help of the Abdus Salam International Centre for Theoretical Physics

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Table 61.1 Details of RCMs used in the study S. no

RCM

Parental GCM

Spatial resolution

Temporal resolution

Year

1

IITM-RegCM4

CCCma-CanESM2

0.5° X 0.5° Monthly averaged

1979–2005

2

IITM-RegCM4

CNRM-CERFACS

0.5° X 0.5° Monthly averaged

1979–2005

3

IITM-RegCM4

CSIRO-QCCCE

0.5° X 0.5° Monthly averaged

1979–2005

4

IITM-RegCM4

GFDL

0.5° X 0.5° Monthly averaged

1979–2005

5

IITM-RegCM4

IPSL-CM5A

0.5° X 0.5° Monthly averaged

1979–2005

6

IITM-RegCM4

MPI-M-MPI-ESM-MR

0.5° X 0.5° Monthly averaged

1979–2005

(ICTP), Regional Climatic Model version 4.4.5 (RegCM4; Giorgi et al. 2012), European Centre for Medium-Range Weather Forecasts (ECMWF) and ERA-Interim reanalysis monthly mean wind speed data from the year January 1979 to December 2005 which has been considered for comparison purpose. ERA-Interim reanalysis data produces accurate data and is officially used as a validation dataset in EURO CORDEX downscaling simulations [14]. Measured wind speeds from wind mast provided by the National Institute of Wind Energy (NIWE) are used for validation of RCMs. The measured data consists of wind speed with three hours of an interval, later converted into monthly mean wind speeds for validation. The wind speed data from January 1989 to November 1993 has been utilized in this study. Due to the limitation of the availability of measured data, ERA-Interim data is used for bias removal from RCMs. Detailed methodology is shown in Fig. 61.1.

61.2.2 Methods 61.2.2.1

Statistical Validation

RCM models are mathematical models. The fine resolution wind speeds from RCM models may not match the measured values [11]. Even small bias in the wind speeds will lead to a bigger difference in the calculation of wind power density. It is necessary to validate/ reduce the bias of the RCM models for further analysis. In this study, monthly averaged wind speeds from all six RCMs at Rameshwaram has been extracted by the bilinear-interpolation method from the surrounding four nearby nodes. An ensemble of all six RCMs has been produced by the averaging method [15]. In this, all six RCMs are given with uniform weightage. Statistical parameters like bias, standard deviation and correlation coefficient were calculated before and

61 Comparative Study and Trend Analysis of Regional Climate …

809

Fig. 61.1 Methodology of the study

after quantile mapping, as given in Table 61.2.

SD = N 

R=

N 

(61.1)

N −1 (Mi − M)(Si − S)

i=1

i=1

61.2.2.2

  N   (xi − x)2  i=1

(Mi − M)

N  2

(Si − S)2

 21

(61.2)

i=1

Quantile Mapping (QM) Technique

RCM model wind speeds are showing bias with the measured wind speeds. The quantile mapping approach has been used due to its simplicity, effectiveness and low computational cost, for bias correction of climate models’ outputs. The equation of QM is as follows [16].

Measured data

CCCma-CanESM2

CNRM-CERFACS

CSIRO-QCCCE

GFDL

IPSL-CM5A

MPI-M-MPI-ESM-MR

Ensemble

ERA-Interim

A

B

C

D

E

F

G

H

I

Dataset

Mean

6.21

5.34

5.34

6.05

5.18

5.03

5.53

4.38

6.56

Std. dev

1.35

2.12

1.91

2.11

2.24

2.13

2.66

2.54

1.27

Without QM RMSD

1.81 1.69 1.58 1.80 0.56

−1.22 −1.23 −0.35

−1.53 −1.38

1.69

−1.03

−0.51

2.26 2.55

−2.19

–

–

bias

Table 61.2 Correlation coefficient of RCM models without and with quantile mapping

0.91

0.53

0.57

0.60

0.59

0.61

0.32

0.46

–

R

6.21

6.11

6.21

6.09

6.10

5.92

6.15

6.21

–

Mean

With QM

1.40

1.46

1.43

1.51

1.59

1.63

1.50

1.52

–

Std. dev

−0.35

−0.45

−0.35

−0.47

−0.46

−0.64

−0.41

−0.36

–

bias

0.58

1.19

1.30

1.26

1.36

1.34

1.66

1.54

–

RMSD

0.91

0.63

0.54

0.60

0.57

0.60

0.29

0.40

–

R

810 B. Abhinaya Srinivas et al.

61 Comparative Study and Trend Analysis of Regional Climate …

Q m (t) = F0−1 [Fs [Q s (t)]]

811

(61.3)

where Q m (t) and Q s (t) are the bias-removed data and modelled data from the RCM. Fs and F0−1 are the cumulative distribution function (CDF) of the RCM model data and the inverse CDF of the reanalysis data, respectively. Wind speeds are fitted with the Weibull distribution. Scale and shape parameters for the reanalysis wind speed time series and RCM wind speed time series have been evaluated. Cumulative distribution function (CDF) is estimated for both the wind speed data. Then generating the wind speeds with CDF of RCM and scale and shape parameters of observed time series.

61.2.2.3

Trend Analysis

For analysis of trends and cumulative change in the wind speeds of RCMs and ERAInterim, a first-degree polynomial regression analysis using the least square method has been considered. The t-test is used for the significance of trends. The cumulative change can be calculated by Eq. 61.4. C=

R g (t end ) − R g (t start ) R g (t start )

(61.4)

where Rg indicates the regression function of wind speed time series, tend is the annual mean wind speed for the final year and tstart indicates the annual mean wind speed for the starting year. From the cumulative change calculations, the trend of the wind speeds has been identified from the slope of the equation. The identified trends are represented in the units of m/s/decade.

61.3 Results and Discussion 61.3.1 Validation Wind speed data from 1951 to 2005 of six RCM models has been considered. The annual average wind speeds are calculated and shown in Fig. 61.2. The statistical analysis of six RCMs, multi-model ensemble and ERA-Interim wind speed data has been performed and validated with the available measured data and presented in Table 61.2. RCMs underestimate the wind speed as they show negative bias. The standard deviation of the wind speed data for RCMs is in the range of 1.91 to 2.66. The correlation coefficient is achieved by the RCM models with the measured data are in the range of 0.33 to 0.61. However, ERA-Interim data is highly correlated to measured data with a correlation coefficient of 0.91. It is necessary to reduce the bias

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before analysing the RCMs wind speeds. In the present study, the quantile mapping technique is used to reduce the bias from RCM data. QM removes the bias from RCM wind speeds by matching the reanalysis wind speeds quantile. Due to the availability of measured data for a small duration, highly correlated ERA-Interim data is used for the bias removal of RCM models. After quantile mapping, the bias in the RCMs is reduced to the range of 0.35 to 0.6. The standard deviation of the RCM ensemble is reduced from 2.12 to 1.46. The correlation coefficient with measured data is also improved from 0.53 to 0.63 for the RCM ensemble. A Taylor diagram containing root mean square deviation, correlation coefficient and standard deviation has been plotted for RCMs and ERA-Interim with respect to measured wind speeds. Figure 61.3 is indicating the Taylor diagram of RCM models before and after quantile mapping, respectively. RCM models after removal of bias are considered for further analysis.

Fig. 61.2 Annual mean wind speeds of six RCMs and its ensemble at Rameshwaram from 1951– 2005

Fig. 61.3 Taylor diagram for all six RCMs and ERA-Interim with measured data a before QM b after QM

61 Comparative Study and Trend Analysis of Regional Climate …

813

Table 61.3 Cumulative change of wind speeds and trends for RCMs S. no

RCM

Cumulative change in the Trend (m/s/decade) wind speed (%) Before QM

After QM

Before QM

After QM

1

CCCma-CanESM2

−1.1

−2.9

−0.01

−0.05

2

CNRM-CERFACS

6.6

−1.0

0.06

−0.02

3

CSIRO-QCCCE

0.7

0.6

0.01

0.01

4

GFDL

0.6

−0.1

0.01

−0.01

5

IPSL-CM5A

0.7

−1.3

0.01

−0.03

6

MPI-M-MPI-ESM-MR

9.0

2.9

0.08

0.06

7

Ensemble

2.7

0.6

0.03

0.01

61.3.2 Cumulative Change and Temporal Trends of RCMs Wind Speeds The cumulative change of wind speeds is calculated by the first-degree polynomial regression equation, as mentioned in Eq. 61.4. The maximum cumulative change with unbiased wind speed is 9.0% which is limited to 2.9% after bias removal from the MPI-M-MPI-ESM-MR model, which is given in Table 61.3. The same RCM is showing the maximum trend of 0.06 m/s/decade after bias correction. The cumulative change in wind speeds for RCMs ensemble is found to be 0.6% which indicates the positive change in the wind speeds with time. To understand the wind speeds changes in detail, seasonal cumulative changes and trends are estimated. Table 61.4 is showing the cumulative change in the seasonal wind speeds and trends of six RCMs. The maximum cumulative change in the wind speeds is observed in the summer season was 12.37% for GFDL. Compared with other seasons, postmonsoon is identified with more variations for all RCMs. It is necessary to consider the seasonal variations for wind energy generation. Season wise changes in the cumulative wind speeds for all the six RCMs and their ensemble are plotted and shown in Fig. 61.4.

61.4 Conclusions Near-surface wind speed from 1979 to 2005 of six RCMs has been analysed at Rameshwaram. Six RCMs and ERA-Interim near-surface wind speeds are compared with the measured data provided by NIWE at Rameshwaram for January 1989– November 1993. RCMs wind speeds are showing higher bias than ERA-Interim with the measured wind speeds. ERA-Interim has a high correlation coefficient (r =

CCCma-CanESM2

CNRM-CERFACS

CSIRO-QCCCE

GFDL

IPSL-CM5A

MPI-M-MPI-ESM-MR

Ensemble

1

2

3

4

5

6

7

−0.12 [−0.16]

0.07 [0.20]

0.08 [0.003]

0.07 [0.10]

0.11 [0.12}

7.04 [3.44] 0.05 [0.076]

6.97 [8.02]

17.45 [0.179]

16.16 [3.81]

11.06 [5.67]

8.59 [0.26] 0.07 [0.006]

Trend

CCW (%)

−27.38 [−8.16]

−0.01 [−0.01]

−0.10 [0.08]

−13.95 [5.86]

−0.13 [−0.58]

−0.09 [−0.24]

−12.80 [−12.37]

0.15 [0.14]

−0.05 [−0.12]

−5.08 [−5.59]

27.14 [7.63]

0.016 [0.08]

0.07 [0.09]

Trend

2.15 [5.86]

12.17 [3.89]

CCW (%)

Summer

*The bias-removed wind speeds cumulative change, and trends are shown in the bracket

RCM

S.no

Winter

Table 61.4 Summary of seasonal mean wind speed trends and cumulative changes

1.51 [0.68]

5.53 [1.24]

0.535 [1.15]

1.2 [0.10]

−2.47 [−0.50]

6.17 [−0.90]

0.51 [1.00]

CCW (%)

Monsoon

0.03 [0.02]

0.07 [0.03]

0.01 [0.03]

0.02 [0.01]

−0.04 [−0.02]

0.09 [−0.03]

0.01 [0.03]

Trend

6.80 [−2.98]

2.19 [−10.03]

8.79 −[4.24]

8.12 [11.25]

7.79 [6.33]

15.21 [−11.16]

−4.56 [−12.29]

CCW (%)

Post-monsoon

0.04 [−0.05]

0.01 [−0.18]

0.06 [−0.09]

0.05 [0.20]

0.04 [0.10]

0.06 [−0.20]

−0.02 [−0.24]

Trend

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20

50

0

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Summer

Winter (QM)

(c)

Summer (QM)

(d) 20 10 0 -10

10 5 0 -5

Monsoon

Monsoon (QM)

Post monsoon

Post monsoon (QM)

Fig. 61.4 Cumulative change in the wind speed percentage (%) for all six RCMs and its ensemble for four seasons a winter, b summer, c monsoon, d post-monsoon

0.91) with the measured data. Due to measured data availability limitations, ERAInterim is used for the removal of bias from RCM models with the quantile mapping technique. Later, the cumulative change in wind speeds and trends are calculated for bias-removed RCMs. The below observations are identified from the conducted study. • All RCMs are showing negative bias (−0.51 to –2.19) with respect to the measured data. It is indicating that the RCM models are underestimating the wind speeds. • RCM model wind speeds are having a greater standard deviation (>2) in comparison with measured data (1.27). • ERA-Interim has shown a greater correlation coefficient (0.91) and lesser bias (−0.35) with measured data. It indicates that ERA-Interim wind speeds are matching with the measured wind speeds. • After removing bias from the RCM models with respect to ERA-Interim by quantile mapping, the bias of models is reduced to the range of −0.36 to − 0.64. • The multi-model ensemble is generated as it reduces the error variance from all RCM models. The correlation coefficient has been improved from 0.53 to 0.63, and bias reduced from 1.23 m/s to 0.45 m/s with quantile mapping. • There is a 0.6% cumulative change in the wind speeds with 0.01 m/s/decade is observed for RCMs ensemble with bias-removed data. It indicates the increase in the nature of wind speeds at Rameshwaram.

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• For seasonal mean wind speeds, a maximum of 12.37% cumulative change occurred in the summer season with GFDL RCM. Even though there is a small change of 0.6 % in the cumulative change of wind speeds observed at Rameshwaram, one should also consider the seasonal variations for wind energy generation. Acknowledgements “The World Climate Research Programme’s Working Group on Regional Climate and the Working Group on Coupled Modelling, the former coordinating body of CORDEX and responsible panel for CMIP5 are gratefully acknowledged. The climate modelling groups, ICTP are sincerely thanked for producing and making available their model output. The authors thank the Earth System Grid Federation (ESGF) infrastructure and the Climate Data Portal hosted at the Centre for Climate Change Research (CCCR), Indian Institute of Tropical Meteorology (IITM) for providing CORDEX South Asia data”. We would like to thank the ECMWF for making available the ERA-Interim reanalysis product at https://www.ecmwf.int/datasets. We would also thank NIWE for making the measured wind speeds available at Rameshwaram.

References 1. P. De Jong, T.B. Barreto, C.A.S. Tanajura, D. Kouloukoui, K.P. Oliveira-esquerre, A. Kiperstok, E.A. Torres, Estimating the impact of climate change on wind and solar energy in Brazil using a South American regional climate model. Renew. Energy. (2019). https://doi.org/10.1016/j. renene.2019.03.086 2. K. Solaun, E. Cerdá, Impacts of Climate Change on Wind Energy Power—Four Wind Farms in Spain (2019). https://doi.org/10.1016/j.renene.2019.06.129 3. X. Costoya, D. Carvalho, M. Gómez-gesteira, On the suitability of offshore wind energy resource in the United States of America for the 21st century. Appl. Energy. 262, 114537 (2020). https://doi.org/10.1016/j.apenergy.2020.114537 4. S. Kulkarni, M.C. Deo, S. Ghosh, Performance of the CORDEX regional climate models in simulating offshore wind and wind potential. Theor. Appl. Climatol. 135, 1449–1464 (2019). https://doi.org/10.1007/s00704-018-2401-0 5. H. Koch, S. Vögele, F.F. Hattermann, S. Huang, The impact of climate change and variability on the generation of electrical power. Meteorol. Zeitschrift. 24, 173–188 (2015). https://doi. org/10.1127/metz/2015/0530 6. D. Ganea, E. Mereuta, L. Rusu, Estimation of the near future wind power potential in the black sea. Energies 11 (2018). https://doi.org/10.3390/en11113198 7. I. Tobin, S. Jerez, R. Vautard, F. Thais, E. Van Meijgaard, A. Prein, Climate change impacts on the power generation potential of a European mid-century wind farms scenario. Environ. Res. Lett. 11 (n.d.) 34013. https://doi.org/10.1088/1748-9326/11/3/034013 8. T. Janes, F. Mcgrath, I. Macadam, R. Jones, Science of the total environment high-resolution climate projections for South Asia to inform climate impacts and adaptation studies in the Ganges-Brahmaputra-Meghna and Mahanadi deltas. Sci. Total Environ. 650, 1499–1520 (2019). https://doi.org/10.1016/j.scitotenv.2018.08.376 9. Y. Gao, S. Ma, T. Wang, The impact of climate change on wind power abundance and variability in China. Energy 189, 116215 (2019). https://doi.org/10.1016/j.energy.2019.116215 10. P.M.M. Soares, D.C.A. Lima, R.M. Cardoso, M.L. Nascimento, A. Semedo, Western Iberian offshore wind resources: More or less in a global warming climate? Appl. Energy. 203, 72–90 (2017). https://doi.org/10.1016/j.apenergy.2017.06.004 11. Y. Wang, L.R. Leung, D. Lee, W. Wang, Y. Ding, Rcm_Review03, J. Meteorol. Soc. Japan.82, 1–30 (2004). papers2://publication/uuid/56A6022E-970F-40C8–8E96-F624F1D6AA1A

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12. P. Lucas-Picher, J.H. Christensen, F. Saeed, P. Kumar, S. Asharaf, B. Ahrens, A.J. Wiltshire, D. Jacob, S. Hagemann, Can regional climate models represent the Indian monsoon? J. Hydrometeorol. 12, 849–868 (2011). https://doi.org/10.1175/2011JHM1327.1 13. S.C. Pryor, R.J. Barthelmie, E. Kjellström, Potential climate change impact on wind energy resources in northern Europe: Analyses using a regional climate model. Clim. Dyn. 25, 815–835 (2005). https://doi.org/10.1007/s00382-005-0072-x 14. D. Carvalho, A. Rocha, M. Gómez-Gesteira, C. Silva Santos, Potential impacts of climate change on European wind energy resource under the CMIP5 future climate projections, Renew. Energy. 101, 29–40 (2017). https://doi.org/10.1016/j.renene.2016.08.036. 15. D.W. Pierce, T.P. Barnett, B.D. Santer, P.J. Gleckler, Selecting global climate models for regional climate change studies. Proc. Natl. Acad. Sci. U. S. A. 106, 8441–8446 (2009). https:// doi.org/10.1073/pnas.0900094106 16. H. Li, J. Sheffield, E.F. Wood, Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching, J. Geophys. Res. Atmos. 115 (2010). https://doi.org/10.1029/2009JD012882

Chapter 62

A Novel Islanding Detection Technique for Grid-Connected Distributed Generation Using KNN and SVM Poonam P. Tikar , Ravishankar S. Kankale , and Sudhir R. Paraskar

Abstract This paper presents a novel technique for islanding detection using machine learning. Islanding occurs when a distribution generation (DG) along with local load become electrically isolated from the grid. Existing methodologies lack in accuracy and speed of islanding detection. The proposed methodology involves the simulation of distribution system with DG, creation of islanding, and non-islanding cases to capture voltages and current data which will be further processed using a four-level discrete wavelet transform for feature extraction. The machine learning classification model is created using a supervised learning classification algorithm based on the dataset generated. This classification model is used to detect the islanding condition. The proposed system is tested on different islanding and non-Islanding conditions. The experimental result shows that the proposed methodology is efficient than earlier islanding detection techniques. Keywords Islanding · Machine learning · Classifier · Distributed generation · Support vector machine · K-nearest neighbor

62.1 Introduction To meet the ever-increasing energy demand of the world, all are looking toward renewable DG. The research on the growth of DG systems and their utilization is increasing around the world because of their advantages and low pollution com-pared to the burning of fossil fuels. In the conventional power system, the power is received by the consumers, but in the DG connected smart grid, consumers can also produce P. P. Tikar (B) · R. S. Kankale · S. R. Paraskar Department of Electrical Engineering, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, India e-mail: [email protected] R. S. Kankale e-mail: [email protected] S. R. Paraskar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_62

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power. The small-scale power generation systems such as photovoltaic, mini-hydro, tidal, and biomass connected to the utility grid at the customer end are called DG [1]. In the islanding condition, the distributed generation system gets electrically isolated from the rest of the power system and continues to feed the local load. Intentional islanding is carried out purposefully to isolate the proportion of the grid during fault or disturbance, where distributed generation assists in a continuous supply of electrical energy to the load. However, unintentional islanding is an uncontrolled operation which may cause serious danger to the utility as well as distributed generation. The worry is chiefly as to the fluctuation and variation of the voltage and frequency. Stability interference of the system may be responsible for creating complications in automatic grid reconnection and restoration [2].

62.1.1 Islanding Detection Methods Islanding detection methods (IDM’s) are broadly classified into local and remote methods which are further classified into different levels. The local method relies on monitoring the various parameters at the local DG terminal. The local methods are further divided into active methods, passive methods, and hybrid methods. The remote method monitors the parameters between the grid and the DG terminal. These methods are based on digital signal processing, machine learning, data mining, etc. Active Method. Active islanding detection methods or techniques work by injecting a signal into distributed generation output. It is necessary to monitor the deviation in the signal so as to detect possible islanding conditions. The system parameters to monitor can be voltage, frequency, and impedance. The disadvantage of active method is the degradation of system performance because of the injected signal [3–5]. Passive Method. The passive method monitors the changes occurring in the system parameters at the point of common coupling (PCC). The system parameters involve voltage, current, power, frequency, impedance, etc. The speed of the detection is less as compared to active method. But this method does not affect any power quality or grid operations [6–8]. Intelligent IDM’s. Intelligent IDM’s uses machine learning as well as data mining techniques for classification. Intelligent IDM’s do not require pre-specified threshold values. Input to the machine learning classifier is voltage and current, and it gives output whether it is islanding condition or not. Intelligent IDM’s are not reliable than other techniques [9]. Our contributions to the paper are as follows: (1) Bringing out the best and efficient alternative to detect islanding, (2) introducing a new approach to detect islanding that uses discrete wavelet transform for feature extraction and machine learning for

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islanding detection, and (3) comparing accuracy and speed of different classifiers like SVM and KNN in the same environment. The remaining paper is organized as follows. Section 62.2 reviews existing islanding detection techniques. The system under study is explained in Sect. 62.3. The proposed islanding detection methodology is explained in Sect. 62.4. In Sect. 62.5, the implementation result with all the test cases and comparison between classifiers is given. Finally, the conclusion and future scope is given in Sects. 62.6 and 62.7, respectively.

62.2 Literature Survey Masoud Ahmadipour et al. [10] proposed an islanding detection method that uses modified slantlet transform (MSLT) and machine learning classification. Also, harmony search algorithm is implemented to identity perfect scales of slantlet parameters. Hamid Reza Baghaee et al. [11] proposed a scheme which uses support vector machine for classification but still lacks to detect islanding accurately. This method is passive in nature. Bekhradian et al. [12] introduced an approach for synchronousgenerator-based microgrids that use derivative of the equivalent resistance notice from the small-scale synchronous generator. In the proposed scheme by Rajashree Dhua et al. [13], tuned filters are connected at distributed generation end which is used for islanding detection. The proposed approach is an enhancement in impedance-based islanding detection methods. Gao Ying and Ye Jianwei [14] briefed about the improved slip mode frequency shift islanding detection scheme which is aimed at reducing the number of output harmonics which ultimately improves the performance of islanding detection. This scheme is simulated using PSIM software. Xing Xie et al. [15] proposed an islanding detection scheme for microgrid which takes care of dynamic behavior of load. This passive method parallelly considers dynamic load and calculates threshold value. This method is suitable for SDG and inverter-based DG. Daniel Motter et al. [16] introduced a passive islanding detection scheme that uses the under-voltage block function. This approach is tested on 4606 test cases, out of that, 2149 islanding and 2457 non-islanding cases are considered. The machine learning approach is used for islanding detection which involves features like the variation in frequency and reactive power. K means clustering is used in order to process the offline data for training support vector machine. This approach only considers islanding events under pseudo islanding phenomenon conditions [17].

62.3 System Under Study The system under study represents the grid-connected distributed generation system consisting of 1000 MVA source operating at 120 kV feeding to a 30 km distribution

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line operating at 25 kV through a step-down transformer T1 (120 /25 kV). This voltage is further step-down to 575 V with another two step-down transformers T2 and T3 (25 kV/575 V) and finally fed to the load. Renewable energy-based two 9 MW wind farms; each contains six 1.5 MW of wind turbines connected at load bus supply power to the 120KV gird through a 30 km distribution line. The complete system under study is simulated using MATLAB Simulink. Simulink model has been structured as appeared in Fig. 62.1. Table 62.1 shows the specifications of the Simulink model. Capturing of voltage and current signals is carried at the breaker location (point of common coupling). Table 62.2 shows different cases for data acquisition which provides voltage and current values. The received three-phase voltage and current values are then converted into positive, negative, and zero sequence components by using a sequence analyzer. Here, 1 = positive sequence, 2 = negative sequence, and 0 = zero sequence. The three sequence components of voltages V1 , V2 , V0 are calculated as given below:

Fig. 62.1 Simulink model

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Table 62.1 Specifications of Simulink model Component

Specification

Generator

Rated MVA = 1000, frequency = 50 Hz, rated voltage = 120KV, base voltage = 120 kV

Grounding transformer Rated MVA = 100, nominal voltage = 25 KV, frequency = 50 Hz, R0 = 0.025 pu, X0 = 0.75 pu, Rm = 500 pu, Xm = 500 pu Distributed generations Two wind farms (each of 9 MW) consists of six 1.5 MW wind turbines Transformer T1

Rated MVA = 10, frequency = 50 Hz, rated voltage = 120/25 KV, base voltage = 25 KV, R1 = 0.00375 pu, L1 = 0.1 pu, R2 = 0.00375 pu, L2 = 0.1 pu, Rm = 500 pu, Lm = 500 pu

Transformer T2, T3

Rated MVA = 10, frequency = 50 Hz, rated voltage = 25/575 KV, base voltage = 575 KV, R1 = 0.00375 pu, L1 = 0.1 pu, R2 = 0.00375 pu, L2 = 0.1 pu, Rm = 500 pu, Lm = 500 pu

Distribution lines (DL) Length = 30 km each, base voltage = 25 kV, L1 = 1.05e-3 H/km, L0 = 3.32e-3 H/km, C1 = 11.33e-009 F/km, C0 = 5.01e-009 F/km, R1 = 0.1153 ohms/km, R0 = 0.413 ohms/km Normal loading data

L1 = L2 = 12 MW, C1 = C2 = 12 MW, 0.9MVAR

Table 62.2 Cases for data acquisition Case

No. of data samples Description

Islanding

1

Power cut

Islanding

1

Grid disconnected

Islanding

2

Trip switching with no load and load

Non-islanding 1

Second DG triggered

Non-islanding 3

Normal operation with no load, load, sudden load change

Non-islanding 11

Three-phase faults with no load

V1 =

 1 Va + aVb + a2 Vc 3

(62.1)

V2 =

 1 Va + a2 Vb + aVc 3

(62.2)

1 (Va + Vb + Vc ) 3

(62.3)

V0 =

where Va , Vb , Vc are three-phase voltage phasors and A = 1∠120◦ (complex operator).

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62.4 Proposed Islanding Detection Methodology The proposed methodology comprises of five stages as appeared in Fig. 62.2. Following are the sequence wise steps.

62.4.1 Data Acquisition Data acquisition is performed at breaker location (PCC), for different test condition events like islanding and non-islanding. For data acquisition, different cases are created as given in Table 62.2 which are further given as the input to data preprocessing and feature extraction block to extract the features using discrete wavelet transform (DWT).

62.4.2 Data Pre-processing Data pre-processing is an important and useful step that is used to increase the performance and speed of the islanding detection algorithm. In this step, useful information is extracted from the signals in order to reduce the dimension of input data. Initially, simulation of the power system under study is carried out to obtain the instantaneous values of voltage and current signals at PCC. This time-series signal data captured from the simulation is then converted to a normal metadata value vector.

62.4.3 Feature Extraction Feature extraction is performed on raw data prior to applying any machine learning algorithm on the transformed data in feature space. The proposed islanding detection scheme employs the DWT for feature extraction. Voltage and current signals obtained

Fig. 62.2 Proposed methodology

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Fig. 62.3 Machine learning process

after pre-processing are processed using DWT to obtain the approximate coefficients of voltage and current signals. Wavelet decomposition of voltage and current signals are carried up to the fourth level. The approximate coefficients of the fourth level are used as input to train the fault classifier. Then, statistical parameters like mean, variance, entropy, standard deviations, and root mean square are calculated for all the test cases to extract the features [18]. Features extracted at the feature extraction level are combined together, and the target vector is also identified for all the features.

62.4.4 Building Classification Model (Training Phase) Machine learning classification includes the training and testing phase. The dataset consisting of feature vector and target vector is given as the input to the training block. The output of the training phase is the trained classification model which is used to predict future islanding events as appeared in Fig. 62.3. Training a model using a classification algorithm is necessary, so that it can understand different rules, features, patterns, etc.

62.4.5 Testing Classification Model The final step is testing the classification model. As appeared in Fig. 62.4, the contribution to this progression is prepared to model and test data to anticipate the outcome.

Load the Test Data

Load the Trained Model

Fig. 62.4 Testing classification model

Predict and Classify the Results

Output the Result

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In this step, different parameters like accuracy and time taken to predict the result are analyzed.

62.5 Result and Discussion In order to evaluate the performance of proposed islanding detection methodology, a laptop having an Intel i3 processor and 4 GB RAM with 64-bit Windows 7 ultimate OS is used. Implementation has been carried out in MATLAB R2016A version. After the Simulink model is designed, different cases are executed as given in Table 62.2. Voltage and current signals of each case are given as input to feature extraction block. Post data acquisition, for each voltage and current signal, 5002 records are generated which are converted into metadata vector. Input to the first level DWT is 5002 samples which are then reduced to 2501, 1251, 626, and 313 in the first, second, third, and fourth level DWT, respectively. Based on the outputs of fourth level DWT, statistical parameters such as mean, variance, entropy, standard deviation, and root mean square are calculated which are used as a feature in the feature vector. The dataset generated consists of extracted features of all the test cases with target output, i.e., islanding or non-islanding. Further, the dataset is given as an input to classification algorithms K-nearest neighbor and support vector machine to generate the classification model. For the validation of the predicted classification, confusion matrix is used. The confusion matrix shows the cumulative analysis of predicted results. It shows the errors made by classifier using parameters like True Positive, True Negative, False Positive, and False Negative. Figures 62.5 and 62.6 show the confusion matrix of the KNN and SVM classification model, respectively. The result achieved shows that the detection accuracy of both the classification models is equivalent to 100%. In the testing phase, the proposed methodology has been tested on different 21 cases such as three-phase faults (fault resistance from 0.001 to 0.01), load switching, trip switching, grid disconnected, and power cut with load and no-load condition. Figure 62.7 shows waveforms of sequence components, preprocess sequence components and fourth level approximate coefficients of sequence components of voltage and current signal for the sample test case with the following condition. • Islanding Case: Trip switching, three-phase breaker 1 open switched from 0.3 to 0.32 s, three-phase breaker 2 closed with no load. Finally, based on the given input, KNN and SVM classification model predicts the test output (islanding / non-islanding) and evaluation time for testing. Table 62.3 represents the accuracy of KNN and SVM classifier based on 21 different events (6 islanding, 15 non-islanding). The experimental result shows that the detection and classification accuracy of the SVM classifier is more than the KNN classifier.

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Fig. 62.5 Confusion matrix for KNN classifier

Fig. 62.6 Confusion matrix for SVM classifier

Figure 62.8 represents training and testing time for KNN and SVM classifiers in seconds. Based on the average of 19 events of training and 21 events of testing, the KNN classifier requires less time for training as well as testing than the SVM classifier.

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Fig. 62.7 Test case: a Waveforms of sequence components, preprocess sequence components, and fourth level approximate coefficients of sequence components of voltage signal, and b waveforms of sequence components, preprocess sequence components, and fourth level approximate coefficients of sequence components of current signal

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Table 62.3 Accuracy of KNN and SVM classifier Classifier

Events

KNN

Islanding Non-islanding Islanding

6

0

Non-islanding

0

15

SVM

Predicted islanding events

SVM Classifier

0.15

KNN Classifier

0.12 0

Predicted non islanding events

Accuracy (%)

5

1

95.23

0

15 100

1.16 0.72 0.2

0.4 Testing Time

0.6

0.8

1

1.2

1.4

Training Time

Fig. 62.8 Training and testing time (in seconds) for KNN and SVM classifier

The proposed Islanding detection method (KNN and SVM) has been compared with existing islanding detection methods like intelligent-based relay [19], decision tree algorithm [20], over/under frequency [21], ROCOF based technique [21]. The comparative analysis graph has been given in Fig. 62.9. The comparative analysis graph shows that the proposed methodology performs better in terms of detection speed and detection accuracy. However, the results thus obtained do not prove that the proposed islanding detection methodology has no disadvantages because only 40 events are simulated which are still limited in size. Secondly, the proposed methodology is not tested on the IEEE standard power distribution network with multiple DG interfaces. Overall Accuracy (%) 95.23

100

83.33

Proposed Proposed Intelligent method KNN method SVM based relay

Fig. 62.9 Comparative analysis

94.5

90.24

93.81

Decision tree Over/under ROCOF based algorithm frequency technique

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62.6 Conclusion This paper shows the use of a discrete wavelet transform for feature extraction and machine learning-based classifiers for islanding detection. Simulation results show that the proposed methodology is efficient and reliable than existing islanding detection methods. Out of the two implemented classification models (SVM and KNN), experimentally it is found that SVM classifier performs better than KNN classifier. It has been observed that training and testing time for the KNN classifier is less than the SVM classifier. From the overall results, it is concluded that the proposed methodology can be implemented in real-time so as to improve the efficiency of islanding detection.

62.7 Future Scope From the literature review, it is found that there is still scope to find a better method for islanding detection on the basis of efficiency, computational time, and complexity. The work presented in this paper can be further extended for the detection of the causes of islanding. A lot of research is focused on synthetic data for training and testing classifiers. Therefore, there is a need to work on real-time detection and classification of islanding. De-noising is still a challenge, and feature extraction and classification algorithms poorly perform in a noisy environment. Therefore, there is still a way to develop new algorithms for the detection and classification of islanding phenomenon under both noiseless and noisy environments. Most of the algorithms used for the detection and classification are dedicated to a particular power system and are not generalized. Therefore, there is a scope for developing new generalized algorithms that can be applied to any power system with distributed generation.

References 1. R. R. Ch, K. Harinadha Reddy, Islanding detection techniques for grid integrated DG–A review. IJRER 9(2) (2019) 2. N. A. Fadzil et al., A research of islanding detection method for distributed generation: mechanism, merits and demerits. Int. J. Innov. Technol. Exploring Eng. 8(12S2) (2019). ISSN: 2278–3075 3. M. Hamzeh, S., Farhangi, B., Farhangi, A new control method in PV grid connected inverters for anti-islanding protection by impedance monitoring, in Proceedings of the 2008 11th Workshop on Control and Modeling for Power Electronics, Zurich, Switzerland, pp. 1–5, 17–20 August 2008 4. T.-Z. Bei, Accurate, “active islanding detection method for grid-tied inverters in distributed generation.” IET Renew. Power Gener. 11, 1633–1639 (2017) 5. S. Akhlaghi, A. Akhlaghi, A. Ghadimi, A performance analysis of the slip mode frequency shift islanding detection method under different inverter interface control strategies, in Proceedings

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of the 2016 IEEE Power and Energy Conference at Illinois (PECI), Urbana, IL, USA, pp. 1–7. 19–20 February 2016. R. Haider, C.H. Kim, T. Ghanbari, S.B.A. Bukhari, M. Saeed uzZaman, S. Baloch, Y.S. Oh, Passive islanding detection scheme based on autocorrelation function of modal current envelope for photovoltaic units. IET Gener. Transm. Distrib. 12, 726–736 (2018) C. Li, C. Cao, Y. Cao, Y. Kuang, L. Zeng, B. Fang, A review of islanding detection methods for microgrid. Renew. Sustain. Energy Rev. 35, 211–220 (2014) D. Salles, W. Freitas, J.C.M. Vieira, B. Venkatesh, A practical method for non detection zone estimation of passive anti-islanding schemes applied to synchronous distributed generators. IEEE Trans. Power Deliv. 30, 2066–2076 (2015) B. Matic-Cuka, M. Kezunovic, Islanding, “detection for inverter-based distributed generation using support vector machine method.” IEEE Trans. Smart Grid 5, 2676–2686 (2014) M. Ahmadipour, H. Hizam, M. L. Othman, M. A. M. Radzi, N. Chireh, A novel islanding detection technique using modified Slantlet transform in multi-distributed generation. Int. J. Electr. Power Energy Syst. 112, 460–475 (2019). ISSN 0142–0615 H. R. Baghaee, D. Mlaki´c, S. Nikolovski, T. Dragiˇcevi´c, Support vector machine-based islanding and grid fault detection in active distribution networks. IEEE J. Emerg. Select. Topics Power Electron. R. Bekhradian, M. Davarpanah, M. Sanaye-Pasand, Novel approach for secure islanding detection in synchronous generator based microgrids. IEEE Trans. Power Delivery 34(2), 457–466 (2019) R. Dhua, D. Chatterjee, S. K. Goswami, Harmonic filter-based improved islanding detection technique for microgrid, in IET Renewable Power Generation 13(13), 2443–2450 (2019) Y. Gao, J. Ye, Improved slip mode frequency-shift islanding detection method, in 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), Jishou, China, pp. 152–155 (2019) X. Xie, C. Huang, D. Li, A new passive islanding detection approach considering the dynamic behavior of load in microgrid. Int. J. Electr. Power Energy Syst. 117, 105619 (2020). ISSN 0142–0615 D. Motter, J.C.M. Vieira, Improving the islanding detection performance of passive protection by using the undervoltage block function. Electric Power Syst. Res. 184, 106293 (2020). ISSN 0378–7796 Y. Li, N. Lu, X. Wang, B. Jiang, Islanding fault detection based on data-driven approach with active developed reactive power variation. Neurocomputing 337, 97–109 (2019). ISSN 0925–2312 MATLAB, The MathWorks Inc (Natick, Massachusetts, United States, 2018). K. El-Arroudi, Intelligent based approach to islanding detection in distributed generation, in IEEE transactions on power delivery 22(2) (2007) A. Shrestha, R. Kattel, M. Dachhepatic, B. Mali, R. Thapa, A. Singh, D. Bista, B. Adhikary, A. Papadakis, R.K. Maskey, Comparative study of different approaches for islanding detection of distributed generation systems. Appl. Syst. Innov. 2, 25 (2019) N.W.A. Lidula, A.D. Rajapakse, A pattern-recognition approach for detecting power islands using transient signals—part ii: performance evaluation. IEEE Trans. Power Delivery 27(3), 1071–1080 (2012)

Chapter 63

A 150 kW Grid-Connected Roof Top Solar Energy System—Case Study Achala Khandelwal and Pragya Nema

Abstract With the growing requirement of energy and draining resources, the globe is approaching toward the renewable sources of supply. India is moving along with the world to extensively utilize the natural sources. One of the natural sources that is readily available in the country is the solar source. With the development in solar systems, rooftop solar photovoltaic system is an appealing alternate source of electricity for any industry or household. On contrast to the conventional source of generation, the sunrays are obtainable at zero cost and produce pollution-free electricity. The possibility of PV system at a certain site is evaluated through the availability of area, availability of sunrays, requirement of demand, etc. The technical specifications can be calculated using software simulation tools. This paper presents the performance analysis of a 150 kW grid-tied photovoltaic system mounted on the rooftop of an industry. Performance analysis of this grid-connected PV plants can assist in designing, functioning, and maintenance of a new grid-connected PV system. Keywords Array · Solar · Grid · Photovoltaic · Energy

63.1 Introduction Sun energy is the unique source of generating electricity which is most easily available, free of cost, and non-polluting as well. Solar photovoltaic system is the broadly used technology across the world [4, 16]. The huge production of PV cells and modules along with the farther growth in development and research, and constant government support, price drops are increasing day by day, which encourages the extensive application of interactive grid-linked solar system for commercial as well as residential purposes [9, 12]. The incorporation of photovoltaic solar system on to the building can facilitate self-production of electrical energy [1, 17]. At same instant, the PV scheme may support the electrical grid by providing surplus energy generated, chiefly for the period of sunny and warm climate [5, 15]. As, throughout this period of time, A. Khandelwal (B) · P. Nema Department of Electrical Engineering, Oriental University, Indore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_63

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A. Khandelwal and P. Nema Particulars Site name

Mumbai, Maharashtra, 400,065, India

Coordinates

19.097 N, 72.833E

Yearly average solar irradiation

2148 kWh/m2

Yearly average temperature

26.06 °C

production can be high with higher amount of solar irradiations, also the load on electricity grid is high during these times due to the extensive use of air-conditioning devices [2]. This also helps in minimizing the environmental and ecological impacts. Though for the possibility of a photovoltaic system, there must be an adequate supply of energy from the sun all through the year. As we know India has abundant supply of energy from the sun, a vast capacity for photovoltaic generation is seen. Technically, various types of PV power generation systems are available which differ in their yielding capacity, price, and the material being used [14]. Meteorological circumstances, for instance, irradiation along with temperature, decide the operation or functioning of any photovoltaic scheme [10]. To supply continuous energy throughout the complete year, a photovoltaic scheme should have precise dimension for which a thorough res earch is required so as to pick out a preeminent option, having maximum efficiency and that too at a lower cost.

63.2 PV Plant Location Information The site being selected for the study is a commercial building of Mumbai city in Maharashtra, India. Mumbai is a densely populated city on India’s west coast at a latitude of 19° 05 52 N, longitude of 72°˚ 50 01 E, being a financial center, its India’s largest city. Mumbai city obtain its electrical power from the Adani Electricity, Maharashtra Electricity board, and Tata Power. The generation site system selected for learning is a commercial building with space available on roof of the building. The site has a total capacity of 150 kW. Three 50 kW PV systems are being installed on three roofs of different building in the same campus, making it a total of 150 kW plant. The PV plant is coupled to the Adani power grid which supplies power in that area. The particulars related to location are given in Table 63.1.

63.3 Installed Plant Description Any grid-connected photovoltaic system consists of PV modules, power conditioning unit with solar inverters, and grid connection equipment. The system shows effectual

63 A 150 kW Grid-Connected Roof Top Solar Energy System—Case Study Table 63.2 Plant illustration

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Particulars Installed power

150KW

Setting up type

Roof parallel

Module type

Polycrystalline

Mounting scheme

Fixed

Gradient

21z s

Albedo

20%

utilization of power generated through solar or sun energy as it has no losses in terms of storage. During the perfect circumstances, grid-tied photovoltaic system after consumption by the connected load supplies surplus power to the utility grid. Thus, the excess power generated can be utilized to a great extent in grid-connected system, whereas in standalone system, batteries are to be used to store the surplus energy or else the energy is wasted. Table 63.2 provides the plant illustration.

63.3.1 PV Modules The 150 kW photovoltaic plant is fitted with polycrystalline solar panels. The solar modules possess an effectiveness of 15.43% with fixed mounting scheme. The panels are rated at VOC as 43.56 V and ISC as 8.57A with the highest working temperature of 80 °C. The distance between panel to panel is of 25 mm.The panels are cleansed every 15 days so as to get rid of soil and dust and hence produce better outputs. The panels have no obstruction in receiving sunrays and so are free from any shading effects.The fixed has an inclination of 21° toward south. The installed PV array is shown in Fig. 63.1. Figure 63.2 demonstrates the variance in solar irradiance over the 12 months.

63.3.2 The Power Conditioning Units Three 3-phase inverters, each for three 50KW PV array, PSIT-50 K are employed for DC–AC conversion, which then feeds the supply to the grid. The rated efficiency of inverter is 98%. Table 63.3 provides the specifications of the inverter. Inverter transforms DC generated by photovoltaic system to the required AC. The inverter power rating is 58 kW. The inverter output is synched with the grid so as to match the voltage and frequency.

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Fig. 63.1 Installed PV array

Fig. 63.2 Monthly variation of solar irradiance

63.3.3 Grid Connection The yield of photovoltaic unit is supplied to the PSIT inverter so as to convert generated DC into AC supply and later after synchronization, supply to state grid. The protection toward the grid is such that in poor circumstances when the grid fails or provides with either short or elevated voltage, the photovoltaic scheme would be detached with the state grid. The installed grid-connected system is shown in Fig. 63.3.

63 A 150 kW Grid-Connected Roof Top Solar Energy System—Case Study Table 63.3 Inverter specifications

Model number

PSIT-50 K

Pdc max

58 KW

Vdc max

1100 V

Idc max

28.5 A

Vdc start up

200 V

Vdc MPPT range

200–1000 V

Vac norm

380 V

Fac norm

50 Hz

Pac norm

50 KW

Iac max

83.3 A

Pf

0.8 to 1 to 0.8

Efficiency

98%

Ambient temperature

−25 to 60 °C

Ingress protection

IP65

Protective class

Class I

Fig. 63.3 Installed grid connection

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63.4 System Performance Analysis The total AC power generated through the installed system is 222750kWh. Figure 63.4 represents monthly variance in the power generation. The performance of the PV system is evaluated using following parameters.

63.4.1 Array Yield For a solar photovoltaic, array yield is determined as the proportion of either of daily/monthly/yearly DC energy yield from a photovoltaic system to that of the rated photovoltaic array power which is known by formula [3, 8] YA =

EDC Ppvrated

where EDC in the above equation represents the total direct current energy output from the photovoltaic array in kWh and Ppvrated represents the output rated power of the photovoltaic system in kWp. The yearly array yield is 4.41 h/d.

63.4.2 Final Yield For a solar photovoltaic, final yield is determined as the proportion of total AC output energy in any given period to that of the rated photovoltaic array power which is known by formula [7] Fig. 63.4 Monthly variation of PV energy production

63 A 150 kW Grid-Connected Roof Top Solar Energy System—Case Study

YF =

839

EAC Ppvrated

where EAC in the above equation represents the total alternating energy output from the solar inverter generated by the photovoltaic system for a specific period in kWh. The installed photovoltaic system has a final yield of 1856 kWh/kWp/year with respect to the total AC energy output of 278438kWh.

63.4.3 Reference yield For a solar photovoltaic, reference yield YR is known by the equation as YR =

SR HR

where SR represents the total in-plane solar radiation for the photovoltaic system (kWh/m2 ) and. HR represents the PV array reference irradiance at standard test condition (STC) (1 kW/m2 ). The total in-plane solar radiation is observed to be 2147 kW/m2 .

63.4.4 PV Module Efficiency The PV module efficiency is calculated as [8, 19]  EDC × 100 % = SR Apv 

ηpv

where Apv is the total area (m2 ) of PV modules. The PV module efficiency is found to be 14.40% with respect to the yearly DC energy generated EDC .

63.4.5 System Efficiency The total photovoltaic system efficiency is determined as the energy output from a PV array divided by the total in-plane solar insolation and is given as [8]  ηs =

 EAC × 100 % SR Apv

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The total system efficiency is found to be 13.26% against the total AC production of 222750 kWh.

63.4.6 Performance Ratio (PR) For a solar photovoltaic, performance ratio provides you with the information of energy efficiency and reliability of the photovoltaic system. Performance ratio (PR) of a plant for a period of time is [11, 13] Energy measured (kWh)      PR =  Irradiance kWh/m2 on the panel × Apv m2 × ηpv PV system fault or breakdown can be detected on the basis of PR. Lesser PR value refers to inaccurate functioning of the PV system or failure of the inverter. For the installed photovoltaic system, the yearly PR is 0.92 or 92% in percentage which is very good for a system.

63.4.7 Capacity Utilization Factor (CUF) For a solar photovoltaic, CUF is the proportion of real electrical energy produced by photovoltaic system throughout the complete year to the equivalent system energy output at its rated capacity [18].  CUF% =

 annual energy generation in kWh × 100 installed plant capacity in kW × 365 × 24

The variation in location climatic condition causes losses in system which in turn results in change in capacity factor. Higher is the CUF, lower is the cost of electricity generation. The CUF for the studied photovoltaic system is found to be 16.9%.

63.4.8 PV Plant Losses Majorly the losses are categorized in two categories. Array Losses: It includes the losses caused by partial shading, irradiance loss, wiring loss, MPPT errors, limitation due to dust, and thermal loss. In normalized functioning index, all of these array losses are considered under the head of collection losses, denoted by Lc [6, 8].

63 A 150 kW Grid-Connected Roof Top Solar Energy System—Case Study

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Inverter Losses: These are the losses that occur by passive elements, DC-AC inverter, and the conduction loss [20]. LS = YA − YF The array losses and inverter losses are found to be 1.39 h/d and 0.35 h/d, respectively.

63.5 Cost of Energy for the PV Plant The installation expense of the 150KW PV plant is approximately 57lacs. By installing the PV plant, the total cost saved is appx 25lacs per year. This way it has been observed that the payback period for the photovoltaic plant is three years.

63.6 Conclusion The 150 kW grid-connected photovoltaic scheme fitted at the rooftop of an industry in Mumbai, India, was monitored during April 2019–March 2020, and a study of running parameters was made. All the yearly and monthly diameters were focused. The significant findings from the study are summarized here. The annual final yield for the PV plant is 14856 kWh/kWp or 4.06 h/d which is quite higher and good. Inverter efficiency was found as 92.13% and that for PV module as 14.40%. The complete system efficiency was found to be 13.26% with annual PV energy production of 223MWh out of which supplying 33MWh to the grid. The installed PV plant has caused a reduction in power cost to a great extent. Taken as a whole, the functioning of placed grid-tied rooftop solar photovoltaic system is identified as a realistic way out for supply of power in western India with the ease of successful installation. This way, analysis made for the grid-connected PV plants can assist in designing, functioning, and maintenance of a new grid-connected PV system.

References 1. B. Shiva Kumar, K. Sudhakar, Performance evaluation of 10 MW grid connected solar photovoltaic power plant in India. Energy Reports, Elsevier, Amsterdam, vol. 1, pp. 184–192 (2015). ISSN 2352–4847 2. K.Y. Lau, N.A. Muhamad, Y.Z. Arief, C.W. Tan, A.H.M. Yatim, Grid connected photovoltaic systems for Malaysian residential sector: Effects of component costs, feed-in tariffs, and carbon taxes. Energy 102, 65–82 (2016)

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3. R. Dabou, F. Bouchafaa, A.H. Arab et al., Monitoring and performance analysis of grid connected photovoltaic under different climatic conditions in south Algeria. Energy Convers. Manage. 130, 200–206 (2016) 4. H. Rezzouk, A. Mellit, Feasibility study and sensitivity analysis of a stand-alone photovoltaic– diesel–battery hybrid energy system in the north of Algeria. Renew. Sustain. Energy Rev. 43, 1134–1150 (2015) 5. R. Sharma, S. Goel, Performance analysis of a 11.2 kWp roof top grid-connected PV system in Eastern India, Energy Reports, pp. 76–84 (2017) 6. C.E.B.E. Sidi, M.L. Ndiaye, M.E. Bah, A. Mbodji, A. Ndiaye, P.A. Ndiaye, Performance analysis of the first large-scale (15 MWp) grid-connected photovoltaic plant in Mauritania. Energy Convers. Manage. 119(1), 411–421 (2016) 7. L.M. Ayompe, A. Duffy, S.J. McCormack, M. Conlon, Measured performance of a 1.72 kW rooftop grid connected photovoltaic system in Ireland. Energy Convers. Manage. 52(2), 816– 825 (2011) 8. R. Arora, R. Arora, S.N. Sridhara, Performance assessment of 186 kWp grid interactive solar photovoltaic plant in Northern India. Int. J. Ambient Energy 40, 1–14 (2019) 9. A. Khandelwal, P. Neema, State of art for power quality issues in PV grid connected system, in 2019 International Conference on Nascent Technologies in Engineering (ICNTE), Navi Mumbai, India, pp. 1–4 (2019). https://doi.org/10.1109/ICNTE44896.2019.8945829 10. N. Hemanthbabu, S. Shivashimpiger, N. Samanvita, V.M. Parthasarathy, Performance ratio and loss analysis for 20MW grid connected solar PV system - case study. Int. J. Eng. Adv. Technol. 8(2), 20–25 (2019) 11. A. Balaska, A. Tahri, F. Tahri, A.B. Stambouli, Performance assessment of five different photovoltaic module technologies under outdoor conditions in Algeria. Renewable Energy 107, 53–60 (2017) 12. S. Yoomak, T. Patcharoen, A. Ngaopitakkul, Performance and economic evaluation of solar rooftop systems in different regions of Thailand. Sustainability 11, article no 6647 (2019) 13. V. Sharma, S.S. Chandel, Performance analysis of a 190kWp grid interactive solar photovoltaic power plant in India. Energy 5, 476–485 (2013) 14. K. Samir, L. Jose, V. Dmitri, F. Leopoldo, Grid-connected photovoltaic systems: an overview of recent research and emerging PV converter technology. IEEE Ind. Electron. Mag. 9, 47–61 15. A. A. Elbaset, M. S. Hassan, H. Ali, Performance analysis of grid-connected PV system, in 2016 Eighteenth International Middle East Power Systems Conference (MEPCON), Cairo, pp. 675–682 (2016) 16. E.R. Shouman, E.T.E. Shenawy, N.M. Khattab, Market financial analysis and cost performance for photovoltaic technology through international and national perspective with case study for Egypt. Renew. Sustain. Energy Rev. 57, 540–549 (2016) 17. H. Faria Jr. F.B.M. Trigoso, J.A.M. Cavalcanti, Review of distributed generation with photovoltaic grid connected systems in Brazil: Challenges and prospects. Renew. Sustain. Energy Rev. 75, 469–47 (2017) 18. S. K. Sharma, D. K. Palwalia, V. Shrivastava, Performance analysis of grid-connected 10.6 kW (Commercial) Solar PV power generation system. Appl. Sol. Energy 55, 269–281 (2019) 19. C. Li, Comparative performance analysis of grid-connected PV power systems with different PV technologies in the hot summer and cold winter zone. Int. J. Photoenergy 2018(8307563), 9 (2018) 20. H. Alwazani et al., Economic and technical feasibility of solar system at Effat University, in 2019 IEEE 10th GCC Conference & Exhibition (GCC), Kuwait, pp. 1–5 (2019)

Chapter 64

Fuzzy SVM Classifier for Clothes Pattern Recognition Abhishek Choubey, Shruti Bhargava Choubey, and C. S. N. Koushik

Abstract Cloth pattern recognition is a strenuous effort for partially or completely blind people. The large intra-pattern variations are posing limitations to the machinebased algorithms. With this mind, we are developing a MATLAB code for recognizing patterns of the clothes and enhancing the image parameters like contrast, brightness, etc. To determine the clothes pattern, till now, a simplified SVM classifier was used, but we tend to implement the same with use of the fuzzy support vector machine whose accuracy is considered to be better than the traditional SVM. In a simplified SVM, label is assigned on the basis of hyper plane and kernel function while in fuzzy SVM, membership in terms of probability is also determined for each sample to be fall in each class. The present project model consists of a MATLAB code and a datasets for training images. We create our own fuzzy rules for the image enhancement such as brightness and contrast. We then send the image through fuzzy rules, and the output is shown in the form of audio format. By the help of this, it is of huge help for the blind people who cannot differentiate them due to the loss of vision. Keywords Blind · Color blind and visually impaired · Color analysis · Pattern analysis and Fuzzy SVM

64.1 Introduction Based on data from the American basis for the blind and the country wide fitness interview survey, there had been as of 1994–95 approximately 2750 human beings in the US with naked moderate grasp or much less while a small proportion of the 1-3 million human beings who qualify as legally blind, and this is the population who are most in want of vision substitution systems, in view that many people with low creative and prescient can accomplish visual duties with magnification and other aids. In everyday life, humans choose to find out terrific garments to wear. This is a very challenging challenge for blind humans to pick clothes with suitable coloration A. Choubey · S. B. Choubey · C. S. N. Koushik (B) Sreenidhi Institute of Science and Technology, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_64

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and pattern. Most blind human beings manipulate this problem each via beneficial aid from family contributors or with the aid of way of the usage of braille labels or many kind of sticking patterns are designed on the garments for more shades and better appearances. This technique also requires useful resource in labeling. And some blind human beings treatment the matching bother through way of preserving only garb with very simple colors and patterns in their wardrobes. There are many methods that have been developed for clothing and texture recognition with the help of computer image formation and prescient and image processing lookup, and presently, there is no machine that can correctly supply matching preferences for blind people. A suitable answer to this bother ought to assist no longer totally blind persons; however, those who are severely shade deficient, though in most situations, the shade deficiency is restrained to one axis in color residence. There are some transportable digital coloration identifiers on hand; however, they can totally observe vital hues current in very small vicinity. Unfortunately, this structure of machine cannot effectually classify colorings of garments that containing more than one hues and complicated patterns. There are various pivotal inconveniences for beneficial garments coordinating. In the first place, people find an article to be the equivalent despite even more modified in the structure of a light reflected from the item. Moreover, shadows and folding may likewise also be worried as fragment of a surface examples or symbolism of the garments and cause mistakes, and the photographs of the garments can be imaged from subjective review bearings. Strategies for coordinating examples require the enter pair of evidence. To solve the problems, our system can deal with garments with different hues and surface data. After the coordinating calculation are said by utilizing content to discourse outputs [11, 12].

64.2 Review of Literature Hasanuzzaman et al. have made easy for blind people to identify money of any currency with a help of system which work automatically. It is somewhat similar like camera and computer technology which also includes large range of features like high quality, high accuracy, and very easy to handle by the user. This system has great conditions like revolution, enlightenment, scales arrangement, and more. Also, it can direct the user to accurately center at the perceived use of speed up robot features [2]. Dimitrios Dakopoulos and Nikolous built has worked on the topic electronic travel aids. Its initial range was to assist the partially blind person in the garment shop without the assistance of a moment individual. This assistive work will portrait the same [3]. In this work, broken surfaces are used for mapping the system on the embedding’s proposed the area to same structure to delineate area patches to a less turns, scales, distortion takes place. Mapping near the surface with will turn patches into a less dimensional subspace which can remove the unwanted variety factors coming because of the photometric and area changes. We had observe surface partials in light of subspace which had hard protection from picture disturbance.

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XiaodongYang et al. have developed an algorithm framework for the visually impaired people who face problem during picking up mild clothes from a large set of them. In this model, they used a camera for taking the inputs, and the software would determine the type of cloth from the given number of clothes pattern, i.e., plaid, stride, pattern less, stripes, and unpredictable [4]. Hasanuzzaman developed a framework regarding bank cash recognition in order to assist the visually impaired people. This innovation is completely based on a single PC based camera. This development contains unique parameters like high precision, durability, more productivity, and more intensity. After all, his model is been innovative in many aspects such as rotation, scaling, stability, 2D DWT, illumination, complex design recognition, accuracy, and precision. This has been helping blind people over the time [5]. Dakopoulos and Nikolous built have developed an algorithm framework with the help of mapping systems in which the substrate subspaces are delineated into more lower type of patches for better enhancement. Many features like scaling, point of view, variety, enhancement, and distortion are ensnared with range of variables. The undesired variables can be eliminated with the help of mapping system which has both parametric and geometric changes. They have combined radon signatures and DWT to solve the problem for complex features and higher contrast issues which can further also be developed with edge-based pattern detection (extraction feature). The results are then in the form of speech format [6]. Shuai Yuana et al. in his project titled clothing matching for visually impaired person have developed applied science in order to overcome the difficulties and challenging tasks which play a role in the life of the visually impaired. They rather insisted on development of software-based applications rather than sticking labels to the clothes pattern [7].

64.3 Functional Diagram and Methodolgy In the above functional diagram, the project is executed by initiating an image from for the input which can be found inside the vast datasets CCYN. Each piece of them in the database has a unique directionality, lighting variation, and intensity. The .jpeg image is converted to from RGB to HSV and is allowed for extraction of features by subjecting the image to scaling and plane rotation in the form of two-dimensional (2D) image transformations. The main principle behind pattern recognition system is preprocessing an image and extracting features from the image. To recognize the patterns from the given image, we use a training algorithm known as fuzzy support vector machine (FSVM). In case of color recognition, the classifier is programmed to recognize 11 colors which are white, gray, black, pink, purple, blue, cyan, green, yellow, orange, and red. The weight of each color equals to the percentage of pixels belonging to this color each pixel in the image shows its own saturation value and intensity out of gray, black, and white color which are easily identifiable. After preprocessing, we use an

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Fig. 64.1 Block diagram for proposed work

extracting features for extracting the characteristics of the selected image. There are several extraction features which include RQA, SIFT, and STA. We use 12 different types of sample data for training the algorithm which is shown in Fig. 64.2. When the simulation of training images is done, we apply fuzzy (fuzzyfying) rules for the selected image. The rules can be created by the user such as increasing the contrast, brightness, and sharpness. After fuzzyfying, the image is sent for the final output where the result is in the form of audio output. Depending on the type of clothes, the result will be simulated along with the change in contrast, brightness etc. [9, 10, 13] (Figs. 64.1 and 64.2). The crucial role behind the pattern recognition system is to extract the features for classifying the patterns. There are a couple of techniques to extract features from an image. They are listed as follows.

64.3.1 Statistical Features Extraction (STA) It uses a wavelet transformation which can pixelate the image into more small pixels for easier extraction. The images can be classified using features like variance, energy, uniformity, and entropy.

64 Fuzzy SVM Classifier for Clothes Pattern Recognition

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Fig. 64.2 Dataset for training images

N σ2 =

j=0 (x

− X )2

(64.1)

N

Mean is represented as X, and N is the number of times. Entropy : E(X ) = −



P(x) log10 P(x)

Uniformity : ASM =

N 

pi,2 j

(64.2)

(64.3)

i, j=0

Energy : Energy =

√

ASM

(64.4)

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Fig. 64.3 DWT for pattern analysis

64.3.2 Recurrence Quantification Analysis (RQA) This technique is used to improve the accuracy of the support vector machine classifier. It also acts as a local feature extractor.

64.3.3 Scale-Invariant Feature Transform (SIFT) The SIFT includes a wide coverage of extracting key points from the reference image. The extracted features are then compared with the datasets which are in the CCYN database. The SIFT has a unique way of extracting in such a way that the invariant parameters such as illumination, scaling, rotation and, noise can be overcome.

64.3.4 Discrete Wavelet Transforms (DWT) DWT is the application of high pass and low pass filter at the same time, so as to extract the low and high frequencies as a feature to compare the cloth pattern. The decomposition is given as below (Fig. 64.3).

64.3.5 Support Vector Machine (SVM) Support vector machines (SVM) have gained conspicuity in the field of machine learning and pattern classification. Steps involved in SVM:

64 Fuzzy SVM Classifier for Clothes Pattern Recognition

f (x) =

 x j∈S

  αj yj K x j, x + b

849

(64.5)

involved in algorithm. Given the two classes X1 and X2, let us assume X1 are the positive class and X2 are the negative class. The present support vector machine is vulnerable to noises and outliers. In order to deal with the hurdles, we are introducing fuzzy-based SVM. In this methodology, we are fuzzyfing all the datasets of different classes, i.e., negative and positive classes. Assume a training data set S with the labeled training point (xi , yi , si ) where every training data xi ∈ Rn belongs to a class labeled by Yi ∈ {1, − 1}for i = 1,…, n. The optimal hyperplane can be obtained by solving the quadratic optimization problem as shown in Eq. (64.6).  1 siξ subject to yi (wT xi + b) ≥ 1 − ξ i min w2 + C 2 and ξ i ≥ 0∀i = 1, . . . , n

(64.6)

The difference between SVM and FSVM is on the term siξi. In FSVM, the measure of error has different weights. The FSVM can also be solved by its dual form [8].

64.4 System Description The very nature of this code is to identify the colors and patterns of the clothes. The system is designed to preprocess and extract the feature points from the image by using 2D image transformation. After the image key points extraction, the points are sent to the fuzzy SVM classifier for enhancing the image in aspects like contrast, brightness, etc., and later matching the pattern from the given number of patterns. We are using fuzzy SVM classifier over a normal SVM classifier to overcome the inaccuracies of the normal SVM classifier. SVM algorithms play a crucial role in the development of many real-time constraints like bioinformatics, image recognition, and enhancement. Fuzzy rules are created with the help of MATLAB toolboxes available for fuzzy. With the help of available toolbox, we can create our own rules for any type of applications like image parameters, enhancement, and tracking [8].

64.5 Results and Discussions For the determination of the clothes and its color, the use of the fuzzy SVM classifier is done as explained earlier due to its advantages by the use of the fuzzy logic. The use of this SVM classifier is much more superior to that of the traditional SVM in terms of usage. The model tends to give better accuracy values than that of the

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Fig. 64.4 Select an image for color calculation

traditional SVM, wherein the features are extracted after the preprocessing stage from the training set, and by the use of the extracted features, the fuzzy SVM classifier is used to classify the cloth pattern type into a particular category and even for the color determination. However, in the case of the determination of the color, the taken images are converted to the respective HSV values from the RGB values of the test set. The HSV values are tried to map to respective wheel in order to determine the exact detectable color. The accuracy of the model is considered to be of about 92 to 95% .

64.5.1 Color Calculation See Fig. 64.4.

64.5.2 Detected Color (with Speaker) • The colors is blue with 91.0867 percent. • The colors is cyan with 4.5714%. • The colors is purple with 0.77041%.

64 Fuzzy SVM Classifier for Clothes Pattern Recognition

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Fig. 64.5 Training four types of cloths to SVM

Fig. 64.6 Selected image for pattern recognition

64.5.3 Pattern Calculation Figs. 64.5, 64.6, 64.7, 64.8, 64.9 and 64.10.

64.6 Conclusion From the above results, we can easily identify pattern recognition through a multitasking process which includes color accuracy of the clothes, training the four types of clothes design: irregular, pattern less, stripes, and plaid, and then, the clothes are subjected to the fuzzy rules which increases the contrast of the clothes and speaks out the type of cloth which we selected. This real-time identification technique can help the visually impaired with ease. This can be further more developed by letting the machine learn more clothes pattern and complex designs. The use of fuzzy SVM classifier can overcome the limitations of a normal SVM classifier. The present model can be built into the processor and also mobile phones for the benefits of the visually impaired people. Therefore, by the use of the fuzzy SVM classifier, the model can be used for the challenged people who have problem with their vision, and hence, the results can be got effectively.

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Fig. 64.7 Applied fuzzy rules for enhancement

Fig. 64.8. Applying fuzzy to all three planes

A. Choubey et al.

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Fig. 64.9 Enhanced image by fuzzy transform

Fig. 64.10 Detected cloth type

References 1. American Foundation for the Blind. [Retrieved October 30, 2004] Statistics and sources for professionals. 2004. from www.afb.org/info_document_view.asp?documentid=1367 2. X. Hasanuzzaman, Y. Yang, Y. Tian, Robust and effective component-based banknote recognition for the blind. IEEE Trans. Syst. Man Cybern. C 42(6), 1021–1030 (2012) 3. Dakopoulos, N. G. Bourbakis, Wearable obstacle avoidance electronic travel aids for the blind: a survey. IEEE Trans. Syst. Man Cybern C 40(1), 25–35 (2010) 4. X. Yang, S. Yuan, Y. L. Tian, Assistive clothing pattern recognition for visually impaired people. IEEE Trans. Human Machine Syst. 44(2) (2014) 5. D. Hasanuzzaman, X. Yang, Y. Tian, Robust and effective component-based banknote recognition for the blind. IEEE Trans. Syst. Man Cybern C 42(6) 1021–1030 (2012) 6. S. Y. Dakopoulos, N. G. Bourbakis, Wearable obstacle avoidance electronic travel aids for the blind: a survey. IEEE Trans. Syst. Man Cybern. C 40(1), 25–35 (2010) 7. Y. L. Tiana, A. Arditib, Clothing matching for visually impaired persons. Technol. Disability 23, 75–85 (2011). https://doi.org/10.3233/TAD-2011-0313 8. H.-P. Huang, Y.-H. Liu, Fuzzy support vector machines for pattern recognition and data mining. Int. J. Fuzzy Syst. 4(3) (2002)

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9. C. Sentelle, G. Anagnostopoulos, M. Georgiopoulos, Efficient revised simplex method for SVM training. IEEE Trans. Neural Networks, 22(10), 1650–1661 (2011) 10. P. Hurtik, V. Molek, I. Perfilieva, Novel dimensionality reduction approach for unsupervised learning on small datasets. Pattern Recogn. 103, 107291 (2020) 11. M.A. Iqbal Hussain, B. Khan, Z. Wang, S. Ding, Woven fabric pattern recognition and classification based on deep convolutional neural networks. Electronics 9(6), 1048 (2020) 12. J. Zhang, C. Liu, A study of a clothing image segmentation method in complex conditions using a features fusion model. Automatika 61(1), 150–157 (2020) 13. Y. Zhang, J. Li, X. Zhou, T. Zhou, M. Zhang, J. Ren, J. Yang, A view-reduction based multiview TSK fuzzy system and its application for textile color classification. J. Ambient Intell. Human. Comput. 1–11 (2019)

Chapter 65

A Detailed Analysis of Municipal Solid Waste Generation and Composition for Haridwar City, Uttrakhand, India Kapil Dev Sharma and Siddharth Jain

Abstract Municipal solid waste (MSW) is a heterogeneous unavoidable by-product generated by human activities in commercial and residential areas. With economic growth, population explosion, urbanization, industrialization, and better living standards in cities, India is facing the problem of MSW management and disposal. Municipal authorities are not able to manage increasing quantities of waste in an efficient way, due to which considerable MSW can be seen on the roads and other public places, which results in several environmental and health-related problems that are increasing. Therefore, ineffective MSW management is one of the major environmental issues in most Indian cities, which require serious attention. MSW generation rate and detailed composition analysis play a major role to develop an effective, economical, and environmentally friendly MSW management system. This paper aims to characterize the waste generated in Haridwar city and review of the existing situation of MSW management. A total of 10 samples (A to J, one sample per week) have been collected (5 in summer and 5 in winter) from MSW dumpsite of Haridwar city. All samples have been detailed physically characterized to find out the composition of each component of MSW. Also, the moisture content of each component of each sample has been determined. The main components of MSW were organics (49%), inert (17%), plastics (10%), paper and textile (9%), and metal (7%). The detailed composition analysis shows that organic (biodegradable 49%) and recyclable (35%) waste are two major components of MSW. Finally, based on field studies and available literature data, the waste generation rate of Haridwar city was estimated at 220 metric tons (0.94 kg /c /d). Keywords Municipal solid waste · Waste composition · Waste characterization · Waste management K. D. Sharma (B) Mechanical Engineering Department, Gurukul Kangri Vishwavidyalaya, Haridwar, Uttrakhand, India e-mail: [email protected] S. Jain Mechanical Engineering Department, College of Engineering, Roorkee, Uttrakhand, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_65

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65.1 Introduction Municipal solid waste (MSW) is generated by the routine activities of human life. It is also due to the improper utilization of energy and resources [11]. The population and economic development of India are growing rapidly. The rapid growth of India is not only putting great pressure on its available resources but also forcing spending on it. There are also some neglected side effects of this development such as the more generation of MSW [12]. MSW generation rate shows a positive correlation with the economic growth of the nation, population explosion, urbanization, and industrialization that has been recorded worldwide [14]. Unscientific handling of MSW leads to health hazards and environmental degradation [6]. Management of MSW in an effective manner highly depends upon the major MSW composition as well as rate of change of MSW composition over the time [9]. As the world is moving toward its urban future, the quantity of MSW, one of the most important by-products of urban lifestyles is increasing faster than the rate of urbanization. In 2009, around 0.68 billion tons (BT) of MSW has been generated by 2.9 billion urban residents, which has to be increased to 2.01 BT of MSW through 3 billion residents in 2016. It is expected that by 2050, about 68% of the population will become urban, which will generate around 3.4 BT MSW per year [17]. Urbanization level in India also has been increased from 27.9 to 31.7% during a decade (2001– 2011), and it is predicted that in the next 10 years, about 50% of the Indian population will live in the cities [7]. Environmental Ministry of India estimated that urban India generated around 62 million tons (MT) of MSW (0.450 kg/capita/day) in 2015 [13]. In which the quantity of waste collected was only 82% (50 MT). Quantity of treated waste was just 28% (14 MT) of the collected MSW, and the rest 72% (36 MT) was openly dumped (MNRE, 2016). It is pointed out that India will see a rise in waste generation up to 165 million tons in 2030 and to 436 million tons by 2050 [10]. Presently, the power generation potential from MSW in India exists around 500 MW. As per the government policies of India, it is expected to enhance to 1,075 MW by 2031 and further to 2,780 MW by 2050 [13]. According to the Indian Renewable Energy Development Agency (IREDA), India has so far achieved only 2% of its waste-to-energy production capacity. According to the market analysis of Frost and Sullivan, the Indian MSW market has been growing at an annual compound rate of 9.3% since 2013 [1]. In India, most MSW moves to land and water bodies without appropriate treatment that causes the emission of greenhouse gases and water pollution. Therefore, proper management of MSW is one of the critical environmental problems of Indian megacities. An effective MSW management mainly involves activities related to the generation, storage, collection, transfer and transportation, processing, disposal, and treatment of MSW. But, in major Indian cities, the MSW management system includes only four activities, namely waste generation, collection, transportation, and disposal [15]. Nowadays, different types of WtE technological options are available, those can be broadly classified into following categories: thermal conversion (i.e., incineration, gasification, pyrolysis, liquefaction, plasma treatment, torrefaction, RDF),

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bio-chemical conversion (i.e., composting, vermicomposting, AD, fermentation), physiochemical conversion (i.e., esterification and other processes to convert plant and vegetable oils to biodiesel), emerging technologies (i.e., Dendro liquid energy, microbial fuel cell, hydrothermal carbonization), and sanitary landfilling [8, 16]. Nevertheless, there is no single way to resolve MSW management issues as the size and composition of the MSW component changes over time. Thus, the appropriate treatment for each MSW fraction differs from each other. Therefore, how to manage such huge amount of waste in a sustainable manner is one of the biggest challenges for future generations. Few studies exist for Haridwar city for a brief characterization of MSW primarily focused on organic and inorganic wastes, but a detailed MSW characterization study does not exist yet. This study is mainly focused on the level of components and sub-components of organic, inorganic, and recyclable MSW. A detailed compositional analysis of the MSW would be very suitable for addressing suitable MSW management technology and issues associated with it.

65.2 Materials and Methods 65.2.1 Description of Study Area In India, Haridwar city is considered one of the seven holiest cities. Haridwar is the second-largest southwestern district in Uttrakhand state after Dehradun, covering an area of about 2,360 km2 . It is located 314 m above the sea level, its latitude and longitude are 29.96ºN and 78.15ºE, respectively, and it has an average elevation of 819 feet. The Ganges River leaves the mountains and enters the plains of Haridwar. So, Haridwar is considered the “Gateway (Dwar) to God (Hari)”. The Ganges River is considered to be a major river of the Indian subcontinent that rises in the Himalayan Mountains and falls into the Bay of Bengal after traveling for about 2,550 km through a vast plain. The Ganges River alone flows over an area of about one million square km. The 451 million population living in the basin of the Ganges River, directly and indirectly, depends on the Ganges River. The study was conducted in the Haridwar, which is the second-largest city of Uttarakhand state after the Dehradun city on the basis of population. Presently, the MSW dumpsite in Haridwar city is located near the new construction site in Sarai village, Bhagtanpur (Latitude: 29.9008 and Longitude: 78.092943) having 50.50 hectares of land area as shown in Fig. 65.1a. The old dumping site was near Chandighat which is now covered with soil and vegetation as shown in Fig. 65.1b. The Sarai dumping site is located in the taluk of Bahadrabad, District Haridwar. It is 9 km to the south from the district headquarters, 7 km from Bahadrabad, 12 km from the city, and 55 km from the state capital Dehradun.

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Fig. 65.1 Satellite map view of present dumping site Sarai Village a, old dumping site at Chandighat b, and samples collection c

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65.2.2 Sampling Methodology A total of 10 samples (A to J, one sample per week) have been collected (5 in summer and 5 in winter) according to instructions of Indian MSW (Management and Handling) Rules and Manual, 2016. Sub-samples (1–8) locations in the dumpsite have been decided by the “Stratified sampling” technique as shown in Fig. 65.2a. All sub-samples have been collected above 1 feet (0.308 m) of the waste surface. In terms of accuracy and efficiency, the primary sample size of MSW was 8 kg (1 kg from each sub-location) which has been reduced to the final sample size of 1 kg through

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 65.2 Quartering and coining technique samples collection technique a–f

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the “Quartering and Coining technique (QCT)” [2]. To apply the QCT technique, a black square-shaped polyethylene (area 36 m2 ) sheet was used. Through white paint, a 1 m radius circle is drawn in the middle of the sheet, which is then divided into four equal parts. All sub-samples were mixed properly and uniformly distributed within the circle. Large MSW pieces were cut into smaller pieces for proper mixing. Two quarters of the circles were removed to reduce the sample size as shown in Fig. 65.2c. This process continued until the required final 1 kg sample was obtained as shown in Fig. 65.2f. Through the QCT technique, all 10 samples were obtained at the intervals of one week.

65.2.3 Sorting Procedure After obtaining the final sample size, the manual sorting process of MSW was initiated by four team members at the dumpsite. The sorting of the mixed MSW was carried out in steps. During the first step sorting, the recyclable wastes, i.e., plastic, papers, metals, textile, leather, glass, polythene, Al foil, thermacoal, rubber, and wire, were separated, and their weights were calculated on the received basis (RB) through a battery-operated weighing machine (precision capacity up to 1 mg). This procedure continued until most of the materials suitable for recycling were separated. After this, all the recycling wastes are again separated into two categories; combustible and non-combustible fractions. A second sorting step was performed to classify the remaining waste constituents that might be used as a combustibles fraction (mixed paper, mixed plastics, hair and jute, wood, dry cow dung) and non-combustible fractions (C&DW, organics food waste and garden waste, and miscellaneous waste). The sieving was continued until the biggest item size had been reduced to the maximum particle size of 12 mm as according to ASTM D 5223–92 [4]. The weight of each composition was calculated on the RB through the weighing machine.

65.3 Results and Discussion 65.3.1 Estimation of MSW Generation and Its Management The city generates around 220MT of MSW every day. Domestics, commercial, shops, restaurants, hotels, dharamsalas, and fruit and vegetable markets are the major sources of MSW generation. The city currently has a total of registered restaurants, hotels, and dharamsalas which are 260, 460, and 280, respectively, as well as three fruit and vegetable markets [5]. There are various sources of solid waste generation as presented in Fig. 65.3. One of the objectives of the present paper is to present an up-to-date MSW generation rate for Haridwar city. Haridwar city is also a big MSW

65 A Detailed Analysis of Municipal Solid Waste Generation …

3%

2% 6%

12% 8% 3% 5%

55% 6%

861

Households Hotels & Restaurants Street sweeping Fruit and vegitable Market Constractions activities Shops and commerical marcket Offices & Institutions Hospitals Others

Fig. 65.3 Various sources of solid waste generation [5]

generation city, governed by Municipal Corporation which comes under Haridwar Metropolitan Region. Haridwar is a holy city, due to which it is always difficult to estimate the exact population of Haridwar. As per census 2011, the population of Haridwar city is 2.23 lakh (0.223 million), and it is estimated that by the year 2041, the population will be 4.2 lakh. The monthly data of MSW entering to the dumping site of Haridwar for the year of 2018 was obtained from the Municipality (Nagar Nigam) of Haridwar. According to Haridwar Nagar Palika Parishad (HNPP)-2018, Haridwar city generated an average of 220 metric tons of MSW per day from 30 municipal wards, most of which were dumped openly at the dumping sites of Sarai village, Chandighat, etc. So, statistically, the per capita waste generation rate was around 0.94 kg per day during this period. By the year 2041, this quantity is estimated to reach around 370 MT per day. During the visit of Haridwar Municipal Corporation, some information has been collected about the waste management of Haridwar city which is given in Table 65.1. Presently, mixed waste is being collected from door to door in 16 wards (out of 30) by KRL waste management Pvt. Ltd. Haridwar (PPP agency). The remaining 14 wards are covered by Haridwar Nagar Nigam. Waste is collected at the secondary storage bins kept in all wards. The condition and number of secondary storage bins observed and found not satisfactory. Regular sweeping and sufficient location of bins are observed on all ghats, but the number of storage bins are not sufficient within the colonies as well as in the market. Currently, HNPP uses the following vehicles and equipment as given in Table 65.2 to transport the MSW. Waste collection bins observed at various locations in Haridwar City are shown in Fig. 65.4. As per the latest report, MSW collection is provided on a daily basis in almost all wards, yet the overall collection efficiency is around 72% [3]. Uncollected MSW is basically scattered on the streets or in open sewers. In particular, this non-collected waste goes into the Ganges River and increases sea litter. Door-to-door (DtD) collection responsibility of MSW has been given to a private company which provides source segregation DtD by separating biodegradable and non-biodegradable MSW in about 22 wards at the end of 2018. MSW collection in the remaining eight wards is still providing by the municipality in an unsegregated manner [3].

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Table 65.1 Waste management information of Haridwar city S. no Particular

Details

1

Total population (Census 2011)

2.25 lakh (0.223 million)

2

Floating population (Approx.)

1.65 lakh (0.165 million)

3

Total number of wards

30

4

Number of wards covered by PPP agency for door to door collection

16 (out of 30)

5

Quantity of solid waste generated and transported

220 MT/day

6

Solid waste (excluding bio-medical waste) disposal method

Collection, transportation, and dumping at Vill-Sarai

7

Location of sanitary landfill site

Vill-Sarai, Distt- Haridwar

8

Nos. and type of waste storage bins in the city

181 MS bins

9

Nos. and type of vehicles available for waste Total 50: Tractor trolly, tripping trucks, transportation to sanitary land fill site dumper placer, tricycles, refuse collector, compactors, JCB, etc

10

Name of the PPP agency for solid waste management (excluding bio-medical waste)

KRL waste Management Pvt. Ltd. Haridwar

11

Bio-medical waste disposal method

Collection, transportation, and processing at CBWFT at Roorkee (Uttrakhand)

Table 65.2 Available infrastructure and handling capacity of Haridwar Municipality S. no

Components

Year-1998

Year-2011

Year-2018

1

Tractor trolly

04

18

20

2

Container carrier

03

06

6

3

Tipper truck

03

09

10

4

Sewer jetting machine

01

02

2

5

Sewer cleaning machine

03

06

6

6

Total waste collection/ day

101 MT

200 MT

220 MT

Today, Haridwar is experiencing the problem of mounting solid waste, due to population growth, lifestyle changes, increasing industrial activities, and economic development. Solid waste from municipal, hospital, and industries is a problem that needs serious attention. The issues involved are diverse. There are various shortcomings in the existing management which are inadequate manpower, financial resources, implements, and machinery required for effectively carrying out various activities for MSW management. Although at present, the magnitude of the problem is not very huge, it is very important to observe it before the worsening situation.

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863

Fig. 65.4 Waste collection bins observed at various locations in Haridwar City

65.3.2 Composition of MSW During the investigation of the study, the components and sub-components of MSW have been presented in Table. 65.3. MSW has been detailed classified into various subcomponents like organic matter, wooden pieces, paper cardboard, textiles, Table 65.3 Components and sub-components investigated in detailed characterization of MSW of Haridwar city Component

Sub-components

Paper

Newspaper, magazine, advertisement, packaging material, office paper, other papers

Plastic

Low density polyethylene (LDPE), high-density polyethylene (HDPE), and mixed plastics

Glass

Transparent, green, and dark

Metal

Copper, aluminum, steel, iron, wires, etc

Organic

Food waste, large compostable waste, garden waste, cow dung, and other organics

Miscellaneous

Pharmaceuticals, wood, hair, jute, textile, rubber, Al foil, inert, and other miscellaneous

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jute/hair, rubber and leather, bones, thermacoal, plastics, plastic bottles, metals, glass, combustible, incombustible, and other inerts. A detailed compositional analysis of MSW from Haridwar city is given in Table 65.4. It can be seen that MSW contains almost all the components of the solid waste stream. The major components of the MSW stream on as RB were organics (49%), inert (17%), plastics (10%), paper and textile (9%), and metal (7%) as shown in Fig. 65.5. The overall characteristic of generated MSW indicates the similarity to middleincome countries in terms of the typical distribution of its composition where food waste is still the dominant waste fraction while other waste fractions indicating more enriched consumption characteristics such as paper, glass, and textiles are more conspicuous compared to lower-income countries of the world. The major recycling components of MSW stream, i.e., paper, plastic, glass, metal, and textiles have been recorded 35%, indicating some good prospects for recycling. Therefore, an effective mechanical waste recycling system needs to be installed in the city to utilize such good potential for recycling. The implementation of a separate collection system for recyclables waste is urgent requirement, which is currently not exist Haridwar city. Currently, there is no official separate collection system for recyclables and non-recyclables waste. All the recycling activities are performed by regpikers (scavengers) on the mixed waste at the dumpsite. This not only negatively affects the quantity recycled, but also the quality of the recovered material is low as the waste is commingled. Further, these samples dried in the oven for physical characterization on a dry basis. To determine the average total solids (TS) and moisture content (MC) of each substrate and complete sample, all samples were dried in three parallels in an oven at 105 °C until the constant weight of each component as shown in Fig. 65.6. In this way, removed moisture is calculated [Moisture content (%) = (Initial Mass-Final mass) × 100 / Initial mass]. It can be seen that the variation of MC exists between 32 and 43% in the winter session and 21 to 31% in the summer session. This is because some moisture was already absorbed by the hot air due to summer weather, so the MC has been reduced.

65.4 Conclusion Total ten samples were collected conducted at the dumpsite of Haridwar city during summer and winter session. The main components of MSW were organics (49%), inert (17%), plastics (10%), paper and textile (9%), and metal (7%). The detailed composition analysis shows that organic (biodegradable 49%) and recyclable (35%) waste are two major components of MSW. Therefore, to manage such a huge quantity of organic waste either a composting or anaerobic digestion facility should be installed. A separate official waste collection and management system for recyclable waste must be implemented to utilize such good potential for recycling waste.

Paper

Plastic

Metal

Thermacoal 3

Polythene

Hair/Jute

Glass

Inert

Textile

Garden waste

Wire

Rubber

Cow dung

Plastic and glass

Al foil

Total

Moisture 43 content (%)

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

1000

10

0

0

0

0

0

0

137

49

19

100

11

54

7

15

Wood

2

596

RB

Food waste

15

15

0

38

50

105

28

22

E (gm)

0

0

0

0

20

42

60

142

0

0

21

0

0

100

0

0

0

0

19

20

28

0

0

0

34

0

50

0

109 230

15

10

0

27

50

105 35

24

15

190 388

DB RB

43

82

15

22

0

0

0

0

0

0

33

0

20

0

0

0

102

6

0

144

107

218 0

0

19

0

0

100 22

35

62

0

193 457

DB RB

0

0

40

6

0

70

80

0

22

0

0

0

22

43

63

14

0

0

118

0

0

0

0

198

5

0

50

5

19

48

47

92

250 418

DB RB

0

0

0

5

26

82

0

28

0

0

70

0

0

0

0

0

0

0

3

15

0

38

195 245

5

0

31

3

19

48

31

63

175 558

DB RB

39

32

39

36

31

574 1000 612 1000 680 1000 610 1000 640 1000

9

0

0

0

0

0

0

135 152

49

13

63

2

11

54

6

12

220 453

DB RB

D (gm)

38

0

39

5

35

80

125

48

10

0

40

10

0

0

60

96

0

0

4

30

20

20

10

9

0

30

10

0

0

40

0

90

0

0

0

0

33

155 265

38

0

30

4

35

80

80

30

180 432

DB RB

I (gm)

68

0

15

6

57

160

8

12

0

90

0

0

0

0

32

12

0

61

42

0

0

0

255 180

95

0

0

3

30

18

14

8

235 380

DB RB

H (gm)

16

0

55

12

33

0

121

12

0

52

41

0

0

0

0

0

104

0

37

0

87

165 145

65

0

13

5

55

155 80

7

10

155 310

DB RB

J (gm)

71

47

24

35

7

24

7

0

0

70

0

35

0

74

3

9

43

10

7

24

39

128 171

52

12

29

0

120 47

74

13

0

180 434

DB RB

Average (gm)

28

22

27

21

32

695 1000 721 1000 780 1000 735 1000 787 1000

0

0

0

3

15

0

35

235 160

0

0

0

3

26

82

0

16

280 350

DB RB

G (gm)

F (gm)

C (gm)

Samples collected in summer season

B (gm)

A (gm)

MSW samples (each sample 1000 gm = 1 kg)

Samples collected in winter season

1

MSW composition

Table 65.4 Physical segregation of collected MSW samples on received basis (RB) and dry basis (DB)

683

3

9

26

9

7

11

29

160

34

5

17

5

47

69

30

17

206

DB

65 A Detailed Analysis of Municipal Solid Waste Generation … 865

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K. D. Sharma and S. Jain Detailed Composition of MSW on a Recieved Basis (RB)

Detailed Composition of MSW on a Dry Basis (DB) Food Waste Wood Paper Plastic Metal Thermocoal Polythene Hair/Jute Glass Inert Textile Garden Waste Wire Rubber Cow Dung Plastic+Glass Al Foil

Food Waste Wood Paper Plastic Metal Thermocoal Polythene Hair/Jute Glass Inert Textile Garden Waste Wire Rubber Cow Dung Plastic+Glass Al Foil

Categorized Composition of MSW on a Received Basis (RB)

9%

Categorized Composition of MSW on a Dry Basis (DB)

5%

5% Inert

Inert

5%

37%

Metal

10%

49%

Plastic Glass Paper and Textile

7%

Other

17%

Organic Waste

8%

Organic Waste

3%

Metal Plastic

13%

Glass Paper and Textile

9%

Other

23%

Fig. 65.5 Detailed and categorized classifications of MSW on as received and dry basis

Fig. 65.6 Drying of MSW samples in the oven

Moreover, the solid waste management practice in Haridwar city appears to be inadequate and needs to up gradation. Door-to-door (DtD) waste collection from households was commissioned in all wards to collect segregated biodegradable and nonbiodegradable (recyclable) waste in different containers. Implementation of mechanical waste sorting plants at the dumpsite can be a valuable solution to segregate the remaining recyclable waste, which is left during primary collection process. There are

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various shortcomings in the existing management which are inadequate manpower, financial resources, implements, and machinery required for effectively carrying out various activities for MSW management. Presently, the different types of wastes viz. bio-medical, hazardous, industrial, and e-waste all are mixed with MSW and dumped at the same site. To avoid this, there is need to give proper training to municipal workers on segregation of waste and efficient utilization of resources. The present system of waste management in Haridwar city is not qualifying the standards as set by SWM Rules 2016. There is need to implement SWM Rules 2016 in an integrated manner. Acknowledgements We thank to Indian Academy of Environmental Sciences Hardwar, and Haridwar Nagar Palika Parishad (HNPP) for giving valuable information about solid waste. We show our appreciation to colleagues, friends, and organizations all around the world that has contributed with valuable information.

References 1. D. Chinwan, S. Pant, Waste to energy in India and its management. J. Basic Appl. Eng. Res. 1, 89–94 (2014) 2. CPHEEO-Part I, Swachh Bharat Mission- Municipal Solid Waste Management Manual Part I: An Overview [WWW Document]. CPHEEO (Central Public Heal. Environ. Eng. Organ. Minist. URBAN Dev. India. (2016). https://cpheeo.gov.in/upload/uploadfiles/files/Part1(1). pdf. Accessed 26 November 16 3. ECON Laboratory and Consultancy, Solid waste characterization report for environmental baseline study of municipal solid waste dumpsite Haridwar, Uttrakhand [WWW Document]. ECON Lab. Consult. https://udd.uk.gov.in/files/Solid_Waste_Characteristics_Haridwar.pdf. Accessed 25 February 20 4. S.J. Feng, Q.T. Zheng, H.X. Chen, Unsaturated flow parameters of municipal solid waste. Waste Manag. 63, 107–121 (2017). https://doi.org/10.1016/j.wasman.2017.01.025 5. Government of Uttrakhand, Urban Municipal Solid Waste Management Action Plan for State of Uttarakhand [WWW Document]. Urban Dev. Dir. Dehradun (2019). https://doi.org/10.1017/ CBO9781107415324.004 6. B. Gupta, S.K. Arora, Municipal solid waste management in Delhi—the capital of India. Int. J. Innov. Res. Sci. Eng. Technol. 5, 5130–5138 (2016) 7. R. Joshi, S. Ahmed, Status and challenges of municipal solid waste management in India : a review. Cogent Environ. Sci. 2, 1–18 (2016) 8. K.A. Kalyani, K.K. Pandey, Waste to energy status in India: A short review. Renew. Sustain. Energy Rev. 31, 113–120 (2014) 9. M.S. Korai, R.B. Mahar, M.A. Uqaili, The Feasibility of Municipal Solid Waste for Energy Generation and Its Existing Management Practices in Pakistan (Sustain. Energy Rev, Renew, 2017). https://doi.org/10.1016/j.rser.2017.01.051 10. A. Kumar, S.R. Samadder, An empirical model for prediction of household solid waste generation rate—a case study of Dhanbad. India. Waste Manag. 68, 3–15 (2017). https://doi.org/10. 1016/j.wasman.2017.07.034 11. K. Laohalidanond, P. Chaiyawong, S. Kerdsuwan, Municipal solid waste characteristics and green and clean energy recovery in Asian megacities. Energy Procedia 79, 391–396 (2015) 12. F. Mayer, R. Bhandari, S. Gäth, Critical review on life cycle assessment of conventional and innovative waste-to-energy technologies. Sci. Total Environ. 672, 708–721 (2019). https://doi. org/10.1016/j.scitotenv.2019.03.449

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13. MNRE, Power Generation from Municipal Solid Waste, yearly booklet issued [WWW Document]. MNRE (Ministry New Renew. Energy), Govt. India. (2016). https://164.100.47.193/lss committee/Energy/16_Energy_20.pdf. Accessed 22 January 17 14. K.D. Sharma, S. Jain, Overview of municipal solid waste generation, composition, and management in India. J. Environ. Eng. 145, 1–18 (2019). https://doi.org/10.1061/(asce)ee.1943-7870. 0001490 15. M. Suratwala, J. Kumarkhaniya, H. Panchal, Solid waste management survey in Kankaria Lake. Ahmedabad 14, 3–8 (2019) 16. WEC, World Energy Resources-2016 [WWW Document]. World Energy Counc. (2016). https://www.worldenergy.org/wp-content/uploads/2016/10/World-Energy-ResourcesFull-report-2016.10.03.pdf. Accessed 11 January 17 17. World Bank Group, What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050-The Urban Development Series [WWW Document]. Int. Bank Reconstr. Dev. / World Bank 1818 H Str. NW, Washington, DC 20433 (2018). https://openknowledge.worldbank.org/ handle/10986/30317. Accessed 14 June 19

Chapter 66

Techno-Economic Analysis of Piezoelectric-Based Smart Railway Tracks Manvi Mishra, Priya Mahajan, and Rachana Garg

Abstract This paper consists of an analysis of technical and economical aspects of the installation of piezoelectric pads on the railway tracks. The lead zirconate titanate (PZT) has been used as piezoelectric material in this paper. For harvesting electrical energy, the two types of piezoelectric energy harvesting system, namely compression-type piezoelectric harvester and cantilever-type piezoelectric harvester are considered. The technical analysis included the sensitivity analysis of the energy produced by both types of piezoelectric system. The physical dimensions, frequency of vibrations, and amount of charge produced are the basic parameters of the piezoelectric energy harvesting system. Further, to get an idea about the economic aspect of the project, the cost analysis of single units of both the system has also been done. It has been observed that compression-type piezoelectric harvester is technically and economically superior to cantilever-type energy harvesting system in terms of electrical power generation for railway tracks. Keywords Compression-type piezoelectric system (ComPES) · Cantilever beam-type piezoelectric system (CanPES) · And lead zirconate titanate (PZT)

66.1 Introduction The railways are one of the important modes of transportation of any country. In India, railway has a wide network of around 63,000 km. In most of the countries, the railways now become fully electrified. The railway department consumes around 2– 3% of the total generated electricity in the country. As the pollution is increasing in the world day by day to the dangerous level, the different sectors, including the railway M. Mishra (B) · P. Mahajan · R. Garg Delhi Technological University, New Delhi, India e-mail: [email protected] P. Mahajan e-mail: [email protected] R. Garg e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_66

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sector also, are attempting to use the renewable source of energy. There are some electromechanical energy wasted in our daily life which can be used for generating electrical energy from the vibrations and mechanical forces, etc. In this direction, there are several attempts that have been made which are now practically used at several stations. The solar PV array is now installed at the rooftop of the railway stations and also on the rooftop of the train itself [1]. Also the wind farms are installed near the railway stations, and the power generating at that farm is supplied to the railway sectors [2]. Also in some countries, the concept of supercapacitors is also now introduced for storing the electrical power during acceleration and deceleration of train. The improved idea of integrating all the renewable energy to get the maximum amount of electrical power has been derived by the researchers [3]. One of the new ideas in this direction can be using a “piezoelectric effect” of the material to extract the amount of energy which is wasted or not in use at present time. The East Japan Railways have tried to use the foot fall at the ticket counter at the platform to extract some amount of energy. There are some universities in California who are attempting to use the piezoelectric sheets at the roads to extract electrical energy. One of the company of Israel, Innowattech had file a patent in 2013, discussing about the different locations like embedded at the cross-junction of railway track and sleeper and at the very side of the railway tracks, at which the piezoelectric can be installed [4]. They gave an idea to embed a piezoelectric unit at the cross section area of railway track and sleeper, and also it can be attached to the rail track to use the ground vibrations to extract energy. Based on it, two types of piezoelectric system viz. compression-type unit (ComPES) and unimorph/bimorph cantilever beam-type unit (CanPES) can be used for railway tracks [5–7]. The focus of this paper is to suggest the most preferable, technically reliable, and economical piezoelectric harvesting system for railway tracks. For technical analysis, the mathematical model of both types of PES is developed, and the method of sensitivity analysis of the energy produced has been used to study the effect of various parameters of interest on energy generation. The cost analysis has also been done in this paper to get an idea about the economic aspect of the project. Based on the studies, the total cost for installing the compression type PES on a railway station in India and, hence, the total saving to the railways have been calculated.

66.2 Piezoelectric Effect Whenever a mechanical disturbance or stress is applied to some material, the electrical polarization takes place inside that material producing the electric charge. This effect is called piezoelectric effect. The word piezoelectric comes from the Greek “piezin,” which means to squeeze or press, and piezo, which is Greek for “push,” The piezoelectric material is non-conducting one which should be placed in between two metal plates. For electrical energy to be produced by piezoelectric material, there should be some mechanical disturbance present.

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Fig. 66.1 Electrical equivalent of piezoelectric system

Zinc blende, topaz, and quartz are some of the naturally occurring material which possesses electric charge on their surface, when a mechanical stress is applied on them. In recent research, some manmade materials are developed like lead zirconate titanate (PZT) and polyvinylidene fluoride (PVDF), which can generate more electrical charge. Fig. 66.1 shows the systematic production of electrical energy from the piezoelectric unit. The voltage is generated whenever the force or vibrations has been applied on the piezoelectric unit. Piezoelectric energy harvester is the type of energy harvesting system which helps in generating electrical energy when the mechanical disturbance has been applied. The weight or the frequency of vibrations has been used as an exciting agent for the piezoelectric units. This excitation in electrical equivalent circuit is shown by the input current source or the input voltage source. These exciting agents produces the disturbances which can be in the form of suppression or the vibrations in the layer of piezoelectric material that lead to vary the original alignment of the electric particles present in the material. This causes the change in polarization which helps in establishment of electric field, which helps in generating electrical energy. Accordingly, the piezoelectric system can be divided into two types of energy harvesting systems. These two types of harvesters are given below: (i) (ii)

Compression-type piezoelectric system (ComPES). Cantilever beam-type piezoelectric system (CanPES).

Fig. 66.2 shows the classification block diagram of two types of piezoelectric electrical energy harvesting system with their respective sources of excitations. Fig. 66.2 Block diagram of excitation of piezoelectric system

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66.2.1 Compression-Type Piezoelectric System The ComPES harvester can be embedded at the cross section junction of sleeper and rail to use the mass of the train to generate electrical energy, whereas the CanPES can be installed on the sides of the track use the ground vibrations of the moving train [4]. The ComPES is the direct and simple illustration of piezoelectric effect. The physical modal focuses on the area on the piezoelectric sample at which the force will be applied. This area is the important place which is responsible for the conversion of mechanical energy like force and weight, into the electrical energy. The three directions play an important role in deciding the parameter and constants used for the calculation of generated voltage in the compression-type PES. Figure 66.3 shows the different directions which are considered while calculations [10]. In given Fig. 66.3, directions 1, 2, and 3 are showing the axis x, y, and z, respectively. The poling direction is the main direction about which decides the alignment of the electric field in the piezoelectric material when the force is applied on piezo material. As the electric field is produced, the polarization will occur in the material which is responsible for the production of charge. The equation which relates the mechanical parameters with the electrical parameter is given below: εi = SiEj σ j + dmi E m

(66.1)

σ Dm = dmi σi + eik Em

(66.2)

The indexes i, j have given any value as 1,2,…,6, and m, k can be any value as 1,2,3 referring the different coordinating directions according to Fig. 66.3. σ is for stress vector (N/m2 ), ε is strain vector, E is applied electric field vector (V/m), e is permittivity (F/m), d is piezoelectric strain constants matrix (m/V), S is matrix of compliance coefficient (m2 /N), and D is electric displacement vector (C/m2 ). Equation 66.1 shows the relation between the mechanical strains εi in i direction with the elastic compliance tensor SiEj at constant electric field, σ j mechanical stress in j direction, piezoelectric constant tensor dmi , and E m electric field in m 3

Fig. 66.3 Three dimensional directions of piezoelectric material

6

Poling direction

5 2

1

4

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direction. Equation 66.2 shows the relation between the electric displacements in m direction Dm with the piezoelectric constant tensor dmi , σ j mechanical stress in i σ , and E m electric field direction, dielectric constant tensor under constant stress eik in m direction. The matrix form of Eqs. 66.1 and 66.2 defining the specific directional terms can be expressed as: ⎤ ⎡ S11 ε1 ⎢ε ⎥ ⎢ S ⎢ 2 ⎥ ⎢ 21 ⎢ ⎥ ⎢ ⎢ ε3 ⎥ ⎢ S31 ⎢ ⎥=⎢ ⎢ ε4 ⎥ ⎢ S41 ⎢ ⎥ ⎢ ⎣ ε5 ⎦ ⎣ S51 ε6 S61 ⎡

S12 S22 S32 S42 S52 S62

S13 S23 S33 S43 S53 S63

⎡

S14 S24 S34 S44 S54 S64

⎤ ⎡ D1 d11 d12 d13 d14 ⎣ D2 ⎦ = ⎣ d21 d22 d23 d24 D3 d31 d32 d33 d34

⎤⎡ ⎤ ⎡ σ1 d11 S16 ⎢ σ ⎥ ⎢d S26 ⎥ ⎥⎢ 2 ⎥ ⎢ 12 ⎥⎢ ⎥ ⎢ S36 ⎥⎢ σ3 ⎥ ⎢ d13 ⎥⎢ ⎥ + ⎢ S46 ⎥⎢ τ23 ⎥ ⎢ d14 ⎥⎢ ⎥ ⎢ S56 ⎦⎣ τ31 ⎦ ⎣ d15 S66 τ12 d16 ⎡ ⎤ σ1 ⎡ σ ⎤⎢ σ2 ⎥ ⎢ e11 d15 d16 ⎢ ⎥ ⎥ σ ⎢ ⎥ σ d25 d26 ⎦⎢ 3 ⎥ + ⎣ e21 ⎢ σ4 ⎥ σ d35 d36 ⎢ ⎥ e31 ⎣ σ5 ⎦ σ6

S15 S25 S35 S45 S55 S65

⎤ d31 d32 ⎥ ⎥⎡ E ⎤ ⎥ 1 d33 ⎥⎣ ⎦ ⎥ E2 d34 ⎥ ⎥ E3 d35 ⎦ d36

(66.3)

⎤⎡ ⎤ σ σ e12 e13 E1 σ σ ⎦⎣ e22 e23 E2 ⎦ σ σ e32 e33 E3

(66.4)

d21 d22 d23 d24 d25 d26

For calculating the amount of voltage produced by the single unit of PES, the “d” expressed in C/N is used as the constant which helps in calculating the amount of charge produced due to polarization through the force applied. As the polarization is direction dependent, this constant depends on the direction. Figure 66.4 shows that the force is being applied from the direction 3; hence, all the calculations for calculating the amount of electrical energy produced will be calculated according to this direction. The stress produced in the single unit is σ3 =

F lb

(66.5)

where F is the force provided to the piezoelectric unit, l is the length, and b is the width of the piezoelectric unit. This stress helps in producing the electric charge Force

Fig. 66.4 Compression-type PES Short circuit

t

b

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Fig. 66.5 Electrical equivalent circuit of compression piezoelectric harvester

Capacitor Current source

Resistance

Output Voltage

Q = d33 F. There is also one another direction dependent constant g33 , which helps in calculating the amount of voltage produced in a single ComPES. The electric field direction and stress imposed on the ComPES should be in one direction [11]. Considering all these conditions, the output voltage produced is calculated as: V =

g33 Ft lb

(66.6)

With all the above equations, the circuit diagram of the piezoelectric transducer is given in Fig. 66.5. For Fig. 66.5, the mechanical to electrical analogy is defined as [6]: (1)

The current source is equivalent of the force given to the piezo unit.

I =

Q t

(66.7)

The charge present in the circuit is Q = d33 F, (2)

The capacitance is equivalent to the compliance which is inversely proportional to the stiffness. The piezoelectric material is covered with metal on top and bottom, which makes the structure as capacitor

C=

ε◦ ε A t

(66.8)

where ε◦ and E are the free space permittivity and the relative permittivity of the material, respectively, and t as the thickness of electrode. (3)

The resistance is equivalent to mechanical damping. Known that piezoelectric material produces charge on the application of force, but the charge does not stays on the surface forever. This charge gets the leakage path from the parallel capacitor and resistor connection; hence, the polarized piezoelectric unit gets back to its non- polarized state.

Table 66.1 shows the different values which has been used to design a unit of ComPES.

66 Techno-Economic Analysis of Piezoelectric … Table 66.1 Constants used in designing a unit of ComPES

875

S. no

Constants

Value

1

Charge constant (d33 )

390 × 10–12

2

Length (mm)

58

3

Width (mm)

15

4

Thickness (mm)

1.82

5

Free space permittivity (ε◦ )

8.854 × 10–12

6

Relative permittivity (ε)

3400

7

Voltage constant (g33 )

48 × 10–3

Fig. 66.6 MATLAB Simulink output of single unit of ComPES

When the equivalent circuit is designed with all the above calculated value is designed on the MATLAB Simulation, the voltage and current waveform results for one unit of ComPES are shown below in Fig. 66.6.

66.2.2 Cantilever-Type Piezoelectric System In this system, the electrical energy is produced due to the vibrations produced at the nearby place. These vibrations produce the disturbances in the piezoelectric material which, due to alignment of electric field, is responsible for production of electrical energy. The beam containing the piezoelectric material layer, the cantilever body for supporting this beam, the tip mass to synchronize the frequency of vibrations with the frequency of the beam is the main parts of CanPES.

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Fig. 66.7 Physical model of CanPES

For dealing with this type of model, it is important to have a physical model (shown in Fig. 66.7) containing the mass m as system mass, spring constant k, and damping constant cT . The excitation to the physical system in this case is acceleration of the train due to which the vibrations are produced. These vibrations are expressed in sinusoidal form as x(t) = Asin(ωt), where A is train acceleration, and ω is the vibrational frequency. These vibrations results in the movement of mass as y(t) [12– 14]. The equation defining this movement is given below: ¨ m y¨ (t) + cT y˙ (t) + ky(t) = −m x(t)

(66.9)

The accelerating train generates the vibrations which help in extracting electrical energy from the structure. The relative movement between the tip mass and base of the structure helps in determining the condition for maximum power production. These two frequencies, i.e., the system base frequency and the natural frequency should be in sync to produce the maximum electrical energy. The expression of frequency is given as [15, 16]:  ωn =

Ke f f me f f

(66.10)

33m b I where K e f f = 3E and m e f f = 140+m L3 t The parameters present in above equation are I as the beam’s moment of inertia, mb as the beam mass, and mt as the tip mass which is provided to the cantilever structure. There must be some restricting parameter which prevent the power to go very high suddenly at resonating frequency, this parameter is damping factor (ζt = k/2mωn ). The idea is to convert the energy generated by the ground vibrations due to accelerating train into the electrical energy. The open circuit voltage V produced by single cantilever beam PES is given as [14]:

V=

−d 31 t p σn ε

(66.11)

where t p is the thickness cantilever piezoelectric beam; -d31 is the piezo charge factor; E is the dielectric constant; at the resonating frequency, the system produced

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the stress σn which is given as: σn =

3E Ah 4L 2 ζt ωn2

(66.12)

where E is the effective modulus of beam, h is the piezo layer distance from neutral axis, L is the beam length, ζt is the damping factor of the system, and A is the train acceleration. Thus, the electrical power produced due to these vibrations is defined as given Eq. (13): P=

Vo2 R L p Rs + R L p

2

(66.13)

where Rs the source resistance, and RLp is the load resistance [17–19]. The MATLAB simulation has been used for producing results. The result for single CanPES unit is given in Fig. 66.8. The resistance is equivalent to mechanical damping. Known that piezoelectric material produces charge on the application of force, but the charge does not stays on the surface forever. This charge gets the leakage path from the parallel capacitor and resistor connection; hence, the polarized piezoelectric unit gets back to its nonpolarized state.

Fig. 66.8 MATLAB Simulink output current and output voltage of one PES unit

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66.3 Sensitivity Analysis The sensitivity of the function can be defined as the relative variation in one parameter with respect to change in other parameter. It is an important tool in predicting the effect of one parameter on the other parameter. The sensitivity of the system can be expressed as Sensitivit y = 

A

SK =



%changein A %changein K

A K ∂ A/A orSK = SA A K ∂ K /K 

A

The notation S K denotes sensitivity of variable A with respect to parameter K [21].

66.3.1 Sensitivity Analysis of ComPES The concept of sensitivity analysis has been applied on the energy expression of ComPES. The energy expression of this system is given as: E=

1 F 2t 1 QV = d33 g33 2 2 lb

(66.14)

The expression of energy contains the constants d33 and g33 which are material specific. The other variables like force impact, i.e., weight of the wheel of the bogie on the piezoelectric unit, length, breadth, and thickness of the piezoelectric unit can be changed according to the requirement and hence are parameters of interest. The sensitivity functions of the energy expression with respect to the parameters of interest are given as: Ft ∂[E] = d33 g33 ∂F lb

(66.15)

1 F2 ∂[E] = d33 g33 ∂t 2 lb

(66.16)

SlE =

1 F 2t ∂[E] = − d33 g33 2 ∂l 2 l b

(66.17)

SbE =

1 F 2t ∂[E] = − d33 g33 2 ∂b 2 lb

(66.18)

S FE = StE =

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It has been observed from Table 66.2 that the force is the prominent input with normalized sensitivity value as 2, for the electrical energy production in the ComPES, while length and width are having the negative value of sensitivity. Following curves show the variation of energy with respect to force in Fig. 66.9a, energy with respect to length in Fig. 66.9b, energy with respect to breadth in Fig. 66.9c, and energy with respect to thickness in Fig. 66.9d. It has been observed in Fig. 66.9 that when the force given to the piezo unit increases, the generation of electrical energy increases. Table 66.2 Computed values of normalized sensitivity from sensitive functions developed PES

Function

Parameter

ComPES

Energy

Force

Energy

Length

Energy

Breadth

Energy

Thickness

Normalized sensitivity 

E



E



E



E

SF = 2 St = 1

Sl = −1 S b = −1

Fig. 66.9 a Energy versus force curve b Energy versus length curve c Energy versus breadth curve d Energy versus thickness curve

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66.3.2 Sensitivity of CanPES The exciting agent for electrical energy production in case of CanPES is frequency of vibrations. Due to these vibrations, the beam deviates from its mean position and this disturbance helps in producing electrical energy. Hence, the cantilever beamtype PES is dependent on the frequency. The physical system arrangement is shown in Fig. 66.7. The tip mass is one of the important parameter in this system, which helps in maintaining the resonating frequency of the system, at which maximum electrical power can be produced. By varying the tip mass, the frequency produced by the vibrations of cantilever beam and power output of the system can be adjusted. From Eq. 10, the frequency expression is given below:  w=

3E I (140 + m t ) 33m b L 3

(66.19)

The sensitivity function of frequency with respect to tip mass is given below: Smwt

∂[w] 1 = = ∂m t 2

 3E I 33m t L 3 33E I (140 + m t ) 33m b L 3

(66.20)

The calculated normalized sensitivity of the frequency function with respect to tip mass is given below: w



Smt =

mt w S = 0.034 w mt

(66.21)

The length used in this expression should be designed in such a way that it should support the beam structure of the CanPES. The expression of generation of power from one unit of piezoelectric cantilever structure by using Eqs. (8), (9), and (10) is:  P = 68.06

d31 t p m b ca εζt I R(140 + m t )

2 (66.22)

The sensitivity function and corresponding normalized sensitivity of the power with respect to tip mass are given below: SmPt =

  d31 t p m b ca 2 1 ∂[P] = −2 ∂m t εζt I R (140 + m t )3

(66.23)

mt P S = −0.068 P mt

(66.24)



P

Smt =

Similarly, the parameters like mass of beam and beam’s moment of inertia can be analyzed. The sensitivity function of both the terms is given below:

66 Techno-Economic Analysis of Piezoelectric … Table 66.3 Computed values of normalized sensitivity from sensitive functions developed

SmPb

881

PES

Function

Parameter

CanPES

Frequency

Tip-mass

Power

Tip-mass

Power

Beam mass

Power

Beam moment of inertia

2  d31 t p ca ∂[P] = = 68.06 (2 × m b ) ∂m b εζt I R(140 + m t )

mb P S = 0.886 P mb  2 d31 t p m b ca 1 ∂[P] S IP = = −2×68.06 ∂I εζt R(140 + m t ) (I )3 

P

Smb =



P

SI =

I P S = −2 P I

Normalized sensitivity 

w



P



P



P

S m t = 0.034

Smt = −0.068 S m b = 0.886 S I = −2

(66.25) (66.26)

(66.27) (66.28)

The variation of frequency of the structure and power with respect to tip mass has been shown in the following curves. The value of tip mass is 4 g, beam mass 44.3 g, and moment of inertia is 0.36 mm4 . Figure 66.10a–d. shows the variation of generated frequency and power with different parameters. It has been observed in Table 66.3 that the beam mass is more sensitive than other parameters, which is used in harvesting electrical energy from CanPES.

66.4 Economic Analysis The economic analysis is one of the major parameter to determine the success rate of the project in real world. In this section, the analysis of cost of single unit of ComPES and CanPES has been given. The cost of single ComPES is Rs.3.5, and it can generate 0.2 W of electrical power, while a single unit of CanPES can only generate 0.017 W by single unit which is costing around Rs.150. Therefore, the CanPES array will be costlier than the ComPES. Also, the power produced by the CanPES is majorly dependent on the frequency of the structure when it gains vibrations. When this vibrating frequency is in range of resonance frequency of the structure, then the maximum power has been produced. Therefore, the energy extraction from this type of structure is difficult and costlier as compared to the ComPES.

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66.4.1 Cost Estimation of ComPES Array Based on the above analysis, the cost estimation of ComPES array is done. For the ComPES array, one module of ComPES which is to be installed at the cross section junction of rail and sleeper, having 40 basic piezoelectric unit (n), is considered. The total number of cross-junctions of sleeper and railway track used S = 500. Total piezoelectric units used P = S × n = 40 × 500 = 20,000. In Indian market, the cost of 1 piezoelectric unit = Rs.3.5 Therefore, total cost of installation = P × 3.5 = 20,000 × 3.5 = Rs. 7 lakhs. The railway sector in India pays around Rs. 6.5/unit [22]. The electricity units used by railway station in a single day, T = 150 units. The total cost spent by railways in a day = 6.5 × 120 = Rs. 780. In a year, railway spends = Rs. 2.8 lakhs . The electrical units generated by the ComPES, Tp = 124. The remaining units for which railway will have to pay = T-Tp = 150–124 = 26 units. Therefore, there will be only 26 units of electricity for which the railway has to pay. The cost of 26 units = 26 × 6.5 × 365 = Rs. 61,685. Thus, the railways which were earlier paying Rs. 2.8 lakhs as electricity bill, will now have to pay only Rs. 61,685 per year. The PZT piezo material has life span of about several numbers of years. Hence, the cost of return can be comes estimated in 1–1.5 years.

66.5 Techno-economic Differences of Two PES Based on the above discussion, the technical and economic difference in the two types of energy harvester systems has been analyzed. It has been observed that. (i)

(ii)

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The output power produced is the important factor to choose any system. Considering the output power generated by the one unit of both the PES, compression type is generating around 0.2 W, whereas the power generated by the one unit of cantilever type is producing only 0.017 W. The electrical energy produced due to piezoelectric effect is dependent on the type of excitation agent. The excitation agent in case of ComPES is weight of the bogie, whereas for the CanPES, it is vibrations produced due to the accelerating train. The location of the piezoelectric units is one of the important parameter to produce the reliable amount of electrical energy. In case of ComPES, the location must be the cross-junction of the railway track and sleeper, because this system has got excited by the weight which is applied on the piezoelectric unit, whereas the suitable location for the CanPES should be very near or very side of the railway track as this system has to make vibrations in the piezoelectric cantilever beam which is produced due to the accelerating train.

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From the above economic analysis, it is clear that installing ComPES is inexpensive compared to the CanPES, and also former can generate more electrical energy than later for same number of piezoelectric units.

The comparison of both the PES suggests that ComPES is better than CanPES in terms of electrical power generation for railway tracks. The excitation parameter for ComPES is force which is stable than the frequency which is the excitation parameter for CanPES.

66.6 Conclusion In this paper, technically reliable and economic piezoelectric harvesting system is suggested based on techno-economic analysis carried out. It has been observed that the ComPES is highly sensitive to the force, as the force is the basic excitation agent to generate the electric field in the piezo material in case of compression. In case of CanPES, the tip mass plays an important role in maintaining the synchronized frequency for the generation of electrical energy. The energy produced by CanPES is dependent on frequency of vibration, and hence, it is challenging to harness electrical energy produced by CanPES because if number of cantilever structures are used, then it is difficult to synchronize the frequency of these structures to produce a constant amount of electricity. The array of compression type units can be more useful as synchronization of different structures is not required in this case. Further, ComPES harvester can produce electrical energy more economically as compared to the CanPES harvester. Hence, ComPES is better suited to harness electrical energy from railway tracks.

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Fig. 66.10 a Frequency versus tip-mass curve b Power versus tip-mass curve c Power versus beam mass curve d Power versus moment of inertia curve

References 1. M. K. Darshana, K. Karnataki, G. Shankar and K. R. Sheela, A practical implementation of energy harvesting, monitoring and analysis system for solar photo voltaic terrestrial vehicles in Indian scenarios: A case of pilot implementation in the Indian Railways, in 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), Dhaka, 2015, pp. 542–545 2. B. OmPrakash, T.J. Kumar, Hybrid power generation using solar, Wind and Piezo. Int. J. Adv. Energy Res. Technol. 5(11) (2017). ISSN No.:2348–8190 . 3. M. Becherif, M.Y. Ayad, A. Henni, A. Aboubou, Hybrid sources for train traction: Wind generator, solar panel and supercapacitors, IEEE International Energy Conference. Manama 2010, 658–663 (2010) 4. H. Abramovich, E. Harash, Power harverting From Railways: Apparatus, System and Method, Innowattech Ltd., Patent No.: US 7,812,508 B2, Date of Patent (2010) 5. A.H. Sodano, D.J. Inman, Estimation of electric charge output for piezoelectric energy harvesting. Strain J. 40(2), 49–58 (2004) 6. G. Staines, H. Hofmann, J. Dommer; L. L. Altgilbers, Y. Tkach, Compact Piezo-Based High Voltage Generator—Part I: QuasiStatic Measurements. Electromagnetic Phenomena, Y.3, No.3 (11) (2003) 7. L. Shih-Fu, L. Bei, Modeling of a PZT-driven cantilever actuator, in Proceedings of SPIE —The International Society for Optical Engineering. 4753 (2002) 8. Alam, K.S., et al., Modeling and computation of a solar-piezoelectric hybrid power plant for railway stations, in 2012 International Conference on Informatics, Electronics & Vision (ICIEV), Dhaka, pp. 155–159 (2012)

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9. Z.W. Zhang, H.J. Xiang, Z.F. Shi, Modeling on piezoelectric energy harvesting from pavements under traffic loads. J. Intell. Mater. Syst. Struct. 27 ( 2015) 10. M. Mishra, P. Mahajan, R. Garg, Chapter 19 Piezoelectric Energy HarvestingSystem Using Railway Tracks, Springer Science and Business Media LLC (2021) 11. M. Dutta, P. Shrimoyee, Footstep voltage generator using piezo-electric transducers. Int. J. Sci. Eng. Res. 8(3) (2017) 12. R. Djuguma, P. Trivailo, K. Graves, A study of energy harvesting from piezoelectrics using impact forces. The European Phys. J. Appl. Phys. (EPJ AP), 48(1) 11101 (2009) 13. J. Li, S. Jang, J. Tang, Design of a Bimorph Piezoelectric Energy Harvester for Railway Monitoring. J. Korean Soc. Nondestructive Testing. 32 (2012). https://doi.org/10.7779/JKSNT. 2012.32.6.661 14. A. Karlström, A. Boström, An analytical model for train-induced ground vibrations from railways. J. SoundVibration. 292(1–2), 221–241 (2006) 15. S. Priya, C. Chih-ta, Piezoelectric Windmill: A novel solution to remote sensing. Japanese J. Appl. Phys. 44(3), L104–L107 (2005) 16. R. Challa, M.G. Prasad, F.T. Fisher, A coupled piezoelectric-electromagnetic energy harvesting technique for achieving increased power output through damping matching. Smart Mater. Struct. 18, 095029 (2009) 17. S. Roundy et al., “Improving power output for vibration-based energy scavengers,” in IEEE Pervasive Computing, vol. 4, no. 1, pp. 28–36,Jan.-March 2005. doi: https://doi.org/10.1109/ MPRV.2005.14. 18. J. Li, S. Jang, Design of a bimorph piezoelectric energy harvester for railway monitoring. J. Korean Soc. Nondestruct. Test. 32(6), 661–668 (2012). ISSN 1225–7842 / eISSN 2287–402X 19. G. Gegrande, P. Chatterjee, W. Van de Velde, P. Hölscher, V. Hopman, A. Wang, N. Dadkah, R. Klein, Vibration Due to a Test Train at Variable Speeds in a Deep Bored Tunnel Embedded in London Clay,” 11th International Congress on Sound and Vibration (St-Petersburg, Russis, 2004) 20. M. Zhu, E. Worthington, A. Tiwari, Design study of piezoelectric energy-harvesting devices for generation of higher electrical power using a coupled piezoelectric-circuit finite element method. IEEE Trans. Ultrasonic’s, Ferroelectrics Frequency Control 57, 46–51 (2010) 21. P. Mahajan, R. Garg, P. Kumar, Sensitivity analysis of railway electric traction system, India, in International Conference on Power Electronics 2010 (IICPE2010), New Delhi, 2011, pp. 1–5. https://doi.org/10.1109/IICPE.2011.5728107 22. https://www.business-standard.com/article/economy-policy/indian-railways-to-plug-into-nat ional-power-grid. Last accessed on 28 July 2020

Chapter 67

JDMaN: Just Defeat Misery at Nagging—A Smart Application for Women Protection N. Jayanthi, N. M. Deepika, G. Nishwitha, and K. Mayuri

Abstract Women are exploring themselves in various areas in this world and yet are facing many challenges and threats in their daily life; according to the National Crime Records Bureau (NCRB) 93.3% outrage, victims are solo women travelers. Due to these enormity situations, there is an urgent need to develop a women security device that can be easily carried. In this technological age, one gadget which has become like oxygen to everyone is a mobile phone. In such a scenario, by making use of mobile phone, we focus on developing a mobile app along with a wearable smart pendant developed by using IoT that assures women to travel confidently and safely. This IoTbased pendent can work automatically and manually. The pendant is an integration of multiple sensors that can work with or without contact with the human body. To the best of our knowledge, no mobile application or IoT device has proposed prior safety information about the route the user has to travel. Unlike the other mobile applications or IoT devices that will get activated at the time of the incident and then share the location details, our proposed method provides prior information of multiple routes from source to destination indicating safe and unsafe routes, and also a vibration is been given to the user so that she shall not become unconscious if at all she faces any unusual incident. The combination of mobile app and IoT-based pendant works very well in providing safety for women. Keywords IoT · Wearable device · Mobile application · Safe route · Unsafe route

N. Jayanthi (B) · N. M. Deepika · G. Nishwitha · K. Mayuri Institute of Aeronautical Engineering, Dundigal, India e-mail: [email protected] N. M. Deepika e-mail: [email protected] G. Nishwitha e-mail: [email protected] K. Mayuri e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_67

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67.1 Introduction In today’s world, there is a lot of advancement in technology. But women are still facing unfortunate incidents. As the rate of crimes against women increasing day by day, both young and elderly women are not confident in building their careers even in metropolitan cities like Delhi, Mumbai, and Hyderabad. According to the National Crime Records Bureau (NCRB), 93.3% [21] of outrage victims were solo women travelers. By observing the recent incidents, traveling in a safe route to reach the destination can reduce the risk of unusual incidents. The sophisticated technologies of twenty-first century have given solutions to the existing problem. One such technology is the Internet of Things which extends the power of the Internet beyond computers and Smartphone with good processing [22]. It is a huge network connected to things and people by which data can be collected, sent, and received about the environment around them [23]. It is a network of physical objects that are nested with software, sensors, and other technologies. Features that made it so popular and powerful are access to the low-cost and low-power sensor, connectivity, cloud computing platforms, machine learning and analytics, and conversational Artificial Intelligence (AI) [24]. In this paper, a Smartphone application and an IoT device are proposed. The IoT device is embedded with sensors which should be miniature into a tiny wearable pendant. These sensors are activated automatically in an unexpected situation. The Smartphone application JDMaN initially asks to deposit preset contacts that can be used to send emergency messages later in an unusual situation. The mobile app also asks users to give source and destination places as inputs before the user start the journey. As soon as the user enters the inputs the app shows number of routes from source to destination marking safe and unsafe routes. The smart pendant united with Smartphone application has an advantage of miniaturizing nature and low cost. The novel idea of this paper is to intimate the user about the safety of the route before starting the journey to reduce the risk of harm to women. The paper is organized as follows: in section two, literature survey is presented; in section three, proposed method is described listing various software and hardware components which are followed by a conclusion and future work.

67.2 Literature Survey This section throws light on various smart devices used for women’s safety. Authors in [1] developed a standalone device without an android application and with ATmega 328 microcontroller. It sends the current location of the woman to family and friends alerting them about the current location by making use of GPS and GSM modules.

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In [2], authors proposed a safety device that is in the form of a wristwatch, which has a two-way talk feature by which the victim can contact friends and family, and also it contains a loud buzzer. The concept device is based on GEOFENCE. Authors of [3] proposed a self-defense system with a button. When the button is triggered, it sends victims’ location to her family members. It also alerts the surroundings with a prerecorded message. In paper [4], authors have proposed an android application for the device (FEMME). Its features are sending SOS message, record audio, record video, and detect the hidden camera. The recording option of audio and video will help to have evidence. In [5], authors developed a standalone device Suraksha. This device can be used in three different ways one the voice mechanism recognizes the voice of the victim and sends distress messages automatically, two a switch can be on/off, and third is that when the device is thrown, the force sensor of the device gives information to the victim’s family members and friends about location information. Authors of [6] developed a safety device that is in the form of a smart band, and this device gets activated by tapping it twice. This device has interesting features ones it gets activated as sending GPS location to police control rooms and pre-defined contacts when the device is thrown, the force sensor is activated and sends current location of the victim, piezo buzzer gets activated, and also shock is generated. Authors in [7] developed a portable safety device known as SMARISA. It is activated by tapping the button after which the camera captures the image of the attacker and sends these pictures along with the current location to police and predefined emergency contact numbers by making use of a Smartphone. In [8] proposed a safety device that needs fingerprint to get activated. The device cooperates with a buzzer to alert nearby people, location of the victim is sent to ICE contacts (In case of emergency contact)) through GPS and GSM module, shock generator, also some additional features like audio recording and display of safe place from the current crime location. In the paper [9], authors have proposed a safety device that resembles a normal belt. This device incorporates the Arduino Board, GSM/GPS modules, screaming alarm, and pressure sensors. This device activates automatically when the threshold of pressure sensor gets crossed. It sends emergency messages every two minutes to the police and three preset numbers with an updated location. It also contains a screaming alarm and can generate electric shock. In paper [10], authors proposed a smart shoe for women’s safety. The components of the device are placed on the sole and side layers of the shoe. This device sends emergency messages to the emergency contacts on pressing the switch located toward the side of the shoe. It also includes a shock circuit that generates a shock of 440 kV, a camera that records live video. In paper [11], authors proposed a wearable safety device that can be operated automatically without the need for the Internet. As the sensor’s data cross threshold value device gets activated automatically, the data is sent to the cloud, where logistic regression is applied to the data. When it is identified that it is an emergency help

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message which is been sent to emergency contacts. This device uses a Zigbee mesh network and hence does not need the Internet. In paper [12], authors have a specialized device for rural women. They have designed a BECON device that works with the wireless network. The BECON information will reach central stations with the help of the nearest access point. In [13], a smart garment was developed that incorporates an electronic device. This device will generate 3800 kV to help victims escape from the location. On multiple attacks, 80 electric shocks will be generated. In [14], a mobile app is developed initiated by Channel [V]. On pressing the power button twice of the Smartphone, it sends location details to the contacts every two minutes.

67.3 Existing System There are many other devices and mobile app for the protection of women. Some of the smart devices [20] are shown in Fig. 67.1, and some of the apps are Advanced Electronics System for Human Safety (AESHS) [15], ILA security, Life360 Family Locator App [16] and apps to detect location by shaking the phones [17, 18]. In the existing system, a safety device resembles a normal belt [9]. This device incorporates the Arduino Board, GSM/GPS modules, screaming alarm, and pressure sensors. This device activates automatically when the threshold of pressure sensor gets crossed; it also contains a screaming alarm and can generate electric shock. The drawbacks of existing system are first it is operated only in automatic mode; second screaming alarm is not audible to a long distance. In proposed method, these disadvantages are overcome by operating the device in both automatic and manual

Fig. 67.1 Smart devices

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mode. The screaming alarm is replaced by sending alert messages to nearby people who have installed the JDMaN mobile app for immediate help.

67.4 Proposed Methodology As per the literature survey, there are various mobile apps and safety devices for the protection of women. These devices can be operated manually or automatically at the time of any unusual incident. But in the proposed method, an advanced safety measure is intimated to the woman. To the best of our knowledge, this is the first paper that proposes to give prior information about the route that the user has to travel (Fig. 67.2).

67.4.1 Software’s This section focuses on software components used in the proposed method (Fig. 67.3). GPS and Google Maps These two are navigation tools; GPS is divided into groups; each group is assigned a separate orbital path so that detection can be done from anywhere on the earth surface; it is useful to track the coordinates of the user. The uses of Google maps are many like finding nearby hotels, temples, petrol filling stations, hospitals, traffic density, etc. In our prototype method, the mobile app JDMaN works in similar ways to Google maps to find populated and unpopulated routes that guide as safety instruction to the user. Fig. 67.2 Components of proposed method

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Fig. 67.3 Connection between Smartphone and pendant

GSM The Global System for Mobile Communication is a digital mobile telephony system. It can accept any GSM network operator SIM card and work as a mobile phone. As it is compatible with Integrated Services Digital Network (ISDN) and can send and receive SMS, it is used to send messages to family, friends, police, and the nearby people in an emergency. Smartphone—Mobile App (JDMaN) Our proposed app is developed with the motivation of Google maps. These maps are used for tracking traffic, searching places, and for quick navigation. From these real-time applications, we are going to adapt traffic tracking that displays the traffic speed with colors. Our mobile app JDMaN shows various routes from source to destination indicating safe and unsafe routes as presented in Fig. 67.4. • Green means an unsafe route as the route is not populated. • Red means a safe route as the route is heavily populated.

67.4.2 Hardware Devices This section focuses on hardware components used in the proposed method. Smart Pendant IoT-based pendant is built up by embedding sensors that get activated automatically and sent emergency messages to preset contacts and police shown in Fig. 67.5. Force-Sensing Resistors Lightweight sensing material, change in force across the device, can be changed in resistance in the terminals. Force-sensing resistors consist of a semi-conductive material—or, semi-conductive ink—contained between two thin substrates. As shown in Fig. 67.6, there are two different types of force-sensing resistor technologies— shunt mode and thru mode.

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Fig. 67.4 Safe and unsafe routes

Shunt mode force-sensing resistors are polymer thick-film devices consisting of two membranes separated by a thin air gap. One membrane has two sets of interdigitized traces that are electronically isolated from one another, while the other membrane is coated with a special textured, resistive ink. Thru mode force-sensing resistors are flexible printed circuits that utilize a polyester film as its two outer substrates. Silver circles with traces are positioned above and below a pressure-sensitive layer, followed by a conductive polymer. An adhesive layer is used to laminate the two layers of the substrate together.

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Fig. 67.5 Smart pendant

Fig. 67.6 This graphic illustrates the differences between the shunt and thru mode force-sensing resistor technologies

Vibration Sensor A vibration sensor, also known as a piezoelectric sensor shown in Fig. 67.7, has many types that are used to measure the acceleration, pressure, and vibration changes of a device or system. It can be used alongside an Arduino or Raspberry Pi through the miniaturized. The working principle of vibration sensor is a sensor that operates based on different optical otherwise mechanical principles for detecting observed system vibrations. The sensitivity of these sensors normally ranges from 10 mV/g to 100 mV/g, and there are lower and higher sensitivities which are also accessible. Vibration analysis can detect problems such as imbalance and bearing failures.

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Fig. 67.7 Vibration sensor

Fig. 67.8 Pulse rate sensor

Pulse Rate Sensor The pulse rate sensor shown in Fig. 67.8 consists of a light-emitting diode and a detector like a light detecting resistor or a photodiode. The heartbeat pulses cause a variation in the flow of blood to different regions of the body. Pulse rate sensors are available in wrist watches (Smart Watches), Smartphones, chest straps, etc. The heartbeat is measured in beats per minute or bpm, which indicates the number of times the heart is contracting or expanding in a minute. Raspberry Pi The Raspberry Pi is a small, stripped, low-cost, and credit card-sized computer that can be used to do many of the simple tasks like connecting to a computer monitor or TV using HDMI, Internet browsing, playing games, and even play HD videos. Figure 67.9 shows credit card-sized Raspberry Pi.

67.4.3 Working The proposed method consists of an IoT circuit in a pendent and a mobile app on a Smartphone shown in Fig. 67.10. A pendant consists of a pulse rate sensor, vibration

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Fig. 67.9 Credit card-sized Raspberry Pi

Fig. 67.10 A flowchart for mobile application working

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sensor, and a force sensor. A mobile app works to give prior information about the safest route to women. The pendant and Smartphone are connected using Bluetooth. The proposed methodology works as follows: 1.

2. 3. 4.

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Initially, the user has to give input in the app about source and destination, and thus, the app shows number of routes from source to destination highlighting safe and unsafe routes. Safe route means populated route with more traffic of people, and unsafe route means low traffic or no traffic of people. If the user selects a safe route depending on her choice, she may or may not share her live location with her family and friends. If the user has to select an unsafe route in the worst case, she has to compulsory share the live location to her family or friends or both in manual mode. If anything unusual happens in the unsafe route, the sensors in the pendent are activated automatically and the data location is sent to police, family, friends, and the nearby people who installed this mobile app for immediate help. In case, if the pendent is thrown in an unusual situation, the force sensor in the pendant will send the last current location to the preset numbers.

67.5 Conclusion and Future Work Our proposed system can provide safety for women who travel alone on roads, workplaces, and public transport. As the proposed system can be operated manually or automatically, it is the easiest way to use. As the women are intimated with safe or unsafe routes before making her alert about the surroundings, which no existing system has done yet. The vibration to the woman keeps her conscious without losing her senses. This system can make women travel more confidently and safely at any time of the day or night. This system is a prototype so it has to be implemented and can be further improved by adding features like communication without the Internet and beneficial to rural women.

References 1. P. Bhilare, A. Mohite, D. Kamble, S. Makode, R. Kahane, Women Employee Security System using GPS And GSM Based Vehicle Tracking, Department of Computer Engineering Vishwakarma IOT SavitribaiPhule Pune University India, Int. J. Res. Emerg. Sci. Technol. 2(1) (2015). E-ISSN:-2349- 7610 2. N. V. Kumar, S. Vahini, Efficient tracking for women safety and security using Iot. Int. J. Adv. Res. Comput. Sci. 8(9) (2015) 3. B. Vijaylashmi, S. Renuka, P. Chennur, S. Patil. Self defense system for women safety with location tracking and SMS alerting through GSM network. Int. J. Res. Eng. Technol. (IJRET), 4(5) (2015) 4. D. G. Monisha, M. Monisha, G. Pavithra, R. Subhashini, Women safety device and applicationFEMME. Indian J. Sci. Technol. 9(10) (2016)

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5. N. Bhardwaj, N. Aggarwal, Design and Development of “Suraksha”-A Women Safety Device. International Journal of Information & Computational Technology 4(8), 787–792 (2014) 6. S. Ahir, S. Kapadia, J. Chauhan, N. Sanghavi, The Personal Stun-A Smart Device For Women’s Safety, in 2018 International Conference on Smart City and Emerging Technology (ICSCET) (pp. 1–3) IEEE (2018) 7. N. R. Sogi, P. Chatterjee, U. Nethra, V. Suma, SMARISA: A Raspberry Pi Based Smart Ring for Women Safety Using IoT, in 2018 International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 451–454). IEEE (2018) 8. W. Akram, M. Jain, C. Sweetlin Hemalatha, Design of a smart safety device for women using IoT. International Conference On Recent Trends In Advanced Computing(ICRTAC) 9. B. Chougula, A. Naik , M. Monu , P. Patil, P. Das, Smart girls security system. Int. J. Appl. Innov. Eng. Manage. (2014) 10. V. Sharma, Y. T. D. Vydeki, Smart shoe for women safety, in 2019 IEEE 10th International Conference on Awareness Science and Technology (icast) 11. T.K. Muskan, M. Khandelwal, P.S. Pandey, W.S.D. Designed, using IoT and machine learning, IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, ScalableComputing & Communications Cloud & Big Data Computing, Internet of People and Smart City Innovations (2018) 12. R. Paknikar, S. Shah, P. Gharpure, Wireless IoT based solution for women safety in rural areas, in Proceedings of the Fourth International Conference on Communication and Electronics Systems, ICCES (2019) 13. M. Mohan, N. B. Bal ,R. Tripathi, SHE- Society Harnessing Equipment, SRM University, Chennai. (Online) 14. Available. https://www.srmist.edu.in/content/worlds-first-anti-rapedevice-invented-young-stu dent-researchers-srm-university-chennai 15. UpsanaDass, “Best 10 Personal Safety Apps For Women (Android)” August 3, 2018. (Online). Available. https://www.hongkiat.com/blog/android-personal-safety-women-apps/ 16. A. Wadhawane, A. Attar, P. Ghodke and P.Petkar, IoT based smart system for human safety. Int. J. Comput. Appl. 179(7) (2017) 17. The Life360 website. (Online). Available: https://www.life360.com/ 18. D. Chand, S. Nayak, K. S. Bhat, S. Parikh, Y. Singh, A. A. Kamath, A Mobile Application for Women’s Safety: WoSApp, IEEE conference (2015). ISBN: 978–1–4799–8641–5 19. S. Mehta, S. Janawade, V. Kittur, S. Munnole, S. Basannavar, An android based application or women safety. Int. J. Eng. Sci. Comput. (2017) 20. https://magicpin.in/blog/top-10-woman-safety-gadgets/ 21. https://www.outlookindia.com/newsscroll/domestic-violence-tops-crime-against-women-in2018-ncrb/1704114 22. https://www.iotforall.com/what-is-iot-simple-explanation/ 23. https://www.ibm.com/blogs/internet-of-things/what-is-the-iot/ 24. https://www.oracle.com/in/internet-of-things/what-is-iot.html

Chapter 68

Control of PM Synchronous Motor with Hybrid Speed Controller with Gain Scheduling for Electric Propulsion Amit V. Sant and V. S. K. V. Harish

Abstract Due to the increased operating efficiency and reduced carbon emissions, the number of battery driven electric vehicles (EVs) plying on the roads is gradually increasing. It is estimated that in the future, the electric vehicles will phase out the internal combustion engine-based vehicles, and the automobile sector will be dominated by EVs. In order to increase the distance travelled by EV per full charge of the battery, it is important to increase the efficiency of the propulsion system. Hence, permanent magnet synchronous motors (PMSMs), which have higher operating efficiency, as compared to the induction motors, are largely preferred for electric propulsion. The PMSM drive generally employs field-oriented control (FOC). In FOC, the operation of speed controller is critical as it decides the reference torque for the inner control loop. As the proportional–integral (PI) speed controllers with constant gains suffer from performance degradation under disturbances, gain scheduling is employed. To further improve the dynamic performance of the speed controller, this paper reports electric propulsion with hybrid gain-scheduled PI speed controller for the FOC controlled PMSM. The weights for the gain-scheduled PI controller and the fuzzy equivalent proportional controller are determined by the hyperbolic tangent function. The output of the reported speed controller is the weighted average of the two controller outputs. This results in computational simplicity and improved dynamic performance. The EV performance with this hybrid speed controller is analysed for acceleration, deceleration and cruising conditions. Keywords Electric vehicle · Field-oriented control (FOC) · Hybrid gain-scheduled PI controller · Permanent magnet synchronous motor (PMSM)

A. V. Sant (B) · V. S. K. V. Harish Department of Electrical Engineering, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India e-mail: [email protected] V. S. K. V. Harish e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_68

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68.1 Introduction The increasing levels of air pollution had led to the global warming issues which are having catastrophic effects in terms of climate change and increase in sea levels. The coal-powered thermal power plants and petrol or diesel based internal combustion engine are significant contributors to the greenhouse gas emissions. With the increasing awareness about climate change, there is a growing interest in the electric vehicle (EV) sector [1]. In addition to the growing environmental concerns, there is a global increase in the usage of EVs due to technological developments, economic incentives and government regulations [2]. The growth of EVs is aided by the activation of Corporate Average Fuel Economy standards in 2016 [3]. Moreover, the unreliable fossil fuel economy and rising prices of fossil fuel have further contributed to the growth of EV sector [2]. Hence, EVs are viewed as a replacement for the internal combustion engine-based vehicles to overcome the issues of gaseous emissions and fuel economy [4]. In EVs, the electric propulsion system comprises of a battery-powered electric propulsion motor. The power being fed to the electric vehicle is modulated by means of a power converter. The issues that are cause of hindrance for the wider market acceptability of EVs are: (i) limited distance travelled per full charge of the battery pack, (ii) higher charging times of the battery pack and (iii) lack of the necessary charging infrastructure [2]. To make the EV more competitive, the distance travelled per full charging of the battery pack needs to be increased. One way to achieve this is to use batteries with higher energy density, such as solid state batteries [5]. Alternately, the system efficiency can be increased by reducing losses so that the distance travelled over a full charging of the battery pack can be increased. Electric motors are central to the propulsion system and can significantly impact the overall efficiency of the EV [7]. Electric motors are responsible for the conversion of the on-board electrical energy to mechanical energy for the commanded motion [4, 6]. Higher torque, power density, efficiency, reliability and robustness are the key features of propulsion motors [4, 6, 7]. Other features such as cost, controllability and maturity of technology are significant and need to be considered for selection of electric propulsion motor [7]. Permanent magnet synchronous motors (PMSM) are widely employed as propulsion motors for EV as they can operate over wide range of speed and torque with higher torque density and power density with the currently available designs [8]. Furthermore, PMSMs offer merits of higher efficiency, higher power factor and maintenance-free operation [3, 9], making it ideally suited for EVs. Field-oriented control (FOC) is standard theory largely employed for the speed control of PMSM [3, 9–14]. FOC offers merits of fast dynamic response and reduced torque ripples [9, 15]. Also, the use of pulse width modulation instead of hysteresis current controller results in drive operation at fixed switching frequency [9, 15]. The structure of field-oriented control consists of outer speed loop and two individual inner current loops [9, 10]. The speed controller processes the speed error and determines the reference value of the torque producing component of stator current which needs to be drawn so as to ensure that developed torque results in the speed error

68 Control of PM Synchronous Motor with Hybrid Speed Controller …

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being reduced to zero. Hence, the performance of the speed controller is critical for the operation of FOC controlled PMSM drive [9, 10]. Fixed gain proportionalintegral controllers are usually employed as speed controller due to its computational simplicity [9–11]. The PMSM drive is a nonlinear, strongly coupled and multivariable system [11]. The performance of fixed gain PI speed controllers is affected by the parameter variations, system disturbances and speed variations, resulting in adverse impact on the drive performance [9, 11]. Sliding mode controllers, fuzzy logic controllers, multi-resolution wavelet controllers, gain-scheduled PI controllers, hybrid fuzzy PI controllers and hybrid PI controllers are reported as a replacement for the PI controllers [9–14]. Fuzzy logic controller is computationally intensive, whereas the sliding mode controller suffers from the chattering issues. Gain scheduling controllers allow for the PI controller gains to be modified based on the speed error, but the dynamic performance is greatly impacted by the constants involved in the gain scheduling algorithm. Hybrid controllers combine the merits of two controllers, but involve execution of two controller algorithms. To reduce the computational burden, incorporate gain scheduling and combine the merits of fuzzy logic controller and gain-scheduled PI controller, this paper proposes hybrid speed controller with gain scheduling for speed controller of FOC controlled PMSM drive for electric propulsion. In hybrid controller, the fuzzy logic algorithm is replaced by fuzzy logic equivalent proportional (FLEP) controller, reported in [12]. FLEP controller mimics the performance of the fuzzy controller during the initial transient condition by forcing the maximum permissible current to drive the speed error to zero in shortest possible time. When the FLEP controller is driving the error to zero, the gain-scheduled PI controller gains are determined. The weights of the two controllers are determined by processing the error signal by hyperbolic tangent function. During the dynamic state, the FLEP controller has more prominent effect on the output of the hybrid controller, whereas for steady state, the gain-scheduled PI controllers ensure that the steady state error is zero. Thus, the drawbacks of fixed gain PI controller, hybrid fuzzy PI controller and gain-scheduled controller are overcome with the proposed hybrid speed controller with gain scheduling, which is computationally simple and provides fast dynamic response. This controller is implemented as a speed controller for the FOC controlled PMSM driving an EV, and the drive operation is analysed for different operating conditions.

68.2 Electric Propulsion System Electric propulsion system is responsible for the control of flow of power from the energy storage element to the propulsion motor for the development of necessary tractive effort for the desired motion of EV. The developed tractive effort needs to overcome the aerodynamic drag, friction between the wheels and the road surface and inertia to ensure the desired motion [1]. Figure 68.1 shows the block diagram representation of electric propulsion system for a rear wheel drive EV. The battery pack delivers the necessary electric power for the propulsion system [3]. The battery

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Fig. 68.1 Generalized block diagram of electric propulsion system for a four-wheeler EV

management is included to provide the correct estimation of battery parameters and to ensure necessary battery protection. A dc-dc converter, which interfaces the battery with the voltage source inverter (VSI), regulates the dc voltage at the input of the VSI and ensures battery operation within the specifications. VSI converts dc supply to a controlled variable voltage variable frequency ac supply and thereby modulates the flow of power from the battery to the propulsion motor for the desired motion of the EV. Due to its merits of higher efficiency, higher power density and higher torque per ampere, PMSM is widely preferred in four-wheeler EVs [3]. The rotor shaft is connected to the rear wheels through mechanical gears. The incorporation of regenerative braking necessitates the dc-dc converter to facilitate the bidirectional flow of power. For the control of the flow of power for the commanded vehicle movement, control signal 1 and 2 need to be controlled. Control signal 1 determines the switching state of the dc-dc converter to regulate dc-link voltage. On the other hand, the control signal 2 determines the switching state of the VSI to modulate the flow of power from the dc-link to the stator windings of PMSM. For the speed control of PMSM, FOC is a widely employed control technique [9]. FOC offers the merits of fast dynamic response, low torque ripples and inverter operation at fixed switching frequency [9, 15]. Figure 68.2 illustrates the block diagram representation of FOC for PMSM. The control structure of FOC comprises of outer speed loop and two individual inner current loops for the d-q axes currents [9, 15]. Based on the driver command, the reference speed command, Ω rR , is generated. Thus, generated command value is

68 Control of PM Synchronous Motor with Hybrid Speed Controller …

903

Fig. 68.2 Block diagram representation of FOC of PMSM for electric propulsion system

compared with the actual speed, Ω r , and the resulting error signal, es , is processed by a PI speed controller. This controller determines the reference value of q-axis current, mqR , which is proportional to the developed torque that would result in acceleration or deceleration of rotor leading to es being reduced to zero. As the permanent magnets mounted in rotor are fully responsible for the magnetization of air-gap, no magnetizing current, i.e. the d-axis stator current, is drawn by the stator. For maximum torque per ampere control, the reference value of d-axis current, mdR , is zero. The three output signals of incremental encoders with indexing are usually processed by the quadrature encoder pulse circuitry to extract the Ω r and rotor position information, θ r . With the help of θ r , the instantaneous stator currents, mr − my − mb , are sensed by the hall effect current sensors and processed to determine the equivalent d-q axes currents, md − mq , in synchronous reference frame through coordinate transformation. mdR − mqR are, respectively, compared with md − mq to determine the individual current errors, emd − emq . The inner current control loop separately processes emd − emq with current PI controllers to decide upon the reference value of d-q axes voltages, vRd − vRq . The employed inverse coordinate transform uses θ r to transform vRd − vRq in synchronous reference frame into r − y − b reference frame to obtain the three-phase stator reference voltages, vrR − vyR − vbR . Pulse width modulator compares vrR − vyR − vbR with the carrier signals to determine the control signal 2, i.e. the six gating pulses of VSI. Thus, generated control signals ensure that the reference voltages are impressed across the stator terminals to ensure that the magnitude, phase and frequency of the stator current are

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controlled so that md − mq match with mdR − mqR . This leads to indirect control of torque and flux and as a consequence Ω r regulated at Ω rR . The role of the speed controller is critical as it decides the value of mqR that is proportional to the reference torque and can result in desired rotor speed, and as a result, desired motion of the vehicle can be achieved. As varied conditions are experienced during the motion of EV, the fixed gain PI controllers may suffer from performance degradation during sudden changes in vehicle speed and system disturbances. Hence, hybrid speed controller with gain scheduling is proposed in this paper. The discussions on the merits, functionality and mathematical model of this controller are presented in the following section.

68.3 Hybrid Gain-Scheduled PI Speed Controller 68.3.1 Gain-Scheduled PI Controller PI controllers are largely employed as speed controllers in FOC of PMSM [9]. The problems associated with the constant gain can be resolved either with the use of gain scheduling or use of hybrid controller. In gain-scheduled PI controller, the proportional gain, P, and integral gain, I, are determined based on the error signal, e, using (1), (2) and (3), where Pm and Pn are the maximum and minimum values of P, K 1 is a constant, and I m is the maximum value of I [14]. P = Pm − (Pm − Pn )e K 1 |e|

(68.1)

I = Im (1 − tanh(|e|))

(68.2)

The output of a gain-scheduled PI controller is given as [14]  yG = (P · e) +

(I · e)dt

(68.3)

In gain-scheduled PI controller, as P and I are varied with the change in e, the problems associated with fixed gains are eliminated. However, the dynamic response is greatly affected by the selection of K 1 .

68.3.2 Hybrid Fuzzy PI Controller Fuzzy logic controller determines the controller output through fuzzification, rule base and defuzzification. Fuzzy logic controllers are reported to have better transient response as compared to the PI controller [9, 11, 15]. However, the performance of

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PI controller is relatively better under steady state [9]. Hybrid fuzzy PI controller combines the merits of PI and fuzzy logic controllers. The output of hybrid fuzzy PI controller is given in [9]. yH F P I = y P I · w P I + yF · wF

(68.4)

where yHFPI is the output of hybrid fuzzy PI controller, yPI and yF are the output of PI and fuzzy logic controllers, and wPI and wF are the weights of the output of PI and fuzzy logic controllers. Also, it is to be noted that w P I + w F = 1.0

(68.5)

During dynamic state, as the fuzzy logic controller has better dynamic response, wF is closer to unity. Based on (5), this implies that wPI is closer to zero. Hence, yF is very close to yHFPI . Conversely, under steady state, as the PI controller has better steady state response, wPI is unity and wF is zero. Hence, yHFPI equals to yPI under steady state. The use of fuzzy logic in hybrid fuzzy PI controller results in increased computational complexity and execution time [11]. Also, the PI controller necessitates tuning and involves constant gains.

68.3.3 Hybrid Speed Controller with Gain Scheduling This proposed speed controller overcomes the demerits of higher, computational complexity, larger execution times and constant gains, which are usually associated with the hybrid fuzzy PI controller. Fuzzy logic controller poses computational burden even though it has least prominence during the steady state. Hence, fuzzy logic controller is replaced by fuzzy logic equivalent proportional (FLEP) controller. With the gain of FLEP controller as K FLEP and its absolute maximum value limited by a limiter to yPmax , which has output of FLEP controller is defined as yP = K F L E P · e · S

(68.6)

where  S=

y Pmax · sgn(e) i f (|K F L E P · e|) > y Pmax 1 i f (|K F L E P · e|) < y Pmax

(68.7)

sgn indicates signum function. The hybrid speed controller with gain scheduling comprises of FLEP controller and gain-scheduled PI controller. FLEP controller is dominant during the transient

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period, whereas the gain-scheduled PI controller adjusts its gains during the transient period and is more dominant as the steady state approaches. The output of the hybrid speed controller with gain scheduling is stated as in (8), where wH is the weight of FLEP controller defined in (9) [10]. (1-wH ) is the weight for the output of the gain-scheduled PI controller. Depending on the determined weights, the output of the hybrid controller with gain scheduling is determined.

yH =

⎧ ⎪ ⎨ ⎪ ⎩

+

⎫ ⎪ ⎬

Pm − (Pm − Pn )e K 1 |e| · e + ∫(Im (1 − tanh(|e|)) · e)dt · 

⎪ ⎭ Gain scheduled PI controller output

⎫ ⎪ ⎪ ⎬

⎧ ⎪ ⎪ ⎨

· K P ·e·S

⎪

F L E ⎪ ⎪ ⎭ ⎩ FLEP controller ⎪ output

wH

 Weight of the output of FLEP controller w H = tanh(|e|)

(1 − w )

 H Weight of the output of gain scheduled PI controller

(68.8)

(68.9)

68.4 Results and Discussion The mathematical model of the PMSM drive-based propulsion system was simulated and analysed in PSIM simulation package. The proposed hybrid PI controller with gain scheduling is employed for the control of rotor speed. The PMSM drive comprises of a PMSM fed from a battery source through a VSI. The outer speed loop is controlled by the proposed hybrid controller, whereas PI controllers are implemented for the control of d-q axes currents. Sinusoidal pulse width modulation is employed with a switching frequency of 10 kHz. The specifications of PMSM are 4.1 kW, 8 pole, 156 V, 15 A, 0.3 , 3 mH and 119 V/krpm. The reference speed of the vehicle as per the customized drive cycle is illustrated in Fig. 68.3. Acceleration, cruising and deceleration are observed during the drive cycle. The maximum recorded speed is 900 rpm. As a part of the FOC, this reference speed is compared with the actual speed to determine the reference value of torque producing component of current. The FOC ensures that the actual rotor speed tracks the reference value during the acceleration, deceleration and cruising periods, as shown in Fig. 68.4. The necessary torque is developed to provide the necessary acceleration and deceleration. Also, the rotor speed is held at zero as per the requirements of the drive cycle. The three-phase stator currents, shown in Figs. 68.5 and

68 Control of PM Synchronous Motor with Hybrid Speed Controller …

Fig. 68.3 Reference value for the rotor speed

Fig. 68.4 Actual rotor speed

Fig. 68.5 r–phase stator current

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Fig. 68.6 Three-phase stator currents

68.6, never exceed the maximum permissible value and are sinusoidal with total harmonic distortion less than 5%. Also, the change in frequency of stator current changes is observed with variation in rotor speed. The outputs of the FEP controller, gain-scheduled PI speed controller and the proposed hybrid speed controller with gain scheduling are depicted in Figs. 68.7, 68.8 and 68.9, respectively. From these figures, it is evident that FEP controller has role to play during the dynamic state only. At steady state, the output of the FLEP controller, also termed as FEP controller, is negligible. FEP controller ensures fast dynamic response by setting the reference value of q-axis current and consequently the developed torque to the maximum permissible value. The gains-scheduled PI controller takes prominence during the steady state and ensures that the steady state error is reduced to zero. Based on the output of FEP, gain-scheduled PI controller and the weights, the output of the proposed hybrid speed controller with the gain scheduling is decided. It is clear from Figs. 68.10 and 68.11 that under the dynamic conditions, the weight assigned to the FEP controller is

Fig. 68.7 Output of FEP controller

68 Control of PM Synchronous Motor with Hybrid Speed Controller …

Fig. 68.8 Output of gain-scheduled PI controller

Fig. 68.9 Output of the proposed hybrid controller with gain scheduling

Fig. 68.10 Weight of FEP controller

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Fig. 68.11 Weight of gain-scheduled PI controller

Fig. 68.12 Actual rotor speed under parameter variation

far higher than that assigned to the gain-scheduled PI controller. The reverse is true for steady-state condition. Moreover, the system performance is also tested under the parameter variation in the form of increase in stator resistance for phase–r. As shown in Fig. 68.12, at 1.5 s, the rotor resistance is increased by 50%, and however, there is no variation or abnormality is observed in the rotor speed. Thus, the proposed theory is validated through the simulation results.

68.5 Conclusions The proposed hybrid speed controller with gain scheduling is employed for a FOC controlled PMSM drive responsible for electric propulsion. The proposed controller combines the advantages of FEP controller and gain-scheduled PI controller to provide fast dynamic and good stead state response in the electric propulsion system.

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As the FEP and gain-scheduled PI controller as well as the weight determination are computationally simple, the resulting hybrid controller does not incur large execution times. The FEP controller and the weight determination result in hybrid controller ensuring that rated or near rated currents are drawn to achieve fast dynamic response. The gain-scheduled PI controller makes sure that the steady state error is reduced to zero. It is validated form the system performance that the use of hybrid speed controller with gain scheduling results in stable system operation even under parameter variation. With the different conditions encountered during the operation of EV, the proposed controller is ideally suited to perform under parameter variations, speed change and system disturbances.

References 1. A. Emadi, Y.J. Lee, K. Rajashekara, Power Electronics and Motor Drives in Electric, Hybrid Electric, and Plug-In Hybrid Electric Vehicles. IEEE Trans. Industr. Electron. 55(6), 2237–2245 (2008) 2. G. Buc¸san, M. Balchanos, D. Mavris, J. Lee, M. Ishigaki, A. Iwai, Management of technologies for electric vehicle efficiency towards optimizing range, in International Conference on Systems. Man, and Cybernetics (SMC). (IEEE, Budapest, 2016), pp. 3836–3841 3. A.V. Sant, V. Khadkikar, W. Xiao, H.H. Zeineldin, Four-Axis Vector-Controlled Dual-Rotor PMSM for Plug-in Electric Vehicles. IEEE Trans. Industr. Electron. 62(5), 3202–3212 (2015) 4. K.T. Chau, C.C. Chan, C. Liu, Overview of Permanent-Magnet Brushless Drives for Electric and Hybrid Electric Vehicles. IEEE Trans. Industr. Electron. 55(6), 2246–2257 (2008) 5. T. Hitosugi, Solid-state Lithium Tthin-film batteries capable of fast charging, in International Meeting for Future of Electron Devices, Kansai (IMFEDK). IEEE, Kyoto (2019) 6. K. T. Chau, Overview of Electric Vehicle Machines–From Tesla to Tesla, and Beyond, in International Conference of Asian Union of Magnetics Societies (ICAUMS). IEEE, Tainan (2016) 7. N. Hashemnia, B. Asaei, Comparative study of using different electric motors in the electric vehicles, in: 18th International Conference on Electrical Machines. IEEE, Vilamoura (2008) 8. A. M. Omara, M. A. Sleptsov, Comparative study of different electric propulsion system configurations based on IPMSM drive for battery electric vehicles, in 19th International Conference on Electrical Machines and Systems (ICEMS), IEEE, Chiba (2016) 9. A.V. Sant, K.R. Rajagopal, PM Synchronous Motor Speed Control Using Hybrid Fuzzy-PI with Novel Switching Functions. IEEE Trans. Magn. 45(10), 4672–4675 (2009) 10. A.V. Sant, K.R. Rajagopal, N.K. Sheth, Permanent Magnet Synchronous Motor Drive Using Hybrid PI Speed Controller With Inherent and Noninherent Switching Functions. IEEE Trans. Magn. 47(10), 4088–4091 (2011) 11. L. Feng, M. Deng, S. Xu, D. Huang, Speed Regulation for PMSM Drives Based on a Novel Sliding Mode Controller. IEEE Access 8, 63577–63584 (2020) 12. A.V. Sant, K.R. Rajagopal, PM synchronous motor drive with wavelet controller and fractional order integrator, in Joint International Conference on Power Electronics, Drives and Energy Systems & Power India. IEEE, New Delhi (2010) 13. G. Dewantoro, Y. Kuo, Robust speed-controlled permanent magnet synchronous motor drive using fuzzy logic controller, in IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), IEEE, Taipei (2011)

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14. A.V. Sant, K.R. Rajagopal, Gain scheduled PI speed and current controllers for the vector control of PMSM, in 8th International Symposium on Advanced Electromechanical Motion Systems (Electromotion 2009). IEEE, Lille (2009) 15. B.K. Bose, Modern Power Electronics and AC Drives (Prentice-Hall, Englewood Cliffs, NJ, 2002)

Chapter 69

Study on Effect of Draft Tube Diffuser Shape on Performance of Francis Turbine Lakshman Suravarapu and Ruchi Khare

Abstract Francis turbines are the most popular turbines among various kinds of hydraulic turbines. A thorough review of different kinds of literature has led to the conclusion that there is a desperate need to increase the performance of the Francis turbines. Many investigators have put their efforts to increase the turbine performance, and also the work is being projected on various aspects of turbine performance variables. To improve the performance of hydraulic Francis turbines by investigation, modification and analysis, many works of the literature are available. It is found that the performance of draft tube of any turbine plays a major role in overall performance of the turbine. In the present work, numerical simulation of complete Francis turbine is carried out at rated conditions by changing three different geometries of (circular, rectangular and rectangular with splitter) at the outlet section. Comparison of various performance parameters of Francis turbine at different rotational speeds for different types of draft tubes is done. It is found that the performance of draft tube with circular cross section at outlet is best. Keywords Francis turbine · Draft tube · Numerical simulation · Computational Fluid Dynamics (CFD)

Nomenclature g H Dt1 Dt2 Dt3

Acceleration due to gravity (9.8m/s) Net head (m) Draft tube with varying circular cross section Draft tube with varying rectangular cross section Elbow draft tube of varying rectangular cross section with a splitter

L. Suravarapu (B) · R. Khare Maulana Azad National Institute of Technology Bhopal, Bhopal, MP 462003, India e-mail: [email protected] R. Khare e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_69

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Overall efficiency of turbine Draft tube efficiency Torque (N-M) Output power of turbine (KW) Rotational speed of runner (rpm)

69.1 Introduction Hydropower is considered as main source of renewable energy as water is available abundant, and electric power generation was done with low cost and without pollution [1]. Hydropower plants are extremely effective energy conversion systems in which hydraulic turbines convert water pressure into mechanical shaft power, which can be used to drive a generator to produce electric power [2]. Water is carried in the Penstock from the source which is then flows through the Francis turbine containing spiral casing, stay rings, stay vanes, guide vane distributor, runner and draft tube [3]. The efficiency of reaction turbine is increased accordingly the net available head should be increased. To increase the net head, we can either lower the tail race which is not preferred due to space limitations or we can place the runner low. The draft tube is installed at runner exit to increase the net head which ultimately leads to increase in the efficiency of the turbine [4]. The hydraulic performance characteristics of a draft tube depend on its shapes and dimensions of the diffuser and the flow pattern at its entrance [5]. Based on the shapes of the diffuser, draft tubes can be classified in to four categories: (a) Conical diffuser draft tube or straight divergent draft tube (b) Simple elbow draft tube (c) Elbow draft tube with changing cross section (d) Moody’s spreading draft tube [6].

69.2 Geometric Modelling and Simulation The existing Francis turbine is having 13 blade runners with 18 guide vanes, 18 stay vanes, stay ring, casing and draft tube. The draft tube of the existing turbine is conical elbow draft tube. In this work, the performance analysis of Francis turbine is done by using three different draft tube geometries (Fig. 69.1). • Simple elbow draft tube or draft tube with varying circular cross section • Draft tube with varying rectangular cross section • Elbow draft tube of varying rectangular cross section with a splitter. Geometry of the turbine along with three different draft tubes is prepared accordingly [7] in Solid Works and ICEM CFD. The unstructured three dimensional mesh is generated using ICEM CFD. The geometry of the Francis assembly without casing is shown in Fig. 69.2 (Table 69.1).

69 Study on Effect of Draft Tube Diffuser Shape on Performance … Table 69.1 Geometrical details of Francis turbine components

915

1

No. of guide vanes

2

No. of stay vanes

18

3

Radius of runner outlet

508 mm

4

No. of blades in runner

13

5

Radius of draft tube inlet for all draft tubes

510 mm

6

Radius of draft tube outlet for dt1

1235 mm

7

Length of the diffuser for dt1

4200 mm

8

Length of draft tube outlet for dt2

2437 mm

9

Height of draft tube outlet for dt2

1027 mm

10

Length of the diffuser for dt2

3697 mm

11

Length of draft tube outlet for dt3

2437 mm

12

Height of draft tube outlet for dt3

1027 mm

13

Length of the diffuser for dt3

3697 mm

14

Length of the splitter for dt3

2021 mm

Simple Conical Draft Tube

18

Simple Elbow Draft Tube

Elbow Draft Tube with Varying Cross Section

Moody draft tube

Fig. 69.1 Different types of draft tubes based on diffuser shapes

Dt1

Dt2

Dt3

Fig. 69.2 Francis turnine assembly for draft tubes with different diffuser cross sections

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110 100 90

DT1

80

DT2

70

DT3

60 50 40 300

400

500

600

700

800

900

1000

N (RPM)

Fig. 69.3 Variation of overall efficiency versus rotational speed Draft tube efficiency Vs Speed

105 100 95 90

DT1

85

DT2

80

DT3

75 70 65 60 300

400

500

600

700

800

900

1000

N (RPM)

Fig. 69.4 Variation of draft tube efficiency vs rotational speed

Torque Vs speed 65000 60000

(NM)

55000 50000 45000

DT1

40000

DT2

35000

DT3

30000 300

400

500

600

700

N (RPM)

Fig. 69.5 Variation of torque versus rotational speed

800

900

1000

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Meshing is a technique to discretize complete flow domain into very small elements. These elements consist of nodes at which unknown variables are to be calculated [8]. A total number of nodes for CFD analysis are huge, and large computational memory is required. The unstructured mesh is generated using tetrahedron for 3D flow domain using Ansys ICEM CFD. The number of elements in the complete Francis geometry is around 6 lakhs, which are varying with the draft tube geometry.

69.3 Pre-processing and Domain Specifications In pre-processing, the generated meshes for all domains are imported to CFX and assembled as a turbine component starting from stay vanes, guide vanes runner and then different draft tubes will be gathered. For more justification, translation of mesh is used so that each domain founded its own location. The basic property which is defined to each domain is a non-buoyant stationary or rotating fluid domain and water as working fluid. Runner domain is rotating domain at different speeds, and other domains are stationary domains. Reference pressure of all domains is considered as zero, and then, pressure is defined as boundary condition. K-e model is selected as turbulence model for simulation.

69.4 Boundary Conditions For solution of discretized assembly are to be specified at boundaries are called boundary conditions. These boundary conditions are usually applied at inlet and outlet boundaries. Inlet boundary condition is set at inlet. Mass flow rate as 8000 kg/sec is taken as boundary condition which is inserted at inlet. Flow throughout the geometry is subsonic, and its direction is normal to inlet boundary. A medium intensity (5%) of turbulence is considered. Reference pressure for the complete domain is considered as 1 atmospheric pressure. Outlet boundary condition is specified at outlet of draft tube as relative pressure is taken as zero atmospheric pressure. Blades, hub, shroud and other surrounding surfaces are selected as smooth walls with no slip conditions. For getting reasonable accuracy, the K-e turbulence model is selected for simulation of this hydraulic turbine. Rotational speeds of the runner is varied in the range of 400–900 rpm at a regular interval of 100 pm, and other domains are kept stationary. Smooth walls with no slip conditions are considered for all simulations. Steady-state analysis is carried out for complete flow passage of Francis turbine with all three different geometries of draft tube.

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69.5 Results and Discussion The original geometry of the turbine is available with the draft tube of circular cross section. The results of this geometry are validated for best efficiency point, i.e. at mass flow rate of 8000 kg per sec and at rotational speed of runner as 700 rpm, which are found to be in fairly good agreement with the experimental results. From Fig. 69.3 depicts that maximum efficiency of the draft tube is obtained at rotational speed of 700 rpm for draft tube with circular cross section. It is observed that the pattern of efficiency variation for different speeds of the runner is same in case of all three geometries of the draft tube with all three different draft tubes. Efficiency increases up to 700 rpm and then starts decreasing in all three cases. This may be because of higher losses at the outlet of draft tube in case of rectangular draft tube (Dt2) and draft tube with splitter (Dt3), which is due to corners at the outlet section of the draft tube. It is also observed that off-design performance of Dt2 and Dt3 is better in comparison to Dt1 type of draft tube. The pattern of the Fig. 69.3 is similar to the regular characteristics of Francis turbine [9]. The variation of draft tube efficiency with rotational speed (Fig. 69.4) shows that it is very high (more than 95%), in case of Dt1(circular outlet) but its values are increasing, in case of Dt2 and Dt3. This is because of more swirl and losses at low rotational speed. Maximum torque of the turbine is achieved at the rotational speed of 700 rpm (Fig. 69.5) for draft tube with circular cross section. The pattern of torque variation for different speeds is almost same in all the three draft tubes assemblies. This is because as output power and torque are directly proportional and highest output generated by the turbine at best efficiency point, i.e. at 700 rpm, as shown in Fig. 69.6. As the mass flow rate is taken constant, the best efficiency point for the turbine is obtained at 700 rpm for Francis turbine irrespective of draft tube diffuser shape. The pressure contours and velocity streamline are shown in Figs. 69.7, 69.8 and 69.9. Output Power Vs speed

4500 4000

Po(KW)

3500 dt1

3000

dt2

2500

dt3

2000 1500 300

400

500

600 700 N (rpm)

Fig. 69.6 Variation of output power versus rotational speed

800

900

1000

69 Study on Effect of Draft Tube Diffuser Shape on Performance …

Fig. 69.7 Streamlines and contours for velocity and pressure for dt1 at 700 rpm

Fig. 69.8 Streamlines and contours for velocity and pressure for dt2 at 700 rpm

Fig. 69.9 Streamlines and contours for velocity and pressure for dt3 at 700 rpm

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Velocity of all the draft tubes is gradually decreased from inlet to exit of the draft tubes, whereas pressure was gradually increased from inlet to exit section of the draft tubes which fulfils the purpose of draft tube. The comparison of pressure contours from Figs. 69.7, 69.8 and 69.9 shows that highest pressure recovery could be achieved in case of the draft tube with circular outlet (Dt1), which is the main reason of achieving highest hydraulic efficiency of the turbine assembly having Dt1.

69.6 Conclusions The numerical simulation of the 3D steady-state fluid flow from the entire Francis turbine with three different geometries of the draft tubes is carried out. Performance of the turbine is analysed in terms of hydraulic efficiency, draft tube efficiency, torque and power output. It is found that the draft tube with existing geometry of the draft tube, i.e. the circular outlet shows the best performance. The highest efficiency is achieved at the rotational speed of 700 rpm. The study was useful for performance analysis of Francis turbine. It is also helpful for selection of suitable draft tube geometry for achieving maximum efficiency.

References 1. A.A. Khan, A.M. Khan, M. Zahid, R. Rizwan, Flow acceleration by converging nozzles for power generation in existing canal system, Renew. Energy 60 (2013) 2. G. Kahraman, H.L. Yücel, F.H. Oztop, Evaluation of energy efficiency using thermodynamics analysis in a hydropower plant: a case study, Renew. Energy 34 (2009) 3. R. Khare, P. Vishnu, K. Sushil, CFD approach for flow characteristics of hydraulic francis turbine. Int. J. Eng. Sci. Technol. 2(8), 3824–3831 (2010) 4. C. Spandan, K.R. Sarkar Bikash, M. Subhendu, CFD analysis of the hydraulic turbine draft tube to improve system efficiency. MATEC Web of Conferences (2016) 5. M. Arispe Tania, O. Waldir de, G. Ramirez Ramiro, Francis turbine draft tube parameterization and analysis of performance characteristics using CFD techniques. Renew. Energy 127, 114–124 (2018) 6. S.J. Wadibhasme et al., Hydraulic turbine draft tube: literature review. Int. J. Sci. Eng. Technol. Res. 5(3), 673–676 (2016) 7. M.F. Gubin, “Draft Tubes of Hydro-Electric Stations” Amerind Publishing Co (Pvt. Ltd., New Delhi, 1973). 8. H. Safi Wahidullah, P. Vishnu, Design and performance analysis of francis turbine for hydro power station on kunar river using CFD. Int. J. Adv. Res. 5(5), 1004–1012 (2017) 9. P.N. Modi, S.M. Seth, Hydraulics and Fluid Mechanics Including Hydraulics Machines (Standard Book House, New Delhi, 2013).

Chapter 70

Dehydration of Vegetables Through Waste Heat of Vapour Compression Refrigeration System Ankur Nagori , Rubina Chaudhary, and S. P. Singh

Abstract Reduction in post-harvest losses of fresh vegetables is a major objective of most of the cold chain programs. Drying is also an effective tool for reducing postharvest loss. However, the existing industrial drying techniques are energy intensive in operation results emission of high green-house gases. Low-grade waste heat recovered from condenser of refrigeration system could be utilized for drying applications. The process consists an effective utilization of heat, obtained by the condensing unit of a refrigeration system in an intermittent manner with no external energy requirement. An experimental approach of vapour-compression based-refrigeration system has been performed for drying of onion sample. The drying was performed at average temperature of 43 °C. Moisture content was reduced to 12% (wb) after 24 h of drying. Average values of drying rate and SMER were found as 0.013 kg/h and 0.196 kg/kWh, respectively, at the mass flow rate of 0.268 kg/s. The overall drying process is energy efficient. This could be a promising future replacement of current industrial drying systems which are relatively more energy intensive. Keywords Vapour compression refrigeration system · Refrigeration waste heat · Drying of vegetable · Drying rate

Nomenclature mm m kg kW h s wb

Millimetre Metre Kilogram Kilowatt Hour Second Wet basis

A. Nagori (B) · R. Chaudhary · S. P. Singh School of Energy and Environmental Studies, Devi Ahilya University, Takshila Campus, Khandwa Road, Indore 452011, Madhya Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_70

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Dry Bulb Temperature Kilojoule Relative Humidity Drying Rate Specific Moisture Extraction Rate Vapour Compression Refrigeration System Stainless Steel Polyurethane Foam British Thermal Unit Litre Kilowatt hour

70.1 Introduction Cold chain industries for agricultural sector have large quantity of waste heat to recover for drying applications of fresh horticultural products for increment of their self-life through hygienic storage. Refrigeration waste heat from condenser/condensing unit is commonly utilized for space heating and water heating purpose in most of the plants. In Indian context, the total installed capacity of cold storages is less (346 Lakh MT) than required (611.30 lakh MT) [1]. World energy demand would be doubled by 2050, and 17% of total global CO2 emission presently coming from industry alone [2] causes the large quantity of green-house gases emissions. So there is an excellent opportunity of utilizing the ample amount of waste heat from Indian cold chain industry for drying applications and to reduce the carbon credits due to intensive energy consumption. India has second position in the world for vegetable crop production with 180.687 million MT reported in 2017–18 [3]. According to [4] that 30% of fruits and vegetables were spoiled after harvesting, due to poor management practice of preservation and absence of pre-processing units at the harvested site. Drying is a cost-effective preservation method by removing enough moisture from perishables and reduces microbiological activity during storage to prevent decay and spoilage [5]. Vegetables can be made more acceptable to consumers by drying [6] by increasing their self-life and ease of transportation. Waste heat from vapour compression refrigeration system could be applied where low temperature and well-controlled drying conditions are needed [7]. Some of the drying studies are based on the utilization of waste heat of condensing unit of domestic as well as commercial RAC systems. Literature is available for dehydration of food products using refrigeration waste heat but only limited to laboratory scale. In most of the researches, it was found that direct utilization of lowgrade waste heat from condensing unit of RAC utilized for dehydration and water heating purposes. [8–11] performed some experimental analysis for the drying of clothes through waste heat of a vapour compression refrigeration system (room/split

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air conditioner and refrigerator) at different ambient temperature range and relative humidity. The average drying temperature range was found as 35–42 °C. Total average drying time varied from 1.5 to 2 h depending on the inside and environmental conditions. Drying parameters like drying rate (kg/h) and specific moisture extraction rate (SMER) in kg/kWh were found and reported. The recent study on reduction of CO2 emission by utilizing waste heat of one TR condensing unit showed that about 1249.6 kg/yr could be reduced by replacing 1KW electrical drying system through proposed one [12]. A group of researchers [13] performed a drying experiment on sultana grapes (0.5 kg/batch) with the hybrid unit of a solar dryer and split air conditioner. The drying temperature was in the range of 64.9–53.6 °C. Total drying time (40 h) was decreased by 16.7% compared with the open sun drying (48 h). Drying rate and dryer efficiency would be increased with the combination of waste heat. Some investigators [14] performed an experimental analysis for drying of ‘Garcina Atroviridis’ a Malaysian fruit sample (1.2 kg) having medicinal usage through room air conditioner (0.83TR) coupled with mobile drying unit. Average value of SMER, drying rate and COP were found as 0.294 kg/KWh, 0.0903 kg/h and 3.25, respectively. The average drying time (11–14 h) was only 12.5% of total drying time compared to open sun drying. The average temperature at condenser exhaust was found as 38.8 °C. Another attempt made by [15] and analysed the refrigeration waste heat recovery unit for drying application with assistance of solar heat. Apart from drying, some other applications like desalination and water heating was performed [16] by 1.5 TR household air conditioning system. The production of fresh water during summer and pre-monsoon were 4.63 kg/h and 4.13 kg/h, respectively. There is an enough potential of waste heat exhausted from existing systems of cold chain industry which could be utilized for drying purpose. It could be an energy efficient and more reliable method as compared to present industrial drying which is energy intensive. There are very limited research works available on refrigeration waste heat utilization for drying application especially for fruits and vegetables through vapour compression refrigeration system. An experimental study performed for drying of fresh vegetable (Onion) sample by utilizing refrigeration waste heat. Various drying parameters are obtained through the experiment which could be compared with other vegetable drying methods.

70.2 Material and Method A prototype of drying cum refrigeration system using condenser waste heat was developed. The combination consists a tray dryer and a chilled storage system of 280L. The refrigeration condenser exhaust is coupled with dryer inlet through PVC ducting (100 mm). About 40 kg of onion were stored inside the chilled storage chamber at 4–7 °C. The condensing unit of the refrigeration system was coupled with small tray dryer made up of plywood (18 mm) through ducting. The drying unit consists of two stainless steel (304) perforated trays (330 mm × 330 mm) each and

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120 mm apart. Rectangular aluminium sheet cuttings (1 mm) were provided to make a uniform distribution of hot air throughout the trays and the drying chamber.

Dimensional details of the system are given in Table 70.1. Fresh onion (Allium cepa L.) quantity in bulk was purchased from local market of Indore M.P. Intermittent drying of fresh onion sample (360 g) at average temperature of 43 °C was carried out through the refrigeration waste heat. The average temperature of the refrigerated storage was maintained at 5 °C. Average hot air velocity was measured as 1.62 m/s with microprocessor-based digital vane anemometer

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Table 70.1 Specifications for drying cum refrigeration system Components

Material

Specification

Drying unit

Plywood (18 mm)

360 mm × 400 mm × 420 33

Drying (Perfrated)

SS (304) Perforation φ 5 mm

2 nos. (330 mm × 330 mm)

Refrigerated storage

Powder coated Steel (18 gauge)

280 L

Insulation

PUF

60 mm thick

Refrigerant used

R134a

Cooling capacity

1260 BTU/hr

Fan power input

21 W

Volt/Ph/Hz

220–240/1/50

(AM4201). DBT and RH were also measured. Drying rate of product was measured at equal interval of time. Inlet and exhaust temperatures of drying unit and tray temperatures were also recorded through data logger (UniLogPro). Electricity consumption was measured by the static watt-hour meter (single phase). The onions are sliced into rings (4 mm thick). The sample is weighted before the drying experiment. This weight was used as a reference to determine the total drying time. When the weight of the drying sample no longer showed any weight loss, the experiment was stopped. The thermocouples (k type) calibrated with water bath were placed near entrance and exhaust of condenser, at the drying chamber inlet, inside the drying chamber and at the outlet of drying chamber. The compressor of refrigerated unit was running intermittently. Drying is not continuous, so that for 24 h of running the system, the actual drying was for 15 h. The drying experiment was performed at the average ambient condition of 27 °C DBT and 80% RH (Table 70.2).

70.3 Result and Discussion It was found that average temperature leaving the condenser was 43 °C and average temperature entering the condenser was 38 °C. The following results obtained after the experiment. Average drying temperature 43 °C. Room air condition DBT 27 °C and 80% RH. Initial weight of sample 360 g. Final weight of sample 40 g. Total moisture removed 320 g. Drying time 24 h. Average drying rate 0.013 kg/h. Power input 0.297 kW.

Mass flow rate

Qout = mC p(T out − T in) (70.6) Tout = temperature of air leaving the condenser Tin = temperature of air entering the condenser Cp = Specific heat of air (1.005 kJ/kg0C) [Moisture removed] / [mC p(T out − T in) + Win] (70.7) DR = Moisture removed /Total drying time (70.8)

Waste heat from refrigeration (Qout)

SMER

Drying rate (kg/h)

−d) %moisture content = = 100+(W (70.4) W where W = mass of wet sample d = mass of dry material in the sample

360 g m = ρ AV (70.1) ρ = density of air at temperature (t°C) Density ρ = 1.277 − 0.003 ∗ t (70.2)

Initial weight

Moisture content (MC)

Onion rings (4 mm thick)

Product to be dried

Table 70.2 Details of methodology applied for conducting the drying experiment: [13, 17]

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Average hot air velocity 1.62 m/s. Mass flow rate of air 0.268 kg/s. Waste heat from condenser exhaust 1.34 kJ/s. Average SMER 0.196 kg/kWh. Drying rate at the beginning was found higher, and it was decreased continuously with the progressive drying operation. About 50% of moisture was removed during first 6 h and remaining during subsequent drying time. Total 88% of moisture was removed during the process. The average value of removal of moisture content per hour was found as 0.013. The average moisture removal per kWh was found as 0.196. As the described drying process is intermittent, it results lower drying rate and higher drying time as compared to continuous forced convection drying. Also it was found relatively a better control over the drying parameters like temperature and humidity through the entire process. Drying process found better than open sun drying in terms of efficiency, hygiene, product quality and drying time. On the other hand, the above-described process of drying would be energy efficient as waste heat was utilized to dehydrate the product without any extra conventional energy source. Also the drying could be performed using waste heat of existing installations of cold chain industry without any alteration to the system. It would be better for reducing carbon credits for cleaner environment. The only thing is to optimize the size of drying unit and the existing cooling system with respect to capacity and cost.

70.4 Conclusion The above-described drying technique could be utilized as a value addition program during post-harvest pre-processing of various fresh vegetables on farm level itself. As the dehydration is being done through waste heat of existing cold chain systems, no extra energy would be utilized which results advancement in drying technology with energy efficient manner. It could be a sustainable option for employment generation and livelihood. The following are the conclusive points which contribute towards the technological innovation in industrial drying process. • Average drying rate and SMER were found as 0.013 kg/h and 0.196 kg/kWh, respectively. • The total drying time was found as 24 h and the moisture content reduced to 12% (wb). • The refrigeration waste heat drying would be better in terms of drying time, product hygiene and quality as compared to open sun drying. • There was no electricity consumption like energy-intensive conventional drying process as waste heat utilization. • Better process control over the drying parameters was observed as compared to other hot air drying processes.

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• The drying system would be more reliable towards environmental sustainability with low carbon credits.

References 1. S. Negi, N. Anand, Cold chain: a weak link in the fruits and vegetables supply chain in India. IUP Journal of Supply Chain Management 12(1), 48 (2015) 2. World Energy Outlook 2002 and 2004. https://www.worldenergyoutlook.org/ 3. Horticulture—Statistical Year Book India. https://www.mospi.gov.in/statistical-year-bookindia/2017/178 4. ASSOCHAM, Horticulture Sector in India—State Level Experience (The Associated Chambers of Commerce and Industry of India, New Delhi, 2013) 5. H.H. Chen, T.C. Huang, C.H. Tsai, A.S. Mujumdar, Development and performance analysis of a new solar energy-assisted photocatalytic dryer. Drying Technol. 26(4), 503–507 (2008). https://doi.org/10.1080/07373930801929557 6. B.I. Abonyi, H. Feng, J. Tang, C.G. Edwards, B.P. Chew, D.S. Mattinson, J.K. Fellman, Quality retention in strawberry and carrot purees dried with Refractance WindowTM system. J. Food Sci. 67(3), 1051–1056 (2002). https://doi.org/10.1111/j.1365-2621.2002.tb09452.x 7. R. Daghigh, M.H. Ruslan, M.Y. Sulaiman, K. Sopian, Review of solar assisted heat pump drying systems for agricultural and marine products. Renew. Sustain. Energy Rev. 14(9), 2564–2579 (2010). https://doi.org/10.1016/j.rser.2010.04.004 8. T.M.I. Mahlia, C.G. Hor, H.H. Masjuki, M. Husnawan, M. Varman, S. Mekhilef, Clothes drying from room air conditioning waste heat: thermodynamics investigation. Arab. J. Sci. Eng. 35(1), 339 (2010) 9. S. Deng, H. Han, An experimental study on clothes drying using rejected heat (CDURH) with split-type residential air conditioners. Appl. Therm. Eng. 24(17–18), 2789–2800 (2004). https://doi.org/10.1016/j.applthermaleng.2004.03.016 10. H. Ambarita, A.H. Nasution, N.M. Siahaan, H. Kawai, Performance of a clothes drying cabinet by utilizing waste heat from a split-type residential air conditioner. Case Stud Thermal Eng. 8, 105–114 (2016). https://doi.org/10.1016/j.csite.2016.06.002 11. N.A. Musa, The use of waste heat from domestic refrigerator for drying clothes. Sci. J. Mehmet Akif Ersoy Univ. 2(2), 41–46 12. M. Ramadan, R. Murr, M. Khaled, A.G. Olabi, Air dryer using waste heat of HVAC systems– Code development and experimental validation. Appl. Therm. Eng. 147, 302–311 (2019). https://doi.org/10.1016/j.applthermaleng.2018.10.087 13. M. Chandrasekar, T. Senthilkumar, B. Kumaragurubaran, J.P. Fernandes, Experimental investigation on a solar dryer integrated with condenser unit of split air conditioner (A/C) for enhancing drying rate. Renewable Energy 122, 375–381 (2018). https://doi.org/10.1016/j.renene.2018. 01.109 14. T.M.I. Mahlia, L.W. Cheng, L.C.S. Salikka, C.L. Lim, M.H. Hasan, U. Hamdani, Drying Garcina atroviridis using waste heat from condenser of split room air conditioner. Int. J. Mech. Mater. Eng. (IJMME) 7(2), 171–176 (2012) 15. M. Li, Z.Q. Guan, X.Q. Jiang, The design analysis of cold storage refrigeration system heat recovery coupled solar auxiliary heated drying device, in Advanced Materials Research (Vol. 512, pp. 1235–1240).TransTechPublicationsLtd (2012). https://doi.org/10.4028/www.scient ific.net/AMR.512-515.1235 16. R. Santosh, G. Kumaresan, S. Selvaraj, T. Arunkumar, R. Velraj, Investigation of humidification-dehumidification desalination system through waste heat recovery from household air conditioning unit. Desalination 467, 1–11 (2019). https://doi.org/10.1016/j.desal.2019. 05.016

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17. V.K. Sharma, S. Sharma, R.A. Ray, H.P. Garg, Design and performance studies of a solar dryer suitable for rural applications. Energy Convers. Manage. 26(1), 111–119 (1986). https://doi. org/10.1016/0196-8904(86)90040-3

Chapter 71

Peak Power Impact from Electric Vehicle Charging Chandana Sasidharan

and V. S. K. V. Harish

Abstract India is aiming for 30% of all new vehicle sales to be electric by 2030, from a baseline of around 3.5% in FY 2018. In this paper, a methodology for peak power estimation is developed for electric vehicles used for passenger transport. Developed methodology considers the charging requirements which are different across various electric vehicle segments. Chargers available in the market have been classified into distinct power levels based on their nominal capacity. However, these power levels are applicable for electric car charging and are not appropriate for electric bus or electric rickshaw charging. Moreover, charging power needed for a battery is not the same as the nominal capacity of a charger. The nominal capacity of the charger acts as an upper limit to the power drawn, and the actual power drawn will be dependent on the battery capacity, the state of charge of the battery, and the time taken for charging. For each vehicle segment, the charging power is calculated for three different sets of battery capacity for distinct rates or possibilities of charging. Along with plug in charging possibility of battery swapping is factored in for relevant electric vehicle segment. Keywords Impact analysis · Powergrid · Electric vehicle · Road transportation · Two wheeler

71.1 Introduction Carbon-based emissions are rising each year with a noticeable dip been witnessed for 2016, with energy sector being the most significant contributor to global GHG emissions [1]. Within the energy sector, transportation accounts for around 15% of the total emissions as of reported in 2016, with transportation through road being the C. Sasidharan Urban Infrastructure and Utilities Vertical, Alliance for an Energy Efficient Economy, Lajpat Nagar III, New Delhi 110024, India V. S. K. V. Harish (B) Department of Electrical Engineering, School of Technology, Pandit Deendayal Energy University (PDEU), Raysan, Gandhinagar 382007, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_71

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most GHG contributor. The statistical figure reported above include both direct and indirect GHG emissions. Government of India is committed to support the adoption of electric vehicles with demand incentives. The above vehicle categories receive subsidies under the second phase of faster adoption and manufacturing of (Hybrid and) electric vehicles (FAME) scheme from 2019. The scheme intends to support the adoption of 10 lakh electric two wheelers, 5 lakh electric three wheelers including e-rickshaws, 35 thousand electric cars, and 7090 buses. For EVs, it is important to consider where and when the electric vehicle demand will be coming from. Peak demand is the basis of power system planning, and increase in peak implies more investment. Apart from this, the cost of electricity supply is high during peak. This will help in making the charging smart to ensure peak management and green charging.

71.2 Methodology The methodology for peak power estimation is developed for electric vehicles used for passenger transport. The method has taken into consideration that the charging needs are not the same across different electric vehicle segments. Typically, chargers available in the market can be classified into distinct power levels based on their nominal capacity. But these power levels are applicable for electric car charging and not appropriate for electric bus or electric rickshaw charging. Hence, against each vehicle segment appropriate chargers are identified. The second major factor for consideration is that the charging power needed for a battery is not the same as the nominal capacity of a charger. The nominal capacity of the charger acts as an upper limit to the power drawn, and the actual power drawn will be dependent on the battery capacity, the state of charge of the battery and the time taken for charging. For each vehicle segment, the charging power is calculated for three different sets of battery capacity for distinct rates or possibilities of charging. Along with plug in charging possibility of battery swapping is factored in for relevant electric vehicle segments.

71.3 Overview of Different EV Segments and Their Charging The current electrification trends in India is focused on four distinct electric vehicles segments: electric buses, electric cars, electric three wheelers, and electric two wheelers. The charging behavior associated with each of these electric vehicles are different and dependent on the transport operational characteristics and the battery

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specific parameters. This section describes the charging related aspects of these vehicles.

71.3.1 Electric Bus Charging India is experiencing a high adoption of electric buses for intracity transport. The range of its travel of the electric buses depends on its battery capacity and energy consumption. Selecting of adequate battery capacity of electric buses and appropriate charging technology is important to maintain continuity of service post electrification. On one hand, batteries are expensive and adds considerable weight on the electric vehicle. In case the battery capacity is smaller, then there is a need to additional charging infrastructure. The two ideal locations for charging electric buses are depots and terminals. There are two possibilities of charging electric buses, overnight charging at the depots, and opportunity charging at bus stops [2, 3]. The typical battery capacity of electric buses for intracity transport is between 100 and 300 kWh, and the energy consumption of buses is between 1.2 and 2 kW/km. However, it is not ideal to drain Lithium ion batteries beyond a certain capacity, typically between 30 and 50%, and it is a common practice to locate chargers before the battery capacity reaches the limits. The typical charging rates of bus batteries could be anywhere between 0.25C to 2C, indicating charging time in the range of 30 min to 4 h. A 1C rate means that the discharge current will discharge the entire battery in 1 h. Hence, show charging is considered to correspond to 4 h and is associated with overnight charging. Fast charging in the case of buses can be equated to correspond to charging for 30 min of less and is associated with opportunity charging. The charging technology power needed for the buses corresponding to these charging rates are presented in Table 71.1. The charging power is calculated for slow and fast charging from 30–90% and 50–80%, respectively. It should be noted as typically lithium ion batteries follow constant current followed by constant voltage charging, the charging power reduces to more than half when the battery gets charged more than 60% [3]. In case of intracity bus transport, an effective way to optimize battery capacity is also to adopt battery swapping technology. India is also home to a pilot project for battery swapping, in Ahmedabad, where buses with 80 kWh batteries are serving the public transport. In this case, typically charging is done under 1–2 h and the capacity Table 71.1 Peak demand from electric buses based on battery capacity Battery capacity in kWh

Charging power for overnight charging in kW

Charger power for opportunity charging in kW

100

15

60

200

30

120

300

45

180

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of the battery charger will depend on the number of chargers and the rate of charging. The peak demand from these systems depends on the number of batteries that are being charged at the same time and their corresponding rate of charge. If one battery is getting charged at 1 °C rate, the requirement is 80 kW. If two batteries are getting charged at 0.5 °C rate also, the requirement is 80 kW [2].

71.3.2 Electric Car Charging One of the distinctive features about electric cars in India is that there are two categories of vehicles in the models distinct in their battery pack design. Indigenous models, which dominate in market now are designed with low voltage battery packs and do not correspond to the existing charging standards for electric vehicle charging. These battery packs also cannot be charged faster than 0.3C. On the other hand, newer models in the market have high voltage battery packs which can be charged at faster rates, typically under an hour and follow established charging standards [4]. The most distinguishing factor about the electric cars in India could be the battery capacity. The ideal battery capacity for passenger transport in India could be under 25 kWh, unlike the international markets where the battery capacity is as high as 80 kWh. As battery is the most expensive element, and the typical commute distance of private cars are under 50 kms. Though commercial electric vehicles travel for more than 200 km in a day, due to advantages in costs, electric cars with smaller batteries are preferred for commercial adoption as well. As the observed battery capacity for Indian cars are between 20 and 40 kWh, this is considered as the basis of charging power calculation [5]. There are three major possibilities for electric car charging: at home, at parking places, and at public charging facilities as shown in Table 71.2. For home charging, the power of the charger is typically 3.3 kW, which also corresponds to the maximum power that can be drawn from residential plug socket. In case of charging at parking spaces, 7.2 kW is the highest observed power of charger available in Indian market, though 22 kW chargers are common in European market. For public charging facilities, the most common chargers globally and most suited chargers are 50 kW. For the indigenous car models the charging power at parking and public charging will be 3.3 kW and 15 kW, respectively. However, they are not considered for further analysis, as the market trend is shift toward high voltage electric car models. The critical case for the Indian context would be the 50 kW electric chargers. Though chargers of 175 kW capacity are available in the international market, there are no suitable car models that can charge at that power. As the battery capacity remains low, these high power chargers are not anticipated to be part of the ecosystem [4]. For charging electric cars, the charging power is calculated keeping into mind the upper limits of charger power. For residences, the charger power is calculated for 30–90% charging for a time of 8 h. For charging at parking lots and public charging facilities, 50–80% charging is considered for charging time of 3 h and 30 min, respectively.

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Table 71.2 Peak demand from electric cars based on battery capacity Battery capacity in kWh

Charging power for residences in kW

Charging power for parking lots in kW

Charging power for public charging facilities in kW

20

1.5

2.7

16

30

2.3

4.0

24

40

3.0

5.3

32

71.3.3 Electric Three Wheelers There are two categories of electric three wheelers in India, and they are electric rickshaws and electric autos. The electric vehicle which has the largest penetration across the both rural and urban areas in the country are electric rickshaws. These are a distinct class of electric vehicles without any regenerative breaking, and it is presently dominated by lead acid batteries. However, commercial fleet operators have already started taking advantage of the falling lithium ion battery prices to transition fleets. This trend is expected to continue as the charging time with lithium ion batteries are almost one fourth of the charging time of lead acid batteries. With the reduced charging time of these batteries, battery swapping is an attractive option for this segment. Electric autos are new in the market and have lithium ion batteries as well, but their battery capacity is slightly higher than that of electric rickshaws. For estimating the peak power impact from battery swapping, the number of batteries, battery capacity, and the charging time will be important. Typically, for swapping, the charging is undertaken in two hours without air conditioning and one hour with air conditioning. Charging time for overnight charging is taken as four hours. The estimates are performed considering the batteries are 3, 5, and 7 kWh. The number of batteries is being charged at the same time is the factor contributing the power demand. Calculations are done for two different cases for charging from 30–90% as shown in Table 71.3. Individual EV charging is considered at night, and battery swapping is considered for day time charging [6]. Table 71.3 Peak demand from electric rickshaws Battery capacity in kWh

Charging power for four hour charging in kW

Charging power for two hour charging in kW

Charging power for one-hour charging in kW

1.5

0.2

0.45

0.9

3

0.5

0.9

1.8

5

0.8

1.5

3

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Table 71.4 Peak demand from electric two wheelers Battery capacity in kWh

Charging power for four hour charging in kW

Charging power for two hour charging in kW

Charging power for one-hour charging of in kW

3

0.45

0.9

1.8

5

0.75

1.5

3

7

1.05

2.1

4.2

71.3.4 Electric Two Wheelers The highest sold electric vehicles reported in India are electric two wheelers, and this is the segment expected to have the highest penetration in the upcoming years. Two wheelers represent at least 70% of vehicular composition in India, and they offer the highest advantage in terms of total cost of ownership. Electric two wheelers of different battery sizes are available in market, and the typical capacity is between 1 and 3 kW. As most of the batteries are designed as detachable, battery swapping is also an option for these segments. However, the number of batteries at a swapping facility is lesser than electric rickshaws. Hence, individual electric vehicle charging and battery swapping of 5 batteries are options considered for peak demand analysis as shown in Table 71.4. Two charging times, charging in four hours is considered for 30–90% charging during night charging. For day time swapping and battery charging are considered for two hours and one-hour charging [6].

71.4 Forecast Peak Demand from Different Vehicle Segments in FAME In this section, the peak demand from the vehicles supported under the FAME scheme is estimated. As India is a cost sensitive market, with relatively smaller travel distance, it is assumed that 70% of all of the vehicle categories would have the lowest battery capacity in each category. 10% of the vehicles are assumed to have the highest battery capacity, and the remaining 20% would have the mid-range value for battery capacity. For the sake of the calculation, it is assumed that each vehicle undergoes at least one charging cycle in a day. 80% of the vehicles undergo overnight charging and 50% of vehicles undergo day time charging. Where ever there are more than one possibility for day time charging, an equal ratio is assumed between different types of charging. The peak demand would depend on the number of electric vehicles that would start charging at the same time. The results for three different thresholds, 10, 20, and 30% are presented for day time charging, and 25, 50, and 75% are presented for night time charging as shown in Table 71.5. The results shown Figs. 71.1 and 71.2 that though battery capacity of electric two wheelers and three wheelers are quite low, because of the sheer volume the peak

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Table 71.5 Peak demand from day time and night time charging Charging period Percentage of vehicles charging

Day time charging Battery capacity in kWh

10%

20%

30%

30%

20%

30%

Electric bus 4963

100

14.9

29.8

44.7

7.4

11.2

18.6

1418

200

8.5

17.0

25.5

4.3

6.4

10.6

Electric car

Electric three wheeler

Number of vehicles

Night time charging

709

300

6.4

12.8

19.1

3.2

4.8

8

24,500

20

11.5

22.9

34.4

3.7

5.5

9.2

7000

30

4.9

9.8

14.7

1.6

2.4

4

3500

40

3.3

6.5

9.8

1.1

1.6

2.6

350,000

3

23.6

47.3

70.9

15.8

23.6

39.4

100,000

5

11.3

22.5

33.8

7.5

11.3

18.8

50,000

7

7.9

15.8

23.6

5.3

7.9

13.1

1.5

21

42

63

14

21

35

3

9

18

27

6

9

15

5

7

14

21

5

7

12.5

Electric two 700,000 wheeler 200,000 100,000

demand from these vehicle segments is also comparable to the demand from the other segments. And under the current assumptions, it appears that both night charging and day charging will contribute to peak demand. It is important to adopt smart charging strategies to reduce the impact of electric vehicle charging during peak hours.

71.5 Conclusion and Way Forward Transportation is one of the most significant sector in contributing to the GHG emissions, both directly and indirectly. India is aiming for 30% of all new vehicle sales to be electric by 2030, from a baseline of around 3.5% in FY 2018. There are a few studies that have looked into impact of electrification at national level. Most of the studies have also tried to examine how much energy is needed not factoring in when. This work is distinct because the focus is on peak impact.

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Electric two wheeler

Electric three wheeler

Electric car

Electric Bus 0.0

20.0

40.0 30%

60.0 20%

80.0

100.0

120.0

140.0

10%

Fig. 71.1 Peak demand from day time charging

Electric two wheeler

Electric three wheeler

Electric car

Electric Bus 0.0

10.0

20.0 50%

30.0 30%

40.0

50.0

60.0

70.0

80.0

20%

Fig. 71.2 Peak demand from night charging

References 1. M. Ge, J. Friedrich, 4 Charts Explain Greenhouse Gas Emissions by Countries and Sectors. World Resources Instititue. February 06, 2020. Available Online: https://www.wri.org/blog/ 2020/02/greenhouse-gas-emissions-by-country-sector 2. S. Das, C. Sasidharan, A. Ray, Charging India’s Bus Transport (Alliance for an Energy Efficient Economy, New Delhi, 2019) 3. C. Sasidharan, A. Ray, S. Das, Selection of charging technology for electric bus fleets in intra-city public transport in India, in 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1–8). IEEE 4. S. Das, C. Sasidharan, A. Ray, Charging India’s Four-Wheeler Transport (Alliance for an Energy Efficient Economy, New Delhi, 2020).

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5. A. Jhunjhunwala, P. Kaur, S. Mutagekar, Electric vehicles in India: A novel approach to scale electrification. IEEE Electrification Magazine 6(4), 40–47 (2018) 6. C. Sasidharan, S. Das, Assessment of Charging Technologies currently used for Electric Two and Three wheelers in India, in 2020 India Smart Grid Week (2020)

Chapter 72

Integration of Multiple Energy Sources for Hybrid Smart Street Light System Anurag Choubey and Hitesh Kumar

Abstract Consumption of electricity by street light is massive. This is due to the conventional control systems that are used which require high range of power. It is not good considering the importance of energy conservation nowadays. Smart and green lighting systems are essential for resolving these problems primarily due to the start of the concepts of smart cities. This paper therefore focuses on the project to design a smart and also green street lightening systems and utilization of renewable energy sources along with new concept of utilization of mobile radiation effectively. The system proposed, comprises of strong ideas and concepts that can control efficiently most of the operations of street lights derived from natural energy sources like the sunlight, wind energy and motion trace by micro controllers, with the support of RF wireless communication. Two conditions are needed to be completed to switch ON the lights. Low levels of intensity of light are detected by LDR sensor. PIR motion sensors are used to detect object present in the street. Without it, the street light will be in OFF condition. So by implementation of SSLS, the consumption of street light can be decreased. The level of carbon dioxide is also reduced due to the use of renewable energy sources. This causes the light to get ON before any vehicle or any pedestrian enters. Also, bright of street light is reduced whenever there is some movement. Keywords Internet of things (IoT) · Mobile radiation energy · Wastage minimization · Smart street light · Energy conservation

A. Choubey Department of Computer Science and Engineering, Technocrats Institute of Technology, Bhopal, India H. Kumar (B) Department of Mechanical Engineering, Technocrats Institute of Technology, Bhopal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_72

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72.1 Introduction As technology advances day by day, it is priority matter to develop an exclusive smart street light system to reduce energy consumption. Street lighting is very essential for both, rural as well as urban areas, where there is use of such types of lighting systems. The smart city concept is an important focus point for a better environment in near future along with massive technological development happening in present days. At present, street lights are operational for the whole night due to security purposes. Deepa et al. [1] studied the use of 900 MHz of mobile signal frequency for the purpose of charging its battery. It was found that operating the street light for whole night is an inefficient, leads to shorter lamps life and causes light pollution. Due to the rise of environmental considerations, lighting management systems can play a vital role within the reduction of energy consumption of the lighting while not compromising on comfort goals. As stated, the energy is seen as the vital factor for considering the assessment of the effects of technical systems on the atmosphere. The current system used has a timer for the light to turn on and off at dark or bright which is not that suitable as the weather is unpredictable. Sometimes, it may get dark earlier than the set time especially during rainy day and get bright earlier than usual day. When the pedestrian or any objects move slowly, the power consumption used it just a waste. Tambare et al. [2] analysed the use of the Internet of things-based intelligent street lighting system for smart city. The harmful effect of the methods of street lighting were studied, and ways were suggested for improving its behaviour system to make it environmentally savvy and also cost efficient. A perfect design of energy efficient controller of street light on one hand allows people to travel with better condition at night time and on the other hand reduces usage of energy and power cost. This also should improve the charisma of surroundings. Till now, the current lighting system has defects and its design is outdated. Hence, its consumption of energy is large. The reasons for this inefficient road lighting structure in urban areas are: (a) (b) (c)

Poor design High power consumption Inefficient system.

This work reveals that the perfect solution for energy saving is intelligent, smart lighting control and energy management system primarily in public lighting set ups. Smart street light system refers to synchronization of public street lighting with the movement of pedestrians, cyclists and vehicles. When movement is detected, it brightens and gets dim when no activity is seen. Secondly, smart street light does not hang on traditional electricity for charging battery but it has multiple sources for charging the battery. Smart street lighting systems, build on a general concept of smart electricity usage rationalization, are seen to be as one of the crucial elements of the future smart grids. Somefun et al. [3] made the use of sensors for deployment of smart street lighting system. They are a comprehensive system consisting of sensors, control unit, communication unit and management console to ensure energy saving

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and maximum visual safety of drivers and pedestrians. Availability of sophisticated technology enables varied functionalities from basic monitoring (power consumption, temperature, etc.), controlling individual or sets of street lamps (on or off and automatically adjusting desired illumination level depending on road conditions such as increased traffic, special events, etc.) to an intelligently optimized energy efficient solution in large installations. Suseendran et al. [4] performed research on smart street lighting systems and mentioned the other features included in it like standardized lighting protocols, quick fault detection, and locating abnormalities by alarming. Most of these lamp driving solutions are based on a digital approach where microcontroller and advanced semiconductor devices which controls all functions that are required to run the lamp and also in parallel manages every data suitable for executing a smart street lighting framework [4]. Exhaustive literature review has been carried out to analyse the limitations of existing street light system. It is observed that the below-mentioned reasons lead to more power consumption. (1) (2) (3) (4) (5)

Single source of lighting (Electricity) More energy consumption (High expense) No/minimum utilization of natural sources of energy More manpower to operate. Manual switching (OFF/ON) of street lights.

Shichao et al. [5] ascertained that energy management and smart control of lighting system are an ideal solution to save energy, particularly in management of public lighting. Its on/off and dimming of lights can save forty percent energy and reduce lights maintenance costs by fifty percent. It also prolongs life of lamp by twenty percent. The streetlight electricity consumption and maintenance cost of overall control system for every lamp will be reduced thus increasing availability of street light.

72.2 Smart Street Light Charging System The charging system comprises of following three components: (1) (2) (3)

Solar Energy System Wind Energy System Mobile Radiation Energy System.

72.2.1 Solar Energy System Solar energy is of the most important, promising emerging source of renewable energy. Classification of solar technologies depends on the method of capturing and

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distributing solar energy and method to convert it into solar power .Solar technologies are mainly classified into passive solar or active solar. Active solar techniques make the use of photovoltaic systems, solar water heating to harness the energy and solar concentrators to get high intensity solar power. Shichao et al. [6] worked on adaptive street lighting predictive control passive solar techniques are related to the orientation of building with respect to sun and selection of materials having suitable thermal accumulation or properties of light dispersion. Design of spaces for circulating natural air is also included in it. Photovoltaic module are set strongly at the top of the pole. It is given suitable inclination (tilt) angle for receiving maximum sunlight for all time of day (Fig. 72.1). Thus, appropriate voltage and current are produced by these photovoltaic module that can be used to charge the battery. The lamps are light up by the use of this energy stored in the battery. The charge controller unit which controls the charging and discharging of the battery is considered as the heart of the system. It increases the life of battery by preventing the deep discharging and overcharging of batteries. At the time of deep discharging, the charge controller unit disconnects the light and indication of the low battery is given by a red LED glowing on the luminary. Glowing of LED shows that charging is required. The resumption of charging of battery is indicated by a green LED on the luminary. Marino et al. [7] effectively used the controller Arduino uno for automatic control of security system at the main entrance gate of the college campus. At the time of overcharging, the charge controller unit disconnects the solar module. This prevents the battery from overcharging. The battery stores the energy generated by solar photovoltaic module and is used for lighting up of the luminary at night. Normally, low tubular lead acid type batteries are normally used for street lights which require low maintenance. The battery is placed in a box, which is kept under the ground for easy maintenance and replacement as stated by Satyasheel et al. [8] in the study of light intensity monitoring and automation of street light control by Internet of things. Fig. 72.1 Street light with solar energy

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72.2.2 Wind Energy System Unequal heating of the surface of the Earth by the sun causes wind. Wind turbines are installed for getting clean electricity from the kinetic energy of wind. The spinning of the blades of wind turbine causes a rotor to capture the kinetic energy of the wind. This causes the rotor to rotate and drive the generator. In case of very high winds, to prevent the rotor from spinning out of control, most turbines have automatic over speed governing mechanisms. Our wind power animation provides enough information about the working of wind systems and the benefits due to this. Sakshi srivastava [9] worked on controlling a circuit of street lights with specific sensors, LDR and microcontrollers both in day as well as night. The method was mentioned to have benefits like reduced energy cost and maintenance cost. With traditional technique of wind mills, this smart light make use of both natural blowing wind energy by HAWT (on the top) and by VAWT that rotates by air pressure from moving vehicle which rapidly accelerates nearby the bottom of the street light. Both the energy mode are utilised for charging the battery as shown by Rao and Konnur [10].

72.2.3 Mobile Radiation Energy System With the growth of telecom industry, there are more signal radiations in the atmosphere than oxygen but the operator rarely uses all frequency modulated radiated signal. Most of the power is unused and so wasted. In this project simple but resourceful circuit is used through which the Fm radiated signal available in the atmosphere is converted to the direct current signal, and therefore, the terminal acts as a constant voltage source (Fig. 72.2). By the use of this circuit, the energy can be harnesses and utilized for various purposes. The circuit consists of auto stabilizing module which makes the whole system fully independent. So, no external source of power is needed for operating the circuit as studied by Abhishek et al. [11]. Bhuvaneswari et al. [12] analysed the behaviour of solar street tights aided with automatic tracking system. The radiation power from mobile network, available in the atmosphere which would have been wasted in the transmitting FM signals is now correctly utilized in this circuit for generation of electricity. By using array of these types of circuits firstly, the radiations are minimized. Secondly, the weak signal radiations at remote areas can be made strong. The radiated power available in the atmosphere is received through the antenna. The power is transferred to the electrolytic capacitors which serve as a good radio signal receiver. These capacitors on their discharge activate the germanium diodes aids in forward biasing at very low voltages. The charge developed is then stored into ceramic capacitors, and hence, this stored charge can be used to operate street light.

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Fig. 72.2 Block diagram of reserved mobile radiation energy system

72.3 Intensity Control by Sensing Vehicle Movement Rath et al. [13] used the ZigBee wireless medium to control and pass the LED status information. This mechanism is aimed at saving the electricity by lowering the intensity of street light when no movement of vehicles are sensed. As the vehicles come nearer to the lamp, the street lights are switched ON, the LEDs get activated and later on turn OFF. In this mechanism, motion sensor (IR sensor) is placed on the road at certain distance nearby street lights. Light gets brighten using the pulse width modulation (PWM) technique, at the time of movement nearby pole and get dimmed after passing vehicle (Fig. 72.3). During movement, signal from PIR sensor sent to microcontroller that increase the intensity of light and vice-versa.

72.4 Hybrid Street Light Sun, Wind and Mobile Radiation Hybrid streetlight gets power by solar, wind and mobile radiations (Fig. 72.4). Choubey and bhujade [14] researched on automatic smart street lighting sytem based on renewable energy. Overall analysis of the smart grid solutions was presented for street lighting. Techniques were shown to automatic charge through solar, wind (dual mode) and utilization of reserved mobile radiation energy. The system incorporates wind turbine which is a horizontal wind micro generator fixed on the hybrid lamp to balance for scarcity of photovoltaic power in months which have less number of daylight hours or larger nights. This arrangement of renewable energy sources and mobile radiation charger on a LED lamp along with battery storage provides

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Fig. 72.3 Block diagram of intensity control system

Fig. 72.4 Block diagram of hybrid street light control system

considerable lighting independency. This intelligent lighting system is made a technological innovation by the use of illumineon board software. Cynthia et al. [15] suggested the vehicle detection-based automatic street light control using Arduino for power saving applications. The use of integrated IoT, remote control and sensors makes the intelligent streetlight and the foundation of development smart city. Areas

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Fig. 72.5 Hybrid smart street light system

of cities that exchange information about traffic, availability of car parks, air quality, or charging points can be made very simple using it. Mukta et al. [16] studied various works on using IOT for making highway lighting systems energy efficient. Taxonomy was prepared that specifies all the approaches made toward making highway lighting systems energy efficient. Juntunen et al. [17] carried out a pilot project of making the street lighting system of the light traffic route of an housing area in Helsinki intelligent. A lighting control solution was developed after tracking the route users movement by using passive infrared sensors(PIR). As shown in Fig. 72.5. One more wind turbine is added at the bottom which rotates due to the threshold kinetic energy generated by vehicle passing through the street light. One very minute energy source, i.e. via mobile radiation which absorbs the mobile network radiations through the absorber panel located on the top. Radiation electricity generator will be more beneficial than solar panels and wind turbines which cannot work in cloudy atmosphere and becomes dead during rains. Mobile phones make the use of electromagnetic radiation in microwave range, i.e. about 2.5 GHz range which is sufficient to generate electricity. A GSM handset can emit radiation having peak power of 2 watts.

72.5 Conclusion In this work, more stress is given on the natural ways of energy generation sources. Due to the rise of environmental considerations, lighting management systems can play a vital role within the reduction of energy consumption of the lighting while not compromising on comfort goals. Beside traditional solar and wind energy, kinetic energy of the moving vehicles and radiation energy from mobile towers are also utilized. The solar panels and wind turbines which depend on availability of sunlight

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and wind. Kinetic energy of moving vehicle and mobile tower radiation energy will remain working even in the worst climatic conditions. The multiple charging sources make this system more reliable in all weather conditions. Acknowledgements Authors would like to thanks Dr S.C. Choubey, Professor & Dean, Faculty of Electrical & Electronics and Coordinator TEQIP lll,Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh for his endless support and guidance. This work is sponsored by TEQIP III under the flagship of Rajiv Gandhi Prodyogiki Vishwavidhyalaya, Bhopal, M.P., India.

References 1. S. N. Deepa, B. Shamili Swarupa Rani, RF Energy Harvesting Using 900MHz of Mobile Signal Frequency to Charging the Mobile Battery, in IEEE International Conference on Innovations in Green Energy and Healthcare Technologies(ICIGEHT’17) 2. T. Parkash, V. Prabu, R. Dandu, Internet of Things Based Intelligent Street Lighting System for Smart City. 5. 8. https://doi.org/10.15680/IJIRSET.2016.0505181 (2016) 3. S. Tobiloba, A. Claudius, A. Ademola, A. Daniel, S. Comfort, Deployment of smart street lighting system using sensors. International Journal of Electrical Engineering and Technology 10 (2019). https://doi.org/10.34218/Ijeet.10.4.2019.001 4. S. Siddarthan, B. Nanda, A. Josephus, M. Praba, Smart Street lighting System, pp. 630–633 (2018). https://doi.org/10.1109/CESYS.2018.8723949 5. S. Chen, X. Gang, X. Jia, H. Shuangshuang, W. Fei-Yue, W. Kun, The Smart Street Lighting System Based on NB-IoT, pp. 1196–1200 (2018). https://doi.org/10.1109/CAC.2018.8623281 6. F. Marino, F. Leccese, Stefano Pizzuti”, Adaptive street lighting predictive control”. Energy Procedia 111, 790–799 (2017) 7. A. Jalan, G. Hoge, S. Banaitkar, S. Adam, Campus automation using arduino. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 6(6), 4635–4642 (2017) 8. H. Satyaseel, G. Sahu, M. Agarwal, J. Priya, Light intensity monitoring & automation of street light control by Iot. Int. J. Innovations Adv. Comput. Sci. 6(10), 34–40 (2017) 9. S. Srivastava, Automatic street lights. Adv. Electron. Electr. Eng. 3(5), 539–542 (2013) 10. A. Rao, A. Konnur, Street light automation system using arduino uno. Int. J. Innovative Res. Comput. Commun. Eng. 5(11), 16499–16507 (2017) 11. M. Abhishek, S.A. Shah, K. Chetan, K.A. Kumar, Design and implementation of traffic flow based street light control system with effective utilization of solar energy. Int. J. Sci. Eng. Adv. Technol. 3(9), 195–499 (2015) 12. C. Bhuvaneswari, R. Rajeswari, C. Kalaiarasan, Analysis of solar energy based street light with auto tracking system. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 2(7), 3422–3428 (2013) 13. D.K. Rath, Arduino based: Smart light control system. Int. J. Eng. Res. Gen. Sci. 4(2), 784–790 (2016) 14. A. Choubey, R. Bhujade, IoT based smart street light system using renewable energy. Int. J. Sci. Technol. Res. 8(12) , 3990–3992 (2019). 15. P.C. Cynthia, V.A. Raj, S.T. George, Automatic street light control based on vehicle detection using arduino for power saving applications. Int. J. Electron. Electr. Comput. Syst. 6(9), 297– 295 (2017) 16. M.Y. Mukta, M.A. Rahman, A.T. Asyhari, M.Z. Alam Bhuiyan, IoT for energy efficient green highway lighting systems: Challenges and issues. J. Netw. Comput. Appl. 158, 102575 (2020) 17. E. Juntunen, E.M. Sarjanoja, J. Eskeli, H. Pihlajaniemi, T. Österlund, Smart and dynamic route lighting control based on movement tracking. Build. Environ. 142, 472–483 (2018)

Chapter 73

Improving Cold Flow Properties of Biodiesels Using Binary Biodiesel Blends Krishna Kant Mishra, Mukesh Kumar, Ravikant Ravi, Amol Saini, Kunal Salwan, and Mahendra Pal Sharma Abstract The environmental threats posed by rapidly deleting the fossil fuels are currently a major global concern and lead to the research of alternative energy resources. The biodiesel is considered as substitute of diesel but the biodiesel suffers with the disadvantage that the fuel quality is very much impacted by its cold flow properties. The present paper aims to improve the cold flow properties (CFP) of biodiesels by binary blending and blending with kerosene and ethanol as CFP improvers. Jatropha biodiesel (JB) and Pongamia biodiesel (PB) were blended with diesel, kerosene, ethanol and microalgal biodiesel (MB), and it is found that JB20 blend has 27% lower CP and PP compared to JB100 . JBK40 blend lowers the CP and PP to 7 and 3 °C, respectively, while JBK20 blend maximally lowers the CP and PP up to −15 and −18.3 °C, respectively, but due to its non-renewable nature and emits lots of smoke due to kerosene it is not recommended for use in engine. JBE20 blend further reduces the CP and PP to 16 and 13 °C, which is higher than diesel. Therefore, ethanol is not recommended for blending purpose but may be used as CFP in lesser proportion. The results showed that JBM20 blend has significantly improved CP and PP (9.2 and 5.5 °C) and PBM20 blend also has lower CP and PP (12 and 7.5 °C) compared to JB100 and PB100 . So, JB20 , JBM20 and PBM20 blends can be recommended for use in engine under low-temperature conditions. Keywords Biodiesel · Cloud point · Pour point · Blending

K. K. Mishra · M. P. Sharma Biofuel Research Laboratory, Alternate Hydro Energy Centre, Indian Institute of Technology Roorkee, Roorkee, India M. Kumar (B) · A. Saini · K. Salwan Department of Mechanical Engineering, Chandigarh Engineering Colleges, Landran, Mohali, India e-mail: [email protected] R. Ravi Department of Mechanical Engineering, GBPIET Pauri Garhwal, Pauri Garhwal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_73

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Abbreviations CP CFI CFP CFPP E K M JB JBXX LTFT MB PB PP

Cloud point Cold flow improver Cold flow properties Cold filter plugging point Ethanol Kerosene Microalgal biodiesel Jatropha biodiesel XX per cent of JB blended with diesel Low-temperature flow test Microalgal biodiesel Pongamia biodiesel Pour point

73.1 Introduction Energy is the basis for economic development of a nation. The natural energy resources are limited and are diminishing very fast leading to energy crisis in almost all the countries of the world. Therefore, the exploitation of renewable energy resources is being accorded top priority to be used as alternative energy apart from adopting the energy conservation measures. From the point of liquid fuels, biodiesel, the alkyl esters of long chain fatty acids derived from vegetable oils or animal fats via transesterification process, is found to have fuel properties similar to petroleum diesel and so offer perfect substitution of diesel [1, 2]. Jatropha curcus and Pongamia pinnata oils as non-edible resource have generated interest in India as future alternative source of biodiesel but suffer with two major problems: poor oxidation stability and poor CFP for its use as diesel engine fuel [3]. The former is correlated with the stability or ability to undergo deterioration while the later include CFP of liquid fuels like CP, PP, CFPP and LTFT as the fuel quality parameters that may be improved for trouble-free engine operation [4, 5]. Literature reveals that little work is available on binary biodiesel blends for the improvement of CFP. Park et al. studied the effect of blending of palm, rapeseed and soybean biodiesels on the improvement in CFP and oxidative stability (OS) and found that binary blends of Palm and soybean biodiesel have better OS (11hrs), while rapeseed biodiesel has very good CFP, i.e. −20 °C [6]. Nainwal et al. prepared and used binary blends of Jatropha curcus and waste cooking biodiesel and found to have better CFP than the individual biodiesel [7]. Ali et al. reported the PP of biodiesel blend of palm biodiesel with diesel as 0 °C for B20 blend [8]. Zuleta et al. suggested to use castor biodiesel for blending with JB to improve its CFP and found CP of − 12 °C for 25% castor and 75% JB blend [9]. In view of the little work, the present

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study reports the results of improving CFP by binary blends of different biodiesels as well as CFP of the resulting blend [9].

73.2 Problems Due to Poor CFP The biodiesels have a tendency to undergo gel formation and solidification under low-temperature conditions [7, 10] and may create the following: 1. 2. 3. 4. 5.

Plugging and gumming of filters, lines and injector [10, 11], Starting problem due to use of biodiesel in cold condition [12], Driving problem due to use of biodiesel in cold condition [12], Fuel starvation and operation problem which cease the fuel flow in engine [13] and Biodiesel use in cold climate creates pumping problem in engine [14].

These problems can be solved by improving the CFP by forming binary blends among different biodiesels, biodiesel blends with diesel and using CFP improvers like kerosene and ethanol.

73.3 Material and Methodology 73.3.1 Material Jatropha, Pongamia and microalgal oils were used to prepare biodiesels as per methodology adopted earlier [7, 15–20]. All chemicals used were of Analytical Research Grade and 99% pure. Diesel, kerosene and ethanol were purchased from local market. The fuel properties of oils and their biodiesels as determined by standards methods [7, 15, 16] are given in Table 73.1. As from Table 73.1, it is found that the FFA content in Jatropha and Pongamia oils is very high compared to microalgal oil so biodiesel from microalgal oil is proven good as a blending agent to improve the quality of biodiesel from Jatropha Table 73.1 Fuel properties of selected oils Properties

Non-edible oils Jatropha curcus

Viscosity (cSt) at 40 °C

Pongamia pinnata

Microalgal oil (Chlorella protothecoides)

50

53

40

912

924

895

% FFA

17

15

2

Acid value

34

30

4

Density (Kg/m3 ) at 40 °C

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and Pongamia oils. Also the density and viscosity of microalgal oil are very low as compared to former two oils so it is better to use in blending. Table 73.2 compares the fuel properties of different biodiesels selected along with diesel, kerosene and ethanol. The table shows that JB and PB have poor CFP, while MB has very good CP and PP compared to diesel. Therefore, the binary blends of biodiesels as well as the effect of CFP improvers have been undertaken to bring overall CFP near to diesel.

73.3.2 Experimental CP and PP were measured as per the standards given in Table 73.2. The sample was cooled in a glass tube under prefixed conditions and observed at intervals of 1 °C until a cloudy structure becomes visible. This temperature was recorded as CP. For PP, the sample was cooled in a glass tube under prescribed conditions and inspected at intervals of 3 °C till no movement was observed even when the plane of surface was held vertical for 65 s. The PP was recorded as 3 °C above the temperature of stopping of flow. All the data are the average of triplicate measurement and no statically significant difference is found. In order to improve the quality of biodiesel, we have prepared binary blends of different biodiesels, blends of biodiesels with diesel and blends of biodiesel with kerosene and ethanol as CFI and the details are given in Table 73.3. The fuel properties particularly the CP and PP were determined for each sample, and the results are discussed in the following sections.

73.4 Results and Discussion 73.4.1 Blends of JB with Diesel Figure 73.1 gives the variation of CP and PP with JB biodiesel and its blends with diesel. The figure shows that as the proportion of diesel in JB blends is increased, the CP and PP are lowered in favour of diesel and even JB20 blend has improved CP and PP compared to JB100 but slightly higher than diesel due to proximity of these properties with diesel and it is because of this reason that JB20 blend may be recommended as suitable blend for diesel engine.

Test method for kinematic viscosity of transparent and opaque liquids (D-445)

Standard test method for density, relative density or API gravity of crude petroleum and liquid petroleum products by hydrometer method (D 1298)

Viscosity at 40 °C (cSt)

Density (kg/m3 ) at 40 °C

CP (°C)

PP (°C)

% FFA

2

3

4

5

6

JB

Standard test method for determination of free fatty acids contained in animal, marine, and vegetable fats and oils used in fat liquors and stuffing compounds (D5555-95)

Standard test method for pour point of petroleum products (D97-12)

Test method for cloud point of petroleum products (D2500-11)

0.4

19

22

883

4.38

Pensky–Martens 172 closed-cup test apparatus (D-93)

Flash point (°C)

1

ASTM 6751

Property (unit)

S.No

0.3

0

3

860

3.2

124

MB

Table 73.2 Physiochemical properties of biodiesels and CFP improvers

0.4

18

21.2

892

4.52

110

PB

–

−41

–

–

−40

–

789

1.22

13

Ethanol

797

1.16

38

Kerosene

CFP improvers

–

5

6

836

3.4

55

Diesel

0.50 mg KOH/g

0.25

–

–

–

1.9–6.0

ASTM standard limit

Biodiesel standards IS 15607

(continued)

–

–

IS 1448

IS 1448

–

IS 1448

73 Improving Cold Flow Properties of Biodiesels Using … 955

Property (unit)

Acid value

S.No

7

Table 73.2 (continued)

Standard test method for acid value of fatty acids and polymerized fatty acids (D 1980–87)

ASTM 6751

0.8

JB

0.6

MB

0.8

PB

–

Kerosene

CFP improvers

–

Ethanol –

Diesel ASTM standard limit

Biodiesel standards IS 15607

956 K. K. Mishra et al.

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Table 73.3 Different binary biodiesel blends S. No

Sample name

Base biodiesel

1

JB100

2

JB80

Jatropha biodiesel

% of base biodiesel (v/v)

Blending agent/ improver

% of blending agent/improver(v/v)

100

NA

0

80

Diesel

20

3

JB60

60

40

4

JB40

40

60

5

JB20

20

6

JBM80

80

7

JBM60

60

80 Microalgal biodiesel

20 40

8

JBM40

40

60

9

JBM20

20

80

10

JBE5

95

Ethanol

5

11

JBE10

90

10

12

JBE15

85

15

13

JBE20

80

20

14

JBK80

80

15

JBK60

60

40

16

JBK40

40

60

17

JBK20

18

PB100

19

PBM80

Kerosene

20 Pongamia biodiesel

20

80

100

NA

0

80

Microalgal biodiesel

20

PBM60

60

PBM40

40

60

22

PBM20

20

80

23

MB100

Temperature (Celcius)

20 21

Microalgal biodiesel

40

100

NA

0

JB60

JB40

JB20

25 20 15 10 5 0 JB100

JB80

JB Blended with Diesel CP

PP

Fig. 73.1 Variation of CP and PP for blends of JB with diesel

Diesel

Temperature (Celcius)

958

K. K. Mishra et al. 25 20 15 10 5 0 JB100

JBM80

JBM60

JBM40

JBM20

MB100

JB Blended with MB

CP

PP

Fig. 73.2 Variation of CP and PP for blends of JB with MB

73.4.2 Binary Blends of JB with MB As shown in Table 73.2, JB has poor, while MB has the better CFP. To further improve CFP, the binary blends of JB with MB were prepared and their CFP shown in Fig. 73.2 shows that as the proportion of MB is increased in JB-MB binary blends, there is considerable improvement in CP and PP, i.e. JBM20 binary blend has CP of 9.2 °C and PP of 5.5 °C which is significantly improved compared to JB100 but lower than diesel. Accordingly, the JBM20 binary blend can be used without cold flow problems. At the same time, MB100 can be used as CFI, when the poor CFP of other biodiesel needs to be improved.

73.4.3 Binary Blends of PB with MB Figure 73.3 compares the CFP of pure biodiesel with binary blends of PB with MB and MB100 . It shows that with increase in the proportion of MB in PB binary blends, the CFP is significantly lowered, i.e. improved. As can be seen that PBM20 binary blend has a CP of 9.2 °C and PP of 10.5 °C which is significantly lower than PB100 but higher than MB100 but in the range of diesel. Therefore, MB100 can be used as CFP improver for other biodiesels having low CFP.

73.4.4 Blends of JB with Ethanol The blends of JB with ethanol as CFP were prepared by mixing ethanol in 5, 10, 15 and 20% (v/v) with JB. A comparison of CFP of these blends is given in Fig. 73.4 which shows that JBE20 has improved CFP, i.e. CP of 16 °C and PP of 13 °C compared

Temperature (Celcius)

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25 20 15 10 5 0 PB100

PBM80

PBM60

PBM40

PBM20

MB100

PB blended with MB CP

PP

Temperature (Celcius)

Fig. 73.3 Variation of CP and PP for blends of PB with MB 25 20 15 10 5 0 JB100

JBE5

JBE10

JBE15

JBE20

JB Blended with Ethanol CP

PP

Fig. 73.4 Variation of CP and PP for blends of JB with ethanol

to other blends and JB100 . This improvement is not significant, and therefore, even the JBE20 blend is not recommended due to poor CP and PP than diesel. At the same time, the availability of ethanol in such amount is questionable and so its use as CFI is non-feasible. Therefore, ethanol is not recommended as CFI for biodiesels.

73.4.5 Blends of JB with Kerosene Kerosene, a CFP improver, has also used to prepare blends with JB by mixing 20, 40, 60 and 80% (v/v) kerosene with JB as shown in Fig. 73.5 which shows that JBK20 has the best improvement in CFP; i.e. CP of −15 °C and PP of −18.3 °C is achieved compared to JB100 as well as diesel. From practical point of view, it is a non-renewable source and again the availability is also questionable, and at the same time, the engine is likely to emit significant smoke, which is not only harmful to the health but also dangerous to the engine, and therefore, kerosene may not be suitable as CFP improver in such large proportion.

Temperature (°C)

960

K. K. Mishra et al. 30 20 10 0 -10 -20 -30 -40 -50

JB100

JBK80

JBK60

JBK40

JBK20

Kerosene

JB Blended with Kerosene CP PP

Fig. 73.5 Variation of CP and PP for blends of JB with kerosene

73.5 Finding • Compared to JB100 , the CP and PP of JB20 blends with diesel are improved to 16 and 13 °C against 22 and 19 °C of JB100, respectively. • Binary blend of JBM20 has CP of 12.8 °C and PP of 13.5 °C compared to JB100 . • Binary blend of PBM20 lowered the CP and PP by 9.2 and 10.5 °C, respectively. • Ethanol in 20% blend (v/v) is not recommended to be used as CFP improver due to its nature as octane enhancer making the engine operation difficult. • JBK20 blend of JB with kerosene lowers CP by −15 °C and PP by −18.3 °C but not recommended due to its availability and non-renewability. • Binary/tertiary blending may be tried as an effective way of improving the CFP of resulting biofuel with no need of additional additive to improve it.

73.6 Conclusions The results of study show that the CP of JB100 , PB100 , MB100 , diesel and kerosene are 22, 21.2, 3, 6 and -40 °C, respectively, while there PP as 19, 18, 0, 5 and −41 °C, respectively. The blending of JB with diesel, kerosene and MB effectively reduces the CFP of JB100 . JB20 blend with diesel reduces (improves) CP and PP of JB from 22 to 16 °C and from 19 to 13 °C, respectively, while binary blending with MB shows better results as CP and PP are significantly improved from 22 to 9.2 °C and from 19 to 5.5 °C in the range of diesel. Blending with kerosene also remarkably improves the CP and PP of JB from 22 to −15 °C and from 19 to −18.3 °C, respectively, but kerosene increases smoke emissions, so it is not recommended for use in engine. Ethanol also improves the CP and PP of JB from 22 to 16 °C and from 19 to 13 °C, respectively, and acts as a CFI but due to its nature of octane enhancer, it is also not recommended. MB also improves the CP and PP of PB from 21.2 to 12 °C and from 18 to 7 °C, respectively. JBM20 and PBM20 are recommended as suitable blends for use in engine.

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References 1. P. Verma, M.P. Sharma, G. Dwivedi, Operational and environmental impact of biodiesel on engine performance. A Rev. Literature 5 (2015) 2. M.S. Gad, M.K. El-Fakharany, E.A. Elsharkawy, Effect of HHO gas enrichment on performance and emissions of a diesel engine fueled by biodiesel blend with kerosene additive. Fuel 280, 1–7 (2020) 3. F. Ya¸sar, Comparision of fuel properties of biodiesel fuels produced from different oils to determine the most suitable feedstock type. Fuel 264, 1–7 (2020) 4. G. Dwivedi, M.P. Sharma, Cold flow behaviour of biodiesel-a review. Int. J. Renew. Energy Res. 3, 827–836 (2013) 5. M. Kumar, M.P. Sharma, Selection of potential oils for biodiesel production. Renew. Sustain. Energy Rev. 56, 1129–1138 (2016) 6. J.Y. Park, D.K. Kim, J.P. Lee, S.C. Park, Y.J. Kim, J.S. Lee, Blending effects of biodiesels on oxidation stability and low temperature flow properties. Bioresour. Technol. 99, 1196–1203 (2008) 7. S. Nainwal, N. Sharma, A. Sharma, Sen, S. Jain, S. Jain, Cold flow properties improvement of Jatropha curcas biodiesel and waste cooking oil biodiesel using winterization and blending. Energy 89, 702–707 (2015) 8. O.M. Ali, R. Mamat, N.R. Abdullah, A.A. Abdullah, Analysis of blended fuel properties and engine performance with palm biodiesel-diesel blended fuel. Renew. Energy. 86, 59–67 (2015) 9. E.C. Zuleta, L.A. Rios, P.N. Benjumea, Oxidative stability and cold flow behavior of palm, sacha-inchi, jatropha and castor oil biodiesel blends. Fuel Process. Technol. 102, 96–101 (2012) 10. N. Isioma, Y. Muhammad, O.D. Sylvester, D. Innocent, O. Linus, Cold Flow properties and kinematic viscosity of biodiesel. Univers. J. Chem. 1, 135–141 (2013) 11. S.P. Singh, D. Singh, Biodiesel production through the use of different sources and characterization of oils and their esters as the substitute of diesel: a review. Renew. Sustain. Energy Rev. 14, 200–216 (2010) 12. J. Kim, E. Yim, C. Jeon, C. Jung, B. Han, Cold performance of various biodiesel fuel blends at low temperature. Int. J Ener 13, 293–300 (2012) 13. R.D. Misra, M.S. Murthy, Blending of additives with biodiesels to improve the cold flow properties, combustion and emission performance in a compression ignition engine—a review. Renew. Sustain. Energy Rev. 15, 2413–2422 (2011) 14. H. Tang, S.O. Salley, K.Y. Simon Ng, Fuel properties and precipitate formation at low temperature in soy-, cottonseed-, and poultry fat-based biodiesel blends. Fuel 87, 3006–3017 (2008) 15. M. Naik, L.C. Meher, S.N. Naik, L.M. Das, Production of biodiesel from high free fatty acid Karanja (Pongamia pinnata) oil. Biomass Bioenerg. 32, 354–357 (2008) 16. M. Kumar, M.P. Sharma, Production methodology of biodiesel from microalgae. Int. J. Appl. Eng. Res. 8, 1825–1831 (2013) 17. P. Verma, G. Dwivedi, M.P. Sharma, Comprehensive analysis on potential factors of ethanol in Karanja biodiesel production and its kinetic studies. Fuel 188, 586–594 (2017). https://doi. org/10.1016/j.fuel.2016.10.062 18. G. Dwivedi, P. Verma, M.P. Sharma, Optimization of storage stability for Karanja biodiesel using box-Behnken design. Waste Biomass Vaporization 9, 645–655 (2018). https://doi.org/ 10.1007/s12649-016-9739-2 19. M. Chhabra, B.S. Saini, G. Dwivedi, Impact assessment of biofuel from waste neem oil. Energy Sourc. Part Recov Utilizat. Environ. Effects (2019) https://doi.org/https://doi.org/10.1080/155 67036.2019.1623946 20. G. Dwivedi, S. Pillai, A.K. Shukla, Study of performance and emissions of engines fueled by biofuels and its blends, in Methanol and the Alternate Fuel Economy (Springer, Singapore, 2019), pp. 77–106

Chapter 74

Dual-Axis Solar Tracking System Rahul Shaw, Swarup Kumar Das, and Sajjan Kumar

Abstract For maximum power output through any PV panel module, it is necessary to adjust the PV panel in such a way that the solar radiation falls perpendicularly to the panel. Since the solar position varies with time and date throughout the year, for the optimum power output, the panel should not be set fixed. To perfectly track the solar position throughout the year, dual-axis controllable tracking system is needed to be design. This study focuses on the controlling of dual-axis solar tracking system. The main aim is to maximize the power efficiency of the photovoltaic module, by adjusting the angle in order to maintain the perpendicular angle between the sun and the PV module. This system introduces two motors with some electronic sensors connected at different positions for PV module adjustments. Further, this method is simulated and it is seen to be advantageous. This simulation gives reliable performance of the system. Keywords Dual-axis solar tracker · GPS-based solar tracking · Maximum power point tracking · Elevation and azimuth angle control

74.1 Introduction In the way to fulfill the increasing demand of electricity and minimize the environmental impacts due to global warming, the only strategy is to go on increasing the use of renewable energies. Nowadays, photovoltaic module has been given more attention as clean and green energy source. In this system, electrical energy is being produced through this module. Energy output can be used in various applications like heating, lighting, cooking, and many others. In this system, more efficiency can be achieved when solar radiations are perpendicular to PV module. Solar radiation tracker has played a vital role in increasing the efficiency of solar panels in recent years, by proving the better technological achievement. In this system, solar tracker consists of an automatically controlled solar panel system which set the sun as its R. Shaw · S. K. Das · S. Kumar (B) Department of Electrical Engineering, Gargi Memorial Institute of Technology, Baruipur, Kolkata, West Bengal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_74

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reference to incarcerate maximum solar radiation corresponding to different positions of sun. Sun’s position compared to earth’s location varies in a cyclic manner during a course of calendar year. This tracking system involves different tracking sensor devices to maintain the panel position according to the sun’s location. The module also gives the feedback signal to the controlling units for position adjusting. This system is also known as multiple degree moving devices which improve the output efficiency of the system. The purpose of representing this paper is to focus on automatically controlled dual-axis solar tracking system. Basically, this type of tracking system was proposed by the standard astronomical database to confirm the sun’s position at a given time and location throughout the year-round by microcontroller device. This advanced open-loop tracker system incorporates the azimuth position and altitude position of sun for controlling the PV panel position and hence this does not require any feedback system. So, this method of controlling is independent on the ambient temperature and weather conditions. In contrast to this, sensor-based closed-loop tracker system tracks the sun position by minimizing the angle error using feedback system by measuring the incoming solar radiation angle with respect to panel position. That is why, this system is weather-dependent. Because of small endurance in misfit angles between incoming solar radiation and normal solar panel, an accurate solar tracking system is needed to maintain for efficient performance. Regarding this, if the tracking frequency of solar tracker is maximized, it increases the tracker accuracy, which also tends to increase the moving frequency and the mechanical wear. In order to overcome this limitation, an open-loop tracking system is being utilized for controlling and altering the tracking frequency, instead of constant tracking frequency, by correlating the movement from azimuth and the altitude, the overall moving frequency of tracker is reduced. If the moving frequency of tracker is reduced, then the externally required driving energy for tracker is reduced and this improves reliability. Moreover, this system has also a drawback at the instance, the misalignment of the tracker during ‘setup’ by the human. As in this solar tracker, the supplier sets manually the solar path database from astronomical database. In [1], the author worked on establishing a mechanical passive solar tracking system which is enabled by bimetallic strip operated by various dampers. The most efficient way for operation of photovoltaic cell (PV) is the maximum power point tracking (MPPT) [2]. In [3], the author mentioned the theoretical ideas of establishing the single vertical axis tracking system including the effect of shadowing between different trackers. They have generally focused on to minimize the spacing between the respective trackers. Yazid et al. presented a two-axis solar tracking system controlled using PID controllers and fuzzy controller [4]. In this system, two DC motors are being used to make the movement of the PV cell in corresponding two axes. Karlis et al. [5] implemented fuzzy cognitive network-based MPPT technique for operating the PV system at maximum efficiency. Various trained neural networks are used in artificial neural network (ANN)-based techniques to track MPPT [6, 7]. An extensive comparative study between different conventional and metaheuristic PPT algorithms has been presented in [8, 9] to track the global MPP under partial shading condition. Apart from MPPT strategy, the solar tracking is also very much important way to

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utilize the maximum power from a PV panel. So, the paper is focused on design of dual-axis solar tracking system.

74.2 Solar Tracking System Normally the solar radiation coming out from sun mainly involves three main components: • Radiated beam that reaches directly straight on the earth’s surface without scattering. • When the beam passes through the atmosphere, the radiated beam scatters throughout the surface of the earth. • Albedo radiation that reflects from earth’s surface. As the beam holds about 80–90% of the solar radiation energy in the first two components in ideal condition, for PV generator it is the major source of functioning. Further, the solar PV panel needed to make the proper adjustment in aligning the panel with the location of sun’s direct beam. Open-loop system mainly provides an easy way through which the maximum solar radiation will be tracked during operation, but also it has lower efficiency because of manual alignment of the setup to attract more radiations. This concept is quantitatively maintained by taking into consideration the incidence angle (i), between the panel and direct beam. In the same manner, the area of solar panel incarcerates the direct beam in proportional to cosine of (i). Therefore, the power (P) absorbed by PV panel can be given by P = Pmax × cos(i)

(74.1)

Taking into account Eq. (74.1), we can also find out loss of power (a) as: a=

Pmax − Pmax × cos(i) = 1 − cos(i) Pmax

(74.2)

Equation states that more the misaligned angle, more power is lost. Thus, it is clear that, the main objective is to maintain minimum incidence angle at ideally zero degree. For this reason, altitude and azimuth angle are specified for exact location of sun in the sky. The altitude angle is the angle considered from horizon of the observer to the sun, right angle to the horizontal plane. At sunrise and sunset, the value is 0° and 180°. On the other hand, the azimuth angle is obtained by clockwise from the north to the point adjusted on the horizon, respectively, below the sun. Both altitude and azimuth angles along with different axes have been shown in Fig. 74.1. For calculating the altitude and azimuth angles, some of the formulas are being considered as follows [10, 11]: Azimuth = cos−1 ((sin δ. cos ϕ − cos δ. sin ϕ. cos δ)/ cos α)

(74.3)

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Fig. 74.1 Azimuth and altitude angles along with different axes of the system

Altitude = sin−1 (sin δ. sin ϕ + cos δ. cos ϕ. cos ∂)

(74.4)

B = 360/365.(d − 81)

(74.5)

δ = 23.45◦ . sin B

(74.6)

∂ = 15◦ (L ST − 12)

(74.7)

L ST = L T + T C/60

(74.8)

T C = 4(Longitude − LSTM) + EoT

(74.9)

EoT = 9.87 sin(2B) − 7.53 cos(B) − 1.5 sin(B)

(74.10)

LSTM = 15◦ TGMT

(74.11)

True north is mainly chosen to estimate the required azimuth angle. The mentioned tracking system incorporates digital compass for magnetic north that fluctuates from every location. The standard database is being utilized to find the declination angle. This standard database is embedded from National Geographical Data Centre.

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74.3 GPS Receiver GPS advances all proper positioning system and meets all the positioning criteria. The specified date, time, and proper latitude and longitude of sun’s position are provided by GPS tracking. As the heading reference is taken as true north. Then GPS will tabulate the exact database of sun’s location throughout the year. Now depending on the tabulated database, tracker will go on adjusting the PV panel position. This method allows continuous movement of panel corresponding to the sun’s location with angular difference of approximately 1° for both altitude and azimuth, for a day. In altitude movement, the actuator moves panel up at sunrise and down during sunset. On the other hand, the azimuth movement control will allow the panel to move in either clockwise or anticlockwise direction. PID controller is embedded in each slave MC. In both of altitude and azimuth control centre, two 10 bit encoders are also evolved for feedback control system.

74.4 Solar Tracker Design The proposed solar tracking system involves many parts based on its operation namely the hardware part, electrical operating unit, and electronic control unit. All moving parts incorporate under mechanical system such as panel setup and actuators. The electrical unit involves total wiring connections of setup and the electronic control system have master slave microcontroller unit, GPS, compass or RTC module, sensors, and many drives. • Hardware Structure: The overall biaxial structure of solar tracker design [12] is shown in Fig. 74.2. This consists of many lightweight parts and is convenient to be carried. Two actuators are used, one for altitude tracking and another for azimuth tracking.

74.5 Electronic Control System Electronic control system is most effectively designed in order to provide better signal to the hardware devices, so that it can perform better to extract maximum power. Also, it should be more reliable and less energy consuming hardware. A block diagram for the simple control system design has been presented in Fig. 74.3. Here, from the diagram, the main controller (MC) performs as the brain of the system. Database of the sun’s path trajectory has been generated throughout the year-round for a particular site. A 2 GB memory card is used to store the recorded database. The global positioning system (GPS) determines the location of solar tracker, which calculates the exact position from several orbiting satellites. The main MC is connected to GPS, which gives continuous signal. This mainly contains time,

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Fig. 74.2 Structure of the hardware

Fig. 74.3 Block diagram of the control system design

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date, longitude, latitude, altitude, and azimuth databases. Since digital compass is also connected to MC via I2C communication protocol. It is used to calculate the magnetic field of earth and generate an output value which gives a feedback for tracker location with respect to magnetic north. Additionally, two extra MCs are added in system, each for controlling the PV for azimuth angular movement and altitude angular movement. In addition to this, the design also involves three 4 × 20 LCD screen for displaying data and warning if any. The first will display time and date, second includes astronomical database with latitude, longitude, declination angle, hold time, equation time, etc. Third one will show the current, voltages, and power.

74.6 Result and Discussion The performance characteristics of solar tracked are evaluated by comparing the fixed tilted PV panel and newly developed tracker system. Since the fixed tilted PV panel can able to give maximum power only at a particular time in days, the active tracking module may increase the tracker efficiency. The different controlling devices are connected to the system to improve the system efficiency by adjusting the panel position according to sun’s movement. This may be possible either through driving electric servo motor or through hydraulic actuator. The control system for this tracking module may be either open or closed loop control system. In open-loop system, the mathematical calculations need to be performed by microprocessor-based controlling units to predict the sun’s position. This system needs only current state of the sun by knowing its azimuth and altitude angles and performs mathematical algorithms without using feedback system. Since it does not require any sensors as a feedback system to sense the sun’s position by sensing sunlight, this type of system becomes simpler, economical as well as less power consuming device. It consumed only around 2% or below of total collected energy. It provides better performance than closed-loop system especially under cloudy weather condition. All controlling and positioning units are embedded under MCU which incarcerates about 40.7% higher energy than fixed module. In open-loop system, sun tracking is easy but provides poor accuracy and sometime involves manual alignment to generate maximum power. In contrast to this, closed-loop system has high efficiency due better accuracy than open-loop module. Considering the cost and simple circuitry, this innovative approach can be utilized. The auto-self-aligned system may also be designed by utilizing the GPS information and astronomical equation to optimize the open-loop system. It is obviously clear that power o/p from both tracking and non-tracking system decreases during cloudy season as it blocks solar radiation. Thus, o/p power must depend on the received level of light. The graph shown below (Fig. 4a, b) gives an overview of power o/p from 120 W (peak) fixed tilted PV panel and tracking system PV panel during clear days as well as in cloudy days [13]. As per the graph shown below, it can be noticed that as compared to fixed PV panel, the tracking system gives

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Fig. 74.4 Power o/p from tracking system and fixed-tilted system during a clear day b cloudy day

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27% more power o/p in mostly clear day and about 19% more power o/p in cloudy day [13].

74.7 Conclusion It is straight forward that dual-axis solar tracking system plays a vital role and guarantees to maximize the power o/p from the PV panel. Moreover, tracker also capable to adjust the position of PV panel corresponding to the solar radiation. It may be possible by both sensor-based as well as sensor less techniques. Control strategy for sensor-based technique needs close-loop feedback system whereas for sensor less microprocessor-based system needs open loop without feedback system. Sensor less technique needs some mathematical calculations to know the exact position of sun for maximum power output. This type of system can be built to validate the theoretical results. According to this proposed technique, around 28.8–43.6% of energy efficiency can be achieved from a PV system depending on the weather condition and season.

References 1. M.J. Clifford, D. Eastwood, Design of a novel passive solar tracker. Sol. Energy 77, 269–280 (2004) 2. H. Rezk, A.M. Eltamaly, A comprehensive comparison of different MPPT techniques for photovoltaic systems. Sol Energy 112, 1–11 (2015) 3. E. Lorenzo, M. Perez, A. Ezapeleta, J. Acedo, Design of tracking photovoltaic system with a single vertical axis. Prog. Photovoltaics Res. Appl. 10, 533–543 (2002) 4. A. Yazidi, F. Betin, G. Notton, G.A. Capolino, Low cost two-axis solar tracker with high precision positioning, in International Symposium on Environment Identities and Mediterranean Area- ISEIMA (2006), pp. 211–216 5. A.D. Karlis, T.L. Kottas, Y.S. Boutalis, A novel maximum power point tracking method for PV systems using fuzzy cognitive networks (FCN). Electr. Power Syst. Res. 77, 315–327 (2007) 6. A. Mellit, S. Sag˘lam, S.A. Kalogirou, Artificial neural network-based model for estimating the produced power of a photovoltaic module. Renew Energy 60, 71–78 (2013) 7. S.A. Rizzo, G. Scelba, ANN based MPPT method for rapidly variable shading conditions. Appl. Energy 145, 124–132 (2015) 8. M. Premkumar, R. Sowmya., Certain study on MPPT algorithms to track the global MPP under partial shading on solar PV module/array. Int. J. Com. Dig. Sys. 8(4) (2019) 9. B. Kumar, S.K. Jha, T. Kumar, Review of maximum power point tracking techniques for photovoltaic arrays working under uniform/non-uniform insolation level. Int. J. Renew. Energy Technol. 9 (4) (2018) 10. J. Shi, W.J. Lee, Y. Liu, Y. Yang, W. Peng, Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Trans. Indus. Appl. 48(3), 1064–1069 (2012) 11. C. Sungur, Multi-axes sun-tracking system with PLC control for photovoltaic panels in Turkey. Renew. Energy 34, 1119–1125 (2009) 12. A. Al-Mohamad, Efficiency improvements of photo-voltaic panels using a Sun-tracking system. Appl. Energy 79, 345–354 (2004)

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13. M.H.M. Sidek, W.Z.W. Hasan, M.Z.A.Ab. Kadir, S. Shafie, M.A.M. Radzi, S.A. Ahmad, M.H. Marhaban., GPS based portable dual-axis solar tracking system using astronomical equation, in 2014 IEEE International Conference Power & Energy (PECON)

Chapter 75

CFD Analysis of Air Distribution for Suitable Position of Evaporator in Cold Chamber Sushil Kumar Maurya, Rahul kumar, Shri Krishna Mishra, Himanshu Vasnani, and Hitesh Kumar Abstract In Indian cold storage industry, the two most important problems are higher energy consumption and storage losses beyond the permissible limit. In India, storage losses in potato cold store account for 3–10% of the stored product. Major losses are in the form of rotting, cold injury weight losses, and sprouting nutritive value degradation. In the present work, airflow velocity is measured in a modeled cold storage room with the help of ANSYS software. The data which is collected from ANSYS FLUENT 14.5 is the temperature records, and air velocity in monitor point and the distribution of temperature and air velocity in all nodes in model area. The velocity at top layer that is in front of evaporator is between 2.5 to 3 m/s and decreases drastically with distance from source. Airflow improves in the cold storages with the help of duct with slotted arrangement. Keywords Predicted mean vote · Indoor air quality · Thermal comfort · Air velocity · Duct temperature

75.1 Introduction The parameter affecting the energy consumption in cold storage is improper heat and mass transfer, air distribution, relative humidity, and cooling coils or evaporator arrangements of the cold storage. Different studies have been done by researchers in the past focusing on optimizing the circulation of cool air inside the chamber. Posner et al. [1] measured and predicted the performance of air circulation in a room modeled having a partition wall and found the overall effect of obstructions in room. Three-dimensional computational flow dynamics simulation was used using laminar-e turbulence numerical models. Results obtained depicted the way in which S. K. Maurya (B) Modern Institute of Technology and Research Centre, Alwar, India R. kumar · S. K. Mishra · H. Vasnani Suresh Gyan Vihar University, Jaipur, India H. Kumar Technocrat Institute of Technology, Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_75

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partitions and obstructions influence air circulation and particulate transportation in vacant enclosed space. Measurement of airflow in room at each corners including furniture and partitions was made by using Doppler anemometry and particle image velocity techniques. It was found that RNG model gives the best prediction of airflow in room with partitions. Chowdhury et al. [2] analyzed the airflow from an air conditioner in a large multistory apartment by making use of design builder and software called Energy Plus used for assessing building performance. The analysis was performed in the city Rock Hampton in Australia. The thermal compatible model was synchronized with delicate tools for building optimization. It was inferred from results that if the ceilings are chilled in any room then the thermal human comfort level increases. Yongson et al. [3] investigated the small office room for their study of airflow in an air conditioned room. Fluent software was used by placing air conditioning blower at different location planes of the room. Noh et al. [4] studied the IAQ and TC in a classroom equipped with an air conditioner having four-way cassette. Carbon dioxides content and PMV value of the room were measured and a comparison was made with the numerically obtained results. It was found that as we go on increasing the discharge angle of this four-way AC then its thermal comfort value decreases drastically while internal air quality largely remains unaffected. Ventilation rate hardly had any effect on thermal effect unless it is below 800 m3 /h. Sempey et al. [5] found computational fluid dynamics approach of calculating thermal distribution of air in an air conditioning room time consuming and so developed a new rapid simulation technique. Velocity fields were considered fixed and temperature variations in air were neglected owing to mixed convection. Jin et al. [6] performed the experimental investigation of the shade made for the condenser part of an split air conditioner which is mostly kept outside of the room. It was found that the louver for the shade should be kept at an angle of 30 degree from the horizontal when winds blew from lateral side. Also, the best efficiency of condenser was obtained when it was place at a distance of 300 mm from the blower. The conventional heat ventilation and air conditioning systems pose a threat to ozone layer and also a bit costly. Considering there hazards of conventional HVACs, Kabir et al. [7] made the use of wind catcher in AC units and performed CFD analysis on it for validation of air circulation and comfort level. The standard k-e model was selected and computation of fluid dynamics analysis was done on ANSYS FLUENT as well as OPEN FOAM. Khatri et al. [8] observed the air circulation and thermal analysis of a room in which air conditioning is done with a split AC as well as a passive cooling arrangement of Earth air tube heat exchanger. CFD model of the setup was prepared and analyzed in ANSYS FLUENT using k-e model for the turbulent flow. It was concluded that while using such a hybrid arrangement for air conditioning both active as well as passive setup should be placed on the same side of the room for getting highest temperature decrease in minimum time. Moukalled et al. [9] performed CFD analysis for analyzing the performance of an air conditioner kept at the rooftop. The condenser part was taken as single phased flow while evaporator and condenser part were not coupled. Results showed that many air recirculation areas were created in the AC unit near to evaporator. By using six different designs of evaporator coils, improved

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performance of the AC unit was obtained. Aryal et al. [10] analyzed how the use of partitions in a room affected the thermal comfort ability and energy load of the air conditioning setups. A case study in library was done to validate it. CFD analysis for the library room with and without partition was performed and the results were compared. Results showed the only by changing the position of partition walls without relocating the AC unit gives undesired results. Pakash [11] performed the computation flow analysis of an air-conditioned room in which roof and side walls are insulated. The model of the setup was prepared in GAMBIT software and analysis was done in ANSYS FLUENT. The insulation used was wood wool. The PMV value was found to increase by 3% by the use of wood wool insulation. Zheng et al. [12] compared the air conditioner system with air passage to room from duct inlets on the side walls with duct air inlet from the bottom. It was found that for inlet air temperature of 25 °C, the thermal comfort obtained by bottom inlet air supply was found superior that side walls side inlet air supply. Nada et al. [13] performed the CFD analysis of the air conditioning unit with supply of air to a theater from under floor through a number of air diffusers. ANSYS FLUENT version 6.3 was used for analysis. These results were compared with the results of air conditioning units with air inlet to rooms only on the roof side. It was found that energy saved by using under floor air entry increases as we increase the height of the theater.

75.2 Experimental Setup The arrangement consists of evaporator and measuring devices inside the chamber area similar to cold storage plant. Flow velocity here is measured at different positions along the vertical plane. As shown, Fig. 75.1 and Table 75.1 show the physical properties of operational parameter. Ducts are used to create artificial draught in chamber so that air throw from evaporator fan (Fig. 75.2) can be reached end part of chamber. Here, induce draught is used in experiment. Duct is placed at the farthest end of chamber opposite to evaporator position. Duct is fitted with axial blower fan which creates low pressure area at inlet of duct so that induce draught is created for air which is thrown from evaporator horizontally. Two types of ducts are considered for experiment as uniform distribution is core objective of current work. First type duct has flat wall without any slot while other has equally spaced slots on room facing side. In the special type of design arrangement, the cooling coils are fixed in one of the walls of the cold storage at its top. The throw of air is straight and horizontal straight forwardly reaches the items which often are placed near cooling coils. The gap between the chamber wall and the duct wall is 0.16 m and placed opposite to evaporator. At the top of the duct, two fans are installed.

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Fig. 75.1 Line diagram of experimental setup

Table 75.1 Physical properties of operational parameter

Parameter

Values

Pressure at duct side when fans are running

0.3 bar

Duct fan capacity

1.14 m3 /min

Duct wall size

1 m × 0.8 m

Flow capacity of evaporator

2.28 m3 /min

Airflow velocity at evaporator

3 m/s

75.2.1 Specification Model number Rated voltage Frequency Current Rated input power Speed Size Maximum airflow Noise Body material Fan blade Bearing type

AC12038 220 VAC 50/60Hz 0.14/0.13 amp 22 watt 2600 rpm 120 * 120 * 38 mm 90/100 CFM 40/45 DB-A Aluminum die-cast with coating Plastic P.B.T Sleeve

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Fig. 75.2 Evaporator located on the top

Specific heat of onion

3.77 kJ/kg K

Size of carats. Length Width Depth

17 cm 12 cm 10 cm

Carats in chamber are 48 in array of 3 × 4.4 carats and are kept in all columns. Approximately 1.3 kg of onion is kept in each carat.

75.2.2 CFD Approaches Computational liquid elements (CFD) approach has been used in study and enhancement in distribution of airflow and proper heat transfer in the chamber. Navier–Stokes equation is used for governing the equations in order to describe mass transfer and energy momentum for any fluid flow case. These partial differential equations can be solved with the help of CFD. Various researchers had been successfully exhibited and that CFD can be used to model airflow in close room. It had been tested successfully for air circulation and heat transfer in cold store too. CFD study is proved to be best in predicting the effects of different design parameters in temperature fields and flow inside cold store. Navier– Stokes equation is used ignoring any approximation for computing turbulent flow.

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75.3 Result and Analysis In the present work, airflow velocity is measured in a modeled cold storage room with the help of ANSYS. The data which is collected from ANSYS FLUENT 14.5 is the temperature records, and air velocity in monitor point and the distribution of temperature and air velocity in all nodes in model area. • The velocity at top layer that is in front of evaporator is between 2.5 and 3 m/s. and decreases drastically with distance from source. • Velocity at rear portion is between 0.47 and 0.947 m/s at height 80 cm above the ground and 90 cm away from evaporator. • Velocity at midsection and around the buckets was observed in the range of 0.15– 0.6 m/s. • Return air velocity just below evaporator is 1.1 m/s • With duct fitting at rear section at 110 cm away from evaporator velocity at 86 cm above and ground now maintaining velocity in higher range as compared to last case, i.e., velocity is 0–9 m/s to 1–74 m/s from 80 to 100 cm from evaporator. • With use of duct, the return air velocity at bottom is between 0.9 and 1.11 m/s. • Return air velocity just below evaporator is much higher as compared to first case. • Near buckets velocity at midsection is almost same. • Velocity near buckets in this case is in the range of 0–9 m/s to 1–2 m/s at midparts of chamber. • Figure 75.3 shows the cold storage bucket velocity in central plane contour of velocity magnitude. • Figure 75.5 denotes improvement in velocity at 1.4 m/s to 1.6 away from evaporator due to duct.

Fig. 75.3 Cold storage bucket velocity in central plane contour of velocity magnitude

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Fig. 75.4 Cold storage bucket with duct velocity in central plane contour of velocity magnitude

• There is some movement in rear section represented by graph. • Figures 75.4 and 75.5 show with duct velocity at 0.4–0.65 m away from evaporator improve earlier velocity at top level is in range of 1.5–2.3 m/s while after using duct velocity is in range of 2.1–2.6 m/s. • Figures 75.6 and 75.7 show cold storage velocity without and with duct, respectively. • Cold storage velocity duct with slot graph is shown in Fig. 75.8. • Figures 75.9, 75.10 and 75.11 show that initially turbulence is just below the evaporator while with duct and slotted duct it is shifted toward mid part of chamber. When we observe Figs. 75.12, 75.13 and 75.14 it is clear that static temperature is slightly increases with ducts but better distribution is exhibit by slotted arrangement.

75.4 Conclusion In the present work results for the airflow and temperature distribution in a cold store, different conclusions have been pointed out: • From the analysis of the variation of the air velocity at the middle and bottom of the cold store, it was found that the variation is less than the top level of cold store because the evaporator was placed at the top level of cold store.

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Fig. 75.5 Cold storage bucket with duct with slot velocity in central plane contour of velocity magnitude

Fig. 75.6 Cold storage velocity without duct graph

75 CFD Analysis of Air Distribution for Suitable Position …

Fig. 75.7 Cold storage velocity with duct graph

Fig. 75.8 Cold storage velocity duct with slot graph

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Fig. 75.9 Cold storage velocity vector graph

Fig. 75.10 Cold storage velocity vector with duct graph

• Airflow improves in the cold storages with the help of duct with slotted arrangement. • At 86 cm above ground air circulation in chamber at this part from the evaporator is better if induced duct with slot is used. Figures 75.6, 75.7 and 75.8 show at 100 cm away from the evaporator velocity of air without duct is 1 m/s with duct around 1.5 m/s and around 1.9 m/s duct with slot condition it shows that proper air distribution happens if duct with slot is used.

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Fig. 75.11 Cold storage velocity vector duct with slot graph

Fig. 75.12 Cold storage temperature in central plane

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Fig. 75.13 Cold storage temperature with duct in central plane

Fig. 75.14 Cold storage temperature duct with slot in central plane

References 1. J.D. Posner, C.R. Buchanan, D. Dunn-Rankin, Measurement and prediction of indoor air flow in a model room. Energy Build. 35(5), 515–526 (2003). https://doi.org/10.1016/S0378-778 8(02)00163-9 2. A.A. Chowdhury, M.G. Rasul, M.M.K. Khan, Thermal-comfort analysis and simulation for various low-energy cooling-technologies applied to an office building in a subtropical climate. Appl. Energy 85(6), 449–462 (2008). https://doi.org/10.1016/j.apenergy.2007.10.001 3. O. Yongson, I.A. Badruddin, Z.A. Zainal, P.A. Aswatha Narayana, Airflow analysis in an air conditioning room. Build. Environ. 42(3), 1531–1537 (2007). https://doi.org/https://doi.org/

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10.1016/j.buildenv.2006.01.002 4. K.C. Noh, J.S. Jang, M. Do Oh, Thermal comfort and indoor air quality in the lecture room with 4-way cassette air-conditioner and mixing ventilation system. Build. Environ. 42(2), 689–698 (2007). https://doi.org/https://doi.org/10.1016/j.buildenv.2005.10.033 5. A. Sempey, C. Inard, C. Ghiaus, C. Allery, Fast simulation of temperature distribution in air conditioned rooms by using proper orthogonal decomposition. Build. Environ. 44(2), 280–289 (2009). https://doi.org/10.1016/j.buildenv.2008.03.004 6. W. Jin, Y. Zheng, Y. Zhang, Y. Jiang, Experimental study of factors affecting the performance of a semi-enclosed outdoor air-conditioning unit. Procedia Eng. 121, 1713–1720 (2015). https:// doi.org/10.1016/j.proeng.2015.09.138 7. I.F.S. Ahmed Kabir, S. Kanagalingam, F. Safiyullah, Performance evaluation of air flow and thermal comfort in the room with wind-catcher using different CFD techniques under neutral atmospheric boundary layer. Energy Procedia 143, 199–203 (2017). https://doi.org/https://doi. org/10.1016/j.egypro.2017.12.671 8. R. Khatri, A.P. Singh, V.R. Khare, Identification of ideal air temperature distribution using different location for air conditioner in a room integrated with EATHE—A CFD based approach. Energy Procedia 109, 11–17 (2017). https://doi.org/https://doi.org/10.1016/j.egypro. 2017.03.036 9. F. Moukalled, S. Verma, M. Darwish, The use of CFD for predicting and optimizing the performance of air conditioning equipment. Int. J. Heat Mass Transf. 54(1–3), 549–563 (2011). https://doi.org/10.1016/j.ijheatmasstransfer.2010.09.015 10. P. Aryal, T. Leephakpreeda, CFD Analysis on Thermal Comfort and Energy Consumption Effected by Partitions in Air-Conditioned Building, vol. 79 (Elsevier B.V., 2015) 11. D. Prakash, Transient analysis and improvement of indoor thermal comfort for an airconditioned room with thermal insulations. Ain Shams Eng. J. 6(3), 947–956 (2015). https:// doi.org/10.1016/j.asej.2015.01.005 12. C. Zheng et al., Comparison of air-conditioning systems with bottom-supply and side-supply modes in a typical office room. Appl. Energy 227(January), 304–311 (2018). https://doi.org/ 10.1016/j.apenergy.2017.07.078 13. S.A. Nada, H.M. El-Batsh, H.F. Elattar, N.M. Ali, CFD investigation of airflow pattern, temperature distribution and thermal comfort of UFAD system for theater buildings applications. J. Build. Eng. 6, 274–300 (2016). https://doi.org/10.1016/j.jobe.2016.04.008

Chapter 76

Role of Supercapacitor for Increasing Driving Range of Electric Vehicles Under Indian Climatic Conditions Vima Mali

and Brijesh Tripathi

Abstract In this paper, a hybrid combination of lithium-ion (Li-ion) battery and a supercapacitor (SC) has been studied for different realistic temperature conditions in India to estimate the driving range of lightweight electric vehicles (EVs) using standard worldwide harmonized light vehicles test cycle (WLTC) driving profile. The total power required at the wheels of the EV is estimated under ambient temperature conditions by a theoretical approach using the MATLAB/Simulink model. Addressing the demand peaks during the use of EV is an important problem towards thermal stability of electrical energy storage system (EESS). To address this problem, an additional electrical energy storage component along with Li-ion battery, namely SC has been explored. Simulation results indicate that there is no significant effect of temperature on the output of the SC as compared to Li-ion battery. A decrease of nearly 20% in the driving range has been registered due to the decrease in temperature from 45 to −15 °C within a driving time of 3600 s. The addition of an SC with Li-ion battery improves the driving range of EV significantly and helps in the additional storage of energy during regenerative braking. Keywords Lithium-ion battery · Hybrid electrical energy storage system · Supercapacitor · Electric vehicle

V. Mali (B) Department of Electrical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382426, India e-mail: [email protected] Department of Solar Energy, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382426, India B. Tripathi Department of Physics, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382426, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_76

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76.1 Introduction A recent report states that one in eight deaths in India is due to air pollution [1]. Automobile industries are the major contributors to the air pollution in the transportation sector. An alternative emission-free transportation system is the need of the hour to address the pollution problem and for the sustained economic growth of the country. The government of India has already started working on electricity-based transport infrastructure; gasoline and diesel vehicles may be debarred by 2030 [2]. The performance of energy storage components plays an important role in the electric vehicles (EVs) [3]. Among various energy storage components, in EVs, rechargeable lithiumion (Li-ion) batteries are an important and most popular choice as an energy storage component due to their high energy density and lightweight [4]. But one of the key issues with Li-ion battery is that the irregular consumption of power at wheels in EVs during driving leads to sudden discharging of Li-ion battery which affects the electrochemical reaction in the battery [5]. Another associated issue with the Li-ion battery is that it is unable to cope-up with the peak usage. To overcome this problem, an additional storage device, i.e., the supercapacitor (SC) is to be adopted in a hybrid energy electrical storage system (EESS) along with Li-ion battery [6]. SCs have been studied recently in EVs for their applications in the improvement of power delivery performance [7]. High energy density characteristics of Li-ion battery and high power density of SC have been united to develop hybrid electrical energy storage systems to increase the operating efficiency and lifespan [8]. Many reports stating that the Li-ion battery performance degrades under sub-zero temperatures [9] and affects the power delivery from the battery pack [10]. But further study is needed to know the behavior of SC in hybrid EESS to find the on-road performance of EV with respect to ambient temperature in India. For the first time, in this paper, realistic temperature data has been collected for the country. Whole country has been divided into seven temperature zones, and the electric vehicle driving range has been calculated using a theoretical model of hybrid EESS in MATLAB/Simulink for each temperature zone. This is an indicative study, which has been done for a lightweight EV because the lightweight electric vehicles have the potential to reduce CO2 emission reduction by 87.3% with the 2050 baseline [11]. This study is useful for all the EVs consisting of a similar hybrid EESS.

76.2 Theoretical Description of Supercapacitor An electric circuit-based discrete-time-dependent model of SC using an inbuilt MATLAB block with features listed in Table 76.1 has been considered for this study. Due to the high power density characteristics of SC, it is used to handle the peak current in the system [12]. The SC can supply 100–1000 times higher power compared to the batteries but they cannot store the high energy as of the Li-ion batteries [13]. The use of SC can lower the size of the battery. Further, SC

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Table 76.1 Simulation parameters of the SC used in MATLAB/Simulink model Sl. No.

Parameter

Value

1

Rated capacitor (F)

500

2

Equivalent DC series resistor ()

2.1 × 10–3

3

Rated voltage (V )

16

4

Number of series capacitor

15

5

Number of parallel capacitors

1

6

Initial voltage (V )

16

7

Number of layers

1

8

Molecular radius (m)

1 × 10–9 (Fm −1 )

6.02 × 10–10

9

The permittivity of electrolyte material

10

Charge current (A)

10

11

Current prior open-circuit (A)

10

12

Open-circuit voltage, V OC ( V )

48

13

Charging current (A)

10.2

has millions of charge and discharge cycles, zero maintenance, and does not use toxic materials [14]. Due to the fast charge and discharge properties of the SC, there are some applications, where the supercapacitor has completely replaced the Li-ion batteries. The SC block used in MATLAB/Simulink model is based on the Gouy-Chapman-Stern model [15]. The output voltage across the SC is obtained by following Eq. (76.1): VSC =

  QT NS Q T d 2Ne Ns RT sinh−1 − RSC • i SC (76.1) + √ Np Ne εε0 Ai F Np Ne2 Ai 8RTεε0 c

 with, SC charge, Q T = i SC dt. To represent the self-discharge mechanism, a modified equation for SC charge has been used (when iSC = 0) as given by Q T =  i Self_dis dt.

76.3 Model of the Hybrid EESS For a hybrid EESS, various control strategies have been studied by researchers [16]. A rule-based strategy has been chosen because of its greater potential in increasing efficiency, mileage, and reducing CO2 emission. A MATLAB/Simulink model has been used in which the SC (16 V, 500 F) and Li-ion battery (12.8 V, 40 Ah) are connected in parallel via bi-directional buck/boost converter and boost converter to maintain a voltage of 42 V in the DC link (V DC ) (see Fig. 76.1). Here, in hybrid EESS, the power allocation is done on the basis of driving cycle, quality of road (pits), ambient temperature, air density, etc., according to control strategy. During

V. Mali and B. Tripathi

Li-ion Battery

DC

Link

Boost Converter DC/AC converter

EM

Transmission

990

Buck/Boost Converter

Supercapacitor Fig. 76.1 A hybrid model of EESS to drive EV

acceleration, the power required is greater than the battery capacity so that SC is used to supply the required extra power demand. So, a reduced strain on the battery increases the lifespan of the battery and reduces replacement cost which is another barrier in adopting electric vehicle. The rule-based strategy is described as given below: • When the energy required by the EV is below the battery capacity, then the power withdrawn by the EV is solely from the battery which is connected through the boost converter. • During deceleration/breaking conditions, the SC should store energy generated by the regenerative braking so the initial state-of-charge(SOC) of SC is maintained at 85%. This increases energy availability in the hybrid EESS. • During the high power demand by the EV the SC and Li-ion battery both supply the required power to the electric vehicle. Using hybrid model of EESS to drive EV (Figure 76.1) the range of EV has been find out.

76.4 Calculation of Driving Range The calculation of driving range for an EV is done based on the driving profile, and the energy stored in EESS. Typical parameters of the EV used in MATLAB/Simulink are listed in Table 76.2. The required power on the wheels to drive the EV can be calculated by using Eq. (76.2) [22, 23], where following components are included: base power (Pbase ) is

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Table 76.2 Typical parameters used for EV simulation Sl. no

Parameters

Values

References

1

Effective mass of the used vehicle in kg (M)

50 kg

[17]

2

Co-efficient of drag (Cd )

0.9

[17, 18]

3

Frontal area of the vehicle (Af )

0.5 m2

[17]

4

Acceleration due to gravity (g)

9.8 m/s2

–

5

Angle of inclination (θ)

0

[19]

6

Efficiency of the electronics (ηconverter )

0.98

[20]

7

Motor efficiency (ηmotor )

0.95

[20]

8

Mechanical drive train efficiency (ηdrive−train )

0.9

[20]

9

Effective battery energy (Whr )

512

–

10

Baseload (w) (i) Turn light indicator (ii) Side indicator lamp (iii) Headlight (iv) Taillight

1.7 10 35 12

[17] [17] [17] [21]

the required initial power, the required power to overcome rolling resistance (Proll ), the required power to overcome the aerodynamic drag (Pdrag ), the required power to align the vehicle against gravity (Pg ), and the required power at the time of acceleration or braking (Pacc ). PW = Pbase + Proll + Pdrag + Pg + Pacc

(76.2)

Energy consumption at wheels (E w ) can be calculated over the given driving cycle by using Eq. (76.3):  Ew =

t

PW dt

(76.3)

0

where t represents the total driving time. The driving range (R) can be evaluated by Eq. (76.4) using the distance covered by the EV, at-hand stored energy in the hybrid EESS (E EESS ), and the required energy on wheels. R=

E EESS × D EW

(76.4)

The at-hand stored energy in the EESS (E EESS ) is given by Eq. (76.5):   E EESS = η × SOCb × E int,b + SOCsc × E int,sc where

(76.5)

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η = ηconverter × ηmotor × ηdrive−train

(76.6)

where η represents efficiency of entire traction system which is taken as 83.79%, SOC represents change in the (SOC) over driving cycle, suffix int means initial, and suffix b means battery. Using this theory, the driving range has been calculated for various temperature conditions by collecting the realistic temperature data of different regions of India as described in the next section.

76.5 Data Collection and Identification of Temperature Zones of India The minimum and maximum temperature of the capital cities of various states of India have been collected from Indian Meteorological Department website and listed in Table 76.3. Based on the temperature values, the collected data is divided into seven sets marking them as zones and listed in Table 76.4. All the temperature zones are shown on the map of India in Fig. 76.2.

76.6 Results and Discussion 76.6.1 Driving Pattern of EV A driving pattern represents the vehicle running speed with respect to time. The driving pattern of the EV subjected to the 240 s of Worldwide harmonized Light vehicles Test Cycle (WLTC) class 3, version 5, vehicle speed test cycle as shown in Fig. 76.3. The WLTC is specially designed for light-duty vehicles for the calculation of fuel consumption and the vehicle emission. The WLTC has been evolved by United Nations Economic Commission for Europe (UNECE) [24]. The characteristics parameters of the profile are listed in Table 76.5.

76.6.2 Power Required Based on Temperature The power required on wheels has been calculated using Eq. (76.2) and the data listed in Tables 76.1, 76.2, 76.3, 76.4 and 76.5 as shown in Fig. 76.4. The required power on wheels is directly proportional to the air density which changes with respect to ambient temperature. The required power on wheels for a WLTC profile is shown in

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Table 76.3 Maximum (Max.) and minimum (Min.) temperature data collected for various states ( Source Indian Meteorological Department Website) Sl. No.

States

Capital

Max. (°C)

Min. (°C)

1 2

Andhra Pradesh

Hyderabad

45.5

6.1

Arunachal Pradesh

Itanagar

39.5

5.2

3

Assam

Dispur

32

11

4

Bihar

Patna

46.6

1.1

5

Goa

Panaji

39.8

13.3

6

Gujarat

Gandhinagar

50

2.2

7

Haryana

Chandigarh

45.3

0

8

Himachal Pradesh

Shimla

32.4

−10.6

9

Jammu and Kashmir

Srinagar

38.3

−20

10

Karnataka

Bengalooru

38.9

7.8

11

Kerala

Thiruvanathapuram

38

16.4

12

Madhya Pradesh

Bhopal

46

0.6

13

Maharashtra

Mumbai

42.2

7.4

14

Manipur

Imphal

35.7

−2.7

15

Meghalaya

Shillong

30.2

−3.3

16

Mizoram

Aizawl

26.3

11.4

17

Nagaland

Kohima

33.9

1.0

18

Orissa

Bhubaneswar

46.5

8.6

19

Punjab

Chandigarh

42.7

0

20

Rajasthan

Jaipur

48.5

−2.2

21

Sikkim

Gangtok

29.9

−2.2

22

Tamil Nadu

Chennai

45

13.9

23

Tripura

Agartala

42.2

2.0

24

Uttar Pradesh

Lucknow

47.7

−1.0

25

West Bengal

Kolkata

43.9

6.7

26

Chhattisgarh

Raipur

42.1

13.1

27

Uttarakhand

Dehradun

44.6

−1.1

28

Jharkhand

Ranchi

31.5

9.8

29

Telangana

Hyderabad

45.5

6.1

Fig. 76.4. The required power profile on wheels is affected by the ambient temperature. As the temperature increases from −15° to 45 °C, the required power for an EV decreases due to low drag resistance present in the air. The decrease in temperature increases the drag resistance of the air, due to which the required power increases.

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Table 76.4 Zone division based on temperature in summer and winter Zones

Approximate temperature (±5◦ C)

States

Zones on the basis of maximum temperature Zone 1

45

Andhra Pradesh, Bihar, Gujarat, Haryana, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu, Tripura, Uttar Pradesh, West Bengal, Chhattisgarh, Uttarakhand, Telangana

Zone 2

35

Arunachal Pradesh, Himachal Pradesh, Assam, Jammu and Kashmir, Kerala, Manipur, Meghalaya, Nagaland, Jharkhand, Karnataka, Goa

Zone 3

25

Mizoram, Sikkim

Zones on the basis of minimum temperature Zone 4

15

Assam, Goa, Kerala, Mizoram, Tamilnadu, Chhattisgarh

Zone 5

5

Andhra Pradesh, Arunachal Pradesh, Bihar, Gujarat, Haryana, Karnataka, Madhya Pradesh, Maharashtra, Nagaland, Orissa, Punjab, Tripura, West Bengal, Jharkhand, Telangana

Zone 6

−5

Uttar Pradesh, Manipur, Uttarakhand, Meghalaya, Rajasthan, Sikkim

Zone 7

−15

Himachal Pradesh, Jammu and Kashmir

76.6.3 Effect of Ambient Temperature on the SC Power Figure 76.5 shows that the power drawn from the SC is high at the beginning (insert of Fig. 76.5), and it means that the high power requirement is supported by the SC. This reduces the strain on the battery which can help to increase the battery life span. The SC in a hybrid combination helps in supplying the power and saves the battery life from high charge and discharge rate. Another study related to the power required during start-up and its effect on the duty cycle of a battery has shown that the battery replacement cost and reliability can be addressed by giving pulsed power initially to the EVs [26].

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Fig. 76.2 Marking of seven temperature zones in India map using seven different colors (three for summer and four for winter)

Fig. 76.3 Worldwide harmonized Light vehicles Test Cycle (WLTC) class 3, version 5

Table 76.5 (Charateristics parameters of the WLTC profile based on chosen EV parameters)

Duration (s)

Distance travelled, D (km)

Average speed (km/h)

Maximum speed (km/h)

240

1.953

29.3

56.7

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V. Mali and B. Tripathi Temperature 45 35 25 15 5 -5 -15

ʌ (kg/m3) 1.074 1.1455 1.1839 1.2250 1.2690 1.3163 1.3673

Fig. 76.4 Power required on wheels (Insert: Table lists the air density data depending on the temperature [25])

Fig. 76.5 SC power output in the hybrid EESS (Insert: plot showing a high current withdrawal from SC)

76.6.4 Effect of Ambient Temperature on the SOC of SC Although the SC power remains unaltered and does not change with respect to change in the temperature but the SOC of the SC gets affected as shown in Fig. 76.6. In Fig. 76.6, the change in the SOC clearly shows that energy is utilized during operation of EV (decreased SOC) and energy is stored in the SC during regenerative braking (increased SOC). Overall, the SOC decreases with an increase in temperature. So the SC behaves better at low temperature. Whereas available literature reports that the Li-ion battery performance decreases significantly at low temperatures [27].

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Fig. 76.6 SOC of SC in the hybrid EESS during use of EV

76.6.5 Estimation of Range with Respect to Temperature Figure 76.7 shows that below 0 °C the range of EVs decreases fast due to the drop in energy and power capacity of the Li-ion battery in combination with the SC. The addition of SC with Li-ion battery improves the range of EV from 1.23 km at − 15 °C to 1.55 km at 45 °C. In the Li-ion battery, due to the sub-zero temperature, a temperature gradient between the outside surface of the cell wall and the surrounding air damages the anode of the lithium plating surface of the Li-ion battery, which further results in the failure inside the cell and even leads to the internal short circuit [27]. The performance deterioration at low temperatures in Li-ion batteries will lead to the depletion in the driving range. The range improvement is an important factor

Initial SOC of Final SOC of Battery, % Battery , % 100% 17.68% 100% 21.136% 100% 24.43% 100% 26.99% 100% 29.21% 100% 31.17% 100% 34.51%

DC

Li-ion Battery

Link

Boost Converter DC/AC converter

EM

Transmission

Temperature, °C -15°C -5°C 5°C 15°C 25°C 35°C 45°C

Buck/Boost Converter

Supercapacitor

Fig. 76.7 Driving range of EVs using hybrid EESS with respect to temperature (Insert: Table lists SOC of Li-ion battery) after 3600 s of driving

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in adapting EV from the consumer point of view. It is concluded that the driving range strongly depends on the driving conditions and the ambient temperature. The results show that an EV with a given EESS would have a higher driving range in summer in zone 1.

76.7 Conclusions In this paper, a hybrid EESS has been studied for various temperature zones of India to estimate the driving range of EVs. The total power demand at the wheels of the EVs is estimated by theoretical approach, and then, the driving range of EV has been calculated by finding SOC of the hybrid EESS using a MATLAB/Simulink model. It is concluded that the driving range gets affected significantly due to the thermal effects on the working of the Li-ion battery and SC. A decrease of nearly 20% in the driving range has been registered due to the decrease in temperature from 45 °C to −15 °C within a driving time of 3600 s. The addition of SC with Li-ion battery improves the range of EV, which is calculated as an additional 1.23 km at −15 °C and 1.55 km at 45 °C. With SC, an increased driving range has been observed for all the zones due to additional energy stored in the SC. Overall, it is concluded that the supercapacitor helps in improving performance of hybrid EESS for various climatic conditions of India.

References 1. K. Balakrishnan et al., The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: the global burden of disease study 2017. Lancet Planet. Heal. 3(1), e26–e39 (2019). https://doi.org/10.1016/S2542-5196(18)30261-4 2. T.S. Hora, A.P. Singh, A.K. Agarwal, Future Mobility Solutions of Indian Automotive Industry: BS-VI, Hybrid, and Electric Vehicles (Springer, Singapore, 2018), pp. 309–345 3. F. Un-Noor, S. Padmanaban, L. Mihet-Popa, M. N. Mollah, E. Hossain, A comprehensive study of key electric vehicle (EV) components, technologies, challenges, impacts, and future direction of development. Energies 10(8) (2017) https://doi.org/https://doi.org/10.3390/en1008 1217 4. A. Sakti, J.J. Michalek, E.R.H. Fuchs, J.F. Whitacre, A techno-economic analysis and optimization of Li-ion batteries for  light-duty passenger vehicle electrification (2015). https:// doi.org/https://doi.org/10.1016/j.jpowsour.2014.09.078 5. N.E. Galushkin, N.N. Yazvinskaya, D.N. Galushkin, Mechanism of thermal runaway in lithiumion cells. J. Electrochem. Soc. 165(7), A1303–A1308 (2018). https://doi.org/10.1149/2.061180 7jes 6. H. Fathabadi, Novel fuel cell/battery/supercapacitor hybrid power source for fuel cell hybrid electric vehicles. Energy 143, 467–477 (2018). https://doi.org/10.1016/j.energy.2017.10.107 7. Z. Lin et al., Materials for supercapacitors: when Li-ion battery power is not enough. Mater. Today 21(4). Elsevier B.V., pp. 419–436, May 01, 2018. https://doi.org/https://doi.org/10.1016/ j.mattod.2018.01.035

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8. J. Fang, Y. Tang, H. Li, X. Li, A Battery/ultracapacitor hybrid energy storage system for implementing the power management of virtual synchronous generators. IEEE Trans. Power Electron. 33(4), 2820–2824 (2018). https://doi.org/10.1109/TPEL.2017.2759256 9. L. Kouchachvili, W. Yaïci, E. Entchev, Hybrid battery/supercapacitor energy storage system for the electric vehicles. J. Power Sourc. 374 (2018), pp. 237–248. https://doi.org/https://doi. org/10.1016/j.jpowsour.2017.11.040 10. L.H. Saw, K. Somasundaram, Y. Ye, A.A.O. Tay, Electro-thermal analysis of Lithium Iron phosphate battery for electric vehicles. J. Power Sourc. 249, 231–238 (2014). https://doi.org/ 10.1016/j.jpowsour.2013.10.052 11. M. Browne, J. Allen, J. Leonardi, Evaluating the use of an urban consolidation centre and electric vehicles in central London. IATSS Res. 35(1), 1–6 (2011). https://doi.org/10.1016/j. iatssr.2011.06.002 12. W. Raza et al., Recent advancements in supercapacitor technology. Nano Energy 52 (2018), 441–473. https://doi.org/https://doi.org/10.1016/j.nanoen.2018.08.013 13. Y. Pan, K. Xu, C. Wu, Recent progress in supercapacitors based on the advanced carbon electrodes. Nanotechnol. Rev. 8(1) (2019), pp. 299–314. https://doi.org/https://doi.org/10.1515/ ntrev-2019-0029 ˇ 14. J. Libich, J. Máca, J. Vondrák, O. Cech, M. Sedlaˇríková, Supercapacitors: properties and applications. J. Energy Storage 17, 224–227 (2018). https://doi.org/10.1016/j.est.2018.03.012 15. K.B. Oldham, A Gouy-Chapman-Stern model of the double layer at a (metal)/(ionic liquid) interface. J. Electroanal. Chem. 613(2), 131–138 (2008). https://doi.org/10.1016/j.jelechem. 2007.10.017 16. F. Zhang, L. Wang, S. Coskun, H. Pang, Y. Cui, J. Xi, Energy management strategies for hybrid electric vehicles: review, classification, comparison, and outlook. Energies 13(13), 3352 (2020). https://doi.org/10.3390/en13133352 17. N. Xu, J. Riley, Nonlinear analysis of a classical system: the double-layer capacitor. Electrochem. Commun. 13(10), 1077–1081 (2011). https://doi.org/10.1016/j.elecom.2011. 07.003 18. S. Matey, D.R. Prajapati, K. Shinde, A. Mhaske, A. Prabhu, Design and fabrication of electric bike. Int. J. Mech. Eng. Technol. 8(3), 245–253 (2017) 19. “Rolling Friction by Ron Kurtus—Physics Lessons: School for Champions.” https://www.sch ool-for-champions.com/science/friction_rolling.htm#.XzFJ_H5S_IV. Accessed 10 Aug. 2020 20. T. Porselv, J. Ashok, A. Kumar, Selection of Power Rating of an Electric Motor for Electric Vehicles (2017) 21. Tail Light for Bike, Halogen Tail Light, in Najafgarh Road Industrial Area, New Delhi , Karlite Auto Industries | ID: 8928499588. https://www.indiamart.com/ proddetail/tail-light-for-bike-8928499588.html. Accessed 10 Aug. 2020 22. D. Chandran, M. Joshi, Electric vehicles and driving range extension—a literature review. Adv. Automob. Eng. 05(02) (2016). https://doi.org/https://doi.org/10.4172/2167-7670.1000154 23. S. Chopra, P. Bauer, Driving range extension of EV with on-road contactless power transfer-A case study. IEEE Trans. Ind. Electron. 60(1), 329–338 (2013). https://doi.org/10.1109/TIE. 2011.2182015 24. “Global Technical Regulations (GTRs) - Transport - UNECE. https://www.unece.org/trans/ main/wp29/wp29wgs/wp29gen/wp29glob_registry.html. Accessed 10 Aug. 2020 25. Air Density Calculations Impact of Altitude Temperature Humidity and Pressure.xlsx. https:// www.wind101.net/air-density/air-density-calculator.htm. Accessed 10 Aug. 2020 26. Thermal Effects in Supercapacitors|Guoping Xiong | Springer. https://www.springer.com/gp/ book/9783319202419. Accessed 10 Aug. 2020 27. P. Clarke, T. Muneer, K. Cullinane, Cutting vehicle emissions with regenerative braking. Transp. Res. Part D Transp. Environ. 15(3), 160–167 (2010). https://doi.org/10.1016/j.trd.2009.11.002

Chapter 77

Noise Vulnerability Assessment for Kota City Kuldeep, Sohil Sisodiya, and Anil K. Mathur

Abstract Noise pollution due to vehicular traffic is rapidly growing environmental concern of metropolitan cities all across the world. It became a primary source of noise emissions in urban cities because two-thirds of the total noise pollution in the big cities is associated with traffic noise. It is a derivative of industrialization and urbanization. As per WHO, noise is globally recognized as a major threat for human beings due to several physiological and psychological impacts on human health such as high blood pressure, stress-related disease, sleep disturbances, loss of hearing ability, and the harm of productivity. Severe impacts including loss of memory, frustration, and harmful attacks cannot be ignored. In this research work, the evolution of traffic noise in Kota city has been studied. Twenty-eight sampling locations are selected to cover the whole city for the estimation of traffic noise levels. Noise data is collected, analyzed, and further used for noise mapping. Noise maps have been generated with the help of geospatial information system (GIS) to complete the noise vulnerability assessment for Kota city. Affected areas can be identified through GIS where humans are highly susceptible to the adverse effect of traffic noise pollution. Cardiovascular impacts related to traffic noise levels are connected with noise exposer limit and time to explain the noise vulnerability for Kota city at 78 and 80 dB. This study reveals the importance of traffic noise reduction policies and strategies for public health. Keywords Noise vulnerability · Traffic noise · GIS · Equivalent continuous noise level (Leq)

77.1 Introduction Urbanization and industrialization are increasing in all the developing countries. At the same time, developing countries are also undergoing physical expansion [1]. It provides greater opportunities for access to employment, housing and safety,

Kuldeep · S. Sisodiya (B) · A. K. Mathur Department of Civil Engineering, UD, RTU, Kota, Rajasthan 324010, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_77

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better education and reduces the expense and time of transportation and communication. The rate of urbanization, industrialization, and physical expansion is very high in developed countries in comparison to developed countries. Now, it became the primary cause of various kind of pollution such as noise pollution, water pollution, and air pollution. Noise pollution leads to adverse health impacts on human beings and continuously emerging concern for urban dwellers and government officials due to the increased number of vehicles [2, 3]. Undesirable/unwanted level of sound is termed as noise. It disrupts normal activities (i.e., work, sleep, and conversation) of humans. When levels of sound exceed the prescribed limits/standards given by regulatory agencies (such as CPCB, and MOEFCC in India), it is termed as noise pollution. It is the most underrated environmental problem among all types of pollution because it cannot be seen, smelled, or tasted. World Health Organization (Report 2001) specified that “Noise must be recognized as a major threat to human well-being” [4, 5]. For Indian context, noise pollution generated by the road traffic is unique when compared to other pollutants, because unlike air pollution and water pollution, the noise pollution leaves no sign of evidence regarding its source generation. Although it has its side effects like any other pollutants, noise is often given the lowest priority for control and management. Although road transport is a very important part of cities, it is also a major source of noise pollution. MCI stated that there is a direct and deep connection between environmental noise and human health. Noise pollution has a detrimental effect on the lives of many people [6]. The increasing number of vehicles in cities causes uncontrolled noise pollution, causing many health effects. The health effects of noise pollution cause short-term and long-term psychological and physical disorders [7]. The role of noise in polluting the environment and its effect on human health is being gradually explored. Noise influences hearing ability disrupts sleep, disturbed and disrupts cognitive performance. Besides, epidemiological studies indicate that the incidence of hypertension, myocardial infarction, and stroke increases due to noise pollution [8, 9]. Observation and experimental studies have shown that noise disrupts the structure of nighttime sleep and vegetative stimulation. These problems result in increased levels of stress hormones and oxidative stress, which can cause endothelial dysfunction. The heart consequences resulting from noise pollution are mentioned here and emphasize the dependability of noise mitigation policies for public health [10, 11]. Traffic noise causes several physiological and psychological damages to human health, like annoyance and aggression, hypertension, high stress level, hearing loss, sleep disturbance, and interference with speech. Bus and heavy truck traffic have been found to contribute most to noise-induced annoyance. Traffic noise also causes ecological impacts like change in animal behavior, their spatial distribution, antipredator behavior, reproductive success, foraging behavior, population density, and community structure. Traffic noise has also been found to cause depreciation of property value [12]. The increasing number of vehicles has created a serious threat of noise pollution in urban cities. Estimating traffic noise pollution is very difficult, and it changes with

77 Noise Vulnerability Assessment for Kota City Table 77.1 Sampling locations for noise measurements in Kota city

1003

Location names 1. MBS Hospital, Nayapura 2. Anantpura, Kota Bypass 3. Anantpura, three-way 4. Chambal Garden 5. Naya Nohra, Kota Bypass 6. Chambal industrial area 7. Dhakad Khedi, 8. Naya Gaon, Kota Bypass 9. RICCO institutional area, Ranpur

10. Gobariya Bawdi Circle 11. Sabjimandi 12. Gumanpura 13. Borkheda, three-way 14. RTU, Kota 15. CAD Circle 16. Nayapura Circle 17. Raipura Circle 18. RICCO industrial area, Ranpur

19. Antaghar Circle 20. I.L. Circle 21. Aerodrome Circle 22. Kotri Circle 23. Dadwara 24. Keshavpura 25. Talwandi 26. Jawahar Nagar 27. KSTPS 28. IPIA

vehicle speed, type of road geometry, and physical conditions. It is more difficult to estimate traffic noise in Indian cities. Cities in India have different traffic conditions such as mixed traffic type, congestion, road conditions, and lack of traffic sense. The study related to the estimation of traffic noise levels in Kota city and also identified noise vulnerability on the sampling locations which are under the pollution threat. GIS can be used efficiently in the management of environmental pollution such as gathering, weighing, analyzing, and presenting spatial and feature information to collect noise [13].

77.2 Study Area and Research Methodology Kota is a southwest district of Rajasthan (India). It is situated on the bank of Chambal River and its geographical area is 512 km2 (Forest survey of India) and seventh largest by population 1,001,694 as per census of India 2011 [14]. It is 47th most populated city of India. The vehicle population in Kota as of 2017 is 842,886 [15]. Twenty-eight sampling locations were selected for traffic noise assessment in Kota city. These sampling locations are mention in Table 77.1 and marked in Fig. 77.1. Overall research methodology used in research work is shown in Fig. 77.2.

77.3 Observations The observations were taken during Feb 2019–May 2019 for 96 days. All readings were taken on an hourly basis from 6 am to 10 pm and at a height of 5 feet above the

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Fig. 77.1 Study area and sampling locations

road level and 3-m from the curb. The smallest distance of 20 feet must be kept on the reflective surface from the SLM. The observed equivalent continuous noise level (Leq) for daytime (6:00 am–10:00 pm) at every place is shown in Table 77.2.

77.4 Results and Discussion It is very clear from the observation Table 77.2 that the measured Leq (on an hourly basis) at each location lies in the range of 65.75 dB(A) to 83.06 dB(A). For this study, noise vulnerability assessment is done at two sound levels: First at 78 dB(A) and second at 80 dB(A). The reason for selecting these two noise levels for the study is vehicular traffic. The standards for vehicles are shown in Table 77.3 given by MOEF. Sound level of 78 dB(A) is commonly generated by the two wheelers and 80 dB(A) is generated by the four wheelers. The following noise limits for vehicles shall be applicable from January 1, 2003.

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Fig. 77.2 Flowchart of research methodology

77.4.1 Noise Vulnerability Assessment for Kota City at 78 dB(A) MBS Hospital Nayapura, Naya Gaon Kota Bypass, Chambal Garden, Rajasthan Technical University, RICCO Institutional Area, Ranpur, and Naya Nohra Kota Bypass are the areas which are less vulnerable to noise level of 78 dB(A) because the population of these area is exposed to this level of noise for 0–25% time in a day as shown in Fig. 77.3. Hence, human beings living in nearby areas of these sampling locations are very less affected by the adverse impacts of high noise levels. Dhakad Khedi is the place where noise level of 78 dB(A) mostly lies between 25 and 50% time in a day and the residents of these areas are more susceptible to the adverse effects on noise pollution.

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Table 77.2 Observed data for each sampling location with population Station name

Longitude

Latitude

Leq [dB (A)]

Population (Thousands)

Kotri three-way

75.85

25.17

80.28

05,372

Kota super thermal power station

75.81

25.17

79.70

05,896

Indraprastha industrial area 75.86

25.12

80.25

05,253

Antaghar circle

75.85

25.19

80.36

06,008

Aerodrome circle

75.85

25.16

80.75

06,613

I.L. circle

75.85

25.14

80.51

21,105

RIICO institutional area, Ranpur

75.85

25.06

65.75

05,948

Talwandi circle

75.84

25.14

79.55

07,529

Subjimandi

75.84

25.18

80.58

05,053

Dadwara

75.87

25.22

78.85

07,553

Gumanpura three-Way

75.84

25.17

81.11

05,934

MBS Hospital, Nayapura

75.85

25.19

77.68

05,399

Keshavpura circle

75.83

25.14

79.48

08,097

DCM circle

75.88

25.14

80.35

23,619

RIICO industrial area, Ranpur

75.83

25.05

78.70

05,948

Jawahar Nagar

75.83

25.15

79.51

07,529

Anantpura, Kota Bypass

75.86

25.09

81.3

05,948

Anantpura, three-way

75.85

25.11

82.83

07,770

Raipura circle

75.89

25.14

79.76

12,307

Naya Nohra, Kota Bypass

75.92

25.17

76.06

07,281

Borkheda, three-way

75.88

25.18

79.23

09,115

RTU, Kota

75.81

25.13

72.13

11,111

Nayapura circle

75.85

25.19

83.06

11,210

Dhakad Khedi, Kota Bypass

75.91

25.14

77.96

12,307

CAD circle

75.83

25.16

81.93

13,315

Naya Gaon, Kota Bypass

75.82

25.12

71.73

05,225

Chambal Garden, three-Way

75.82

25.16

75.23

08,048

Gobariya Bawdi circle

75.85

25.13

82.73

13,023

Anantpura Kota Bypass, Anantpura Three-way, Gobariya Bawdi Circle, Sabjimandi, Gumanpura, Chambal Industrial Area, Borkheda Three-way, CAD Circle, Nayapura Circle, Raipura Circle, RICCO Industrial Area Ranpur, Antaghar Circle, I.L. Circle, Aerodrome Circle, Kotri Circle, Dadwara, Keshavpura, Talwandi, Jawahar Nagar, Kota Super Thermal Power Station, and Indraprastha Industrial Area

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Table 77.3 CPCB standards for vehicles Sr. no

Type of vehicles

Noise limits dB(A)

1

Two-wheeler: • Displacement up to 80 cm3 • Displacement more than 80 cm3 but up to 175 cm3 • Displacement more than 175 cm3

75 77 80

2

• Three-wheeler: • Displacement up to 175 cm3 • Displacement more than 175 cm3

77 80

3

Passenger car

75

4

Passenger or commercial vehicle: • Gross vehicle weight up to 4 tonne • Gross vehicle weight more than 4 tonne but up to 12 tonne • Gross vehicle weight more than 12 tonne

77 80 82

Fig. 77.3 Noise vulnerability assessment for 78 dB

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are the locations where peoples are highly vulnerable to severe impacts of noise pollution because 78 dB(A) of noise level is maintained almost 75–100% of the time.

77.4.2 Noise Vulnerability Assessment for Kota City at 80 dB(A) Naya Gaon Kota Bypass, Gobariya Bawdi Circle, Sabjimandi, Gumanpura, Dadwara, Keshavpura, Jawahar Nagar, Kota Super Thermal Power Station, and Indraprastha Industrial Area, MBS Hospital, Nayapura, Chambal Garden, Rajasthan Technical University, RICCO Industrial Area Ranpur, RICCO Institutional Area, Ranpur, and Naya Nohra Kota Bypass are the areas which are less vulnerable to noise level of 80 dB(A) because the peoples of these area are exposed to this level of noise only for 0–25% of the time. Hence, human beings living in nearby areas of these sampling locations are very less affected by the adverse impacts of high noise levels. Borkheda Three-way and Raipura Circle are the place where noise level of 80 dB(A) mostly lies between 25–50% and 50–75%, respectively, of the time in a day. The residents of these areas are more susceptible to the adverse effects on noise pollution. Anantpura Kota Bypass, Anantpura Three-way, Chambal Industrial Area, I.L. Circle, Aerodrome Circle, Kotri Circle, Antaghar Circle, Nayapura Circle, CAD Circle, Talwandi are the locations where peoples are highly vulnerable to severe effects of noise pollution because 80 dB(A) of noise level is maintained almost 75– 100% of the time during a day. Figure 77.4 shows the noise vulnerability assessment for 80 dB.

77.5 Conclusion Traffic noise is an environmental pollutant which has many adverse health impacts on human beings. Many studies reveal that exposure to high noise level [50–75 dB(A)] for 16 h leads to increased cardiovascular diseases to humans such as arterial hypertension, myocardial infarction, and stroke. It can be concluded that noise levels range between 65.75 dB(A) to 88.06 dB(A) for 90% of the stations. Almost 75% of the sampling locations are vulnerable toward the harmful effect of noise pollution for the noise level of 78 dB(A) whereas ~43% sampling locations are vulnerable to negative impacts of 80 dB(A) level of noise. These data also indicate that the prolonged exposure to high noise level [60–83 dB(A)] may contribute significantly toward noise originated diseases which may encompass the residents of the vulnerable areas prone to noise pollution. It would definitely help the regulatory authorities, stakeholders

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Fig. 77.4 Noise vulnerability assessment for 80 dB

and municipal corporation of Kota for better traffic control and management, and working toward reducing vulnerability prone to noise pollution.

References 1. R.B. Hunashal, Y.B. Patil, Assessment of noise pollution indices in the city of Kolhapur, India, Procedia Soc. Behav. Sci. 37, 448–457 (2012). https://doi.org/https://doi.org/10.1016/j.sbspro. 2012.03.310 2. P., Noise pollution assessment in greater Agartala City: a case study. Int. J. Res. Eng. Technol. 03(09), 402–407 (2014). https://doi.org/https://doi.org/10.15623/ijret.2014.0309063 3. United Nations: Department of Social and Economic Affairs: Population Division, “Our urbanizing world,” Popul. Facts (2014), p. 4 4. C. Clark, C. Crumpler, H. Notley, Evidence for environmental noise effects on health for the United Kingdom policy context: a systematic review of the effects of environmental noise on mental health, wellbeing, quality of life, cancer, dementia, birth, reproductive outcomes, and cognition. Int. J. Environ. Res. Public Health 17(2) (2020). https://doi.org/https://doi.org/10. 3390/ijerph17020393 5. G. CPCB (Ministry of Environment & Forests, Noise Pollution Regulation in India (2001)

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6. A. K. Dasarathy, Noise Pollution: Causes, Mitigation and Control Measures for Attenuation, no. March, (2015), p. 136. https://doi.org/https://doi.org/10.1007/s004210050211 7. D. Banerjee, Research on road traffic noise and human health in India: review of literature from 1991 to current. Noise Health 14(58), 113–118 (2012). https://doi.org/10.4103/14631741.97255 8. N. Singh, S.C. Davar, Noise pollution-sources, effects and control. J. Hum. Ecol. 16(3), 181–187 (2004). https://doi.org/10.1080/09709274.2004.11905735 9. S. Srivastava, Effect of noise pollution and its solution through eco-friendly control devices in the north east India. Procedia Eng. 38, 172–176 (2012). https://doi.org/10.1016/j.proeng.2012. 06.024 10. T. Münzel, T. Gori, W. Babisch, M. Basner, Cardiovascular effects of environmental noise exposure. Eur. Heart J. 35(13), 829–836 (2014). https://doi.org/10.1093/eurheartj/ehu030 11. W.H. Organization, Burden of Disease from Environmental Noise (2011), p. 128 12. P. Banerjee, M.K. Ghose, R. Pradhan, GIS based spatial noise impact analysis (SNIA) of the broadening of national highway in Sikkim Himalayas: a case study. AIMS Environ. Sci. 3(4), 714–738 (2016). https://doi.org/10.3934/environsci.2016.4.714 13. M.R. Monazzam, E. Karimi, M. Abbaspour, P. Nassiri, L. Taghavi, Spatial traffic noise pollution assessment a case study. Int. J. Occup. Med. Environ. Health 28(3), 625–634 (2015). https:// doi.org/10.13075/ijomeh.1896.00103 14. Directorate of Census Operations, District Census Handbook: Kota (2011) 15. Government of Rajasthan Transport Department, Statistical Abstract 2018–19 (2018)

Chapter 78

Application of Global Sensitivity Analysis to Building Performance Simulations for Screening Influential Input Parameters in a Humid Coastal Climate Souryadeep Basak and Aviruch Bhatia Abstract This paper explores the possibilities of informing decisions during the planning and design phases of a building. The study has been carried out for the weather pattern corresponding to the city of Kolkata, India. The results of the study can be appropriately generalized for similar coastal humid locations. Seven parameters have been taken as the dominant set of model inputs which are varied across several iterations of simulations. The study focusses on the energy consumption of the building as the sole dependent variable. The input parameters are varied by a Python script to generate several input data files for building performance simulation on EnergyPlus. The relative importance of each design parameter, as well as their combined effect in conjunction with other parameters, has been determined by performing sensitivity analysis on the building energy consumption and the corresponding input data samples. The Morris method of factor screening has been used to rank the design variables in order of their influence on the building energy consumption. Keywords Sensitivity analysis · Morris screening · EnergyPlus · Building performance simulation

78.1 Introduction A significant portion of the world’s energy consumption is on account of the built environment. This sector is responsible for 30% of global greenhouse gas emissions and 20% of global energy consumption. According to the U.S. Energy Information Administration in its International Energy Outlook 2019, the building energy consumption in non-OECD countries is projected to rise by more than two percent annually [1]. This is approximately five times the projected rise for OECD countries. Even among the non-OECD countries, India is projected to have the fastest rate of increase in building electricity use till 2050, with a projected rise of 5.3% per capita building electricity use. S. Basak (B) · A. Bhatia TERI School of Advanced Studies, New Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_78

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In a bid to reduce global anthropogenic emissions [2], green building design has emerged as the way forward [3]. This includes optimal design of building parameters which may minimize the energy use index (EUI) and maximize thermal comfort or optimize some other objective function. In order to achieve optimal building design, building performance simulation (BPS) is extremely useful. However, running simulations for all combinations of input parameters is computationally expensive. Therefore, the importance of uncertainty analysis (UA) or sensitivity analysis (SA) lies in identifying the most relevant design variables for green buildings. Iooss and Lemaitre [4] provide a comprehensive list of regression-based, screening-based, variance decomposition-based, and metamodel-based methods for sensitivity analysis. Each method has its distinctive merits. SA has been used in literature across other fields as well. Brevault et al. [5] have explored different sensitivity analysis methods in the context of aerospace vehicle design and the effect of input parameters on the vehicle trajectory. The Morris method of screening, Sobol method of variance decomposition, regression method of partial correlation coefficients (PCC), and analysis of variance (ANOVA) by design of experiment approach have been used to evaluate the sensitivity of the model output to the input parameters. Wan et al. [6] have modeled a tiered approach to sensitivity analysis for a flood forecasting model. The 12 parameters of the Liuxihe distributed watershed model were first screened for the most dominant input parameters. Sobol sensitivity analysis was then performed on the six parameters that were observed to have significant effects on model outputs. The relevance of the assumptions of orthogonality of input parameters for the Sobol method was investigated and modifications suggested in the event of correlated model inputs. SA techniques have been extensively used in building performance simulation models. Menberg et al. [7] used TRNSYS software to simulate a building model with 11 uncertain parameters, and a comparative analysis of the Morris screening method, standardized regression coefficient ranking method, and the Sobol method was presented with great agreement between the methods for the dominant parameters. It was also concluded that the computationally economical Morris method can also provide reliable information about second-order interactions. Gagnon et al. [8] used SA methods to determine the relative importance of 30 uncertain parameters. Apart from a cogency in parameter ranking across standardized regression coefficients, particle rank correlation coefficients, and Sobol indices, it was shown that fixing design variables in a sequential manner, as in the traditional design process, lowers the probability of finding energy efficient designs. The importance of integrated design process was established and it was suggested that in the event of sequentially fixing design variables, the most dominant ones should be fixed at a later phase in the design process. Jin and Overend [9] have used the sampling-based standardized regression method and the variance-based Sobol method to assess the relative importance of design parameters on energy, comfort, and economic indicators of facade performance. Two generic building models were simulated for London, Rome, and Helsinki to reliably rank the design variables for facade performance. Hopfe et al. [10] used Monte Carlo sampling with normal input parameter distributions to perform uncertainty analysis using standardized rank regression coefficients.

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Another study [11] establishes the Morris screening method as a good initial guess for influential parameters, while noting the computationally expensive requirements for the variance-based Sobol method. While the Sobol method may be used for simplified model designs with few input parameters, complex models may necessitate a tremendous quantum of computing power.

78.2 Climate of the Study Location Kolkata is a metropolitan city located on the eastern coast of India. The approximate coordinates of the city are 22.65°N, 88.45°E. As per Indian Standard SP 7-2005, India is divided into five major climate zones, namely hot and dry, warm and humid, composite, temperate and cold (Fig. 78.1). Kolkata is classified as having a warm and humid climate. The elevated temperatures coupled with high relative humidity are responsible for a greater number of cooling degree days, therefore increasing building energy consumption to maintain conditions of thermal comfort. The climate of location was analyzed by means of the Climate Consultant software [12]. Figure 78.2 represents the wind wheel for the entire year, with several parameters segmented directionally. Figure 78.3 shows the trends of relative humidity, dry bulb temperature, and wet bulb temperature across the year. It is to be noted that the relative humidity is consistently high during most parts of the year. Therefore, building design should be informed by these climatic variables and their local spatial and temporal trends.

Kolkata

Fig. 78.1 Classification of Indian climate zones as per Indian Standard SP 7-2005

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Fig. 78.2 Annual wind wheel for the weather of Kolkata

Fig. 78.3 Annual variation of relative humidity, dry bulb temperature, and wet bulb temperature

78.3 Sensitivity Analysis In order to measure the relative importance of input design parameters on the model output, sensitivity analysis methods are essential. There exist many SA techniques to determine the degree of influence of input parameters. Regression-based methods

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make use of standardized regression coefficients, variance decomposition methods like the Sobol method decompose the variance of the output into a summation of variances of increasing dimensionality, and screening methods like the Morris method vary the input parameters one at a time to find out their mean elementary effect on the model output. In this study, the Morris method of factor screening has been used for ranking the building design parameters in order of their relative influences.

78.3.1 Morris Method of Screening The Morris method of global sensitivity analysis [13] is generally used for dimensionality reduction in higher-dimensional models. It is extremely effective in screening out the important input variables from the input variable space [14]. It is often referred to as one-at-a-time (OAT) screening because in each subsequent run only one input variable is changed locally to examine its effect on the output of the model [15]. Let us consider a function with k variables as inputs. The Morris screening starts with a random point in the k-dimensional space. Only one dimension is varied at a time to get the next point. The distance between two points for a particular dimension is predetermined and standardized. Therefore, we have k points following the first random point. Therefore, for the entire trajectory, we have k + 1 points [16], where two consecutive points differ only in one dimension. For simplicity, let us consider a three-dimensional space with inputs X, Y, and Z. Let us suppose that the randomly assigned first point has the coordinates (3, 4, 5) in the three-dimensional space. The trajectory shown in Fig. 78.4 can be explained in tandem with the algorithm. Let us further assume that the standardized distance

Fig. 78.4 Trajectory of Morris screening algorithm in three-dimensional space with 4 points

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Table 78.1 Morris screening trajectory operations in three-dimensional space

Trajectory

Point

Operation

1

(3,4,5)

Random assignment

2

(3,2,5)

−2 in the Y-dimension

3

(3,2,7)

+2 in the Z-dimension

4

(1,2,7)

−2 in the X-dimension

between any two consecutive points is 2 units along each dimension. Table 78.1 explains the trajectory path and the operations being performed along the trajectory. While creating the trajectories, each input variable is assumed to have a set of discrete values, which are called levels. Therefore, each variable takes on an even range of values; X i = {0, 1/(p − 1), 2/(p − 1)…, (p − 2)/(p − 1), 1}. The kdimensional grid becomes a k-dimensional p-level grid, where the step size is 1/(p − 1). The number of trajectories is denoted by r. The elementary effect (Eij) is computed for each trajectory (i = 1, 2 …, r) and each input variable (j = 1, 2 …, k). It is calculated for two consecutive points in the k-dimensional space and is given by (1). Ei j =

Y (X 1 , X 2 . . . X j + δ j . . . , X k ) − Y (X 1 , X 2 . . . X j . . . X k ) δj

(78.1)

The mean of the absolute value of the elementary effect [13] of the jth input variable is calculated for a given input parameter across the set of all trajectories and is given by (78.2): μj∗ =

1    Ei j r i=1

(78.2)

This quantity represents the quantum of linear or first-order effects of the jth variable on the output. A higher mean absolute elementary effect signifies greater first-order effects and the converse holds true as well. The standard deviation of the elementary effect is also calculated for each parameter.   r r 1  1 σj =  (E i j − E i j )2 r i=1 r i=1

(78.3)

This measure is indicative of the interaction effects of the jth input variable. Because the Morris method searches a wide range of the input space, it is considered as a tool for global sensitivity analysis. Depending on this screening method, input variables can be classified into three categories: inputs with little or no influence on the output, inputs with direct effects and limited interaction effects, and inputs with highly nonlinear effects. However, a drawback of the Morris method is that it

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does not provide further information about higher-order interactions, which may be calculated by the Sobol method [7].

78.4 Methodology As shown in Fig. 78.5, the general framework followed in this paper starts with the definition of the input parameters to be investigated through SA methods. Once the input variables are defined and their bounds are specified, an appropriate sampling algorithm generates the combinations of input parameters that will be used for SA. If there are D input parameters and S input data samples, then the output of the sampling algorithm will be a S × D matrix. Each 1 × D row of this matrix will be used as a set of inputs to generate a model output. Therefore, the output matrix will be a S × 1 column vector. Sensitivity analysis is subsequently performed on the set of inputs and their corresponding outputs to determine the relative importance of the model inputs and the sensitivities of the model output to perturbations in these input design variables.

Generate input parameter dataset by sampling through the input parameter space

Generate input data iles (.idf) from input data matrix

Simulate the .idf iles on EnergyPlus to calculate the annual building energy consumption

Perform SA to assess the relative in luence of input parameters

Fig. 78.5 Generalized sequence of operations for performing Morris screening

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78.4.1 Model Input Parameters The workflow adopted in this study starts with the definition of the input parameters and their bounds. For our study, we have chosen seven building design variables which are as follows: Window-to-wall ratio. It represents the ratio of the fenestration area to the total area of the wall. For performing uncertainty analysis, the WWR was varied from 10 to 60%. Overhang angle. This parameter denotes the tilt of an overhang of 0.0487 m depth. It is varied within a minimum of 2° and a maximum of 25°. Wall thickness. The wall thickness was varied between 0.05 and 0.1 m. The walls are simulated using concrete, plaster and XPS insulation. Roof thickness. The roof construction is simulated using the same parameters as that of wall thickness and it is also varied between the bounds of 0.05 and 0.1 m. Glass solar heat gain coefficient. The SHGC was varied from 0.13 to 0.80. Since the SHGC, visible light transmittance (VLT), and the U-value of the glass are related to each other, it would be impractical to treat them as independent variables. For simplicity of the model, we have assumed that the VLT and U-value of the glass are functions of the glass SHGC. This makes sample generation for the Morris screening method simpler, which would otherwise have had to choose from a discrete set of glass parameter sets. Orientation. The building orientation was perturbed in the range of 0° (due north) to 90° (due east). Aspect ratio. It represents the ratio of the length and the width of the building. For our study, the aspect ratio has been varied between 1 and 3. The input parameters to be evaluated and their bounds are shown in Table 78.2. Figure 78.6 shows wireframe rendering of north-facing building simulation for 10% WWR, 9.67° overhang angle, 0.05 m roof and wall thickness, SHGC of 0.35, and aspect ratio of 1.7. Table 78.2 Input parameters to be evaluated for SA and their bounds

Parameter

Minimum

Maximum

Units

Window-to-wall ratio

10

60

Percent (%)

Overhang angle

2

25

Degree

Wall thickness

0.05

0.1

Meter

Roof thickness

0.05

0.1

Meter

Glass SHGC

0.13

0.8

Dimensionless

Orientation

0

90

Degree

Aspect ratio

1

3

Dimensionless

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Fig. 78.6 Wireframe rendering of north-facing building simulation for 10% WWR, 9.67° overhang angle, 0.05 m roof and wall thickness, SHGC of 0.35, and aspect ratio of 1.7

78.4.2 Sampling Strategy Within the defined input parameter space, appropriate data points are selected according to the sampling strategy applied. For the Morris screening method, each design variable in the input space is quantized into four levels. Corresponding to each trajectory, there shall be a randomly assigned data point. Each of the seven parameters are then modified one at a time (OAT) to obtain a total of 8 data points per trajectory. We have sampled 100 trajectories, which yields 800 different sets of input parameters. It is to be noted that Monte Carlo sampling techniques and quasirandom Latin hypercube sampling techniques are not suited for the Morris screening method. The OAT sampling is performed using the SALib Python library [17]. The generated samples are used to create input data files (.idf) to be simulated on EnergyPlus software [18, 19] using a Python script [20].

78.4.3 Building Energy Simulation The generated input samples were used as building parameters in EnergyPlus simulations. The building energy consumption is calculated for each simulation. These sets of input parameters and corresponding simulated building energy consumption are used to perform SA. All other design variables of the building remains unchanged throughout all the simulations. In addition to the seven input parameters we have chosen, the VLT and U-value of the glass shall also vary with SHGC between different samples as we have taken them to be dependent variables.

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78.4.4 Sensitivity Analysis and Ranking of Parameters The SALib Python library abstraction was used to apply the Morris method of factor screening. The 800 × 7 input parameter matrix has been sampled in such a manner that only a single input parameter is varied between two adjacent data points. The Morris screening test gives us two indices that may be used to rank the input parameters by the degree of their influence on the output. The mean absolute elementary effect represents the first-order effects of a variable on the output, while the standard deviation of the elementary effect explains the higher-order interactions of an input variable. Therefore, plotting the mean absolute elementary effect against the standard deviation for each variable provides an easy graphical approach to determine the relative ranking of parameters.

78.5 Results and Discussion 78.5.1 Analysis of Morris Screening Results As ready discussed in Sect. 3.1, the Morris screening test returns indices which may be used to rank the input parameters in order of their influence on the building energy consumption. The elementary effect, given by (1), is calculated for each variable and each trajectory. The first-order effects of an input parameter are represented by the mean of the absolute value of the elementary effect (μ*) across all trajectories. The standard deviation of the elementary effects (σ ) can be used as a relative measure of the second- and higher-order interactions of input variables. The summary of the sensitivity indices is enumerated in Table 78.3. In order to visually distinguish the more influential input parameters, a scatter-plot is synthesized where σ, representative of higher-order interaction effects, is plotted against μ*, denoting direct effects, for all the input parameters. Figure 78.8 shows such a representation for the analysis performed in the study. As is evident from Figs. 78.7 and 78.8, the highest direct effects are due to the Table 78.3 Sensitivity indices for all input parameters obtained from the Morris screening test

Parameters

μ

μ*

σ

Glass SHGC

10,973.77

11,280.79

7166.35

9559.72

9993.30

7397.22

Overhang angle

−2443.44

2443.44

1465.95

Roof thickness

−1331.13

1331.13

306.81

307.73

635.17

891.57

−377.33

378.60

233.77

293.22

328.34

315.37

WWR

Aspect ratio Wall thickness Orientation

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Fig. 78.7 Ranking of the input design parameters based on µ*

Fig. 78.8 σ versus μ* scatter-plot for input variables

SHGC of glass and the WWR. Incidentally, the second-order effects of these parameters are also the greatest. Overhang angle appears to rank next in terms of relative influence on the output. The remaining four parameters can be seen clustered together at the bottom left corner of the plot. The building orientation appears to be the least influential input parameter. However, it is imperative to note that the choice of building model can also influence the results of the sensitivity test. Therefore,

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these results hold true for the general single-storied building model simulated for the climate of Kolkata. It would be impractical to generalize these results across all climate zones. Nevertheless, if the climate of any location is similar to that in Kolkata, these results may be extrapolated in those contexts. The results of the Morris sensitivity analysis help us to make better informed decisions at the planning stage of any new building.

78.5.2 Scope for Further Work The Morris screening method is useful for identification of the input parameters which have the greatest degree of influence on the model output. However, it does not provide additional information regarding the higher-order interactions of input parameters. The standard deviation of the elementary effect merely gives us a measure of the variation of the output that is not explained by the first-order effects of the model inputs. In order to extract useful information about the interaction effects of parameters, it may be advisable to perform sensitivity analysis by the Sobol method, which is based on the decomposition of variance. The Sobol method is computationally expensive and requires a much greater number of input parameter samples. The sampling strategy to be used for the Sobol sensitivity analysis is either Monte Carlo sampling or Latin hypercube sampling. Due to the computationally expensive nature of the Sobol analysis, it may be feasible to employ a surrogate model for the simulation of building energy performance, as demonstrated by Sangireddy et al. [21]. The development of a surrogate model may speed up the process of simulating numerous combinations of input parameters, as established in literature.

78.6 Conclusion Modeling a typical single-storied building in a tropical humid location by varying seven design variables, the relative importance of each parameter was evaluated through the Morris screening test. The input space was quantized into four levels along all parameter axes. A total of 100 trajectories was evaluated to calculate the sensitivity indices of the input variables. Finer, albeit marginally better, estimates of sensitivities may be achieved by increasing the resolution of quantization levels and the number of evaluated trajectories. The results of the sensitivity analysis can help architects and designers focus on important input variables and dedicate commensurate time and effort in optimizing these design variables during the design process. Due to the inherent uncertainties in the design parameters during the design phase, in the absence of sensitivity analysis tests, it might be difficult to identify key design parameters that might affect the building energy consumption. Additional objective functions may be integrated into the design requirements to assess the influence of design variables on each objective

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function. Furthermore, while sequentially fixing design variables in the traditional design process, the assignment sequence may be optimized by giving greater decision weightage to more influential design parameters. In conclusion, application of SA in building design can better inform decision-making processes that increase the probability of achieving optimal energy efficiency in building performance.

References 1. U.S. Energy Information Administration website, https://www.eia.gov/todayinenergy/detail. php?id=41753. Last accessed 2020/08/09 2. L. Pérez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energy and Buildings 40(3), 394–398 (2008) 3. S. Salat, Energy loads, CO2 emissions and building stocks: morphologies, typologies, energy systems and behaviour. Build. Res. Inf. 37(5–6), 598–609 (2009) 4. B. Iooss, P. Lemaître, A Review on Global Sensitivity Analysis Methods (Springer, 2015) 5. L. Brevault, M. Balesdent, N. B´erend, R. Le Riche, Comparison of different globa sensitivity analysis methods for aerospace vehicle optimal design, in 10th World Congress on Structural and Multidisciplinary Optimization 6. H. Wan, J. Xia, L. Zhang, D. She, Y. Xiao, L. Zou, Sensitivity and interaction analysis based on sobol’ method and its application in a distributed flood forecasting model. Water 7, 2924–2951 (2015) 7. K. Menberg, Y. Heo, R. Choudhary, Sensitivity analysis methods for building energy models: comparing computational costs and extractable information. Energy Build. 133, 433–445 (2016) 8. R. Gagnon, L. Gosselin, S. Decker, Sensitivity analysis of energy performance and thermal comfort throughout building design process. Energy Build. 164, 278–294 (2018) 9. Q. Jin, M. Overend, Sensitivity of facade performance on early-stage design variables. Energy Build. 77, 457–466 (2014) 10. C.J. Hopfe, J.L.M. Hensen, Uncertainty analysis in building performance simulation for design support. Energy Build. 43, 2798–2805 (2011) 11. V. Zeferina, R. Wood, J. Xia, R. Edwards, Sensitivity analysis of a simplified office building. J. Phys: Conf. Ser. 1343, 012129 (2019) 12. UCLA, Climate consultant, (2016). https://www.energy-design-tools.aud.ucla.edu/climateconsultant/ 13. M. Morris, Factorial sampling plans for preliminary computational experiments. Technometrics 33(2), 161–174 (1991) 14. F. Campolongo, J. Cariboni, A. Saltelli, An effective screening design for sensitivity analysis of large models. Environ. Modell. Softw. 22(10), 1509–1518 (2007) 15. F.L. Pereira, F. Valente, J.S. David, N. Jackson, F. Minunno, J.H. Gash, Rainfall interception modelling: is the wet bulb approach adequate to estimate mean evaporation rate from wet/saturated canopies in all forest types? J. Hydrol. (2016) 16. A. Franczyk, Using the Morris sensitivity analysis method to assess the importance of input variables on time-reversal imaging of seismic sources. Acta Geophys. 67, 1525–1533 (2019) 17. J. Herman, W. Usher, SALib: an open-source python library for sensitivity analysis. J. Open Sour. Softw. 2(9). https:dpi.org/https://doi.org/10.21105/joss.00097 (2017) 18. EnergyPlus Energy Simulation Software: Weather Data, Build. Technol. Program. (n.d.). https://energyplus.net/weather-region/asia_wmo_region_2/IND. Accessed July 1, 2020s 19. DOE. Building Technology Program, EnergyPlus simulation software, https://apps1.eere.ene rgy.gov/ buildings/energyplus (2020)

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20. A. Bhatia, Early Design Methodology for Energy Efficient Building Design. International Institute of Information Technology Hyderabad (2019) 21. S.A.R. Sangireddy, A. Bhatia, V. Garg, Development of a surrogate model by extracting top characteristic feature vectors for building energy prediction. J. Build. Eng. 23, 38–52 (2019)

Chapter 79

Two Decades of Urban Growth in Kota City: The Urban Heat Island Study Payal Panwar, Sohil Sisodiya, and Anil K. Mathur

Abstract Rapid urbanization creates many issues that can have positive and negative impacts on the environment. The city of Kota, India is situated on the banks of the Chambal River; urbanization of Kota city has a significant impact on land surface temperature (LST) based on Landsat data of previous years. Kota city has increased land surface temperature (LST), build-up areas, and less vegetation. For studies, urban sprawl was analyzed using Landsat data from 2001, 2009, and 2020. The objective of this study is to compute spatial and temporal through change detection techniques and to explore the speed and direction of urban development using long-term satellite data. Determination of socio-economic changes in the study area analyzed the reasons for such rapid urban development in Kota city. The current report shows the correlation between the normalized difference of build-up index (NDBI) and land surface temperature (LST) for the city of Kota, Landsat 5, Landsat 7 ETM + for monitoring and analyzing high-resolution satellite images, Landscape 8 was used. Keywords Land surface temperature · Urbanization · Urban heat island · Built-up indices

79.1 Introduction Spatial and temporal variations in land surface temperature arise due to the changes in the city of Kota and the spatial temperature affecting the local weather of the city. At present, urbanization is growing [1]. Kota city is a fast-growing city, faces challenging and varied climatic conditions and potentially harmful impacts of environmental variation. In the coming times, people from both rural and urban communities across India will feel the effects of climate change acutely. Figure 79.1 shows that the total population growth rate was 18.59% between the years 1951 and 2011 [1].

P. Panwar · S. Sisodiya (B) · A. K. Mathur Department of Civil Engineering, UD, RTU, Kota, Rajasthan 324010, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_79

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Fig. 79.1 Population growth per decades of Kota city [1]

Urbanization is a necessity to provide better living standards to the fast-growing population worldwide. The change in the land surface characteristic is due to urbanization and it increases the LST [2]. Monitoring urbanization is the key role of planners, management, govt. and non-govt. administrations to optimize guidelines for natural resource use and implement policies for recorded growth at the same time to minimize impacts on the atmosphere [3]. The land cover pattern and the materials of urban areas with an increase in temperature within have become highly important to concern the impacts on the environment, as well as human health [4]. The identity of the built-up area has great importance in urban, suburban, and agricultural studies. The calculation of its transformation to the disadvantage of the non-built sector is an important indicator of urban revolution and environmental degradation [5]. Satellite remote sensing is quite useful for studying city environments and outer environments when high spatial resolution imagery is accessible [6]. Remote sensing offers scientific tools to calculate the underlying area, studying multispecialty space using inter-temporal satellite images, regardless of whether the rural population is susceptible to any change in rainfall pattern or temperature [7]. The urban population should not be unnoticed. Remote-sensing images are important in studying changes such as catastrophe risk management, deforestation, crop yield estimation, and urban development and planning [8]. Digital image change detection is based on studying the change between two images obtained at different time periods at the same geographic location. If the deviation in the spectral response exceeds the prescribed threshold value, one thematic class is converted to another thematic class [9]. Urban sprawl indicates the extent to which an area is built and the extent of its spread. The greater the area on which buildings are built and scattered, the more there is urban sprawl [9]. The growth of habitat and increase in impermeable surfaces are

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encroachments on land where before there was farming land, grasslands, marshlands, water bodies, and forests. Changes in the landscape are important for taxation, quality of life, water quality, agricultural viability, wildlife habitat, and social equity every day [10]. Affective surfaces are encouraged as useful ecological indicators, and one atmospheric condition that indicates impervious surfaces is urbanization [11]. Most of the studies are based on big cities like Delhi, Mumbai, Kolkata, and Chennai. Medium-sized cities have received less attention like Dehradun (Uttarakhand), Hoshangabad (Bhopal), Bankura (West Bengal). Some of which play an important role are so-called counter-magnets of mega-cities [11, 12].

79.2 Study Area Kota city is allocated the eastern bank of the Chambal River in the southern part of Rajasthan. Figure 79.2 shows the area map of Kota city. It is the third-largest city

Fig. 79.2 Study area location map (Kota city)

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of Rajasthan after Jaipur and Jodhpur. The longitude and latitude coordinates are 25.18°N 75.83°E. It covered an area of 318 km2 (3.63% in the Rajasthan State), its average elevation is 271 m (889 ft.), it is bounded north and northwest by Sawai Madhopur, Tonk, and Bundi district. Chambal River separates these from the Kota district and forms the natural boundary. The district is bounded by Jhalawar and Mandsor district of M.P. on the south, Baran district on the east, and Chittorgarh district of Rajasthan on the west. The total area of Kota is 5217 km2 including 4590.49 km2 rural area and 626.51 km2 urban area and comprises of 6 tehsils, namely Pipalda, Digod, Kanvas, Ladpura (Kota), Sanghod & Ramganj Mandi. Kota has a population of 1,951,014 people in census 2011. There are 396,501 houses in the district. Its climate is semi-arid with high temperatures during the year. Summer (long, hot, and dry) peaks in late March–June. The monsoon season comparatively has lower temperatures, but higher humidity. The monsoons subside in October and temperatures rise again. Mild winter starts in late November and lasts till February last week. The temperature has over 26.7 °C to 12. That time is considered the best time to visit Kota because of excessive heat in the summer. Annual average rainfall in the Kota district is 660.6 mm. Maximum rainfall can be attributed to the southwest monsoon which has started at the beginning of the last week of June and may last till mid-September. Premonsoon showers begin toward the middle of June with post-monsoon rains occasionally occurring in October. The winter is largely dry, although some rainfall does occur as a result of the Western Disturbance passing over the region [12].

79.3 Research Methodology and Data Used 79.3.1 Research Methodology For the present study, the following methodology is adopted which involves the preparation of land surface temperature (LST), normalized difference built-up index (NDBI) maps, retrieval of those method maps, and correlation analysis [13]. Normalized differential built-up index (NDBI) is assessed for urbanization and build-up area expansion. Figure 79.3 shows the flowchart of the adopted research methodology. The MIR wavelength range (1.55–1.75 µm) compared to the NIR range (0.76–0.90 µm) is maximum [14]. NDBI mapping has been calculated using Eq. (79.1); NDBI =

MIR − NIR MIR + NIR

(79.1)

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Fig. 79.3 Flowchart of adopted research methodology

Here, MIR is medium infrared reflection, which has a band 6 of Landsat-8. NIR is a near-infrared reflection which has Landsat-8 s band set 5 [13]. The value of NDBI ranges between −1 and +1. The higher the NDBI, the higher the ratio of the built-up area.

79.3.2 Data Used Imagery Landsat data were used for this study because of its higher spectral resolution than other commonly used images such as “spot and multi-spectral scanners (MSS)” [15]. For the study, satellite source data are used to search the underlying land. The basic details of remote sensing satellites are shown in Table 79.1 Images have high-resolution quality and are cloud-free. The land surface temperature data were obtained from Landsat satellite data using Arc (GIS) software. These Landsat temporal images were captured at 24-05-2000 (Landsat 7 ETM+), 24-05-2009 (Landsat 5 TM), 23-05-2020 (Landsat 8) by using USGS Earth Explorer Web. Satellite data were done by Arc software [15].

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Year

Source

Landsat 5 TM

May 2000

USGS

Landsat 7 ETM +

May 2009

Glovis

Landsat 8

May 2020

USGS

79.4 Results and Discussion 79.4.1 Land Surface Temperature (LST) Land surface temperature is known to observe temperature interface between the earth’s surface and atmosphere. It is used for terrestrial thermal analysis and controls the effective radiating temperature of the earth’s surface [10]. As shown in Fig. 79.4, in Kota city, land surface temperatures were increased because of urban growth, and vegetation rate is decreasing day by day. Table 79.2 has shown the temperature changes during the last few years that causes urban heat island. The land surface temperature as obtained from Arc GIS software indicated that the rise in temperature increased for minimum temperature is 1.74% for the maximum temperature is 6.02% within two decades. The increased temperature difference is caused due to rapid urbanization has risen in these two decades from 2000 to 2020. Population growth deploys the rapid development of areas surrounding the city [11].

79.4.2 Correlation Between Land Surface Temperature versus NDBI NDBI for the year 2000, 2009, and 2020 is clearly showing that the value of NDBI was initially low. As time passed, the value of NDBI in 2009 is increased when compared to 2000. In 2020, the value of NDBI is maximum when compared to 2000 and 2009. Figure 79.5 shows that build-up area continues increasing day by day. As per building growing up, the land surface temperature is proportionally increased.

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Fig. 79.4 LST images of the study area for the recent years 2000, 2009, and 2020 Table 79.2 Observed difference in temperature in Kota city during recent year

Year

Minimum temp. (°C)

Maximum temp. (°C)

May 2000

26.88–29.76

44.01–47.80

May 2009

27.55–30.02

44.43–48.54

May 2020

29.26–29.76

48.79–50.96

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Fig. 79.5 NDBI images of the study area for the recent years 2000, 2009, and 2020

79.5 Conclusion For analysis LST and NDBI coverage maps are developed from Landsat data. This analysis explains the spatial distribution of the land surface temperature of Kota city. It is concluded that for the past years land surface temperature is rapidly increasing for both the vegetation index and build-up index. Due to temperature increases, their impacts cause low vegetation life and create urbanization build-up growth. As shown in Fig. 79.5, NDBI satellite map urban development is increasing because of the buildup growth area, and both ecological and social-economics effects are increasing. The effect on the environment is evident from decadal changes in local weather, climate and socio-ecological factors. Future studies will be beneficial for implementing effective strategies to mitigate climate-destroying and detrimental effects. Future reference must be conducted in-depth.

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References 1. R. census 2011, Census Board of Rajasthan, cencus, 2011. https://censusindia.gov.in/2011ce nsus/dchb/Rajastan.html 2. S. Guha, H. Govil, N. Gill, A. Dey, A long-term seasonal analysis on the relationship between LST and NDBI using Landsat data. Quat. Int. (2020). https://doi.org/10.1016/j.quaint.2020. 06.041 3. D.K. Ghosh, A.C.H. Mandal, R. Majumder, P. Patra, G.S. Bhunia, Analysis for mapping of built-up area using remotely sensed indices—a case study of Rajarhat block in Barasat sadar sub-division in west Bengal (India). J. Landsc. Ecol. Republic) 11(2), 67–76 (2018). https:doi.org/https://doi.org/10.2478/jlecol-2018-0007 4. M. Imran, A. Mehmood, Analysis and mapping of present and future drivers of local urban climate using remote sensing: a case of Lahore, Pakistan. Arab. J. Geosci. 13(6) (2020). https:// doi.org/https://doi.org/10.1007/s12517-020-5214-2 5. J. Yang, M. Santamouris, Urban heat island and mitigation technologies in Asian and Australian cities—impact and mitigation. Urban Sci. 2(3), 74 (2018). https://doi.org/10.3390/urbansci2 030074 6. C. Pathak, S. Chandra, G. Maurya, A. Rathore, M. O. Sarif, R.D. Gupta, The effects of land indices on thermal state in surface urban heat island formation: a case study on Agra City in India using remote sensing data (1992–2019), Earth Syst. Environ. 0123456789 (2020). https:// doi.orghttps://doi.org/10.1007/s41748-020-00172-8 7. X. Zhu, X. Wang, D. Yan, Z. Liu, Y. Zhou, Analysis of remotely-sensed ecological indexes’ influence on urban thermal environment dynamic using an integrated ecological index: a case study of Xi’an, China. Int. J. Remote Sens. 40(9), 3421–3447 (2019). https://doi.org/10.1080/ 01431161.2018.1547448 8. S. Ahmed, Assessment of urban heat islands and impact of climate change on socioeconomic over Suez Governorate using remote sensing and GIS techniques. Egypt. J. Remote Sens. Sp. Sci. 21(1), 15–25 (2018). https://doi.org/10.1016/j.ejrs.2017.08.001 9. R. Sharma, P.K. Joshi, Mapping environmental impacts of rapid urbanization in the national capital region of India using remote sensing inputs. Urban Clim. 15, 70–82 (2016). https://doi. org/10.1016/j.uclim.2016.01.004 10. J. Bandyopadhyay, K. Kanti Maiti, D. Chakravarty, Application of remote sensing and gis techniques for sub-surface water potentiality mapping in mining area: a case study of Pandabeswar block in Barddhaman District, West Bengal, India. Indian Cartogr. XXXIV (2014) 11. A. Mathew, S. Khandelwal, N. Kaul, Investigating spatial and seasonal variations of urban heat island effect over Jaipur city and its relationship with vegetation, urbanization and elevation parameters. Sustain. Cities Soc. 35, 157–177 (2017). https://doi.org/10.1016/j.scs.2017.07.013 12. K. Geographical, Kota Rajasthan Official Portal, https://kota.rajasthan.gov.in/content/raj/kota/ en/about-kota/geographical-and-physical-features.html# 13. A. Garg, Di. Pal, H. Singh, D.C. Pandey, A comparative study of NDBI, NDISI and NDII for extraction of urban impervious surface of Dehradun (Uttarakhand, India) using Landsat 8 imagery, in 2016 International Conference Emerging Trends Communication Technologies ETCT 2016 (2017), pp. 8–12, https://doi.org/https://doi.org/10.1109/ETCT.2016.7882963 14. M.S. Malik, J.P. Shukla, S. Mishra, Relationship of LST, NDBI and NDVI using landsat-8 data in Kandaihimmat watershed, Hoshangabad, India. Indian J. Geo-Mar. Sci. 48(1), 25–31 (2019) 15. USGS,“U.S.GeologicalSurvey.”_https://www.usgs.gov/centers/eros/data-citation?qtscience_ support_page_related_con=0#qt-science_support_page_related_con

Chapter 80

Anomaly Detection Systems Using IP Flows: A Review Rashmi Bhatia, Rohini Sharma, and Ajay Guleria

Abstract The dependency on computer networks is increasing in all the sectors of the society and so are the threats. An anomaly detection system detects new attacks, identifies the intruder, and blocks them from further attacks. The researchers are proposing various techniques to detect the anomalies. In this paper, various aspects of the anomaly detection systems are discussed. Flow collection process and the tools used for collection are discussed in detail. The various statistical, data mining, deep learning, outlier-based, ensemble-based, and other techniques used by researchers in developing anomaly detection systems have been reviewed in detail. The research gaps in the study of anomaly detection are also discussed to give future directions. Keywords Intrusions · Anomaly detection · Data mining · Flow collector · Flow exporter

80.1 Introduction Computer network is a group of various devices connected with each other for information sharing. First computer network was ARPANET, which came in existence in 1969 and now the whole world is connected through World Wide Web (WWW). As shown in Fig. 80.1 [1] the number of connected devices in 2015 was 15.41 billion, which increased to 26.66 billion in 2019. It is projected that in 2025 the number of connected devices will be 75.44 billion. Most of the countries in the world are spending a huge amount on cybersecurity. Thus, network security mechanism such as intrusion detection is and always be a hot topic for researchers. Intrusion means when without the permission, some unauthenticated or unauthorized user enters into a system with an intention to harm or compromise the security of the system. Intrusion detection system detects such attack instances and mitigates R. Bhatia · R. Sharma (B) Department of Computer Science and Applications, Panjab University, Chandigarh, India e-mail: [email protected] A. Guleria CSC, Indian Institute of Technology, Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_80

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Fig. 80.1 Number of connected devices globally, yearwise [1]

their effects. The idea of intrusion detection was originated in 1980 by Anderson [2], followed by Denning in 1987 [3].

80.1.1 Types of Intrusion Detection Systems As per [4], two types of IDS are: Host-based IDS (HIDS). These are deployed on a computer system to detect security breaches. The scope of IDS is limited to a host only, but the intrusion detection is fast. Network-based IDS (NIDS). These are deployed on a network connection at various points. The scope is expanded to a complete network or a network segment.

80.1.2 Detection Methods Detection methods in network-based IDS are of four types: Signature-based/misuse-based IDS. Every incoming instance is compared to the signatures of known intrusions prestored in the database. The zero day/novel intrusions are hard to detect, which is a danger to the system. Anomaly-based IDS. A model is designed to analyze the patterns of past traffic to predict the future normal behavior of the system. In such systems, zero day/novel anomalies can be detected, but it suffers from high false alarm rate (FAR). Stateful Protocol Analysis (SPA). As per [4] SPA studies the states of protocols, specifically a pair of request–response type of protocols. SPA [5] verifies the protocol semantic against the specification and labels out of range value as intrusion.

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Hybrid-based IDS. Some researchers also suggest hybrid-based IDS, which is a combination of signature-based and anomaly-based approach.

80.1.3 Types of Data As per [6], the data on which an IDS can be trained are: Packet-based data. Some IDS perform deep packet inspection (DPI) on packet payload field which requires more time with higher detection rate but encryption, privacy, and complexities are required to be handled. Flow-based data. A flow is a group of packets sharing source IP, destination IP, source port, destination port, protocol, etc. It is the metainformation about network connections which is present in the packet header. In this paper, we have focused on flow-based anomaly detection systems. The rest of the paper is organized as follows: In Sect. 80.2, the flow collection process and tools are described in detail and in Sect. 80.3 the techniques used by researchers in anomaly detection systems are discussed. Discussion and research gaps are discussed in Sect. 80.4 followed by conclusion in Sect. 80.5.

80.2 Flow Collection Process and Tools Various flow-based datasets are available which can be used to evaluate the proposed systems [7, 8]. In flow-based anomaly detection, collection of flows is required to monitor and detect anomalies from a network. Unlike deep packet inspection, the payload field of the packets is not required in the collection of flows and thus the privacy of the data is not compromised and it speeds up the collection process. As per [9], the packets transmitting information over a network having same header information will belong to one flow. However, if there is a large time gap between the transmission of two packets having same header information, then it will belong to two different flows instead of one. Flow collection process passes through various phases which are described as below: Observe. The packets over a network are captured and packet truncation, filtering, sampling, etc., can be implemented. Meter. Flows are defined from captured packets. Some filtering or sampling methods can be applied. The flows are cached until they are considered expired. Export. Flow exporter delivers the expired flows to the flow collector. Collect and Analyze. A collector receives the flow records, stores and preprocesses them. The preprocessing task can be feature selection, feature reduction, anonymization, etc. Finally flow records are analyzed by IDS.

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80.2.1 Tools Used in Flow Collection Process Flow exporter. A device that observes the traffic either listening to traffic directly or through an input file provided to it, do metering and finally exports the flows in standard format to flow collector. Flow collector. A device that receives flows from flow exporter and processes them before giving them to IDS. Finally, IDS analyzes these flows to detect anomalies. These tools to collect and export flow records can be software- or hardware-based. Where hardware-based tools give better performance at high cost, software-based tools provide high flexibility at moderate price. Further software tools can be open source or commercial.

80.2.2 Standard Protocols for Handling Flows/Flow-Based Formats As per [9], there are two main protocols widely used for handling flows: NetFlow and IPFIX. NetFlow is designed by Cisco and is implemented in application layer. NetFlow records traffic statistics in the form of flow record. It has two main versions v5 and v9 which is an extension of v5. NetFlow v5 is inflexible in terms of field definition whereas NetFlow v9 offers flexibility and extensibility. IPFIX (IP flow information) is another application protocol created by IETF. It offers more flexibility by allowing variable length fields and other extensions. Some other flow-based formats are sFlow and OpenFlow.

80.2.3 Flow Exporters argus [10], released in 1993, captures packets from a distributed environment where network traffic passes through different points of observation. pmacct [10], released in 2003, exports bidirectional flows. Vermont [10], released in 2005, performs packet and flow filtering, sampling, accounting, and aggregation. Yet another flowmeter (YAF) [10], released in 2016, an IPFIX compliant flow exporter, processes packet data captured either live from a network interface or from pcapdump files. joy [10], released in 2016 by Cisco, is an open-source flow exporter and collector. pFlour [11] introduced in 2008, is a packet and flow gathering tool, which fetches the packets from pcap library, processes packets and flows, and exports them to a collector. It exports the expired flow information in NetFlow v5, v9 or IPFIX format to the collector as well as the structured packet dumps.

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HashFlow [12], introduced in 2019, is a new tool for more accurate and better collection of the flow records from the network. It reduces memory consumption and maintains accurate records of elephant flows (flows with large number of packets) and mice flows (flows with small number of packets). nProbe [9] is an exporter as well as a collector as it includes both a NetFlow v5/v9/IPFIX probe for IPv4/v6 and a collector. It provides extensibility with plugins, supported only by the nProbe Pro version. It is compatible with any standard collector and can efficiently convert the collected sFlows in NetFlow or IPFIX format. Softflowd [9] is an exporter that is capable of generating flows in NetFlow (v1, v5 or v9) format using softfloctl program and exports them to the collector. It needs libpcap and its associated headers. It is capable of reading pcap files recorded using tcpdump. In [13], the authors have proposed a customized extension of NetFlow and IPFIX that combines intrusion detection into flow metering process so as to mitigate DDoS attack which reduces delay in detection.

80.2.4 Flow Collector nProbe [9] is an exporter as well as a collector. Nfcapd [9] captures data in NetFlow format (any version) and stores it in binary file. It is a NetFlow capture daemon of the nfdump tools. SiLK [9] collects NetFlow (v5, v9) and IPFIX flows, stores, and analyzes them.

80.3 Techniques Used in Flow-Based Anomaly Detection The classification and detection module of the IDSs are designed using statistical, machine learning, deep learning, outlier, an ensemble, or a combination of techniques.

80.3.1 Statistical Techniques The classification model uses some statistical function to predict the behavior of the system which is compared with the real traffic flows and similarity measure is calculated. If that similarity measure is found above the predefined threshold, the flow is marked as malicious or normal otherwise. In [14], a statistical method Holt-Winters for Digital Signature (HWDS), an improved version of Holt-Winter’s method is proposed. The proposed method HWDS characterizes traffic more efficiently than traditional method, at low computational cost with accuracy around 90% and FPR 5%.

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In [15], a flow-based IDS-based on sketch and combination is proposed. To achieve the forecast sketches, Holt-Winter’s forecasting technique is applied. Proposed method as compared to SNMP-based method detected 100% of Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) anomalies and maximum of other anomalies. SNMP detected fewer anomalies. As per [16], paraconsistent logic deals with the situations of uncertainties. They can be applied to anomaly detection techniques because the unusual activities are more likely to cause a contradiction when they are not an anomaly. In [17], Digital Signature of Network Segment using Flow Analysis (DSNSF) is generated using Auto Regressive Integrated Moving Average (ARIMA) model and paraconsistent logic. Uncertainties and contradictions are treated with pal2v, an extension of paraconsistent logic. On real flow-based dataset, using ARIMA model, 77.01% accuracy and 65.18% precision is achieved whereas using ARIMA+pal2v, accuracy is increased to 90.03% and precision is increased to 78.21%. In [18], the method proposed to characterize traffic is Principal Component Analysis for Digital Signatures and Anomaly Detection (PCADS-AD). While collecting data, system backups were done two hours daily and predictions were based on that data. However, in real traffic, there were no such backups and IDS adapted itself according to the real traffic.

80.3.2 Data Mining and Machine Learning Techniques Machine learning is an Artificial Intelligence (AI) approach, in which, through data, machine learns. Data mining includes application of algorithms to extract patterns from data which is used in knowledge discovery in databases (KDD). As per [19], both the terms data mining and machine learning are used interchangeably, as they use same class of algorithms. Machine learning can be supervised, unsupervised, or semi-supervised. In [20], for data classification, a variation of the decision tree algorithm Enhanced Data Adapted Decision Tree algorithm (EDADT) is suggested. The authors also proposed a hybrid IDS, which is SNORT (signature-based) + ALAD (Application Layer Anomaly Detector) + LERAD (Learning Rules for Anomaly Detection) where ALAD and LERAD are anomaly-based approaches. Performance of proposed system is compared with that of SNORT, SNORT + PHAD (Packet Header Anomaly Detection), and SNORT + PHAD + ALAD in terms of FAR which is minimum in proposed approach, i.e., 0.18%. Semi-supervised approach is used where the labeled data is applied to Support Vector Machine (SVM) classifier to generate a model to label the unlabeled data. The proposed semi-supervised approach is compared with the existing algorithms like Reduced Support Vector Machine, Semi-Supervised clustering algorithm (PCKCM), and Fuzzy Connectedness-based Clustering, where the proposed approach shows 98.88% accuracy with 0.5% FAR. To mitigate the effectiveness of DDoS attack, Varying Hopping Period Alignment and Adjustment

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(Varying HOPERAA) algorithm is proposed. The accuracy of the IDS was 98.12% whereas the False Positive Rate (FPR) was 0.18%. In [21], the authors suggest a two-stage IDS which in the first stage uses enhanced unsupervised One-Class Support Vector Machine (OC-SVM), which removes normal flows and passes malicious flows to the second stage. The second stage uses a Self-Organized Map (SOM), an unsupervised clustering technique which clusters malicious flows into different classes of attacks with 98% accuracy and 0% FPR. In [22], the training data is passed through Genetic Algorithm-based rule creation system defined in the paper, which generates the training rules, which are further passed to the system created by ensemble of Random Forest, J48, and Artificial Neural Network for classification. The system generates datasets specific training rules and alleviates DoS, User-to-Root (U2R), Remote-to-Local (R2L), and Probe attacks with 90% accuracy. In [23], Genetic algorithm is used for generating DSNSF and Fuzzy Logic is used for detection. Fuzzy approach when compared with rigid threshold approach, outlier approach, Classification with Corelation Analysis (CkNN) approach, SVM approach, and Ant Colony Optimization for Digital Signatures (ACODS) approach gave highest accuracy, i.e., 96.53%, highest ROC AUC (Area Under ROC Curve) value, i.e., 99.21 and 0.56% FPR. In [24], DSNSF-GA (Genetic Algorithm) is proposed and its predicted behavior is compared with DSNSF-ACO (Ant Colony Optimization) for same dataset and for real flows. The average CC (Correlation Coefficient) value for DSNSF-GA for flows/sec was 0.65 whereas in DSNSF-ACO it was 0.66. However, the NMSE (Normalized Mean Square Error) value of DSNSF-GA was 0.03 for flows/sec. In [25], DSNSF-KM (K-Means clustering) and DSNSF-ACO are generated on real data. For one particular day, for TCP protocol, the CC value for DSNSF-ACO was 0.80 and for DSNSF-KM was 0.82. In [26], fuzzy k-medoids clustering approach is proposed for detection of the attacks. In [27], KDD Cup dataset is analyzed using K-means clustering technique using Oracle 10G Data Miner tool and as per that, TCP protocol is most susceptible to attack whereas DoS and Probe are most prevalent attacks.

80.3.3 Deep Learning Techniques As per [28], in deep learning approaches, instead of features, raw data is fed into the machine for learning, and the machine learn hierarchical discriminative features from the data using neural networks or deep neural network are infused with various layers, where the input of one layer gives output which represents input in some level of abstraction, that further becomes input of next layer and so on. Deep learning techniques enable machines to learn complex hierarchical features. Various techniques employed in intrusion detection are Convolution Neural Networks (CNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machines (RBM), Dilated Convolution Autoencoders (DCA), Deep Belief Network (DBN), Autoencoders

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(AE), Stacked Autoencoders (SAE), Generative Adversarial Networks (GAN), and Convolutional Variational Autoencoder (CVAE). As per [29], Software-Defined Network (SDN) provides a global network overview by logical centralization of controllers. It enables the network manager to control (configure, manage, secure, optimize) the network resources through dynamic automated SDN programs. The authors apply a Deep Neural Network (DNN) in flowbased anomaly detection in SDN environment using six features out of forty-one features of NSL-KDD dataset. The DNN with an input layer (with six dimensions), three hidden layers (with twelve, six and three neurons respectively), and an output layer (with two dimensions) is used. Firstly, they checked the accuracy and precision of the model by varying the learning rate during training and testing phase. At learning rate 0.0001, the accuracy is 91.7%, but during testing phase at learning rate 0.001, precision is highest, i.e., 83%. When the proposed DNN approach is compared with existing machine learning approaches, the performance was quite low with 75.75% accuracy, where Naïve Bayes tree provided 82.02% accuracy. However, in [30], in OpenFlow-based SDN controller, a deep learning-based Gated Recurrent Unit Long Short-Term Memory (GRU-LSTM) network intrusion detection system is proposed. The model applies univariate feature selection with ANOVA F-test followed by Recursive Feature Elimination (RFE) methods for feature selection. The proposed method is tested on subset of NSL-KDD dataset with selected features and received accuracy of 87% with 0.76% FPR. In [31], the authors argue that as OpenFlow controller in SDN has flow information of the network traffic, so it is more suitable for flow-based intrusion detection than traditional network. They have proposed FBM, A flow-based multilayer perceptron model (MLP) to detect anomalies in SDN environment. Six features of NSL-KDD dataset are the six neurons in the input layer of Flow-based MLP (FBM) and forty features become forty neurons in the input layer of packet-based MLP (PBM). Both methods are implemented using H2O.ai, which is an open-source deep learning analysis tool. The proposed FBM is compared with PBM with respect to modeling time and detection time. AUC value, modeling time, and detection time for different activation functions and different number of hidden layers in FBM are measured and resulted in idea that PBM takes 123% of modeling time taken by FBM. AUC of PBM is 0.91 than 0.89 in FBM. Also, when in experiment, 0.92 True Positive Rate (TPR) is achieved, FBM has lower false positive rates than PBM. In [32], the researchers have used extended version of Replicator Neural Networks (RNN), a deep learning technique which is a fully connected multilayer perceptron, in which there are five layers, an input layer (four features), three hidden layers (seven, three, and four features, respectively), and an output layer (four features). They have used MAWI dataset by extracting four flows with four most prominent features using ‘yaf’ tool. The dataset is injected with DDoS resource exhaustion attacks and SYN port profiling scan anomalies. In the proposed version, the activation function of the middle layer (layer three) varies from that of the original implementation. RNNs are implemented successfully to detect anomalies in training and in unknown data.

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In [33], a two-level deep machine learning model is proposed for anomaly detection in 5G networks. In the first level, symptoms anomaly detection module implementing a Deep Belief Networks (DBN) or an Stacked Autoencoders (SAE), takes the flows (TCP/IP and ICMP flows) as input from flow exporter, identifies symptoms (local anomalous traffic conditions) and transmits the symptom packet composed of the feature vector involved, a time stamp, and the type of anomaly detected to the second module, i.e., network anomaly detection module. The module in second level is implemented using Long Short-Term Memory (LSTM) recurrent network, in which the input from first level is used to recognize temporal patterns of typical attacks. The system has obtained a peak performance of 2.47 million feature vectors per second. Here, the focus was on faster response time instead of accuracy.

80.3.4 Ensemble Learning As per [34], ensemble method includes training many classifiers at the same time, to solve the same problem and then combining their output to obtain better prediction, flexibility, and accuracy. As per [35], random forests or random decision forests are ensemble learning methods used for classification, regression, and other tasks. In [36], random forest algorithm is used for classification. 10 variants of UNB ISCX dataset (nine features) with flows of TCP, UDP, and ICMP protocols are used. Variants of dataset are formed dropping one feature in each. When performance of these ten variants is compared with each other, it is found that the alternative where no feature is dropped gives better results. In [37], BigFlow, a reliable stream learning intrusion detection engine is proposed. It is based on a possibility that a rejected instance may not be an anomaly rather it may be a change in normal behavior over time. Stream learning technique is applied to each rejected event, which is labeled either by signature-based NIDS or a human expert manually and the classifier is updated. The proposed approach when compared with four ML classifiers (decision tree, random forest, gradient boosting, and an ensemble) on MAWI flow (flow-based) real dataset shows a loss of accuracy over a longer period of time in all classifiers as compared to proposed approach with only 4.2% of total storage and 4.2% of total training time. In [22], an ensemble of random forest, J48, and artificial neural network are used.

80.3.5 Outlier Techniques As per [38], outlier detection techniques are used to find the patterns form data that highly vary from rest of the data. Few of the outlier detection methods are: Z-score or extreme value analysis, probabilistic and statistical modeling, linear regression models, proximity-based models, information theory models, and high-dimensional outlier detection methods.

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In [39], the researchers proposed Neighborhood Outlier Factor (NOF) outlier detection approach, which is based on idea that in statistical approaches and rulebased expert systems, on encountering larger datasets, the results are not accurate. In the proposed approach, focus is on additional training time, accurate identification of low, common attacks, and attack classification. A set of various big datasets is used to train IDS in the initial stage at distributed storage environment. The attacks detected are Probe, DoS, U2R, and R2L. Proposed approach is compared with other machine learning approaches like back propagation neural network, artificial neural network, and fuzzy clustering and hyperbolic hopfiled neural network. As compared to existing ML approaches, execution time increase with increase in data is less in proposed approach. Detection rate increases as the dataset size increases. CPU utilization level is minimum and it stays minimum even when dataset size increases. The CPU utilization of proposed approach was only 8–20%, which was much lesser than other ML approaches.

80.3.6 Other Techniques Some other techniques are also utilized by researchers in designing their intrusion detection systems. As in [40], Intrusion Detection Based on Feature Graph (IDBFG) is proposed which records the patterns of various attacks with a novel graph structure, and the behaviors in accordance with the patterns in graph are detected as intrusions. The proposed model IDBFG performs better than SVM and decision tree in terms of detection rates, FAR, and run time. In [41], a new solution to find anomalies in a computer network by applying paraconsistent logic in a non-intrusive manner is proposed. It uses operating attributes from router logs, on which details regarding the client’s request for a resource from server are saved. This system first divides the network operations (for all hosts) in intervals of equal length. Then, most critical interval is found, which is further subdivided into subintervals of equal lengths. Then again, the most uncertain interval is further divided into smaller interval and this process continues till the host with anomalous behavior is found. This paper has identified the host with anomalous behavior in real time on continuously gathered data from a real operating network.

80.3.7 Combination of Techniques Many researchers apply more than one technique on the same datasets to compare the results obtained and draw conclusions. Some of these are: In [42], the proposed Correlational Paraconsistent Machine (CPM) correlates the DSNSF generated by two unsupervised models ARIMA (statistical technique) and ACODS (clustering technique) to improve detection. Flash crowd anomalies are detected with 96.5% TPR and 5% FPR.

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In [43], three DSNSF:Principal Component Analysis for Digital Signatures (PCADS), a statistical method, Ant Colony Optimization for Digital Signature (ACODS), a metaheuristic method, and Auto Regressive Integrated Moving Average for Digital Signature (ARIMADS), a forecasting method, are generated with paraconsistent logic to deal with uncertainty. ARIMADS and ACODS performed much better than PCADS. FPR in ARIMADS is 0%, in PCADS is 8%, and in ACODS is 10%. TPR in all is 98%. In [44], two DSNSF, PCADS based on statistical approach and ACODS based on machine learning approach are generated. An average of 0.7 CC values were achieved for both approaches. FPR in PCADS is 21% and in ACODS is 24% whereas TPR for both is 92%. In [42], two DSNSF are generated, one using ARIMA, a forecasting approach and the other using ACODS, a clustering approach. These DSNSF are integrated with paraconsistent annotated logic (pal2v) to find anomalies. It recorded 95% TPR and 4% FPR using proposed CPM, which was better than ARIMA and ACODS without paraconsistent logic. In [45], a Local Adaptive Multivariate Smoothing (LAMS) method is proposed, which reduces the unstructured false positive by smoothing the output of detector. Unstructured false positives are usually a random noise caused by the stochasticity of the network traffic. The output of the anomaly detector goes to LAMS which smoothes them and reduces the unstructured false positive by replacing the output of anomaly detector with average anomaly score of similar events observed in the past. Two different anomaly detection engines are used: NetFlow anomaly detection that uses NetFlow records (three different datasets) and HTTP anomaly detection that uses proxy logs containing information extracted from HTTP header, which is used by Cisco Cognitive Threat Analytics (CTA) an anomaly-based IDS. On all three datasets, AUC score with LAMS was found better than without LAMS. AUC score for SSH cracking request without LAMS was 0.80 and with LAMS was 0.83. Also, for SSH scan request, AUC score without LAMS was 0.93, which increased to 1 with LAMS. In [46], TCLUS, an effective tree-based clustering algorithm to identify compact as well as overlapping clusters is proposed. Outlier score on the cluster is calculated to find anomalous events. The proposed approach is compared with popular outlier detection methods Local Outlier Factor (LOF), Orca (a program that mines distancebased outliers from large multivariate datasets), and ROS (an outlier score function). The average accuracy 92.83% and precision 89.97% of the proposed approach were much higher than that of the other approaches. Also, the execution time required by LOF and Orca increases as the dataset size increases, but execution time taken by ROS and proposed approach was almost same always.

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80.4 Discussion and Research Gaps An IDS that works in real-time environment with dynamic dataset is need of the hour. Handling a real-time data is quite challenging. A statistical technique works well with small to moderate size datasets. For larger datasets, machine learning is found effective but when scale of data grew more, deep learning outshines the traditional machine learning techniques. • IDS should be able to handle huge and fast network traffic smoothly by detecting attack without missing a single event. So computational speed is important. Very few researchers have worked on CPU utilization time, energy consumption, training time, and testing time, which greatly affect performance while working in real time. • Creating a detailed profile that consists of all possible normal behaviors is a complex task as it is difficult to draw an exact boundary between benign and malign behavior. In that case, different distance and similarity measures can be tried and tested and the best one should be chosen. Very few papers emphasized on varying threshold value of the selected measures used during their experiments, which may affect the detection rate. • Many a times during long run, the normal behavior gets changed. The IDS should not treat such change as anomaly but its normal behavior should be updated. Most of the researchers did not test their IDS in long run, which is a research gap. • Sometimes the events are uncertain, i.e., they do not match with normal predicted behavior but they are not anomalous as well. Such events should be dealt accordingly, which will result in low FPR. • Attackers may attempt to detect, bypass, or disable the IDS installed on a network. In such cases, an IDS invisible to attacker is required, which can be achieved by restricting the communication allowed between security components and private network [47].

80.5 Conclusion and Future Work The paper has reviewed anomaly detection system with detailed explanation of flows collection. The review of the classification and detection techniques proposed by researchers is provided with their results followed by research gaps in the end. This review justifies that there is a need of enhanced research in the field of flow-based anomaly detection systems. In the future work, various flow-based datasets used by researchers will be reviewed in detail along with the techniques used to preprocess the features before applying the classification and detection techniques.

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References 1. Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2025, https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldw ide/. Last accessed 2020/04/18 2. J. Anderson, Computer Security Threat Monitoring and Surveillance. Technical Report (James P. Anderson Company, Fort Washington, 1980) 3. D. Denning, An intrusion-detection model. IEEE Trans. Softw. Eng. 13(2), 222–232 (1987) 4. N. Moustafa, J. Hu, J. Slay, A holistic review of network anomaly detection systems: a comprehensive survey. J. Netw. Comput. Appl. 128, 33–55 (2019) 5. M.F. Umer, M. Sher, Y. Bi, Flow-based intrusion detection: techniques and challenges. Comput. Secur. 70, 238–254 (2017) 6. R. Sharma, A. Guleria, R. Singla, An overview of flow-based anomaly detection. Int. J. Commun. Netw. Distrib. Syst. 21, 220–240 (2018) 7. R. Sharma, R. Singla, A. Guleria, A new labeled flow-based DNS dataset for anomaly detection: PUF dataset. Procedia Comput. Sci. 132, 1458–1466 (2018) 8. A. Sperotto, R. Sadre, F. Van Vliet, A. Pras, A labeled data set for flow-based intrusion detection. in International Workshop on IP Operations and Management (Springer, Berlin, Heidelberg, 2009), pp. 39–50 9. D. Fernandez, H. Lorenzo, F.J. Novoa, F. Cacheda, V. Carneiro, Tools for managing network traffic flows: a comparative analysis, in IEEE 16th International Symposium on Network Computing and Applicationsx (IEEE, Cambridge, 2017), pp. 39–50 10. G. Vormayr, J. Fabini, T. Zseby, Why are my flows different? A tutorial on flow exporters. IEEE Commun. Surv. Tutor., 1–40 (2020) 11. B. Han, J. Lee, J., Sohn, S., Ryu, J., T. Chung, pFlours: a new packet and flow gathering tool, in 10th International Conference on Advanced Communication Technology. (IEEE, Gangwon-Do, 2008), pp. 731–736 12. Z. Zhao, X. Shi, X. Yin, Z. Wang, Q. Li, HashFlow for better flow record collection, in 39th International Conference on Distributed Computing Systems (IEEE, Dallas, 2019), pp. 1416– 1425 13. R. Hofstede, V. Bartoš, A. Sperotto, A. Pras, Towards real-time intrusion detection for NetFlow and IPFIX, in Proceedings of the 9th International Conference on Network and Service Management (IEEE, Zurich, 2013), pp. 227–234 14. M. De Assis, J. Rodrigues, M. Proença, A novel anomaly detection system based on sevendimensional flow analysis, in IEEE Global Communications Conference (IEEE, Atlanta, 2013), pp. 735–740 15. S. Chang, X. Qiu, Z. Gao, K. Liu, F. Qi, A flow-based anomaly detection method using sketch and combinations of traffic features, in International Conference on Network and Service Management (IEEE, Niagara Falls, 2010), pp. 302–305 16. J. Filho, Treatment of uncertainties with algorithms of the paraconsistent annotated logic. J. Intell. Learn. Syst. Appl. 4, 144–153 (2012) 17. E. Pena, S. Barbon, J. Rodrigues, M. Proenca, Anomaly detection using digital signature of network segment with adaptive ARIMA model and para consistent logic, in IEEE Symposium on Computers and Communications (IEEE, Funchal, 2014), pp. 1–6 18. G. Fernandes, J. Rodrigues, M. Proença, Autonomous profile-based anomaly detection system using principal component analysis and flow analysis. Appl. Soft Comput. J. 34, 513–525 (2015) 19. A. Buczak, E. Guven, A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18, 1153–1176 (2016) 20. S. Panwar, P. Negi, Y. Raiwani, Implementation of machine learning algorithms on cicids-2017 dataset for intrusion detection using WEKA. Int. J. Recent Technol. Eng. 8, 2195–2207 (2019) 21. M. Umer, M. Sher, Y. Bi, A two-stage flow-based intrusion detection model for next-generation networks. PLoS ONE 13, 1–20 (2018)

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22. S. Kshirsagar, P. Kumbharkar, Intrusion detection system for large scale data using machine learning algorithms. Int. J. Eng. Adv. Technol. 8, 706–711 (2019) 23. A. Hamamoto, L. Carvalho, L. Sampaio, T. Abrão, M. Proença Jr., Network anomaly detection system using genetic algorithm and fuzzy logic. Expert Syst. Appl. 92, 390–402 (2018) 24. P. Hernandes, L. Carvalho, M. Proença, Digital signature of network segment using flow analysis through genetic algorithm and ACO metaheuristics, in 33rd International Conference of the Chilean Computer Science Society (IEEE, Talca, 2014), pp. 92–97 25. A. Zacaron, L. Carvalho, M. Adaniya, T. Abrão, M. Proença, Digital signature of network segment using flow analysis, in Proceedings of the International Conference on Data Communication Networking, e-Business and Optical Communication Systems (SciTePress, Rome, 2012), pp. 35–40 26. J. Singh, S. Singla, Enhanced intrusion network system using fuzzy–K-Mediod clustering method. Int. J. Innov. Technol. Explor. Eng. 8, 3370–3374 (2019) 27. M. Siddiqui, S. Naahid, Analysis of KDD CUP 99 dataset using clustering based data mining. Int. J. Datab. Theory Appl. 6, 23–34 (2013) 28. Deep Learning for Anomaly Detection: A Survey, https://arxiv.org/abs/1901.03407. Last accessed 2020/05/14 29. T. Tang, L. Mhamdi, D. McLernon, S. Zaidi, M. Ghogho, Deep learning approach for network intrusion detection in software defined networking, in International Conference on Wireless Networks and Mobile Communications (IEEE, Fez, 2016), pp. 258–263 30. S. Dey, M. Rahman, Flow based anomaly detection in software defined networking: a deep learning approach with feature selection method, in 4th International Conference on Electrical Engineering and Information & Communication Technology (IEEE, Dhaka, 2018), pp. 630–635 31. Y. Lai, K. Zhou, S. Lin, N. Lo, F1ow-based anomaly detection using multilayer perceptron in software defined networks, in 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (IEEE, Opatija, 2019), pp. 1154–1158 32. C. Cordero, S. Hauke, M. Muhlhauser, M. Fischer, Analyzing flow-based anomaly intrusion detection using replicator neural networks, in 14th Annual Conference on Privacy Security and Trust (IEEE Auckland, 2016), pp. 317 324 33. L. Maimó, F. Clemente, M. Pérez, G. Pérez, On the performance of a deep learning-based anomaly detection system for 5G networks, in IEEE SmartWorld Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (IEEE San Francisco, 2017), pp. 1–8 34. G. Folino, P. Sabatino, Ensemble based collaborative and distributed intrusion detection systems: a survey. J. Netw. Comput. Appl. 66, 1–16 (2016) 35. Random forest, https://en.wikipedia.org/wiki/Random_forest. Last accessed 2020/07/05 36. D. Fernandez, L. Vigoya, F. Cacheda, F. Novoa, M. Lopez-Vizcaino, V. Carneiro, A practical application of a dataset analysis in an intrusion detection system, in IEEE 17th International Symposium on Network Computing and Applications (IEEE, Cambridge, 2018), pp. 1–5 37. E. Viegas, A. Santin, A. Bessani, N. Neves, BigFlow: real-time and reliable anomaly-based intrusion detection for high-speed networks. Future Gener. Comput. Syst. 93, 473–485 (2019) 38. A Brief Overview of Outlier Detection Techniques, https://towardsdatascience.com/a-briefoverview-of-outlier-detection-techniques-1e0b2c19e561. Last accessed 2020/07/20 39. J. Jabez, B. Muthukumar, Intrusion detection system (ids): anomaly detection using outlier detection approach, in International Conference on Intelligent Computing Communication & Convergence (Elsevier Bhubaneswar, 2015), pp. 338–346 40. X. Yu, Z. Tian, J. Qiu, S. Su, X. Yan, An intrusion detection algorithm based on feature graph. Comput. Mater. Continua 61(1), 255–273 (2019) 41. A. Pimenta, J. Abe, S. Prado, A real-time and non-intrusive analyzer for anomalous behavior of computer networks with paraconsistent logic. INFOCOMP J. Comput. Sci. 17(2), 32–40 (2018) 42. E. Pena, L. Carvalho, S. Barbon, J. Rodrigues, M. Proença, Correlational paraconsistent machine for anomaly detection, in IEEE Global Communications Conference. (IEEE, Austin, 2014), pp. 551–556

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43. G. Fernandes, E. Pena, J. Rodrigues, L. Carvalho, M. Proença, Statistical, forecasting and metaheuristic techniques for network anomaly detection, in Conference on Selected Area in Cryptography (ACM, Salamanca, 2015), pp. 701–707 44. G. Fernandes, L. Carvalho, J. Rodrigues, M. Proença, Network anomaly detection using IP flows with principal component analysis and ant colony optimization. J. Netw. Comput. Appl. 64, 1–11 45. M. Grill, T. Pevný, M. Rehak, Reducing false positives of network anomaly detection by local adaptive multivariate smoothing. J. Comput. Syst. Sci. 83(1), 43–57 (2017) 46. M. Bhuyan, D. Bhattacharyya, J. Kalita, A multi-step outlier-based anomaly detection approach to network-wide traffic. Inf. Sci. 348, 243–271 (2016) 47. G. Bruneau, The History and Evolution of Intrusion Detection (Information Security Reading Room, SANS Institute, 2019). 48. E. Pena, L. Carvalho, S. Barbon, J. Rodrigues, M. Proença, Anomaly detection using the correlational paraconsistent machine with digital signatures of network segment. Inf. Sci. 420, 313–328 (2017)

Chapter 81

Performance Analysis of 250 kWP Roof Top Grid-Connected Solar PV System Installed at MANIT Bhopal Arvind Mittal, Radhey Shyam, and Kavali Janardhan

Abstract The use of grid-connected solar photovoltaic (GCSPV) systems is increasing rapidly, so that appropriate performance analysis calculations play a key role in the emerging solar photovoltaic market. This paper presents the performance analysis of 250 kWp roof top grid-connected solar photovoltaic system installed at sports complex, Maulana Azad National Institute of Technology (MANIT) Bhopal in Madhya Pradesh, India. The installed system consists of total 770 numbers of polycrystalline silicon photovoltaic modules of rating 325 Wp and five numbers of inverters in which three numbers are of 66 kVA and two numbers of 25 kVA power handling capacity. The parameters used in performance analysis are energy injected into grid or total yield, specific yield and performance ratio (PR). Recorded system monitoring data during December 2018 to May 2019 is used for performance analysis. Keywords Performance analysis · Performance ratio (PR) · Solar photovoltaic · Specific yield · Total yield

81.1 Introduction Solar energy is a primary form of energy in universe and it is easily available on earth surface. This solar energy can be converted directly into electrical energy with the help of solar photovoltaic modules. The electrical energy generation from solar photovoltaic system is growing up in India. Nowadays, the grid-connected solar photovoltaic (GCSPV) systems are more popular in growing solar market [1–3]. In this paper, performance analysis of 250 kWp roof top grid-connected solar photovoltaic system using polycrystalline silicon modules installed at sports complex in MANIT Bhopal, Madhya Pradesh, is considered. This installed solar photovoltaic (SPV) system came in operation on November 28, 2018 [4–7].

A. Mittal · R. Shyam · K. Janardhan (B) Energy Centre, Maulana Azad National Institute of Technology Bhopal, Bhopal, Madhya Pradesh 462003, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_81

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The operational performance of system depends on many things such as incident solar irradiance, module and inverter efficiency, ambient and module temperature and used module technology. Performance parameters considered under this study are energy injected into grid or total yield, specific yield (SY) or yield factor and performance ratio (PR). These performance indices are used to compare two or more than two solar systems installed at different locations and different sizes [8–11]. The data is recorded for six months from December 2018 to May 2019 which is used to evaluate performance of installed PV system. In this paper, author presents variation in specific yield, total yield and performance ratio during six months [12–15]. In this paper, author compares performance analysis of two GCSPV systems in Meknes (Morocco). These two installed PV systems were consisted with polycrystalline silicon and mono-crystalline silicon modules technologies. Authors in have evaluated performance analysis of different SPV systems of different size at different sites. This paper also consists four major sections as follows. In Sect. 81.2, the technical aspects of installed SPV system are discussed. The operational performance parameters are explained in Sect. 81.3. Important results and conclusion of study are discussed in Sects. 81.4 and 81.5, respectively.

81.2 System Description The 250 kWp rated grid-connected solar photovoltaic (GCSPV) system under study was installed on the roof of sports complex in MANIT campus in November 2018. Actual views and layout of installed SPV system are shown in Figs. 81.1 and

Fig. 81.1 Actual views of installed system at sports complex in MANIT Bhopal

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Fig. 81.2 System layout

Table 81.1 General information about system

S. No.

Parameter

Value

1

Total number of panels

770

2

Peak power rating of each panel (Wp)

325

3

System power rating (kWp)

250

4

Modules per string

20, 19

5

Total number of strings

10, 30

6

Number of inverters/capacity (kVA)

2/25, 3/66

7

Total area covered by system (Sq. Mts.) 1700

8

Height of structure (mm)

100

9

Latitude

23.2599°N

10

Longitude

77.4126°E

81.2, respectively. The installed PV system consists of total 770 number of multicrystalline silicon modules of RenewSys brand and is fixed on the roof of sports complex with the help of metal mounting structure at sheds tilt angles 5° and 7°. The grid-connected three-phase Schneider inverters are connected in installed GCSPV system. The general information about system is shown in Table 81.1. The electrical characteristics of modules are shown in Table 81.2. In this system, there are 40 total numbers of strings in which ten numbers of strings consist of 20 modules per string and 30 numbers of strings consist of 19 modules per string. The system is connected to utility grid with NET metering.

81.3 Performance Parameters The performance parameters play vital role in estimation of operational performance of installed GCSPV system. The performance ratio and the system losses are the

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Table 81.2 Electrical features of solar modules at standard test conditions S. No.

Parameter

Value

1

Peak power rating of each panel

325.00

2

Power tolerance in Watts

0/ +5.0

3

V o (open circuit voltage in Volts)

45.55

4

I sc (short circuit current in Amps)

9.55

5

V MPP (voltage at max. power point in Volts)

36.86

6

I MPP (current at max. power point in Amps)

8.82

7

Peak system voltage in Volts

1000.00

8

Percentage module efficiency

16.75

9

Fuse rating in Amps

15.00

inversely proportional to each other, if the performance ratio increases then the losses associated with the system decrease. Important performance parameters used in this analysis are described in this section. 1.

2.

3.

Energy Injected into grid The net electrical energy available at output terminals of inverters connected in solar PV system in a given specific period is known as energy injected into grid. The energy injected into grid is also termed as total yield of solar system and it can measure in terms of kWh or MWh. Specific Yield The ratio of net energy output (E) to the DC power rating (Po ) of SPV system at standard test conditions for a particular period is known as specific yield. The specific yield is a convenient performance parameter to compare different solar PV systems of different sizes. Specific yield of system determines the time period for which system generates rated output and it should be high as possible. Specific yield can be measure daily, monthly and yearly basis, and it is also known as final yield and yield factor of installed solar PV system [2]. Performance Ratio (PR)

The ratio of real energy output to nominal or theoretical energy output of SPV system is known as performance ratio. Performance ratio also termed as quality factor, and generally, it is used as a performance indicator of SPV system. Performance ratio is an important performance parameter which is used to compare SPV systems installed at different sites. Performance Ratio(PR) =

Real energy output (kWh) . Theoretical energy output (kWh)

(81.2)

Theoretical or nominal energy output can determine by product of efficiency of modules and incident solar irradiance (kWh) at module surface of the SPV system. Performance ratio can be measure daily, monthly and yearly basis. The performance

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ratio determines system losses that means when system losses high then low performance ratio and when low system losses then high performance ratio. Ideally, the performance ratio should be unity because at ideal condition, there are no losses in system. Performance ratio can be affected by various factors such as temperature, irradiance, efficiency of modules and inverters, used solar technology and recording period.

81.4 Results and Discussion The results of operational performance analysis of 250 kWp roof top GCSPV system are presented in this section. Figures 81.3, 81.4, 81.5 and 81.6 show the evaluated performance of installed system using system monitoring and data recording during Dec 2018—May 2019. Plot in Fig. 81.3 shows monthly total energy generated by installed solar photovoltaic system. Energy generation is maximum in December 2018 followed by March 2019. The plots of specific yield and performance ratio are

Enegy (kWh)

30000.00 20000.00 10000.00 0.00 Dec-2018

Jan-2019

Feb-2019

Mar-2019

Apr-2019

May-2019

Mar-2019

Apr-2019

May-2019

Apr-2019

May-2019

Specific Yield (kWh/kW)

Fig. 81.3 Plot of monthly total energy generation

4.00 2.00 0.00 Dec-2018

Jan-2019

Feb-2019

Performanc e Ratio (%)

Fig. 81.4 Plot of monthly specific yield

100.00 50.00 0.00 Dec-2018

Jan-2019

Feb-2019

Mar-2019

Fig. 81.5 Plot of monthly performance ratio of installed system

Solar Irradiance (kWh)

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Jan-2019

Feb-2019

Mar-2019

Apr-2019

May-2019

Fig. 81.6 Plot of monthly solar irradiance

shown in Figs. 81.4 and 81.5, respectively. Specific Yield of system varies from 1.63 to 2.88 during these six months. The performance ratio (PR) of installed PV system found maximum in December 2018 (80.65%) followed by January and February 2019. Solar irradiance at site is shown in Fig. 81.6. The solar irradiance is high in April and May but low energy generation during this period because of fault in system during these months.

81.5 Conclusion The operational performance analysis of 250 kWp roof top GCSPV system installed over sports complex in MANIT Bhopal has been evaluated on the basis of performance parameters energy injected into grid or total yield, specific yield (SY) and performance ratio (PR). The average monthly total yield of installed SPV system is found in a range of 12,660–22,201 kWh during six months. The energy generated is less in the month of May because synchronization errors were found in inverter 1, inverter 2 and inverter 3. The operational performance ratio of installed SPV system is maximum and minimum in the months December 2018 and May 2019, respectively. The performance ratio in May 2019 is 26.02% but in this month, solar irradiance available was good enough, but the generation was low because of the problem in the inverters at grid interfacing and the dust particles formation on the panel’s surface. Proper cleaning of the panels in the regular intervals is recommended. The operational performance of installed SPV system also affected by dust particles on modules so cleaning of modules should be regularly and properly.

References 1. A.K. Berwal, S. Kumar, N. Kumari, V. Kumar, A. Haleem, Design and analysis of rooftop grid tied 50 kW capacity solar photovoltaic (SPV) power plant. Renew. Sustain. Energy Rev. 77, 1288–1299 (2017) 2. S. Singh, R. Kumar, V. Vijay, Performance monitoring of 43 kW thin-film grid- connected rooftop solar PV system, in 2014 IEEE 6th India International Conference on Power Electronics (IICPE), Kurukshetra (2014), pp. 1–5

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3. X. Zou, B. Li, Y. Zhai, H. Liu, Performance monitoring and test system for grid-connected photovoltaic systems, in 2012 IEEE Asia Pacific Power and Energy Engineering Conference (APPEEC) (2012), pp. 1–4 4. K. Janardhan, T. Srivastava, K. Sudhakar, Matlab Modelling and Simulation of Solar Photovoltaic Panel (LAP LAMBERT Academic Publishing, 2013) 5. H.A. Kazem, T. Khatib, K. Sopian, W. Elmenreich, Performanceand feasibility assessment of a 1.4 kw roof top grid-connected photovoltaic power system under desertic weather conditions. Energy Build. 82, 123–129 (2014) 6. K. Atluri, S.M. Hananya, B. Navothna, Performance of rooftop solar PV system with crystalline solar cells, in 2018 National Power Engineering Conference (NPEC), Madurai (2018), pp. 1–4 7. K. Attari, A. Elyaakoubi, A. Asselman, Performance analysis and investigation of a gridconnected photovoltaic installation in Morocco. Energy Rep., 261–266 (2016) 8. A. Allouhi, R. Saadani, T. Kousksou, R. Saidur, A. Jamil, M. Rahmoune, Grid-connected PV systems installed on institutional buildings: technology comparison, energy analysis and economic performance. Energy Build. 130, 188–201 (2016) 9. K. Janardhan, T. Srivastava, G. Satpathy, K. Sudhakar, Hybrid solar PV and biomass system for rural electrification. Int. J. ChemTech Res. 05(02), 802–810 (2013) 10. B. Shiva Kumar, K. Sudhakar, Performance evaluation of 10 MW grid connected solar photovoltaic power plant in India. Energy Rep., 184–192 (2015) 11. R. Sharma, S. Goel, Performance analysis of a 11.2 kWp roof top grid-connected PV system in Eastern India. Energy Rep., 76–84 (2017) 12. K. Janardhan, A. Mittal, A. Ojha, A symmetrical multilevel inverter topology with minimal switch count and total harmonic distortion. J. Circ. Syst. Comput. (2020) 13. K. Janardhan, A. Mittal, Analysis of various control schemes for minimal total harmonic distortion in cascaded H-bridge multilevel inverter. J. Electr. Syst. Inf. Technol. 03(03), 428–441 (2016) 14. S. Sundaram, J.S.C. Babu, Performance evaluation and validation of 5 MWp grid connected solar photovoltaic plant in South India. Energy Convers. Manage. 100, 429–439 (2015) 15. K. Janardhan, A. Mittal, A. Ojha, Performance investigation of stand-alone solar photovoltaic system with single phase micro multilevel inverter Energy Rep. 06, 2044–2055 (2020)

Chapter 82

An Ensemble Model of Machine Learning for Primary Tumor Prognosis and Prediction Tejinderdeep Singh, Prabh deep Singh, and Rajbir Kaur

Abstract Machine learning is an artificial intelligence division that utilizes various computational, probabilistic, and optimization techniques that enable computers to “read” from previous examples and to identify trends that cannot be discerned from large, noisy, or complex datasets. This is particularly suitable for medical applications, particularly those dependent on complex proteomic and genomic measurements. The primary tumor is the deadliest disease, with a high mortality rate. Machine learning is therefore frequently used in the diagnosis and detection of primary tumors. Machine learning was more recently used to forecast and predict primary tumor diagnosis. The classification of the primary tumor may be defined with different machine learning algorithms, given the extreme impact of the disorder. Logistic regression, SVM, random forest, AdaBoost node, Naive Bayes, K-neighbor grouping, decision tree, and the Gaussian system classifiers are discussed. The dimensional reduction is implemented to simplify the dataset to reduce the measurement time. The objective of this paper is to propose an ensemble model for the prediction of the primary tumor. Keywords Primary tumor · Machine learning · Ensemble model

82.1 Introduction The human body comprises of several cell forms. Living cells are increasing and splitting into ordered and regulated development of new cells. Nevertheless, the mechanism of cell development will go bad if new cell growth occurs, even though it is not required. The effect becomes a collection of excess tissue known as a tumor. A primary tumor refers to a tumor that was first developed at the initial site. Cancerous cells can invade or spread into other parts of the body to create secondary tumors. A successful diagnosis is a daunting challenge as the distribution happens [1]. Therefore it is necessary to diagnose tumors at the original site for effective treatment planning. T. Singh IKG Punjab Technical University, Kapurthala, India P. Singh (B) · R. Kaur Punjabi University, Patiala, Punjab, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_82

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The classification of primary tumor data in machine learning is a challenge, given the multi-class and unequaled characteristics of data collection. In the past, several learning algorithms were suggested to address this challenge. The main groups, such as lung and stomach tumors, appear three to five times more than the overall incidence of the 21 distinct forms of primary tumors, whereas the minority groups appear below a number [2]. The primary tumor evidence included in our research comprises six minority groups, i.e., double and small intestines, glands of the salivary, kidney, testis, cervix uterus, and vagina. Many data mining algorithms also struggle in minority groups due to their incredibly small frequency [3]. Machine learning is a technique that can better classify the underlying data and forecast the unlabeled data class accurately. The accuracy of the data mining process is directly based on the consistency of the training results. Low-quality results include clutter, loss of values, and class imbalances [4]. Classification models are skewed against the dominant class in a social difference in such a manner that the models will accurately forecast the predominant population, but minority class knowledge instances appear to be mispredicted.The machine learning technology group has recently attracted significant exposure to solving this issue.

82.2 Proposed Approach Steps involved in ensemble base classification algorithm (Fig. 82.1). 1. 2.

3.

4.

The data is preprocessed and cleaned with the needless variables. The functionality transferred to the ensemble base classification algorithm is used for training and testing 66% of the knowledge has been qualified for training and the rest of the data is checked by applying ensemble-based classification algorithm. Opinions are confirmed by the difference between emotions. The thoughts are transmitted further by sending them to an ensemble-based classification algorithm. The users are suggested for final results and a confusion matrix is created.

Fig. 82.1 Working of proposed ensemble model

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The performance of the proposed ensemble model is evaluated by recall, precision, and accuracy.

A total of nine algorithms are used on the primary tumor data set that consists of 569 patients to diagnose primary tumors more reliably and rapidly. Output measures are used to evaluate each algorithm’s performance. This paper aims to examine different algorithms and to deliver better results in the field of primary tumor detection. Quantitative analysis is performed that could help people minimize the deaths of primary tumor patients by saving time with correct, quick diction, and early recognition. Algorithms are checked for sensitivity, accuracy, time complexity, precision. As it is so important in our current age, we are keen to research it further to produce stronger and more accurate outcomes.

82.2.1 Preprocessing Machine learning application preprocessing is an important move toward increasing the accuracy of the data to facilitate the generation of useful data insights. Machine learning data preprocessing relates to the methodology for planning (cleaning and organizing) the raw data for the construction and training of computer models [5]. In brief, machine learning data preprocessing is a data mining technique that converts raw data in a comprehensible and usable format.

82.2.2 Data Visualization Information visualization is the knowledge and analysis of digital depiction. Data visualization tools are available to view and understand trends, outliners, and patterns in data using visual elements like charts, graphs, and maps [6]. Data visualization refers to techniques that communicate data insights through visual representation. The key aim is to distill massive datasets into simple representations such that complicated interactions in the data are readily interpreted. It is also used interchangeably with concepts like graphical knowledge, mathematical analysis, and representation of information. Figure 82.2 shows visualization of dataset.

82.2.3 Feature Scaling Feature scaling is one of the most critical phases in data preprocessing before the development of a machine learning application. That is, the values should be in the same or similar ranges such that no one variable dominates the others. The scale makes a difference between a bad and a stronger model of learning. By developing

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Fig. 82.2 Visualization of dataset

a learning model, it is important to limit the alternatives to a range that is zerotargeted. This should be achieved such that the volatility of the choices is the same. The goal is to meet mathematicians with zero average and variance of the function. There are several methods to achieve so, standardization and normalization being the two best. Standardization removes the Z values. For algorithms using zero-center, standardization, and mean normalization is used [7].

82.2.4 Train-Test Split A machine learning algorithm operates in two phases as it deals with datasets. It usually split the data between testing and training phases by 20–80% [8].

82.2.5 Machine Learning Algorithm The architecture uses the binary classification of marked knowledge. A total of nine algorithms are implemented to find the best. The algorithms selected based on their prediction ability. The random forest (RF) is the machine learning algorithm used for this particular problem. The random forest (RF) is the machine learning algorithm used for this particular problem whereas vector machines, Naive Bayes, AdaBoost, decision tree. Help for logistic regression (LR) [9]. The algorithms’ performance values have been properly equated to assess the most reliable disease prediction.

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Logistic regression Logistic regression is a classification method used to estimate the outcome of a categorically dependent variable. The dependent variable in logistic regression is a binary variable that contains data coded as 1 or 0 [10]. It is one of the most common models for categorical data adaptation, especially for data modeling binary response data. It is the most significant component of a family of models known as generalized linear models. n relation to linear regression, logistic regression will explicitly estimate probabilities and, by the probabilities expected by other classifications, such as Naive Bayes, these probabilities are well equalized. Logistic regression conserves aggregate training sample probabilities. The model coefficients often reflect the relative value of each input variable. SVM Support vector machine (SVM) is a guided algorithm for primary learning that can be used both for problems of classification and regression. It is used primarily in classification issues, however [11]. In the SVM algorithm, each data object is drawn as a point in n-dimensional space and the value of each element is the value of a given coordinate. Then we distinguish by finding the hyperplane which differentiates quite well between the two groups. Help vectors are human measurement coordinates. The main goal of SVM is to define a plane that can give the greatest margin between the data points of both classes. Bringing the margin into consideration should include some extra help to categorize the period leading up to data points with more confidence. Hyperplanes are the selection area that allows data points to be categorized. Transformed groups may be priced for the movement of data points on all sides of the hyperplane. The hyperplane proportion relies on the number of attributes. If the input value is 2, the hyperplane is a line. The hyperplane generates a two-dimensional plane if the sum of the input value is 3. It would be impossible to imagine as the meaning reaches 3. The support vectors are data points near to the hyperplane and affect the hyperplane direction and alignment. The support vectors restrict the classifier distance. The replacement of the support vectors removes the hyperplane position. Random Forest Random forests somehow create a tree and do it naturally. The number of trees in the forest and the production it may generate are explicitly communicated: The greater the volume of trees, the better the findings are right. This may be used for both categorization and regression functions. Overfitting is a critical target that may increase performance. If there are plenty of trees in the jungle in the random forest algorithm, the classifier does not match the structure. The random forest classifier can also manage missing output. The classification should also be modeled as categorical output [12]. Naive Bayes Naive Bayes is a very basic, fast prediction and classification algorithm. Other attribute values. It is founded on Bayes’s theorem of probability. This is primarily

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used to categorize text that contains high-level training datasets. Many definitions include email pressure, sentimental commentary, and the news clause. This is perfect for usability and efficacy. It assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Naive Bayes is the best and most reliable algorithm for cracking texts [13].

82.3 Experimental Setup WEKA Platform (Waikato Framework for Information Analysis) is used for implanting machine learning. WEKA is an open-source platform that provides data preprocessing methods, applying various machine learning algorithms and simulation tools to build machine learning strategies to address the issue of real-world data mining. WEKA supports several standard tasks in data mining, in particular data preprocessing, clustering, classification, regression, visualization, and selection of features. The specific versions may be used on the same dataset. We may evaluate the outputs of various models and select the right one for your function. WEKA tool gives us the model processing statistical efficiency. This provides us with a visualization method for data review.

82.4 Performance Metrics This paper focuses primarily on the analysis of various classification problems and focuses mainly on classification from this category results matrix. The labeled vector 1(malignant) is a favorable case to diagnose primary tumor which specifically shows that the individual has breast cancer. 1.

2.

Confusion matrix The confusion matrix is often known as a readily understood matrix, whereas the precision and rightness of a concept may be defined by the most specific matrix. A confusion matrix is a summary of the classification problem prediction results. The confusion matrix structure allows users to conceptualize confusion matrix effectiveness. Here are the actual classes represented by each matrix row. A single column, on the other side, follows the definition in a pre-defined class or the other direction [14]. Accuracy Accuracy means the proportion of the proper forecast assembled by the classification data model in the entire anticipation number assembled by the classifier. When we nearly match the goal variable groups in a dataset, we should assume reasonable accuracy [15].

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Precision Precision is also what produces the ratio of true positives and the number of false positives. High precision implies that more suitable results have been produced by an algorithm than incorrect [16]. Recall The recall is a calculation of the number of patients that have been expected to have complications versus those who have complications. A strong reminder indicates that an algorithm achieved optimum performance [17].

82.5 Conclusion Machine learning is an artificial intelligence branch that uses various methods of statistical, probabilistic, and optimization to “learn” computers from past examples and to detect patterns from large, noisy, or complex datasets that are hard to discern. Machine learning has recently been used to detect and cure the primary tumor. Application of machine learning models for disease prediction and prediction has become an irrevocable part of tumor studies aimed at improving the treatment and treatment of patients subsequently. The paper discusses an approach to the problem in which a tumor-inclusive clinical feature originally developed, which combines tumor stage, tumor size, and age at diagnosis is the main factor for predicting survival. Our studies reveals that the linear support vector regression, lasso regression, kernel ridge regression, K-neighborhood regression, and decision tree regression are helpful these models produce the strongest expected outcomes. The precise cross-validation reveals the best production on the analyzed primary tumor results of the same types.

References 1. Y. Xie, G. Schreier, , D.C. Chang, S. Neubauer, Y. Liu, S.J. Redmond, N.H. Lovell, N.H., Predicting days in hospitals using health insurance claims. IEEE J. Biomed. Health Inform. 19(4), 1224–1233 (2015) 2. T. Miyano, T. Tsutsui, Y. Seki, S. Higashino, H. Taniguchi, Prediction of care class by local additive reference to prototypical examples. IEEE Trans. Inf Technol. Biomed. 9(4), 502–507 (2005) 3. C.H. Ting, M. Mahfouf, A. Nassef, D.A. Linkens, G. Panoutsos, P. Nickel, A.C. Roberts, G.R.J. Hockey, Real-time adaptive automation system based on identification of operator functional state in simulated process control operations. IEEE Trans. Syst. Man Cybern. Part Syst. Hum. 40(2), 251–262 (2009) 4. C. Yang, C. Delcher, E. Shenkman, S. Ranka, Machine learning approaches for predicting high cost high need patient expenditures in health care. Biomed. Eng. Online 17(1), 131 (2018) 5. N. Boodhun, M. Jayabalan, Risk prediction in life insurance industry using supervised learning algorithms. Complex Intell. Syst. 4(2), 145–154 (2018) 6. K.D. Singh, S.K. Sood, 5G ready optical fog-assisted cyber-physical system for IoT applications. IET Cyber-Phys. Syst. Theory Appl. 5(2), 137–144 (2020) 7. S.K. Sood, K.D. Singh, Optical fog assisted smart learning framework to enhance students’ employability in engineering education. Comput. Appl. Eng. Educ. 27(5), 1030–1042 (2019)

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8. S.K. Sood, K.D. Singh, An Optical-Fog assisted EEG-based virtual reality framework for enhancing E-learning through educational games. Comput. Appl. Eng. Educ. 26(5), 1565–1576 (2018) 9. S.K. Sood, K.D. Singh, SNA based resource optimization in optical network using fog and cloud computing. Opt. Switching Network. 33, 114–121 (2019) 10. Y. Zhu, H. Wu, M.D. Wang, Feature exploration and causal inference on mortality of epilepsy patients using insurance claims data, in IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (IEEE, 2019), pp. 1–4 11. Y. Ren, K. Zhang, Y. Shi, Survival prediction from longitudinal health insurance data using graph pattern mining, in 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (IEEE, 2019), pp. 1104–1108 12. S. Chakraborty, S. Aich, A. Kumar, S. Sarkar, J.-S. Sim, H.-C. Kim,Detection of cancerous tissue in histopathological images using dual-channel residual convolutional neural networks (DCRCNN), in 2020 22nd International Conference on Advanced Communication Technology (ICACT) (IEEE, 2020), pp. 197–202 13. K.D. Singh, S.K. Sood, Optical fog-assisted cyber-physical system for intelligent surveillance in the education system. Comput. Appl. Eng. Educ. 28(3), 692–704 (2020) 14. P. Kaur, P. Singh, K. Singh, Air Pollution Detection Using Modified Traingular Mutation Based Particle Swarm Optimization (2019) 15. S.K. Sood, K.D. Singh, Identification of a malicious optical edge device in the SDN-based optical fog/cloud computing network. J. Opt. Commun. 1 (ahead-of-print) (2018) 16. V. Gupta, H. Gill Singh, P. Singh, R. Kaur, An energy efficient fog-cloud based architecture for healthcare. J. Stat. Manag. Syst. 21(4), 529–537 (2018) 17. N. Singh, P. Singh, R. Kaur, Design and development a hybrid classifier to improve lung cancer diagnosis. J. Gujarat Res. Soc. 21(15), 323–328 (2019)

Chapter 83

Implementing Fog Computing for Detecting Primary Tumors Using Hybrid Approach of Data Mining Jasdeep Singh, Sandeep Kad, and Prabh Deep Singh

Abstract In these days, basic tumor ailment is a big health issue. A primary tumor is a cyst developing at the anatomy site where tumor growth starts and progresses to produce carcinogenic stuff. Internet of things (IoT) devices has the ability to sense and disseminate patient data. The huge amount of data created by intelligent IoT equipments is processed by fog devices. Fog computing in healthcare is becoming very popular as it brings processing capabilities to the edge of the network. In this paper, we have proposed three layered architecture based on fog computing to detect primary tumors which leads to reduction in propagation latency time, network use and energy consumption. As a result, real-time response to primary tumor problems is now possible. The data produced in IoT devices is preserved in cloud for long-term processing to produce statistical results. It helps in strong backups, recovery and high availability. For detecting primary tumors, hybrid approach of data mining is used to uncover hidden patterns, correlations and make decision related to person’s health. In hybrid approach of data mining, we combine three data mining techniques, namely simple logistics, J48 and random forest to obtain high-accuracy, kappa statistics result, TP rate, recall value, F-measure, area under ROC curve and low-root mean square error, root relative squared error. WEKA tool is used for the implementing hybrid approach of data mining. Keywords Cloud · Fog · IoT · Data mining · Healthcare · Primary tumors

83.1 Introduction Cancer is a deadly disease of future and the main reason is environmental component which mutate genes encoding serious cell regulatory proteins. The consequential, abnormal cell behavior give rise to too much stuff of abnormal cells which kills surrounding tissues and can expand to crucial organs leading to this deadly disease J. Singh · S. Kad Department of CSE, Amritsar College of Engineering and Technology, Amritsar, Punjab, India P. D. Singh (B) Department of CSE, IKG Punjab Technical University, Kapurthala, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_83

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of cancer which is an impending danger to patient’s life [1–3]. Tumor expands from where it first originated to grow known as primary tumor, to spread cancers in other body parts also known as secondary tumors. The primary cyst is normally removed easily. To stop the growth of associated tumors (secondary tumors), our main motive is to trace primary tumors. It is very important to detect tumor at an early stage and provide precautionary course of action to infected and uninfected persons [4–8]. We are not giving sufficient attentiveness to real-time analytics as well as to decision making which is the main purpose of IoT, though technologies and solutions which enable linkage and data delivery are growing very fast. The IoT paradigm ensures to make consumer electronic medical devices etc. as part of Internet setup. By applying intelligent analytics on data produced by IoT devices, IoT visualizes a new world of linked devices and humans beings in which quality of life is improved [9–13]. As big data generated by IoT devices is transferred through a network, the possibility of error generation is more because the data transmission delay and packet dropping chances are directly proportional to the amount of data transmitted. In case of crises, delay in alerting the patient affects his life and minor mistake in data transmission leads to incorrect diagnosis. Thus, there is a requirement to diminish data transmission between end users and cloud servers. Heath-related applications also need quick analysis of data as well as real-time decision making without delay which is not feasible in cloud computing scenario. So, fog layer is introduced between mobile end users and cloud server [14–18]. Fog computing paradigm brings cloud computing services to an edge of networks, which leads to decrease in delay in data transmission as well as low packet dropping chances [13, 19, 20]. Concept of data mining is used to spot latent patterns which can be drawn into valid information. Techniques of data mining have better prospects in the region of disease diagnosis and medical healthcare patterns. There is a number of data mining techniques, for example, classification, association, regression and clustering that are applied on data for result prediction. The workload of physicians is reduced to a great extent in detecting primary tumors using this system. Doctors do different medical investigations but avert concentrating this ailment which may have harsh consequences if it break out in humans [21–24].

83.1.1 Emerging Technologies in Healthcare The main function of IoT devices is to gather data concerned with health associated symptoms. The gathered data consists of various health-related attributes [14, 25, 26]. Every day, huge amount of structured and unstructured data is produced online. This large amount of data with growing volume, velocity and variety is known as big data. The volume, complexity and data’s growth rate has reached to its maximum that conventional systems are unable to handle and resulted in failure of processing big data [8, 13, 27, 28]. In the present scenario, as everything is going online, the storing space where each and everything is stored, processed, maintained and retrieved in a perfect manner is called as cloud. It is a large space possessed by some organizations with ample computing capabilities. These organizations provide space and

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Fig. 83.1 Basic fog computing paradigm

processing solutions to customers on rental basis. Notwithstanding, the platform of cloud computing is centralized; it generally comes to a failure to handle requests of millions of IoT sensors and devices. Consequently, the main disadvantage with cloud computing paradigm in IoT environs is more delay. Owing to these disadvantages in cloud computing paradigm, a concept of fog computing has come out. Fog computing has made possible new category of services. As the cloud computing paradigm provides geographical centralized system, fog computing paradigm helps in linking and detecting small quantity of local data, thus reducing delay [29, 30]. Fog computing paradigm is shown in Fig. 83.1.

83.2 Proposed System The proposed system comprises three layers viz IoT data gathering layer, fog layer and cloud layer. Figure 83.2 represents the proposed system in detail. This system offers real-time analysis of user health status by collecting health data related to primary tumor. The IoT data accumulation layer collects data from health sensors. Table 83.1 provides the description of dataset parameters to be sensed by IoT sensors. The gathered data is organized into a particular format and is delivered to the fog layer. The fog computing layer processes the data and classifies the user into contaminated or uncontaminated on the basis of outcome of hybrid classification technique of data mining implemented in this paper. Emergency signals sent to the

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Fig. 83.2 Proposed system for detecting primary tumors

infected citizens. Simultaneously, analyzed medical details of each user are retained on cloud computing layer. The crucial role of cloud is to notify the government and healthcare agencies to control generation of primary tumors among people. The ellaborated explanation of these layer follows below.

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Table 83.1 Description of dataset parameters and their possible values S. No.

Parameter

Possible values

1

Class

Lung, head & neck, esophasus, thyroid, stomach, duoden & sm.int, colon, rectum, anus, salivary glands, pancreas, gallblader, liver, kidney, bladder, testis, prostate, ovary, corpus uteri, cervix uteri, vagina, breast

2

Age

= 60

3

Sex

Male, female

4

Histologic-type Epidermoid, adeno, anaplastic

5

Degree-of-diffe Well, fairly, poorly

6

Bone

Yes, no

7

Bone-marrow

Yes, no

8

Lung

Yes, no

9

Pleura

Yes, no

10

Peritoneum

Yes, no

11

Livor

Yes, no

12

Brain

Yes, no

13

Skin

Yes, no

14

Neck

Yes, no

15

Supraclavicular Yes, no

16

Axillar

Yes, no

17

Mediastinum

Yes, no

18

Abdominal

Yes, No

83.2.1 IoT Data Accummulating Layer This layer is accountable for gathering data of user helpful in detecting primary tumors from various sensors. The datasets include 18 health-related attributes and one class attribute. The data comes from the sensors embedded into the user’s body. These wearable sensors are capable of sensing as well as transmitting data in real time. The data collected is delivered to fog computing layer to distinguish person as infected or uninfected. 18 health-related attributes and one class attribute are shown in Table 83.1.

83.2.2 Fog Layer Fog computing layer works as mediator between IoT sensing devices and cloud layer. It helps system of healthcare for detecting primary tumors by processing the data acquired from IoT sensors. It notifies the person regarding status of health at that

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time whether infected or not. With this, it also provides suitable diagnostic to the infected user to get treated in right time. Cloud computing layer is connected with this layer for retaining and analyzing medical reports of all users and making further decisions. This layer has three components which are describes as follows. User classification. The wearable devices and sensors sense various health attributes of the user. The data gathered by IoT detectors is conveyed to fog devices (e.g., gateways) to process and classify the data. The classification of data is performed using hybrid approach of data mining in which results of three classification algorithm of data mining, namely simple logistic, random forest and J48, are combined to obtain high-accuracy, kappa statistics result, TP rate, recall value, Fmeasure, area under ROC curve and low-root mean square error, root relative squared error. WEKA software is used for the implementing hybrid approach of data mining at fog layer. In this component, the persons are categorized into two different groups using analyzed sensor data. The classification is based upon the parameter values generated by various datasets. Each user is classified into infected or uninfected. The infected users are those who have tumor in any part of body and they are signaled immediately about their current state via mobile notification and an initial diagnosis is provided. Uninfected users are in safer zones and need no immediate notifications as they have no major symptoms of tumor. Prompting an alert. This component provides instant alerts on user’s mobile. The alerts are of two types in which the diagnostic alert is present in each of it. The two alerts are as follows: • Diagnostic and emergency alert is send to the infected user. It signals the user about being infected and the infected user is continuously monitored to keep record of his health. • Diagnostic and precaution alert is send to the uninfected users. It gives information to user about general precautionary measures that user should take in future.

83.2.3 Cloud Layer The main purpose of cloud layer is to process, store and analyze the data which is not processed by fog computing layer. It also has two main components described below. Cloud databases In cloud database component, all information related to various events is stored. The information is enormously in huge amounts, which is further used in decision making in emergency cases. Each user can have easy access to tumorrelated data and they can also check their medical reports saved in cloud database. Individual data of a person is secret and is not disclosed to any unauthorized user. Information sharing and health communication. Data is transferrable among the doctors, government agencies, hospitals and users. Government and health agencies access the analyzed cloud data to take precautionary action to minimize the cases of tumors in public. Based on the data saved in cloud database, hospitals can also

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send health-related suggestions to users and spread awareness among the citizens to take precautionary measures.

83.3 Experimental Setup WEKA tool which stands for Waikato Environment for knowledge analysis is used for carrying out machine learning process. WEKA software come up with tools for implementing machine learning algorithms, preprocessing of data and tools for visualization enabling us to invent techniques of machine learning and applying them to solve data mining issues. WEKA tool helps in fixing many data mining problems, particularly clustering, regression, classification, preprocessing of data and visualization. Different models may be implemented on the same data file. By examining the results of various models, we can choose the appropriate that fulfills our need. WEKA tool provides us with the statistical output of model’s processing. It also gives us the visualization tool to examine the data.

83.4 Results To achieve high accuracy in prediction of primary tumor disease in the user, hybrid approach of data mining is proposed which combines outcomes of three classifiers, namely simple logistics, J48 and random forest. Prediction results of proposed hybrid approach of data mining show more favorable values of parameters than in other classification techniques.

83.4.1 Parameters Used to Evaluate Prediction Efficiency Are as Follows Accuracy. To calculate accuracy of data mining classifier, we have to divide total number of right predictions by total number of instances. Our proposed hybrid approach of data mining provides the accuracy of 95.0287% which is higher as compare to other data mining classifiers. Graphical representation of accuracy is shown in Fig. 83.3. Kappa Statistics. This parameter is measured by comparing the value of observed accuracy with expected accuracy (random agreement). It can be calculated by using formula—(Observed Agreement—Expected Agreement)/(1—Expected Agreement). High value of kappa statistics indicates more accurate system. The

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Accuracy (%) 95.2 95 94.8 94.6 94.4 94.2 94 93.8 93.6 93.4

proposed hybrid approach of data mining outperforms over other classification techniques by achieving high kappa statistics value of 0.594. Graphical representation of kappa statistics is shown in Fig. 83.4. RMSE. RMSE stand for root mean squared error is commonly used parameter, the value of which is calculated by subtracting prediction value of the model from observed value. Root mean squared derivation shows the quadratic mean of the value generated by subtracting predicted value from observed value. Low value of RMSE indicates the classification technique to be more reliable. Using hybrid approach of data mining, we bring down the RMSE value to 0.0561 which is low as compare to other data mining classifiers. Graphical representation of RMSE is shown in Fig. 83.5. RRSE. RRSE stands for root relative squared error. We can calculate relative squared error by finding ratio of ‘total squared error’ and ‘total squared error generated by simple prediction.’ Root relative squared error is calculated by taking square Fig. 83.4 Kappa value of hybrid approach

Kappa StaƟsƟcs 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

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Root Mean Squared Error

0.08 0.06 0.04 0.02 0

root of ‘relative squared error.’ Low value of RRSE indicates the classification technique to be more reliable. The proposed hybrid approach of data mining achieves the low RRSE percentage of 70.0904. Graphical representation of RRSE is shown in Fig. 83.6. TP Rate. TP rate stands for true positives rate. TP are data points which are categorized as positive using particular classification technique that really are positive. It is favorable to have high value of TP rate for classification techniques. The hybrid technique of data mining achieves high TP rate of 0.95 which is higher as compare to other classification techniques. Graphical representation of TP rate is shown in Fig. 83.7. Recall Value. Its value is calculated as the ratio of ‘number of true positives’ and ‘the sum of number of true positives and number of false negatives.’ High recall value indicates more efficient classification technique. In hybrid approach of data mining, recall value of 0.95 is achieved which is higher than other classifier’s output. Graphical representation of recall value is shown in Fig. 83.8. Fig. 83.6. RRSE value of hybrid approach

Root RelaƟve Squared Error (in %) 120 100 80 60 40 20 0

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0.955

TP Rate

0.95 0.945 0.94 0.935

Fig. 83.8 Recall value of hybrid approach

0.955

Recall

0.95 0.945 0.94 0.935

F-Measure. It is a parameter that finds test accuracy. It is integrated count of precision and recall value which is determined by the formula—(2 * Precision_value * Recall Value)/(Precision_value + Recall value). High value of Fmeasure is required for efficient classification techniques. In the hybrid technique of data classification, F-measure of 0.941 is attained which is higher than other classification techniques. Graphical representation of F-measure is shown in Fig. 83.9. ROC Area. ROC area stands for receiver operating characteristic area. It is the area under curve which is computed by the use of area under curve (AUC) function. The function of AUC can have value between 0.0 for no skill and 1.0 for perfect skill. In the hybrid technique of data mining, we achieve high value of ROC area under curve which is equal to 0.991. Graphical representation of ROC area is shown in Fig. 83.10. The comparison of proposed hybrid approach with other classification techniques in terms of various parameters for predicting primary tumors in users in tabular form is shown in Table 83.2.

83 Implementing Fog Computing for Detecting Primary Tumors … Fig. 83.9 F-measure of hybrid approach

Fig. 83.10 ROC AUC of hybrid approach

0.945 0.94 0.935 0.93 0.925 0.92

1.05 1 0.95 0.9 0.85 0.8 0.75

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F-Measure

ROC Area

83.5 Conclusion In this day and age, primary tumor is one of the deadly diseases caused because of some environmental factors, genetic factors or adoption of modern lifestyle by people. Well in time tracing of cancer is the only way to control fatality rate. The proposed system of IoT-based fog computing for detecting primary tumors provides information to end users in more effective manner by providing quick processing and minimal response time. Processing capabilities of cloud are transferred to the edge of network, i.e., fog layer using gateways which provide real-time solution for detecting primary tumors. When we contrast the proposed fog computing platform with cloud computing technique, it is evident that the proposed system attains lesser delay in response time, bandwidth efficiency and high accuracy. The analyzed medical report of each patient is permanently retained in cloud storage which is helpful for doctors, medical institutes and other organizations to provide suggestions and precautionary measures on time. The observation exposes that hybrid method of combining outcome of three classifiers viz simple logistics, J48 and random forest for predicting primary tumors outdone over all other data mining classifier techniques.

0.59

0.594

94.7701

94.7414

J48

Random forest

Hybrid approach 95.0287

0.4973

0.5801

0.5568

94.4828

0.5244

94.6552

NBTree

0.5807

0.4707

Simple logistics

94.454

Decision table

Kappa statisti-cs

Naïve Bayes

94.5115

94.0517

PART

Accuracy (%)

Classifier

0.0561

0.0578

0.0613

0.0586

0.0635

0.0625

0.0762

0.0603

Root mean squared error

70.0904

72.2743

76.6747

73.2463

79.3248

78.1211

95.2732

75.3933

Root relative squared error (in %)

Table 83.2 Comparison of various classification techniques for predicting primary tumors

0.95

0.947

0.948

0.947

0.945

0.945

0.941

0.945

TP rate

0.95

0.947

0.948

0.947

0.945

0.945

0.941

0.945

Recall

0.941

0.941

0.939

0.937

0.929

0.932

0.928

0.939

F-meas-ure

0.991

0.99

0.935

0.975

0.958

0.968

0.845

0.958

ROC area

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However, in proposed system, there are various challenges that can be redressed in the times to come. One of these is privacy and security. In future, accuracy of prediction can also be enhanced by combining output of more classifiers.

References 1. M. Naib, A. Chhabra, Ensemble Vote Approach for Predicting Primary Tumors Using Data Mining (IEEE, 2014), pp. 97–103 2. P. Galambos, Cloud, fog, and mist computing: advanced robot applications. IEEE Syst. Man Cybern. Mag. 6(1), 41–45 (2020) 3. K. Wang, Y. Shao, L. Xie, J. Wu, S. Guo, Adaptive and fault-tolerant data processing in healthcare IoT based on fog computing. IEEE Trans. Netw. Sci. Eng. 7(1), 263–273 (2020) 4. V. Prokhorenko, M.A. Babar, Architectural resilience in cloud, fog and edge systems: a survey. IEEE Access 8, 28078–28095 (2020) 5. X. Wang, Z. Jin, An Overview of Mobile Cloud Computing for Pervasive Healthcare, vol. 7 (IEEE, 2019) 6. X. Li, X. Huang, C. Li, R. Yu, L. Shu, Edgecare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems, vol. 7 (IEEE, 2019) 7. W. Tang, K. Zhang, D. Zhang, J. Ren, Y. Zhang, X. Shen, Fog-enabled smart health: toward cooperative and secure healthcare service provision. IEEE 57, 42–48 (2019) 8. M. Naib, A. Chhabra, Predicting primary tumors using multiclass classifier approach of data mining. Int. J. Comput. Applicat., 9–13 (2014) 9. S. Kumar, M. Singh, Big data analytics for healthcare industry: impact, applications, and tools. IEEE 2(1), 48–57 (2019) 10. P. Verma, S.K. Sood, Fog assisted- IoT enabled patient health monitoring in smart homes. IEEE Internet Things J. (2018) 11. G. Harerimana, B. Jang, J.W. Kim, H.K. Park, Health Big Data Analytics: A Technology Survey (IEEE Access, 2018) 12. S. Sharaf, N.F. Shilbayeh, A Secure G-Cloud-Based Framework for Government Healthcare Services (IEEE Access, 2018) 13. S. He, Cheng, Bo, H. Wang, Y. Huang, J. Chen, Proactive Personalized Services Through FogCloud Computing in Large-Scale IoT-Based Healthcare Application (IEEE China Communications, 2017) 14. Y. Chen, Y. Chang, C. Chen, Y. Lin, J. Chen, Y.Y. Chang, Cloud-Fog Computing for InformationCentric Internet-of-Things Applications (IEEE, 2017) 15. O. Akrivopoulos, I. Chatzigiannakis, C. Tselios, A. Antoniou, On the Deployment of Healthcare Applications over Fog Computing Infrastructure (IEEE, 2017), pp. 288–293 16. S.K. Sood, I. Mahajan, Wearable IoT based healthcare system for identifying and controlling chikungunya virus. Comput. Indus., 33–44 (2017) 17. N. Kumar, S. Khatri, Implementing WEKA for medical data classification and early disease prediction (IEEE, 2017), pp. 1–6 18. H. Gupta, A.V. Dastjerdi, S.K. Ghosh, R. Buyya, iFogSim: A Toolkit for Modelling and Simulation of Resource Management Techniques in the Internet of Things, Edge and Fog Computing Environments (2017) 19. P. Verma, S.K. Sood, Cloud-centric IoT based disease diagnosis healthcare framework. Science direct, pp 1–12 (2017) 20. L.A. Tawalbeh, R. Mehmood, E. Benkhlifa, H. Song, Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications (IEEE Access, 2016) 21. T. Cerquitelli, E. Baralis, L. Morra, S. Chiusano, Data Mining for Better Healthcare: a Path Towards Automated Data Analysis? (IEEE, 2016), pp. 60–63

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22. B.V. Kiranmayee, T.V. Rajinikanth, S. Nagini, A Novel Data Mining Approach for Brain Tumour Detection (IEEE, 2016), pp. 46–50 23. V. Casola, A. Castiglione, K.K.R. Choo, C. Esposito, Healthcare-related data in the cloud: challenges and opportunities. IEEE Cloud Comput., 10–14 (2016) 24. M.A.N. Banu, B. Gomathy, Disease Forecasting System Using Data Mining Methods (IEEE, 2014), pp. 130–133 25. Y.E. Gelogo, J. Oh, J.W. Park, H. Kim, Internet of Things (IoT) Driven U-Healthcare System Architecture (IEEE, 2015) 26. X. Wu, X. Zhu, G. Wu, W. Ding, Data mining with big data. IEEE 6(1), 97–107 (2014) 27. C. Priyadharsini, A.S. Thanamani, An overview of knowledge discovery database and data mining techniques. Int. J. Innov. Res. Comput. Commun. Eng. 2, 1571–1577 (2014) 28. A. Priyanga, S. Prakasam, Effectiveness of data mining—based cancer prediction system (DMBCPS). Int. J. Comput. Appl. 83(10), 11–17 (2013) 29. P. Hu, S. Dhelim, H. Ning, T. Qiu, Survey on fog computing: architecture, key technologies, applications and open issues. J. Netw. Comput. Appl., 27–42 (2017) 30. Y. Liu, L. Zhang, Y. Yang, L. Zhou, L. Ren, F. Wang, R. Liu, Z. Pang, M.J. Deen, A Novel Cloud-Based framework for the elderly healthcare services using digital twin. IEEE Access 7, 49088–49101 (2019)

Chapter 84

Analysis on Filter Circuits for Enhanced Transient Response of Buck Converters Karthik Ramireddy, J. V. A. R. Sumanth, T. R. S. Praneeth, and Y. V. Pavan Kumar

Abstract This paper discusses the importance of choosing a proper filter circuitry along with the power electronic converters. A case study of buck converter is considered and its response is studied with respect to various key filter circuits available in the literature. Although there are many varieties of filter designs available in the literature, the circuit configuration must be investigated to reduce the overall cost and associated losses. With respect to this, a detailed survey has been conducted to identify all such key circuits and used them to see their impact on the buck converter performance. The comparative analysis is carried out qualitatively and quantitatively to analyze the best features. In qualitative analysis, various factors such as number of filter components, their specifications, and sizes are considered and for quantitative analysis, ripple factor, transient response, and steady-state response are considered. The overall analysis and circuit designs are executed by MATLAB/Simulink software. From the analysis, a best-suited filter circuit for buck converter operation is concluded. Keywords Buck converter · Filter circuits · Transient response · Time domain responses · Frequency domain analysis · Converter performance

84.1 Introduction In the modern progressive electronic fields, where components like processors, voltage converters, regulators, LCD displays, power audio amplifiers, POL converters, ASIC’s and FPGA’s are being used, which requires low-voltage sources. To step down the voltage from the source, an efficient converter is required. The power electronic buck converter is such DC step-down converter which is capable of reducing the higher input voltages to low voltages as per the requirement. These are the basic functionality units in half-bridge and full bridges. Buck converters use K. Ramireddy · J. V. A. R. Sumanth · T. R. S. Praneeth · Y. V. Pavan Kumar (B) School of Electronics Engineering, Vellore Institute of Technology—Andhra Pradesh (VIT-AP) University, Amaravati 522237, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_84

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digital pulse methods to step down the voltages which are an efficient way to conversion. These converters are operated by power electronic switches like MOSFET, IGBT, BJT, etc. The desired magnitude of output voltage can be obtained from these switches through controlling its duty cycle value, which ranges from 0 to 1. But the major constraint in this buck converter is its noise generation which consists of unwanted ripple voltages and ripple currents, which can be adjusted by interfacing suitable filter circuits at the output stage of the buck converters. Normally, these filters are made up of passive elements like inductor and capacitor components which store and release the current and voltages during each switching cycle. Modeling of the filters is a tedious task as it needs proper sizing of these passive components. In recent years, many research works are evolving in this filter design area to improvise the performance of electronic converters. Recent researches such as design of output filter for buck converter [1], filters for modern switching DC-DC controllers [2], energy transfer principle for achieving continuous current with minimum phase for DC converters [3], different filters designed to reduce the noise generated and to adjust the ripple voltages are presented in [4, 5] denote the importance of suitable filter circuit design. Further, T-filter for ripple reduction in current for boost converter [6], input filters to minimize the EMI noise for DC-DC regulators [7], filter circuits for high voltage DC power converters [8], analysis of EMI filters for DC-DC converters [9], filter circuit for AC-DC converters [10], input filter circuits for buck converters [11–13], enhances the usefulness with respect to a specific objective and application. Besides, there are different types of control methods discussed to improve the quality of responses in [14] and [15], respectively. With respect to all these advancements presented in the literature, this paper brings a complete analysis on all the key filter designs available for buck converter application. Finally, with the help of analysis results, the best filter circuit for the buck converter which produces less noise as well as less ripple voltages and currents is recommended.

84.2 Working and Design of Buck Converter The operations of the buck converter can be divided into two modes as per the operating condition of switches. Generally, MOSFET can be used as switch in the circuit of buck converter. The reversed voltage supply is governed by a PN junction diode, which stops the reversed power supply to the buck converter. Electrically passive components like capacitors and inductors are utilized to step down the respective input voltage. The path representing the flow of current when switch is ON and when switch is OFF is given by Figs. 84.1 and 84.2, respectively. This depicts the loop of current flow when switch is ON is the longer path of current conduction when compared to the condition when the switch is in OFF mode. During ON mode of the switch, flow of current is through the inductor thereby storing some current in it then it flows through the capacitor where the voltage is stored, and the load is connected in parallel to the capacitor. For the OFF mode of the switch, current flow is through the

84 Analysis on Filter Circuits for Enhanced Transient Response …

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Inductor

Switch ON

Capacitor

Voltage

Diode

Fig. 84.1 Current flow through the buck converter when switch if OFF

Inductor Switch OFF

Capacitor

Voltage

Diode

Fig. 84.2 Current flow through the buck converter when switch if ON

loop of diode, inductor, and capacitor where the stored voltage and currents during the ON mode of the switch are fed to the load connected at the capacitor. To study the performance of the converter, buck converter circuit is modeled in MATLAB–Simulink software as figured in Fig. 84.3, where the MOSFET is acting

PWM

MOSFET m +

Voltage Source (V)

Fig. 84.3 Buck converter circuit diagram

Diode

+

S

+

g D

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Table 84.1 Specifications of buck converter

PWM

S. No.

Parameter

Value

Unit

1 2

LB

1

mH

CB

33

µF

3

RLOAD

100



4

Duty cycle

60

%

5

Voltage source

20

V

6

Switching frequency

50

kHz

MOSFET m

1mH +

+

S

+

g D

Diode

33

1

20V

Fig. 84.4 Simulink diagram used for buck converter analysis

as a switch and fed by a PWM signal using which the duty cycle and switching frequency of circuit are adjusted. Here, L B is the inductance, C B is the capacitance of buck converter, and RLOAD is resistance of load connected to the buck converter. Specifications of buck converter are given in Table 84.1. Theoretically expected output voltage (V out ) from conventional buck converter in terms of V in is given as V out = DV in = 0.6 * 20 = 12 V. Simulink diagram used for the performance analysis with respect to the specifications is displayed in Fig. 84.4. The drawback of buck converter circuit is its noise generation, ripple voltages, and ripple currents. In order to reduce these parameters, different types of input filters and output filters are connected to the buck converter circuit. The passive elements like capacitors and inductors present in filter circuit will compensate these disturbance parameters.

84.3 Design of Filter Circuits for Buck Converter Even though buck converters are efficient in stepping down the voltages, the main disadvantage in this converter is noise generation, ripple voltages, and currents. This problem can overcome by interfacing suitable filters to the buck converter circuit. There are different types of filters such as filter-1 is a type of R, L filter which is placed between C B , and RLOAD obtained from [1], with specifications of 0.5 m

84 Analysis on Filter Circuits for Enhanced Transient Response …

MOSFET

PWM

m

D

S

Filter circuit

1mH

+

+

Diode

+

+

20V

1

0.63 0.5m

+

g

1085

33

Fig. 84.5 Simulink diagram of buck converter with filter-1

and 0.63 µH, respectively. The Simulink diagram representing the filter circuit 1 interfaced with the buck converter is represented in Fig. 84.5. From the analysis carried out in [2], the filter-2 is L, and C-type filter is placed between L B and C B with values of 22 µH and 33 µF, respectively. The Simulink diagram representing the filter-2 circuit interfaced with buck converter is represented in Fig. 84.6. From [3], this filter-3 circuit is C, and L-type filter is placed between L B and C B with values of 11 µH and 0.3 µF, respectively. The Simulink diagram representing the filter-3 circuit interfaced with the buck converter is represented in Fig. 84.7. Filter-4 circuit obtained from [4] is a R, C filter that is placed parallel to CB with specifications of 227 m and 2.2 µF, respectively. The Simulink diagram representing the filter-4 circuit interfaced with the buck converter is represented in Fig. 84.8. Filter-5 circuit obtained from [5] is a R, L, C filter that is placed parallel to C B with specifications of 0.05 , 0.06 µH, and 200 µF respectively. The Simulink diagram showing the filter-5 circuit interfaced with the buck converter is represented in Fig. 84.9. Filter-6 is T-shaped filter having two inductors and one capacitor which is obtained from [6] and is placed between L B and C B with specifications of 19 µH, 14 µH, and 4.45 µF, respectively. The Simulink diagram showing the filter-6 circuit interfaced with buck converter is represented in Fig. 84.10.

PWM

MOSFET g D

m S

Filter circuit 1mH +

+

Diode

33 F

+

+

20V

33 F

+

22

Fig. 84.6 Simulink diagram of buck converter with filter-2

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MOSFET

Filter circuit

m

1mH

S

+

+

+

g D

+

PWM

Diode

33

20 V

+

0.3

Fig. 84.7 Simulink diagram of buck converter with filter-3

PWM

MOSFET g D

m S

1mH +

Diode

227m

33

2.2 F

+

20 V

+

+

Filter circuit

Fig. 84.8 Simulink diagram of buck converter with filter-4

PWM

MOSFET g D

m S

1mH +

0.06 H 200 F

+

Diode

33

20 V

+

+

Filter circuit

Fig. 84.9 Simulink diagram of buck converter with filter-5

Filter-7 circuit extracted from [7] consists of five resistors, two capacitors, and an inductor with specifications of 0.162 , 0.162 , 5 m, 0.58 m, 5 m and 100 µF, 100 µF, and 16 µH, respectively. The Simulink diagram showing the filter-7 circuit interfaced with buck converter is given in Fig. 84.11. The cost effectiveness

84 Analysis on Filter Circuits for Enhanced Transient Response …

MOSFET m S

Filter circuit

1mH

+

+

+

14 4.45 F

100

Diode

20V

33 F

+

19

+

g D

+

PWM

1087

Fig. 84.10 Simulink diagram of buck converter with filter-6

PWM MOSFET

Filter circuit 0.162 16

1mH 0.162

+

+

+

+

100 F

100

Diode 100 F

20V

0.58

5m

5m

+

+

+

m S

+

g D

33 F

Fig. 84.11 Simulink diagram of buck converter with filter-7

of the converter depends on number of components present in it. So, a Table 84.2 is formulated to show the topological analysis representing the number of switches, diodes, resistors (R), inductors (L), and capacitors (C) present in different filters used in the converters. Table 84.2 Topological analysis of buck converter with different filters Filter

Switches

Diodes

R

L

C

Filter-1

1

1

2

2

1

Filter-2

1

1

1

2

2

Filter-3

1

1

1

2

2

Filter-4

1

1

2

1

2

Filter-5

1

1

2

2

2

Filter-6

1

1

1

3

2

Filter-7

1

1

6

2

3

Best filter

All filters

All filters

Filters 2,3,6

Filter 4

Filter 1

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84.4 Simulation Results and Analysis To evaluate the efficacy of various filter circuits discussed in Sect. 84.3, the voltage and current responses of the buck converter with the presence of all these different filters are plotted. In these, Figs. 84.12, 84.13, 84.14, 84.15, 84.16, 84.17, 84.18, 84.19 represent the voltage responses, and Figs. 84.20, 84.21, 84.22, 84.23, 84.24, 84.25, 84.26, 84.27 represent the current responses. Further, the output voltage and current responses obtained with all the varieties are consolidated as shown in Figs. 84.28 and 84.29, respectively. The performances of the converters are analyzed by taking the time domain parameters into consideration. The tables representing the time domain parameters of output voltage and current responses obtained for different

Fig. 84.12 Voltage response of buck converter without filter

Fig. 84.13 Voltage response of buck converter with filter-1 circuit

84 Analysis on Filter Circuits for Enhanced Transient Response …

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Fig. 84.14 Voltage response of buck converter with filter-2 circuit

Fig. 84.15 Voltage response of buck converter with filter-3 circuit

filters are depicted in Tables 84.3 and 84.4, respectively. Voltage deviations and current deviations of different filter circuits are represented in Table 84.5.

84.5 Conclusion This paper performs a comprehensive analysis on usage of various filter circuits for buck converter application. During the comparative analysis of different responses with time domain parameter analysis, responses of systems which produce less values in majority of all-time domain parameters are considered as the best response.

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Fig. 84.16 Voltage response of buck converter with filter-4 circuit

Fig. 84.17 Voltage response of buck converter with filter-5 circuit

From the time domain analysis shown in Tables 84.3, 84.4, and 84.5, the following observations have been made. • From Table 84.3, it is observed that, filter-1 produces less peak time and settling time, filter-3 produces less delay time, and filter-7 produces less peak overshoot time. • From Table 84.4, it is observed that, filter-1 produces less rise time, peak time, peak overshoot time, and settling time. • From Table 84.5, it is observed that, filter-3 produces less voltage/current deviations. Hence, from this analysis, filter-1 circuit design is recommended as the most effective one among all other filters because of its desired lower time domain parameters.

84 Analysis on Filter Circuits for Enhanced Transient Response …

Fig. 84.18 Voltage response of buck converter with filter-6 circuit

Fig. 84.19 Voltage response of buck converter with filter-7 circuit

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Fig. 84.20 Current response of filterless buck converter

Fig. 84.21 Current response of buck converter with filter-1 circuit

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84 Analysis on Filter Circuits for Enhanced Transient Response …

Fig. 84.22 Current response of buck converter with filter-2 circuit

Fig. 84.23 Current response of buck converter with filter-3 circuit

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1094

Fig. 84.24 Current response of buck converter with filter-4 circuit

Fig. 84.25 Current response of buck converter with filter-5 circuit

K. Ramireddy et al.

84 Analysis on Filter Circuits for Enhanced Transient Response …

Fig. 84.26 Current response of buck converter with filter-6 circuit

Fig. 84.27 Current response buck converter with filter-7 circuit

Fig. 84.28 Output voltages of all seven filters interfaced buck converter

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Fig. 84.29 Output currents of all seven filters interfaced buck converter Table 84.3 Time domain parameters for output voltages of different filters Filters

T r (ms)

T d (ms)

T p (ms)

M p (V)

T s (ms)

Filter-1

0.1923

0.295

0.562

9.2875

25

Filter-2

0.2765

0.242

0.814

9.7214

48

Filter-3

0.7025

1.353

1.992

5.9945

53

Filter-4

0.1995

0.307

0.583

9.344

28

Filter-5

0.5091

0.782

1.513

9.812

062

Filter-6

0.2095

0.321

0.606

9.3922

29

Filter-7

0.5051

0.797

1.492

8.1445

54

No filter

0.1917

0.293

0.566

10.223

35

Best filter

No filter

Filter-3

Filter-1

Filter-7

Filter-1

Table 84.4 Time domain parameters for output currents of different filters Filters

T r (ms)

T d (ms)

T p (ms)

M p (A)

T s (ms)

Filter-1

0.187

0.288

0.566

0.1052

26

Filter-2

0.271

0.415

0.814

0.2014

55

Filter-3

0.693

1.112

1.992

0.1249

61

Filter-4

0.197

0.301

0.586

0.1936

34

Filter-5

0.503

0.767

1.512

0.2029

58

Filter-6

0.315

0.316

0.606

0.1945

45

Filter-7

0.499

0.781

1.492

0.1696

37

No filter

0.189

0.287

0.561

0.1055

42

Best filter

Filter-1

No filter

Filter-1

Filter-1

Filter-1

84 Analysis on Filter Circuits for Enhanced Transient Response … Table 84.5 Deviations in voltages and currents for different filters

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Filters

Voltage deviation (%)

Current deviation (%)

Filter-1

0.05

0.04

Filter-2

0.017

0.016

Filter-3

0.000125

0.00012

Filter-4

0.033

0.03

Filter-5

0.033

0.024

Filter-6

0.0083

0.008

Filter-7

0.00016

0.00016

No filter

0.017

0.012

Best filter

Filter-3

Filter-3

References 1. D.N. Antonietta, F. Nicola, P. Giovanni, S. Giovanni, Optimal buck converter output filter design for point-of-load applications. IEEE Trans. Industr. Electron. 57(4), 1330–1334 (2010) 2. A. Nadler, Impact of the layout, components, and filters on the EMC of modern DC/DC switching controllers. Wurth Elecktronik, 1–21 (2017) 3. R. Rueda, S. Ghani, P. Perol, A new energy transfer principle to achieve a minimum phase and continuous current boost converter, in 2004 IEEE 35th Annual Power Electronics Specialists Conference, vol. 3(10) (2004), pp. 2232–2236 4. S. Bhat, H.N. Nagaraja, Effect of filter elements on the performance of buck converter, in 2014 International Conference on Advances in Energy Conversion Technologies (2014), pp. 169–173 5. C. Nelson, 5 - LT1070 design manual, in Analog Circuit Design (2011), pp. 59–123 6. S.S. Sanjeevi, D. Tamilselvan, Input current ripple reduction based boost converter through T-filter network, in 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), vol. 8(2) (2016), 36–40 7. I. Mukharjee, I.A. Gadoura,Simple, low EMI-noise input-filter design for DC/DC power regulators, in 7th IEEE Conference on Industrial Electronics and Applications (2012), 948–953 8. M. Muur, D. Vinnikov, Output filter for the high voltage DC/DC converter, in Doctoral School of Energy and Geo-technology (2007), pp. 118–121 9. K. Kostov, J. Kyyra, T. Saunito, Analysis and design of EMI filters for DC-DC converters using chain parameters, in European Conference on Power Electronics & Applications (2003) 10. S. Pyakurayal, M. Matin, Filter Design for AC to DC Converter. Int. Refereed J. Eng. Sci. (IRJES) 2(6), 42–49 (2013) 11. S.A. Lopa, S. Hossain, M.K. Hasan, T.K. Chakraborty, Design and simulation of DC-DC converters. Int. Res. J. Eng. Technol. 3(1), 335–340 (2016) 12. C.S. Liu, K. Huang, S.Y. Chen, The design criteria of input filters of buck-boost converters with peak current-mode control. J. Chin. Inst. Eng. 38(1), 1–9 (2015) 13. F. Shih, D.Y. Chen, Y.P. Wu, Y.T. Chen, A procedure for designing EMI filters for AC line applications. Power Electron. IEEE Trans. 11(1), 170–181 (1996) 14. H. Gashtil, Design, optimization and control of DC-DC converter (Buck), single phase inverter and three phase inverter. Mapta J. Electr. Comput. Eng. (MJMIE) 1(1), 1–10 (2018) 15. R. Karthik, A.S. Hari, Y.V.P. Kumar, D.J. Pradee, Modelling and control design for variable speed wind turbine energy system, in 2020 International Conference on Artificial Intelligence and Signal Processing (AISP), Amaravati, India, (2020), pp. 1–6

Chapter 85

The Cause and Control of Failure of Hydraulic Turbine Due to Cavitation: A Review Md. Mustafa Kamal, Ali Abbas, Ravi Kumar, and Vishnu Prasad

Abstract Cavitation is a cold boiling phenomenon which includes the growth of bubbles in pressure zone below the vapor pressure of water and subsequent collapse in higher-pressure zones. In hydraulic turbines, this phenomenon mainly occurs in reaction turbine, i.e., Francis Kaplan, propeller, bulb, etc. This may cause erosion, noise, instability in operation, vibration and lowers the performance and efficiency of hydroturbine. The installation, off-design operation and improper design of runner blade lead cavitation. In this paper, research carried out in the field of cavitation development, its variation with operating parameters along with material used are discussed. Further study of detection, cause and effect and method to minimize cavitation has been discussed. Effect of different design, i.e., tail water level and blade tip ration and operating characteristic, i.e., temperature, suction pressure and velocity on cavitation has been studied and presented. Keywords Cavitation · Hydroturbine · Francis turbine · Efficiency

85.1 Introduction Energy plays an important part in economy growth for any country, especially developing country like India where more than 300 million people are still not getting electricity. Energy demand rises steeply due to demographic expansion, increasing urbanization and huge demand in rural areas. So to meet this demand hydroenergy is most reliable and cost-effective source to produce electricity. In production of hydroelectric power, India stands on seventh position in the world. As of December Md. Mustafa Kamal Hydro and Renewable Energy Department, IIT Roorkee, Roorkee 247667, India A. Abbas Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India R. Kumar (B) Government Engineering College Bikaner, Bikaner, Rajasthan 334004, India V. Prasad Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_85

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31, 2017, India’s installed capacity is 44963.42 MW or 13.59% of total utility power generation capacity [1]. India made an action plan to use non-fossil fuel sources to generate 40% of total power and same plan has been submitted to UN climate body, UNFCCC. It also helps to reduce greenhouse gas emission which fulfill the international targets and agreement like recently India ratified Paris agreement which seek reduction in greenhouse gas emission in order to reduce the global average temperature by 2 °C [2]. In India, hydropower plant classified in terms of power generation capacity as large, medium, small, mini, micro and pico. Selection of turbine in hydroplant is decided by many factors like head, discharge, load variation, cost, etc. In the hydroplant set up, turbine is the one of main components of plant. Turbine utilizes the energy from water in the form of only kinetic energy or both pressure and kinetic energy to convert it into mechanical energy by rotation of shaft which is further connected with generator. Hydraulic turbine broadly classified into two categories according to energy available at inlet of blade. They are impulse turbine and reaction turbine. Impulse turbine mostly used in high head and low discharge with good performance but for medium and low head power plant, it is difficult to use impulse turbine, so here reaction turbine comes into picture. Small hydropower plant and mini hydropower plant where head is less, reaction turbines are more beneficial. As we know hydropower plant works continuously and people find very difficult to maintain same performance, there are many factors which declined the performance of turbine like silt erosion, cavitation, fatigue, etc. [3, 4]. The most significant reason for degradation of performance of hydraulic turbine is cavitation. Cavitation is a phenomenon in which absolute pressure of flowing fluid is approaches toward vapor pressure at some particular temperature. It is very similar to boiling, the only difference is here the driving mechanism is pressure change and it is controlled by dynamics of fluid [5]. According to Bernoulli’s theorem, drop in pressure caused by rise in dynamic velocity of flowing fluid. So, it is clear that more rise in velocity, more chances of cavitation. That is why cavitation often happened in high specific speed turbine. After formation and growth of bubbles in turbine spacing or blade system, it condenses at higher-pressure zone and at the same time cavities formed which created vacuum at that region. All nearby particles rush toward the center of cavities and collide with each other; this results into sharply rise in local pressure which can be order of several hundreds of atmospheres and also rise in temperature up to 1000 °C [6]. Due to sudden rise in pressure, it impinges high magnitude of impulse force on the surface, causes mechanical damage, known as cavitation pitting” and is shown in Fig. 85.1. Due to rise in local pressure and temperature, this also leads to chemical processes in liquid as well, but it plays secondary role in cavitation damage.

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Fig. 85.1 Growth and collapse phenomenon of bubbles (Lohrberg et al. 1999)

85.1.1 Types of Cavitation in Hydraulic Turbine We know that major pressure drop takes place in runner, so for both point of view efficiency and cavitation, runner of reaction turbine is very important. Cavitation may also be occurred in distributors or guide vanes, runner spacing, inlet of draft tube, etc. is shown in Fig. 85.2 depends on operating condition. So, the behavior of cavitation on different parts of turbine and influence on parameters of performance are different [7, 8].

85.1.1.1

Profile Cavitation Within the Runner

It is the most cavitation prone zone of turbine due to pressure changes rapidly across the blade. Cavitation occurs on the profile blade is known as profile cavitation. But

Fig. 85.2 Different types of cavitation at different location on turbine [9]

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in some operating condition, due to angle of incidence and inlet velocity condition, chances of cavitation are more at the leading-edge spite of full profile. Thus, correct designing of blade profile is very important to improve the cavitation characteristics of turbine.

85.1.1.2

Tip Cavitation

In axial flow or diagonal turbine runner without shroud, due to pressure difference between suction side and pressure side of blade, fluid particles rush to suction side through tip clearance which causes discontinuity in flow and results into vortices. These vortices are reason for immense pressure drop in tip clearance which result into local cavitation and termed as tip cavitation. So, to minimize this cavitation, clearance should have kept as small as possible.

85.1.1.3

Cavitation Due to Sharp Changes of Flow Direction in Turbine Space

Due to abrupt change in direction of motion of fluid, local velocity rises and pressure drops simultaneously which leads to local cavitation and separation of flow at some places in turbine spacing. The reason behind this cavitation is overload operating condition where large discharge and velocity are required. This condition may occur at places like near lower distributor ring, in the bend of draft tube, etc.

85.1.1.4

Cavitation Due to Surface Roughness

If the surface of elements in turbine spacing not super finished, chances of cavitation of more because stream line of flow are irregular and chances of flow separation are more. This also leads to increase in hydraulic resistance which deteriorates the performance and efficiency of turbine.

85.1.2 Evaluation of Cavitation Coefficient Prof. Dietrich Thomas of Munich suggested a cavitation factor σ (sigma) to determine the zone where turbine can work without being affected from cavitation. Its critical value is given by after applying Bernoulli’s equation at exit of runner and free surface of tail race, we get: σinst. =

Ha − Hv − Hs H

(85.1)

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where σinst. is the Thoma’s coefficient of the turbine installation at hydropower plant. H a and H v are correspond to atmospheric and vapor pressure in terms of meter of water. H s is suction pressure head in meter of water or height of runner outlet above tail race [10]. To avoid cavitation in turbine, Thoma’s cavitation factor should be greater than critical cavitation factor (σturb. or σc ) of that type of turbine. So, for cavitation free operation, σinst. > σturb.

(85.2)

Following empirical formulae is given for calculating the critical cavitation value of turbine [11], For Francis turbines, σturb. = 0.044 × (Ns /100)2

(85.3)

  σturb. = 0.3 + 0.0032 × (Ns /100)2.73

(85.4)

For Propeller turbines,

For Kaplan turbines, σturb. = 1.1 × [(σturb. )propeller ]

(85.5)

85.2 Detection of Cavitation Erosion Very few researchers investigated the method of detection of cavitation in hydroturbines. Some work has been given presented below: Gruberet al. [9] carried an experiment for detection of cavitation in hydraulic turbine with the help of ultrasonic signal. They used two methods of classifier such as neural feed forward networks and decision trees to give the fair result of different states of water. This classification is used at three different sections such as a sphere in water flowing circular tube, a NACA profile in a cavitation tunnel and a Francis model test turbine. Set up of above three cavitation sections is shown in Figs. 85.3, 85.4, and 85.5. They conduct testing and got different statistical signal parameters at operating points which indicates that whether the cavitation is dangerous or not. Their result gives the clarification on variation of signal amplitude and its standard deviation with size and concentration of air or vapor bubbles. If the concentration is less, then most of the sent ultrasound signal is unaffected. They also found that the influence of air filled bubbles on standard deviation is much more than vapor filled bubbles. Unfortunately,

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Fig. 85.3 Spherical ball

Fig. 85.4 NACA profile in cavitation tunnel

they do not get result as they expect, but by different classifier methods, chances of differentiation between dangerous and non-dangerous cavitation increase. Bourdon et al. [12] carried out an experiment on NACA 009 profile to detect leading-edge cavitation. They carried an experiment of NACA profile in high speed cavitation tunnel and different sensor is attached with it. To vanish low frequency noise from other mechanical or hydraulic sources, they used high band of frequencies such as 15–35 kHz. To get maximum length of eroded area, they varied blade tilt angle (2.5–4.5°), sigma values (0.7–1.4) and water velocities (20–42.5 m/s). They developed a correlation between acceleration and erosion rate after getting different

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Fig. 85.5 Francis model test turbine

vibratory parameters. Perω1 =

m(x) ¨ 2 c(x) ¨ 2 k(x) ¨ 2 + + 2 ω1 ω1 ω13

(85.6)

where Perω1 is erosive power and x¨ (m2 /s4 ) at single frequency ω1 . Above equation suggested that erosive power is proportional to square of acceleration. Avellan et al. [13] also suggested an empirical formula to evaluate the rate of energy released by leading-edge cavity when it collapsed.   3 × F C pmax + σ × St × L c E˙ = 0.5 × ρ × U∞

(85.7)

  where F C pmax + σ is a function and C pmax , σ are maximum pressure coefficient and cavitation number respectively.St is Strouhal number. Schmidt et al. [14] investigated leading-edge cavitation with acoustic emissions sensor on pump-turbine model and he found out that analysis of acoustic emission is good method to detect leading-edge cavitation. Nennemann et al. [15] carried out CFD analysis on discharge ring cavitation of Kaplan turbine blade. He did CFX two-phase analysis for 24 guide vanes per 5 blades machine. After post-processing, they found different pressure zones at different location of discharge ring as shown in Fig. 85.6. Point B shows that pressure reduction on the wall of discharge ring due to high velocity flow at tip clearance which causes vortices and results into local cavitation. There is drop in pressure at suction side in loading condition, causes cavitation which is shown by point B. Escaler et al. [16] carried out experiment tests on reduced scale Francis turbine for different types of cavitation to improve the cavitation detection techniques. Different parameters of reduced scale Francis model are: No. of guide vanes = 20, No. of runner blades = 19, Speed of runner = 874 rpm (appx.) and flow conditions are: Maximum head = 100 m, Maximum discharge = 1.4 m3 /s. They installed two accelerometers A1 and A2 at 180° on the guide bearing in radial and axial direction, respectively.

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Fig. 85.6 Absolute static pressure on discharge ring of Kaplan turbine [15]

Four dynamic pressure sensors were mounted on the upstream runner and draft tube. P1 and P2 sensors were mounted on upstream runner and P3 and P4 sensors were mounted on draft tube. After measuring the vibration at bearing in both radial and axial direction at all operating conditions and dynamic pressure at draft tube are given below: 1. 2. 3. 4.

Vibrations measured at bearing of turbine can detect bubble and outlet cavitation. Dynamic pressure measurement at draft tube is more beneficial than at upstream runner. Sensor in radial direction measures signal of higher amplitude than axial sensor. It is found that frequency band of 10–15 kHz is best suited for high frequency content and amplitude modulation for cavitation detection.

85.3 Methods for Prevention of Cavitation in Hydroturbine 85.3.1 Air Injection Method At different operating regimes, turbine experiences self-excited pressure and power oscillations. Due to introduction of pressurized air into runner, air inside a bubble cavity reduces the rate of collapse and minimizes the bubble volume. Hence, it reduces the energy of bubble available for noise, erosion and vibration. Some investigator worked upon this and found some important results. Some of their work is listed below: Rivetti et al. [17] carried out test on Kaplan turbine model to reduce tip vortex cavitation by air admission into the runner. Air is injected on the horizontal plane of discharge ring. Their injection system consists of twenty holes with each 3 mm diameter. They kept discharge coefficient same at every injection system. For detection and measure vibration, three accelerometers are placed at different place. Also,

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high speed visualization camera is placed to check the influence of air injection. They carried out vibration analysis with air injection and without air injection. They found that vibration is decreased as pressurized air is injected. They have drawn a graph which is shown in Fig. 85.7a, b. In Fig. 85.7b, value of constant σ is 0.946. They also got some results regarding the efficiency drop when air is injected. At σ is 0.946, drop in efficiency registered was 1.7%. Efficiency of turbine at different cavitation number is shown in Fig. 85.8a and efficiency drop due to air injection is shown in Fig. 85.8b. They extended their research on same model and found that beyond 0.5% air injection has no influence on vibration reduction and efficiency drop increased from 1% [18], shown in Fig. 85.9. Minakov et al. [19] carried out an experiment on Francis turbine in hydraulic turbine of runner diameter 300 mm to know the effect of air injection on pressure pulsation amplitude in draft tube. They kept pressure head constant and varied guide vane opening. They took readings at regular interval of time. They found out that when air is injected into flow path, pressure pulsation at critical zone was reduced as

Fig. 85.7 a Result of vibration level at different σ. b Vibration with air injection at constant σ

Fig. 85.8 a Efficiency at different σ . b Efficiency drop due to air injection

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Fig. 85.9 Influence of air injection rate on level of vibration at constant cavitation number

Fig. 85.10 Reduction in pressure pulsation amplitude with air

shown in Fig. 85.10. But it needs further investigation because they do not investigate at different air injection point.

85.3.2 Material Coating Loss of metal due to cavitation damage is very critical area in hydroelectric power plant. Replacement of eroded parts is very costly and non-feasible, so they coated layers of alloy material to make operational. Various techniques are used to repair the eroded part like weld overlay and inlay technique, epoxy coating, thermal spray, ceramic coating, etc. Many researchers investigated the impact of different types of coating on the performance of hydroturbine and some of them are:

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Kumar et al. [20] reviewed thermal spray method of WC (Tungsten carbide)-based metal coating on eroded part. This method uses chemical or electrical energy to melt a material and then deposited on the surface. One of the thermal spray methods is high velocity oxy-fuel (HVOF) method. They found out that HVOF sprayed WC12%Co has very low porosity value which is about 1–3% as compared to conventional thermal spraying process (5–20%) and also shows great mechanical properties like hardness, toughness, wear resistance, etc. Belzona coating [21] is an epoxy-based composite for metal repair. Three steps are required for Belzona coating on the Kaplan blade which is destructed by cavitation. After surface preparation like cleaning of eroded surface, three steps are included, which are, STEP 1: Belzona 1311 (Ceramic R-Metal) is used to rebuild and restore the surface profile which is lost by cavitation erosion. STEP 2: Belzona 1342 is coated throughout the blade surface to protect the blade profile from both erosion and corrosion. STEP 3: Belzona 2141 has a great property of cavitation erosion resistance, so the area which is exposed to cavitation is coated with this. The improvement in efficiency by this coating is much more than the conventional one like weld coating. Rapid prototyping method [22] is a repair technique in which a portable scanner produces three-dimensional (3D) image, which is analyzed and prepared a replacement piece which is eroded due to cavitation, then it is welded with weld overlay method to existing turbine runner blade. Consumption of time is 30% lesser than conventional techniques because in conventional method after welding, grinding operation is done to make the accurate profile which is lengthy process.

85.3.3 Turbine Setting Setting of turbines above or below tail race plays a vital role in cavitation characteristic of hydroturbine. In actual practice, head between tail race water level and head race water level varied which causes variation in suction head of turbine and bring changes in cavitation coefficient. Suction head defined as distance between blade/runner axis or center line of distributor to the tail race. To measure the suction head from the tail race level to some reference line, USSR recommend some criteria [23]: 1. 2. 3.

For mixed flow turbines, it is measured from center line of distributor to tail race. For axial flow turbines, blade axis is taken as reference line to measure suction head. For horizontal (bulb turbines in particular), suction head is measured from runner axis to the tail race.

From Fig. 85.11, it is clear that decrease in suction head changes cavitation characteristic of turbine but there is no change in efficiency of turbine up to some limit. Further decrease in suction head results into deterioration of performance of turbine.

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Fig. 85.11 Variation of efficiency with Thoma’s cavitation factor [11]

The value of cavitation number from where the performance of turbine affected is called critical cavitation number [8, 18].

85.4 Effect of Other Parameters on Cavitation Characteristic Gohil et al. [24, 25] investigated effect of different parameters on cavitation in Francis turbine of small hydropower plant. Using CFX code, they did numerical analysis and validate with experimental data. They used shear stress model for separation and Rayleigh–Plesset model for cavitation flow in CFX code. Data obtained from numerical analysis is used by author to develop correlations for efficiency loss and cavitation rate in Francis turbine as a function of flow velocity, suction head and temperature. They developed correlation between normalized efficiency loss and suction head, temperature and flow velocity on the basis of regression analysis and expressed as: 2 ηloss.cav = 1.4550 × T 0.2247 × Hs0.1724 × V −5.1779 × e[3.2497×(ln V ) ]

(85.8)

Also established correlation for cavitation rate on the basis of regression analysis and expressed as: m˙ cav = 0.0081536 × T 0.9726 × Hs0 .3573 × V 4.9927

(85.9)

They got some results after numerical investigation is presented below: 1.

At initial velocity, loss off efficiency decreases and attained minimum value but further increase in flow velocity results into increase in efficiency loss.

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2. 3. 4.

1111

Rate of cavitation initially increases linearly but after some value of velocity it increases with higher rate. Both cavitation rate and efficiency loss occur at higher rate at high range of temperature. For higher suction head and high range of temperature, both cavitation rate and efficiency loss slightly increase.

Motycak et al. [26] carried out CFD analysis to get dimension of tip clearance to minimize tip vortex cavitation. He found out that tip clearance should be around 0.05% of runner diameter. He also suggests to use of anti-cavitation lips to deal with tip vortex cavitation without any negative effect on the performance of turbine.

85.5 Conclusions It can be concluded from literature that cavitation cannot be eliminated but it can be minimized. It has been analyzed that there are different forms of cavitation occurs at different sections of turbine and their magnitude is different. Various methods have been discussed for prediction of cavitation occurrence. It is further observed that ultrasonic signal predicts good result to differentiate between air filled bubbles and vapor filled bubbles. There are correlations available between acceleration and erosion rate and it is proportional to square of acceleration. Energy released during pitting action of leading-edge cavity has been also discussed. Various prevention techniques, applied to minimize the cavitation erosion, have been studied. Air injection method to reduce tip vortex cavitation has been discussed and it was found that it provides good solution to reduce the cavitation but the same time there is some efficacy drop has been observed. It is further found that setting of turbines above tail race is also played an important role in cavitation characteristic of turbine. Acknowledgements The authors greatly acknowledge the financial support from National Project Implementation Unit (NPIU), is a unit of Ministry of Human Resource Development, Government of India, for implementation of World Bank Assisted Projects in Technical Education under the third phase of Technical Education Quality Improvement Program (TEQIP-III).

References 1. Executive summary for the month of December, 2017. (Govt. of India, ministry of power, CE, New Delhi) https://www.cea.nic.in/reports/monthly/executivesummary/2017/exe_summary12.pdf. 2. Md. Mustafa Kamal, Scenario of small hydro power projects in India and its environmental aspects. IRJET 2017, p-ISSN: 2395-0072 3. D. Tong, Cavitation and wear on hydraulic machines. Int. WP&DC (1981) 4. F.R. Menter, Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J. 32(8), 1598–1605 (1994)

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5. J.P. Franc, Fundamentals of cavitation (2004), pp. 2–4 6. J. Lal, Hydraulic Machines, 6th edn. (Metropolitan Book Co. Private Ltd., New Delhi, 1975), pp. 162–164 7. V.V. Barlit, Hydraulic Turbines, vol. 1 (2005), pp. 4–8 (Chapter 5) 8. I. Kassanos, J. Anagnostopoulos, D. Papantonis, Numerical analysis of the effect of splitter blades on draft tube cavitation of a low specific speed Francis turbine, in Proceedings of the 6th IAHR Meeting of the Working Group (IAHRWG ’15) (Ljubljana, Slovenia, 2015) 9. P. Gruber, The detection of cavitation in hydraulic machines by use of ultrasonic signal analysis (2014) (2015). https://doi.org/https://doi.org/10.1088/1755-1315/22/5/052005 10. R.K. Bansal, Fluid Mechanics and Hydraulic Machines (1998), pp. 839–841 11. A.K. Jain, Fluid Mechanics and Hydraulic Machines (2002), pp. 835–836 12. P. Bourdon, Erosion Vibratory Fingerprint of Leading Edge Cavitation of a Naca Profile and of a Francis, vol. 176 (1993), pp. 51–68 13. F. Avellan, M. Farhat, Cavitation 91 Symposium, 1st ASME-JSME Fhlills Engineering Conference, 23–27 June 1991, Portland (Oregon) Cavitation Erosion Power F. Avellan, Ph., (June) (1991), pp. 23–27 14. C. Series, Cavitation Measurements on a Pump-Turbine Model Cavitation Measurement on a Pump-Turbine Model. (2015). https://doi.org/10.1088/1742-6596/656/1/012071 15. B. Nennemann, Kaplan turbine blade and discharge ring cavitation prediction using unsteady CFD, in 2nd IAHR International Meeting of the Workgroup on Cavitation and Dynamic Problems in Hydraulic Machinery and Systems, vol. 52(66) (2007). Retrieved from https://mh.mec. upt.ro/iahrwg2007/pdf/09_Nennemann.pdf 16. X. Escaler, M. Farhat, P. Ausoni, E. Egusquiza, in 6 th International Symposium on Cavitation, (2006). A1 P1-2, (September), 2–6 17. A. Rivetti, M. Angulo, C. Lucino, S. Liscia, Mitigation of tip vortex cavitation by means of air injection on a Kaplan turbine scale model, in IOP Conference Series: Earth and Environmental Science, vol. 22 (2014) https://doi.org/https://doi.org/10.1088/1755-1315/22/5/052024 18. A. Rivetti, M. Angulo, C. Lucino, S. Liscia, Pressurized air injection in an axial hydro-turbine model for the mitigation of tip leakage cavitation. J. Phys. Conf. Ser.656(1), 0–4 (2015). https:// doi.org/https://doi.org/10.1088/1742-6596/656/1/012069 19. A. Minakov, D. Platonov, A. Maslennikova, D. Dekterev, Experimental study of the effect of air injection on the pressure pulsations in the hydro turbine flow path under different operating conditions, in MATEC Web of Conferences, vol. 115 (2017), pp. 3–6. https://doi.org/https:// doi.org/10.1051/matecconf/201711505001 20. H. Kumar, C. Chittosiya, V.N. Shukla, Science direct HVOF sprayed WC based cermet coating for mitigation of cavitation, erosion & abrasion in hydro turbine blade. Mater. Today Proc. 5(2), 6413–6420 (2018). https://doi.org/10.1016/j.matpr.2017.12.253 21. https://khia.belzona.com/en/view.aspx?id=6087 22. https://www.hydroworld.com/articles/hr/print/volume-29/issue-7/articles/using-rapid-protot yping-methods-to-repair-runner-cavitation-damage.html 23. V.V. Barlit, Hydraulic Turbines, vol.-1 (2005), p. 15 (Chapter 5) 24. P.P. Gohil, R.P. Saini, Effect of temperature, suction head and flow velocity on cavitation in a Francis turbine of small hydro power plant. Energy 93, 613–624 (2015). https://doi.org/10. 1016/j.energy.2015.09.042 25. P.P. Gohil, R.P. Saini, Numerical study of cavitation in Francis turbine of a small hydro power plant. J. Appl. Fluid Mech. 9(1), 357–365 (2016) 26. E. Science, Kaplan turbine tip vortex cavitation—analysis and prevention (2012). https://doi. org/https://doi.org/10.1088/1755-1315/15/3/032060

Chapter 86

Classification and Synthesis of Nanoparticles: A Review Anna Raj Singh, M. Maniraj, and Siddharth Jain

Abstract The nanoparticle study is intense research with a huge application in optical, biomedical and in electronic fields. They have good scientific interest in the bulk, atomic and molecular structures. Changes in the size affect the chemical and physical properties of the nanoparticles. They have good properties with respect to the bulk material. The nanoparticles have a high surface area to the volume ratio. In the research work of nanotechnology with the study of physical and chemical properties, the application of nanoparticles in biofuel extraction has improved with improvement in the percentage of yield. The use of nanoparticles has improved the efficiency of the diesel engines. The review paper is about understanding the properties of nanoparticles and their applications. The present paper deals with the a review on classification and synthesis of nanoparticles. Keywords Nanoparticles · Properties · Applications

86.1 Introduction With the introduction of new advance technologies in the field of biofuel production, the use of nanoparticles made a huge scope in the research and the effect of using the nanoparticles. The study of nanoparticles is been studied deeply in different fields like biomedical, optical and electronic fields. In the ninth century, the nanoparticles have been used as designs on the surfaces of the pots. Since the Middle Ages, a film of metallic has been prepared and used on the surface giving a luster effect. Nanoparticles may be man-made or exist naturally in the environment. The availability of nanoparticles is in the form of oxides and carbonates. The combustion of the diesel fuel leads to the formation of nanoparticles. Nano refers to the length of a particle in A. R. Singh · M. Maniraj School of Mechanical Engineering, Galgotias University Greater Noida„ Greater Noida, Uttar Pradesh 203201, India S. Jain (B) Department of Mechanical Engineering, College of Engineering Roorkee, Roorkee 247667, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_86

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other words the extension of the object from 1 to 100 nm comes in the category of nanoparticles.Two strategies followed in the methodology of nanoparticles which is ‘top-down’ and ‘bottom-up.’ The application of milling comes under top-down and the use of the chemical process comes under the bottom-up. In the top-down method, the crushing of microparticles is done by the millings and converting the particles in fine powder. The bottom-up methods are done by precipitation, aerosol, sol-gel processes. In the medical studies, the use of nanoparticle applications in different forms like liposomes, polymeric, iron oxide, quantum dot and gold are some of the nanoparticles [1–4]

86.2 Classification The types of nanoparticles are studied on three parameters which is based on 1. 2. 3.

Its origin Its dimension Its structure.

86.2.1 Nanoparticle Based on Origin The nanoparticles are available naturally in our resources which originated from storms or from the eruption of the volcano or bioenergy resources. As to avoid the depletion of natural resources the development of man-made nanoparticles which is in the form of pesticides and used in the treatment of soil fertility, they have good penetration properties. The use of nanoparticles is very harmful for human life but in some reigns due to the over use leading to the toxicity of environmental surroundings. Organic and inorganic are the two divisions of nanoparticles [5] (Fig. 86.1).

Fig. 86.1 Organic and inorganic NPs [6]

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86.2.2 Organic Nanoparticles Organic particles are synthesized from protein, lipids and carbohydrates and other organic compounds with 100 nm size. We see the production methods in the form of top-down and bottom-up methods. Organic nanoparticles are easy to distinguish on the basis of dimension, surface properties and biological properties which make them useful in the modification of the physical properties and improving the food quality standards [7] Dendrimers The Greek word ‘dendrom’ which means ‘tree’ also named as ‘arborols or cascade molecules.’ Dendrimers are three-dimensional molecules which are made of atom like nitrogen in which series of reaction been joined together forming a circular shape structure. The size may increase with an increase in the layers around the circular shape structure. The inner layers are continuously attached to the core and the exterior is connected with the interior generations. They are biodegradable in nature. In biological research, it is discovered that Dendrimer safe the drug until it reaches its specific destiny to release it inside the human body which makes it very unique with its property. Micelles In an aqueous solution, the molecules that consist of polar or non-polar reigns which form aggregates are termed as micelles. It is made of hydrophilic moiety known as head and hydrophobic moiety as the tail. The ‘head’ is always in touch with the surrounding solvent, whereas the ‘tail’ is in the micelle center. The methodology in the micelle formation is known as micellization. The spherical shape contains 50– 100 monomers. The term aggregation number gives the total number of monomers used in the formation of a micelle. Liposomes The term ‘lipos’ which is fat and ‘soma’ as body has been developed by the British hematologist Dr. Alec D Bangham in Cambridge. A liposome has a tiny bubble (vesicle) generated from a similar material in the form of cell membrane. The classification of the liposome is done on the basis of its structure, how it is been prepared and its composition used in it. The handling of the liposomes needs to be taken care as they are unsaturated and sensitive to oxidation. Volatile materials like chloroform if stored in container will evaporate easily that is the reason why the liposomes been stored in dark or in compact caps of glass vessels.

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86.2.3 Inorganic Nanoparticles The particles which are not made of carbon are referred as inorganic particles. The composition of the inorganic particles is metal or oxides of metal. Some of the classification of nanoparticles are as follows. Carbon-Based Nanoparticles The carbon-based particles show remarkable properties in heat and electrical conductivity. They are less harmful to the earth’s atmosphere as they are not destructive to the natural resources. The carbon-based nanoparticles are fit perfectly with the bioresources. The excellent conductivity makes them useful in the manufacturing of computer processor chipset as they are able to withstand moderate temperatures. In biomedical science, they have been friendly in the development of drugs and gene delivery as they have good penetration properties inside the blood cells of the human body, and also due to the presence of different forms of polymers, it has wide use in the tissue generation. High tensile strength makes them very useful in making fake joints or aa bone replacement with addition of calcium powder. In the batteries, the electrodes consist of carbon particulates for storing power and have wide use in the formation of catalyst. They are used in structural designing as they are powerful than steel [9] (Fig. 86.2).

Fig. 86.2 Forms of carbon nanotube [8]

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Fig. 86.3 Ceramic nanoparticle [10]

Ceramic Nanoparticles These nanoparticles have composition of carbonates, oxide films and phosphates. They are in crystalline form and are developed with the heating and sudden cooling. Some ceramics are composite of metal or non-metal for which the bonds between them is either ionic or partially ionic in characteristics. One of the first use of ceramics is in the clay later has wide use in industrial, domestic and in the construction of buildings [11] (Fig. 86.3). Metal Nanoparticles The nanoparticles like gold, silver and copper have been used in molecular imaging, anticancer agents and in the form of drug carriers. In cancer diagnosis test and in surgical treatments, use of gold nanoparticles is very common now a days (Fig. 86.4). Silver nanoparticles are used in chemotography, bioimaging and transfection vectors. They have the absorbing capability and have large surface capability. Silver and gold are the most use metal nanoparticle as they exihibit excellent properties like good conductivity, antibacterial and catalytic properties. Polymeric Nanoparticles The nanoparticles have a spherical shape and organic in nature. Nanosphere consists of a matrix structure and Nanocapsular is in the form of a core shell-type design. The polymeric nanoparticle has the potential to protect the molecules of the drug, join therapy and imaging. Drug delivery in polymeric nanoparticles is renewable and biodegradable in nature (Fig. 86.5).

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Fig. 86.4 Metal nanoparticle [12]

Fig. 86.5 Structure [13]

86.3 Properties of Nanoparticles The nanoparticles are made of three layers which is surface and shell layer with core. The surface layer of the nanoparticle is filled with ions and surfactants. The natural form of the nanoparticle is suspension or as dispersed aerosols. Size is a considerable factor to distinguish the size of a nanoparticle. Variation in the physical

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Fig. 86.6 Size variation with melting point for gold nanoparticle [15]

and chemical properties is dependent on the design of a nanoparticle. Due to the presence of a large number of surface atoms, variation in such properties occurs. The properties like bandgap, mechanical properties, melting point, electrical properties and magnetic properties are discussed below [14].

86.3.1 Melting Point As the bonding energy gets low, the atoms present on the surface contribute towards the low temperature (Fig. 86.6).

86.3.2 Band Gap In semiconductor nanoparticles in any forms whether it is spherical, nanowire or nanofilms, there is reduction in the size of particle when there is large gap between conduction and valence band. To understand the electrical properties of nanoparticles in photoelectrochemical cells, doping of nanoparticles with nanocomposites is done which results reduction of about 50% (1.6 electron Volt) in a optical band for Ag nanoparticles with titanium dioxide nanocomposite. The band gap represents the spacing of the conduction and valence band. When the quantity of the surface atoms on a nanoparticle is big in numbers, the spacing of conduction and the valence bond remains less example like bulk material when the number of surface atoms gets reduced the spacing of the bands become high which is a type of nanomaterial [16, 17].

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86.3.3 Mechanical Properties It is defined as mechanical properties which vary differently in nature and according to various loadings. In case of metals, the properties to be considered are plasticity, hardiness, adversity, flexibility. The addition of a nanoparticle in a material results in the changes in the grain boundary and helps in improving the quality. To improve the strength of properties, the size of the particle must be low as there be fewer chances of the defect in the surfaces. Less number of particles results in more improvement in the rigidity and yield strength [18].

86.3.4 Magnetic Properties The magnetic nanoparticles have a size range of less than 100 nm. The magnetic nanoparticles are available in our natural surroundings in the form of living beings, ceramic and in the rust metal surfaces. The use of magnets, biomedical products and catalyst are some of the man-made products with the use of magnetic nanoparticles. They have wide applications in the science and medical industries. The computer chipsets and in optical fibers iron, nickel, copper and their oxides show good chemical and electrical properties (Fig. 86.7).

86.4 Application of Nanoparticles in Daily Life This section is dealing with the different applications of nanoperticles in daily life (Fig. 86.8).

Fig. 86.7 Magnetic NPs [19]

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Fig. 86.8 Application of nanoparticles [20]

86.4.1 Use of Nanotechnology in Food The nanoparticle usage in the food industry is to a wide area. By the coming year of 2050, the expected population tends to be 9.3 billion which will increase the demands of food production. Nanotechnology is the future scope to make benefits in the food and agriculture sectors. The food used in consumption consists of carbohydrates, fats and proteins in form of small nanostructures. Using of nanoparticles in food gives good taste and increases its nutrition level and remains fresh for long durations. Nanocoatingsare used to preserve the food for long durations. The nanoclays work as ultraviolet resistant. In the packaging, the nanocomposites avoid the discharge of carbon dioxide from bottles. Titanium dioxide and iron oxide have been the best nanoparticle for food additive in the food industry [22] (Fig. 86.9).

86.4.2 Nanoparticle Use in the Electronics With the rise in the production cost of circuits, the nanotechnology has been a good option in the electronic industries. Nanoelectronics is the application of nanoparticle

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Fig. 86.9 Application in food resources [21]

in electronic devices. Due to their small size, the production of the chips with a large number of transistors makes it more powerful and improved the performance of electronic devices. With the use of nanotechnology the size, performance and consumption of power have been reduced. The overall cost of storage drives and memory cards has been reduced. The carbon nanotubes have been a good example in the production of thin displays for TV and monitor screens. The use of nanotechnology is been made in wireless devices, cars and robots in future scope. The carbon nanotubes work as storage cells like lithium-ion batteries as they can store large amounts of energy. The use of flash memory under 90 nm is been fabricated which have high-speed performance in Intel chipsets and in the gaming consoles [23].

86.4.3 Nanoparticles in the Medical Industry The use of nanotechnology is implemented toward drug delivery. The nanoparticles like dendrimers, micelles are used in delivering the drug to its specific location without any side effects to the other organs. This technology made a reduction in production cost and has been very useful for cancer treatment. The iron and gold nanoparticles are used in the treatment of cancer. The precise equipment for the surgical operations is developed which are useful in small surgeries and avoid damages to a large area of the body (Fig. 86.10).

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Fig. 86.10 Classification of nanoparticle application in medical science [24]

Treatment of tumors is possible by the iron oxide. Perfluorocarbon emulsion nanoparticles have improved the imaging of ultrasonography with better contrast which is been very useful to get better images to identify the tumors and minor defects that cannot be seen before. To improve the magnetic resonance imaging the use of two carbon type nanoparticles, gadofullerenes and gadonanotubes have been very useful in maximizing the working capability of MRI [25–27]

86.4.4 Nanoparticles as Catalyst The use of nanoparticles as a catalyst has been very useful in biofuel production due to its larger surface area. The nanotubes work as a heterogeneous catalyst that is reusable in nature and can also be separated providing a clean biofuel. Better the surface area more enhancement in the chemical reaction can be found. The use of nanoparticles as catalysts has been used in diesel engines to get better performance and efficiency. The use of CuO2 nanoadditives in diesel engines resulted in the reduction of exhaust gases like smoke, carbon monoxide and hydrocarbon particulates due to large surface area which resulted in complete combustion. The use of nanoparticles in diesel engines has improved the efficiency and the performance. The use of nanoparticle as a catalyst can be reused and can be separated easily as compared to the homogenous catalyst [28, 29]. Lot of work has been done in the area of transesterification of vegetable oil with homogeneous and heterogeneous catalyst [30–38]; however, there is need to do more comprehensive work in the area of use of nanocatalyst in transesterification process.

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86.5 Conclusion Nanoparticles have been very productive in the daily needs of humans. They have a wide application in food, biomedical and electronic industries. The nanoparticles have reduced the cost of production of computer chipsets and made it lighter in size and powerful in processing. In the biofuel production, the nanoparticles have made the chemical reaction more cleaner and reusable in the form of catalyst. More research work is been conducted over the use of nanoparticles in future technologies which will make daily routine tasks easier and faster. The use of nanoparticles will avoid the overuse of natural resources which is not renewable in nature.

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Chapter 87

Marble and Granite Slurry Reuses in Industries S. S. Godara, Mohit Kudal, Tikendra Nath Verma, Gaurav Dwivedi, and Shrey Verma

Abstract The solid waste generated from the production of marble and sandstone has been used to develop new products. It has been used as a mixture for the more efficient use of natural resources. Granite powder and industrial sand have been used in concrete as alternative materials suitable for concrete production. The primary role of this paper is to use marble dust, sandstone dust, fly debris. The best answer for the marble solution issue is to use it in clusters. The use of this waste will reduce the cost of concrete; Reduction in ecological contamination, use of common property, and vitality request. Marble powder has been used as a cheap filler instead of other commercial fillers such as bleaching and marble powder can be used for static applications as an incomplete replacement of carbon dark in various elastic objects that wear barrier and dynamic properties and do not require high forces. Marble powder is used in commodities, for example, gaskets, carpets, tubes, window pipes, etc. Keywords Granite powder · Marble powder · Problems · Slurry · Solid waste

87.1 Introduction Marble in Rajasthan represents 90% of the total reserves in India. It is available in Ajmer, Makrana, Rajsamand, Udaipur. Mineral extraction represents about 55% of all wastes. The marble square allows you to obtain a marble solution, fine white powder, and garbage, which then leave ripped or isolated rural homesteads, brushing land, waterway beds, roadways, void fields, and seepage areas. About 4000 marble S. S. Godara · M. Kudal Mechanical Engineering Department, University Departments, Rajasthan Technical University, Kota, Rajasthan, India T. N. Verma Department of Mechanical Engineering, MANIT, Bhopal, India G. Dwivedi · S. Verma (B) Energy Centre, MANIT, Bhopal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_87

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quarries and 1100 marble processing compartments distributed across 16 districts of Rajasthan contain vast amounts of marble dust (5–6 million tons/month) in the form of sludge during processing of marble stones. Indiscriminate disposal of marble dust (MSD), mainly on roads, causes drainage, drainage, air pollution, and agricultural land damage. Squander dispersion can be evaluated as half of the mine waste, making up 5% of the waste. In this manner, the first item, marble, is just 30% for use. Various physical properties have been described in the marble slurry squander of the Makrana area of Nagaur district, Rajasthan, India, and are used in the concrete section of concrete. The effect of marble solution on the hydration process, highlights of new and freezing cement, and strength using surrounding hardware were researched [1]. Granite sludge is a waste consisting of superfine powder and creates problems with disposal and ecology around the world today. Disposal of granite waste can cause health hazards, such as breathing problems and allergies, for people around them. This additionally causes air and water contamination. Concrete is the most widely used structure material and requires advancement in the form of fixing (concrete and coarse aggregate). Supporting common property in concrete construction is a fundamental issue in the current state of development. The particular business is additionally one of the leading manufacturers of carbon dioxide. The use of rock mud squanders in cement can significantly take care of issues related to squander age, reducing the use of the regular property and CO2 emissions. A deliberate trial study has completed the use of stone slurries instead of concrete at varying degrees of replacement. Granite sludge waste also does not consist of sludge and organic impurities. It can create to achieve the desired gradation and degree of grinding according to need, and therefore, it can also use as an alternative material for natural sand (F.A.). Thus, granite sludge waste has provided a reliable source of quality for fine aggregates and preserves a depleting source of natural sand [2]. • The effect of spent marble powder is the swap for whiteness at the level of spent marble powder. As indicated by the marble, the suspension is using with lime to finish the work as a whiteness, it is half less expensive, and adequate protection from sun-driven heat is additionally safe because the cooling of the structure is cold [3]. • Possibilities are using marble waste as an adsorbent for purifying water from fluoride. The split acts in two directions, reducing excess fluoride and decreasing the amount of marble waste. MWP650 does not require its low cost, wholesale availability, regeneration since fluorine-containing adsorbents can be used in other industries [4]. • The decoration material of marble paste is better than putty, which has more power and cheaper. Marble paste is a mixture of slurry powder with white cement and slaked or hydrated lime together with a bonding agent. It has a tremendous environmental impact. The saved energy and money can be used elsewhere for the development of the nation [5]. • Underscore, similar and helter kilter are the conduct that M.S. Concrete, Compressive and Boeing Quality, Contact, Pourness, and Ultra-Beat Speed as far as Combat. Some elements have decided the strength and strength of cement,

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including the physical and compound formation of substantial segments, such as the microstructure of constituent particles. SEM and E.D. The clarity of the models observed using the microstructural tests was obtained. Testing and informational conduct of determinations within clear points. It has been found that the optimal percentage of replacement for M.S. Concrete, ignoring minor deviations, was 15% [6]. • The possibility has the processing of marble waste in the production of ceramic products from clay as raw material for the production of ceramic products. Five different mixtures of soil bodies containing marble waste were evaluated by firing color, water discharge, morphology, microstructure, coefficient of thermal expansion, and thermal behavior—up to 27%. The test results show that the use of marble waste in a ceramic granite case is possible for manufacturing ceramic products [7]. • It has been studied that marble waste and various types of trash have been used as a filler as a substitute for 15% with natural soil during highway construction. They are improving the conductivity of water, increasing the level of larger particles. It reduces as far as possible, makes it as far as possible, and reduces the mess Pliny’s file [8]. • The expected use of stone powder in the manufacture of simulated stone was investigated. The test results indicated that compressed quality enhancement depended on the ratio of the solid. The compressive strength of synthetic rocks largely depends on the percentage of cement and stone powder, compaction pressure, and curing time [9].

87.2 Applications Sector of Marble Slurry The waste of marble business is responsible for specific ecological issues. A solitary 30% of 70% of waste and the healthiness of primary items add to the most extreme waste that is indestructible. Dumping destinations give grime. Water systems and drinking water assets and waterways/waterways air affects widely diverse vegetation as well as damaging air–water bodies polluting the air. The most effective arrangement of marble slurry contamination is the use in mass. The core business that can eat marble slurry on such a vast scope is just growth businesses. Areas, where the use of marble slurry should examine as an alternative to traditional crude oil content, are according to the following.

87.2.1 Utilization of Marble Slurry in Cement Manufacturing More than 18 concrete assembling plants result from great limestone shops in the state of Rajasthan. Uncompromising the marble slurry powder has a high MGO rate along these rows concrete producing marble powder with raw materials (limestone)

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not removed. The adequate level of MGO in concrete assembling is within the range of 1–5%. The extended level of MGO in the crude oil mixture postpones the hydration process as the production division occurs. The moisture level in the raw mix required by concrete plants is below 0.5–1%; although in marble slurry, it is within a range of 8–22% [7].

87.2.2 Utilization of Marble Slurry Dust (MSD) in Road Construction The Focal Street Exploration Establishment (CRRI) has effectively showcased marble slurry based street development of 750 m stretches on 05 walks 2006 in the city of Kuncholi, Rajsamand. It has shown that a sub-level layer of street asphalt has been made by increasing the in-situ soil with 20–35% marble slurry dust. The valuation investment fund per kilometer of solitary path road sub-level with 20% of marble dust is about Rs. 75,000. Besides, it has been used as a mobilizing and filler material. The 5–6 million metric tonne age of slurry per year can eat into the development of 2500–3000 km of a path road. The State Open Work Office (PWD) needs to favor this blender to allow road development [8].

87.2.3 For the Manufacturing of Concrete In concrete mixtures, the compressive strength increases by 15% when the sand has mixed with 35% of marble dust. The density of concrete has also improved. Better development rehearsals associated with medical benefits make SCC an attractive answer to the cleaner concrete construction in place. Then, handling the waste powder on the SCC has been open another area to remove the bulk of this waste and has been guaranteed the solidarity of the SCC, requiring a large amount of powder to satisfy the self-compacting properties [10].

87.2.4 Utilization of Marble Slurry in Brick Manufacturing The Central Pollution Control Council (CPCB) and the Royal Norwegian Embassy NORAD) sponsored a project for the Indian Environment Society, Delhi. It told that marble bricks (83% slurry + 7% cement + 10% building sand) were not 93 kg/km produced, cm2 —compressive strength, which is also 2.5 times higher than that of traditional red bricks, which, thanks to flammable and inorganic raw materials, marble bricks are fireproof. Electricity as a source of energy and water as blocks reduce air pollution and protect natural sources of energy (coal and firewood) [9, 11].

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87.2.5 Manufacture of Ceramic Tiles The manufacture of clay divider tiles requires evaluation of the use of marble solution as a raw material at the degree of the pilot plant. The chief fired manufacturer in the nation directed a research center investigation into the issue, which was portrayed as useful [12].

87.2.6 Manufacture of Thermoset Resin Composites A macromolecular examination community in Jabalpur led a transient program, considering the possibility of converting marble slurries into composites of the pitch. Primer results have demonstrated the distinct achievement of this option. In any case, the pilot plant level should be considered.

87.2.7 Manufacture of Lime This study’s importance is to fully characterize the “waste” that has used in the future processes, as well as to determine the feasibility of replacing commodity micronized CaCO3 with a marble slurry. Limestone is the necessary raw material to make lime. The limestone is pressed with a marble frame.

87.2.8 Manufacture of Activated Calcium Carbonate Correlation between marble powder to compound, mineral, physical, morphological, and raw material requirements for the paper elastic and tire industries to show that the use of marble as an alternative to commercial micronized CaCO3 is a real chance for the squadron. A mixture of limestone or marble waste and marble dust (from moss) is used to make calcium or carbonate.

87.2.9 Hollow Blocks and Wall Tiles Marble mud and other earthenware’s are used in combination with zero expansion.

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87.2.10 Water Management The need for water treatment has met with the help of water tanks and wells. 30% of production waste in marble blocks processed by the team leads to results. It contains 20–25% marble chips and 5–10% debris/edges. There are stoves. The marble dust generated during this process forms a liquid suspension that spreads over the blocks. For recycling water and laying marble dust, a curved tank has been built to store water and dust. Due to this curved movement and the formation of sediment, the dust has settled. Alum as a coagulant can increase the rate of formation of deposits. The supernatant returns from the brigade again. In this reuse process, normal water misfortune ranges from 1000–1500 L for each day. The semi-strong marble mock is assembled using siphons in large horses and transported to the intended release site for removal. However, previously it was practiced to dump the liquid solution into an abandoned place. Data has been provided at the disposal site. Distilled water is also used to process marble. In addition to the curved water treatment system, vertical sumps used as shown in the photographs. A filter press has also been installed with some devices to reduce water consumption-discounted water recycled to gangster sites. Semi-solid sludge is collected in tractors and discharged to marked discharge sites. Due to the low volume of sludge production from individual units, it is not economically feasible to install a filter press in each unit. A group of 10–20 units can install a filter press between them. Units requiring ten kilo water per day must pay for water [13].

87.2.11 Visualization on Latent Fingerprint This waste dumped on open land, so this waste marble solution to use to develop latent fingerprints and powder to solve the crime. This waste powder is readily available at the construction site and uses as a hidden fingerprint development powder. This study presents a new marble slurry powdering method that is simple for the development of latent fingerprints on unsafe or non-surfaces. Ten samples are successfully developed and collected for various hazardous and non-unsafe surfaces, i.e., paper, plywood, plastic box, non-toxic synthetic surface, non-toxic wood surface, iron, non-stick utensils, leather, umbrella non-toxic fabric, stainless steel. Fine particles of marble slurry powder have mixed with fatty acids and oil present in the sweat of the fingerprint, and the latent fingerprint pattern can be seen on the surface using marble slurry powder. Studies show that it gives accurate results on most of the surfaces with bright fingerprints patterns and ridges [14].

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87.2.12 Ceramic Artwork The inclusion of marble waste in porcelain stoneware production produces environmentally friendly processed products. It has also assumed that the trash has been used to create other traditional ceramic products and molds. It has been estimated that up to 27% of these squanders can be evaluated in earth bodies made of porcelain stoneware without decay in the dimensional stability of objects [15].

87.2.13 Rubber Industry The classification of marble chips is based on the characteristics such as evaluation of elastic complex, partial replacement of residue filler, which help to improve ambulance and item evaluation for the preparation of marble chips. This investigation indicated that marble powder would be used as a minor filler instead of other commercial fillers, for example, brightening. Despite this, there was no significant improvement in the concoction of marble powder. Marble powder has additionally been used as a partial replacement for carbon dark (up to 10 volumes for each hundred gum) in various elastic items that work under stable conditions [16, 17].

87.3 Conclusion • The marble system is entirely insoluble in water. It takes 24 h to calm down. • Using machinery instead of a landfill is the best policy for working with marble. • It is comfortable and practical to use marble slurry to remove formwork for beams, columns, and slabs and obtain smooth surfaces. • Marble mortar is a fantastic aid because it indicates the rapid extinction of water and the hydration of substantial proceeds for the required timeframe. • Production of roof tiles for rural areas using stone dust and fly ash as raw materials. Tiles are economical because the raw materials used are considered useless for the industry. Therefore, it is readily available as waste [18]. • Due to the use of marble slurry with the cost of lime in marble slurry, the situation whiteness consumes reduced by 50% and smooth whiter and more durable. • The beauty of the marble slurry modulates 0.91, which allows it has been using for the whitewashing, which creates heat to reflect a thin layer and white, which leads to the passive cooling of the building. Marble slurry can have used for whitewashing to finish work in the construction industry. • Inelastic mixes, marble powder, can be used as a minor filler, for example, whitening, to reduce the cost of elastic items. • Marble powder is using for static applications as an incomplete replacement (10 sections by weight) of carbon dark in various elastic objects that do not require

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high forces and for which wear constraint and dynamic properties (for example, twisting and exhaustion quality). It is not essential. Marble powder has been used in objects, for example, tubes, carpets, vehicle mats, window pipes, gaskets, and so on [17].

References 1. M. Singh, A. Srivastava, D. Bhunia, An investigation on effect of partial replacement of cement by waste marble slurry. Constr. Build. Mater. 134, 471–488 (2017). https://doi.org/10.1016/j. conbuildmat.2016.12.155 2. S. Sharma, T. Gupta, R.K. Sharma, Assessment of mechanical properties of concrete containing granite slurry waste. Int. J. Eng. 29 5599–605 (2016). https://doi.org/10.5829/idosi.ije.2016. 29.05b.02 3. R.P. Singh Kushwal, P. Chaurasiya, White washing with marble Slurry. https://www.gyanvi har.org/journals/index.php/2018/12/03/whitewashing-with-marble-slurry/. 4. D. Mehta, P. Mondal, S. George, Utilization of marble waste powder as a novel adsorbent for removal of fluoride ions from aqueous solution. J. Environ. Chem. Eng. 4(1), 932–942 (2016). https://doi.org/10.1016/j.jece.2015.12.040 5. R.P. Singh Kushwah, I.C. Sharma.Energy efficiency and value engineering with industrial waste “marble slurry”. Int. J. Appl. Eng. Technol. 5(1), 84–89 (2015). ISSN 2277-212X (Online) 6. S. Singh, AnshumanTiwari, R. Nagar, and V. Agrawal, Feasibility as a potential substitute for natural sand: a comparative study between granite cutting waste and marble slurry. Procedia Environ. Sci. 35, 571–582 (2016). https://doi.org/10.1016/j.proenv.2016.07.042 7. S. Ye¸silay, M. Çakı, H. Ergun, Usage of marble wastes in traditional artistic stoneware clay body. Ceram. Int. 43(12), 8912–8921 (2017). https://doi.org/10.1016/j.ceramint.2017.04.028 8. D.P. Sharma, G.P. Sharma, International journal of engineering sciences & research technology use of marble waste and cement as binding material. 9655(6), 4–7, (2015) 9. N. Al-Joulani, Utilisation of stone slurry powder in production of artificial stones. Res. J. Eng. Appl. Sci. 3(4), 245–249 (2015). ISSN 2276-8467 10. S.N. Aisyiyah Jenie, et al. Geothermal silica-based fluorescent nanoparticles for the visualization of latent fingerprints. Mater. Express. 10(2), 258–266 (2020). https://doi.org/10.1166/ mex.2020.1551 11. M.A.M. Al-Bared, A. Marto, N. Latifi, Utilization of recycled tiles and tyres in stabilization of soils and production of construction materials – a state-of-the-art review. KSCE J. Civ. Eng. 22(10) 3860–3874 (2018). https://doi.org/10.1007/s12205-018-1532-2. 12. A. Rana, P. Kalla, L.J. Csetenyi, Sustainable use of marble slurry in concrete. J. Clean. Prod. 94 304–311 (2015) https://doi.org/10.1016/j.jclepro.2015.01.053. 13. S. S. Kushwah and S. Gupta, “Effect of Marble Slurry Dust and Lime Stabilization on Geo Technical Properties of Fine Sand. Int. J. Res. Eng. Technol. 6(10), 62–72 (2017). https://doi. org/10.15623/ijret.2017.0610010 14. Z. Khan, M. Umar, K. Shahzada, A. Ali, Utilization of marble dust in fired clay bricks. J. Environ. Monit., XVII, 4–10 (2017). 15. W.C. Fontes, J.M. Franco de Carvalho, L.C.R. Andrade, A.M. Segadães, R.A.F. Peixoto, Assessment of the use potential of iron ore tailings in themanufacture of ceramic tiles: from tailings-dams to ‘brown porcelain’. Constr. Build. Mater. 206, 111–121 (2019). https://doi.org/ 10.1016/j.conbuildmat.2019.02.052 16. P. Hadi, M. Xu, C. Ning, C. Sze Ki Lin, G. McKay, A critical review on preparation, characterization and utilization of sludge-derived activated carbons for wastewater treatment. Chem. Eng. J. 260, 895–906 (2015). https://doi.org/10.1016/j.cej.2014.08.088

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17. G. Marras, N. Careddu, Sustainable reuse of marble sludge in tyre mixtures. Resour. Policy. 59 77–84 (2018). https://doi.org/10.1016/j.resourpol.2017.11.009 18. L.G. Li, Z.H. Huang, Y.P. Tan, A.K.H. Kwan, F. Liu, Use of marble dust as paste replacement for recycling waste and improving durability and dimensional stability of mortar. Constr. Build. Mater. 166, 423–432 (2018). https://doi.org/10.1016/j.conbuildmat.2018.01.154

Chapter 88

Experimental Investigation on Thermal Performance of Solar Air Collector Provided with Corrugated Absorber Suman Debnath, Mukesh Kumar, Vikas Kumar, Amol Saini, Kunal Salwan, and Ravikant Ravi Abstract The study is concern about the effects of solar air collector length. In this research, effects of collector length on the temperature as well as Nusselt number have been discussed for corrugated absorber plate. The mass flow rates are used in the range of 20–40 kg/h m2 through five steps having two different tilt angle and single as well as double glazing cover. Better heat transfer is occurred, collector with double glazing having 45° inclination. During heat transfer, enhancement heat is transferred from absorber to working fluid and Nusselt number (Nu) is found to be 325 which is higher as compared to others. In the entire cases, double glazing collector shows better enhancement from single glazing collector. In the consideration of two inclinations, 45° inclination displays improved outcomes as associated to 30° inclination. The highest absorber temperature is observed at double glazing with 45° inclination which is 99°. Keywords Solar air collector · Collector length · Nusselt number · Absorber temperature

88.1 Introduction The solar energy is considered as one of the most promising options among all the alternative energy resources recent time. Among all the fields, solar air collectors (SACs) are one of the evolving fields where air is used as working fluid. SACs are the devices which trap the solar energy, and using this trapped energy, air is to be heated while it is passed over/below the absorber plate, the heat transfer phenomenon is happened from absorber plate to working fluid air via convection mode. The obtained hot air collected from outlet is utilized for domestic as well as industrial purpose such as drying crops, heating, industrial process, agricultural and many more. Due to low S. Debnath · M. Kumar (B) · V. Kumar · A. Saini · K. Salwan Department of Mechanical Engineering, Chandigarh Engineering Colleges, Landran, Mohali, India R. Ravi Department of Mechanical Engineering, GBPIET, Pauri Garhwal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_88

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heat transfer capability between absorber and working fluid, low thermal efficiency is provided by conventional SACs. Consequently, several methods are applied to overcome this condition and to enhance the efficiency. Parametric optimization with novel solar air collector using grey relation analysis is a well-known method to increase the thermal efficiency [1]. A regression model has been made to forecast efficiencies like energy, exergy, etc. It is shown that the data acquired with regression analysis are companionable with the data obtained from experimentations. Three different solar air collectors have been investigated; two of the collectors have fins and third one without fins. The first and third collectors have double glass covers and second collector has single glass cover. Authors have varied the mass flow rates and inclination angle of the collector and found that fins with double glass covers give better result compared to others two [2]. A numerical model has been proposed for prediction the temperature distribution inside the flat plate solar air collector using non-dimensional form for huge range of turbulent flow [3].The outlet temperature is reported as a function of collector aspect ratio and mass flow rates. Experimental and theoretical analyses have been performed for double-pass solar air collector with fins and baffles providing recycling of the working fluid [4]. Because of the fined plus baffled double-pass strategy, the optimum reflux ratio is around 0.5. A monetary reflection in relations of the heat transfer efficiency and power consumption is increased for defined doublepass process. Author has studied solar air collector for low as well as moderate temperature. Solar air collector with simple construction is employing with cheap materials have produced above 100 °C, to works with good efficiency. Furthermore, it has been also reported that selective coating absorber plate with corrugation having finned is advantageous for air collectors [5]. The study has been carried out to evaluate performance of solar air collector having new geometrical design. Four types of collector have been investigated such as two finned collector with an angle of 75° and 70°, third collector involved with tubes, last one is conventional collector. The collector finned with an angle 75° provides the best results out of four designs; the highest thermal efficiency is found to be 80% [6]. A novel type of solar air collector has been studied and prime importance of the study involved losses are diminished that has happened in front of the collector and makes the most of extraction of heat from the absorber plate. Author has suggested that if this conditions are fulfilled thermal efficiency cab be exceed 75% under the normal condition [7]. Experiment has been performed for both single and double pass collector using transverse fins as well as wire mesh layer. Moreover, it has been reported that temperature difference between outlet and inlet is inversely proportional to the mass flow rates and temperature difference increases as the mass flow rate decreases [8]. Novel arrangements of transverse and longitudinal baffles have been experimented to evaluate the performance of solar air collector. It has been observed that with increase in mass flow rate, thermal enactments and pressure drop both is increased. The collector with baffles gives better heat transfer enhancement as compared with conventional collector [9]. Experimentally investigation has been carried out for single and double pass solar air collector using partially perforated cover and wire mesh. The average

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efficiencies have found 49.98 and 53.67% for single and double pass collector, respectively, at mass flow rate of 0.037 kg/s [10]. One study has been performed to calculate the performance of solar air collector application wise to dried chili on the basis of energy and exergy analysis. Authors have investigated the specific energy consumption and found it has varied up to 5.26 kWh/kg. It has been informed that with 33 h moisture content of red chili is reduced from 80 to 10% [11]. Energy and exergy analysis has been carried out of a solar air collector having different obstacles on the absorber plate. Four different collectors have been analyzed and it has been observed that turbulent is the main reason to increase the thermal efficiency and this happens due to presence of the obstacles on the absorber plate. It has also been noticed that the highest irreversibility is to found for the collector without obstacles and also produces the lowest efficiency among the four collectors [12]. A novel design collector performance has been explored providing double pass recycling and v-corrugated absorber plate. Turbulent intensity along with heat transfer in convection mode is enhanced due to v-corrugation of the absorber plate. The parameters like mass flow rate of air and recycle ratio show significant effect on the collector performance [13]. Passive augmentation techniques have been applied to analyze solar air collector on condition that thermal and exergy investigation. Staggered absorber sheets with fins are tested to evaluate the performance of the collector. It has been reported that largest irreversibility is obtained for conventional collector with lowest efficiency [14]. A theoretical study has been carried out to measure the performance as well as heat transfer characteristics. To solve the mathematical model, author has used explicit finite difference method for five different collectors. The obtained results have showed significant agreement with the experimental data [15]. Experimentally, a comparative analysis has been explored to calculate the performance between perforated glazed and unglazed collector. Among all considered mass flow rates, 0.036 kg/s provides the maximum efficiency as 85% [16]. The solar air collector under the North-East India climatic condition has been explored. It has been reported that from energy and exergy analysis, a qualitative heat transfer is possible in the range of mass flow rate of 0.0078–0.0094 kg/s [17]. Experimental analysis has been performed under the climatic condition of north-eastern India along with an expert system is used based on fuzzy logic [18]. The model based on fuzzy logic can be proficient of forecast the results which are in good agreement with experimental results. Experimental study has been accomplished with corrugated absorber plate accompanied by modeling and optimization using expert system also performed [19]. The optimum outcomes such as energetic efficiency and exergetic efficiency are found as 35.9 and 12.8%, respectively. Experimental investigation has been carried out with wavy plate to analyze energy and exergy for solar air collector providing hybrid expert system [20]. From the extensive literary review, it has been seen that lots of research work have carried out in the field of solar air collectors [21–25]. But collector with corrugated absorber plate with effects of length of the collector has paid little attention. So in this study, solar air collector has been investigated with corrugated absorber plate focusing on the effect of the collector length.

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88.2 Materials and Methods Based on the experimental output, following assumptions can be pointed out: • The collector performance is evaluated at steady state condition. • Through this analysis, the specific heat of glass cover, back plate and insulation are considered to be negligible. The energy gain by the working fluid, Q u from absorber plate to flowing air can be calculated as.   Q u = mC p (Tout − Tin ) = h c A p T p − T f Tf =

Tin + Tout 2

(88.1) (88.2)

Using Eq. (88.1), experimental convection heat transfer coefficient can be calculated and depending upon that of h c experimental Nusselt number (Nu) can also be evaluated by following equation. Nu =

hc D k

(88.3)

The measured factors in all the trial set-ups combine sun flux incident normal to the absorber plate, inlet and outlet temperature, ambient air temperature, the temperature of the absorber plate at various points as well as the top glass temperature. The air mass flow rate is calculated from the inlet air velocity. The pressure drop across the absorber plate is checked for various mass flow rate of air. The air is provided to the system by a centrifugal blower. 1.52 m × 0.52 m is the cross sectional area of the collector box and 0.055 m is its depth. In this experiment, airflow is entered and at the same time it is removed by keeping the circular hole of diameter 0.02 m. In the system pain and corrugated, absorber plates are utilized with dimension of 1.5 m × 0.5 m × 0.001 m. To increase the absorbance of the collector plate, black paint in this experiment paints the whole surface of the absorber plate. Further, a corrugated aluminum plate of amplitude 0.006 m and wave length of 0.075 m is used. The experimental set-up is shown in Fig. 88.1.

88.3 Results and Discussions In Fig. 88.2a–d, the variation of Nusselt number is represented as a collocation of the collector length for the entire day at various tilt angles having single and double glazing. It can be seen that Nusselt number decreases as the working fluid travel from inlet to outlet of the collector and at outlet it shows minimum Nusselt number as expected to obtain. The highest and the lowest Nusselt number have been obtained

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Fig. 88.1 Experimental set-up

are 325 and 27 for collector with 45° inclination double glazed and 30° inclination single glazed, respectively. It can be observed that Nusselt number is varied at inlet 325–162, 307–150, 289–139 and 239–115; at outlet it varies 72–39, 66–36, 60–32 and 50–27 for 45° inclination with double glass, 45° inclinations with single glass, 30° inclinations with double glass and 30° inclinations with single glass, respectively, different mass flow rates of air. For every case from inlet to outlet, Nusselt number decreases from a higher value at inlet. High temperature difference at inlet and low temperature difference at outlet are the main reasons for variation of Nusselt number. Double glazing collector gives better performance as compared to single glazing collector; this is due to reduction in top losses as well as convection losses to the environment. Furthermore, 45º inclined collector provides better results than that of 30° inclined collector. This happens due to perpendicular incident of solar radiation. The curves of the graph are not smooth from inlet to outlet; the reason behind this is not uniform solar radiation. Figure 88.3a–d Display variation of absorber plate temperature with length of the collector for whole day having different inclination angle and glazing cover From Fig. 88.3, it can be noticed that from inlet to outlet absorber, temperature gradually increases. The temperature absorber plate ranges from at inlet and outlet are 79–33, 85–34, 89–39 and 99–43 for 30° inclination with single glass, 30° inclination with double glass, 45° inclination with single glass and 45° inclination with double glass. This clearly indicates that 45° inclination with double glass collector bring better outcomes. A close observation can be noticed that highest absorber temperature is found at 12 noon for every case as solar radiation reaches at peak but lowest temperature is obtained at 4 pm or 8 am in the morning due to lower solar radiation. This absorber temperature is actually the most important parameter in terms of collector performance because more the energy absorbed by absorber plate, more energy can be able to release to the working fluid.

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(a)

= 45° Double glazing

350 300

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Fig. 88.2 Displays the Nusselt number (Nu) variation with the length of the collector from inlet to outlet for 8 am to 4 pm for corrugated absorber plate a 45° inclination with double glass, b 45° inclination with single glass, c 30° inclination with double glass and d 30° inclination with single glass

88 Experimental Investigation on Thermal Performance of Solar Air … = 30° Single glazing

(a) Temperature (°C)

85 8:00 AM

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35 25

0

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95 85

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90 8:00 AM

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40 30

0

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0.6 0.9 1.2 Length of the collector (m)

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110 100

Temperature (°C)

Fig. 88.3 Displays the variation of absorber plate temperature with length of the collector across inlet to outlet throughout the day time for corrugated absorber plate a 30° inclination with single glass, b 30° inclination with double glass, c 45° inclination with single glass and d 45° inclination with double glass

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88.4 Conclusions In this research, effects of collector length on the temperature as well as Nusselt number have been discussed for corrugated absorber plate. The mass flow rates are used in the range of 20–40 kg/h m2 through five steps having two different tilt angle and single as well as double glazing cover. • Better heat transfer is provided from absorber to working fluid by collector with double glazing 45° inclinations, as Nusselt number is found to be 325 which is higher as compared to others. • In all the cases, double glazing collector shows a better enhancement from single glazing collector. • From the considered two inclinations, 45° inclinations display improved outcomes as associated to 30° inclination. • The highest absorber temperature is observed at double glazing with 45° inclination which is 99°.

References 1. A. Acır, M.E. Canlı, ˙I Ata, R. Çakıro˘glu, Parametric optimization of energy and exergy analyses of a novel solar air heater with grey relational analysis. Appl. Therm. Eng. 122, 330–338 (2017). https://doi.org/10.1016/j.applthermaleng.2017.05.018 2. D. Alta, E. Bilgili, C. Ertekin, O. Yaldiz, Experimental investigation of three different solar air heaters: energy and exergy analyses. Appl. Energy 87, 2953–2973 (2010). https://doi.org/10. 1016/j.apenergy.2010.04.016 3. M. Baritto, J. Bracamonte, A dimensionless model for the outlet temperature of a nonisothermal flat plate solar collector for air heating. Sol. Energy 86, 647–653 (2012). https://doi.org/10.1016/ j.solener.2011.11.009 4. C.D. Ho, H. Chang, R.C. Wang, C.S. Lin, Performance improvement of a double-pass solar air heater with fins and baffles under recycling operation. Appl. Energy 100, 155–163 (2012). https://doi.org/10.1016/j.apenergy.2012.03.065 5. D.J. Close, Solar air heaters for low and moderate temperature applications. Sol. Energy 7, 117–124 (1963). https://doi.org/10.1016/0038-092X(63)90037-9 6. S. Karsli, Performance analysis of new-design solar air collectors for drying applications. Renew. Energy 32, 1645–1660 (2007). https://doi.org/10.1016/j.renene.2006.08.005 7. A.A. Mohamad, High efficiency solar air heater. Sol. Energy 60, 71–76 (1997). https://doi.org/ 10.1016/S0038-092X(96)00163-6 8. A.J. Mahmood, L.B.Y. Aldabbagh, F. Egelioglu, Investigation of single and double pass solar air heater with transverse fins and a package wire mesh layer. Energy Convers. Manag. 89, 599–607 (2015). https://doi.org/10.1016/j.enconman.2014.10.028 9. AJ. Mahmood, Experimental study of a solar air heater with a new arrangement of transverse longitudinal baffles. J. Sol. Energy Eng. Trans. 139 (2017). https://doi.org/10.1115/1.4035756. 10. R. Nowzari, L.B.Y. Aldabbagh, F. Egelioglu, Single and double pass solar air heaters with partially perforated cover and packed mesh. Energy 73, 694–702 (2014). https://doi.org/10. 1016/j.energy.2014.06.069 11. A. Fudholi, K. Sopian, M.H. Yazdi, M.H. Ruslan, M. Gabbasa, H.A. Kazem, Performance analysis of solar drying system for red chili. Sol. Energy 99, 47–54 (2014). https://doi.org/10. 1016/j.solener.2013.10.019

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12. E.K. Akpinar, F. Koçyiˆgit, Energy and exergy analysis of a new flat-plate solar air heater having different obstacles on absorber plates. Appl. Energy 87, 3438–3450 (2010). https://doi.org/10. 1016/j.apenergy.2010.05.017 13. C.D. Ho, C.F. Hsiao, H. Chang, Y.E. Tien, Investigation of device performance for recycling double-pass v-corrugated solar air collectors. Energy Procedia 105, 28–34 (2017). https://doi. org/10.1016/j.egypro.2017.03.275 14. A. Ucar, M. Inalli, Thermal and exergy analysis of solar air collectors with passive augmentation techniques. Int. Commun. Heat Mass Transf. 33, 1281–1290 (2006). https://doi.org/10.1016/j. icheatmasstransfer.2006.08.006 15. P. Naphon, B. Kongtragool, Theoretical study on heat transfer characteristics and performance of the flat-plate solar air heaters. Int. Commun. Heat Mass Transf. 30, 1125–1136 (2003). https://doi.org/10.1016/S0735-1933(03)00178-7 16. R. Vaziri, M. Ilkan, F. Egelioglu, Experimental performance of perforated glazed solar air heaters and unglazed transpired solar air heater. Sol. Energy 119, 251–260 (2015). https://doi. org/10.1016/j.solener.2015.06.043 17. S. Debnath, B. Das, P.R. Randive, K.M. Pandey, Performance analysis of solar air collector in the climatic condition of North Eastern India. Energy 165, 281–298 (2018). https://doi.org/10. 1016/j.energy.2018.09.038 18. S. Debnath, J. Reddy, D.B. Jagadish, Investigation of thermal performance of SAC variables using fuzzy logic based expert system. J Mech. Sci. Technol. 33, 4013–21 (2019). https://doi. org/10.1007/s12206-019-0543-3 19. S. Debnath, J. Reddy, D.B. Jagadish, An expert system-based modeling and optimization of corrugated plate solar air collector for North Eastern India. J. Brazilian Soc. Mech. Sci. Eng. 41, 1–18 (2019). https://doi.org/10.1007/s40430-019-1782-z 20. J. Reddy, S. Debnath, B. Das, Jagadish, Energy and exergy analysis of wavy plate solar air collector using a novel hybrid expert system. J Brazilian Soc. Mech. Sci. Eng. 41 (2019). https:// doi.org/10.1007/s40430-019-1901-x 21. K.A. Suresh, S. Khurana, G. Nandan, G. Dwivedi, S. Kumar, Life span and overall performance enhancement of solar photovoltaic cell using water as coolant: a recent review. Mater. Today Proc. 5, 18202–18210 (2018) 22. A. Dwivedi, A. Bari, G. Dwivedi, Scope and application of solar thermal energy in India—a review. Int. J. Eng. Res. Technol. 6(3), 315–322 (2013) 23. S. Mishra, S. Verma, S. Chowdhury, G. Dwivedi, Analysis of recent developments in greenhouse dryer on various parameters—a review. https://doi.org/10.1016/j.matpr.2020.07.429 24. S.S. Tejra, B. Vishal, H. Udania, G. Dwivedi, Solar photovoltaic-thermal (PV-T) hybrid technology: an Indian perspective, vol. 4(1) (2017), pp. 102–106 25. C.P. Mohanty, A.K. Behura, M. Singh, B. Prasad, A. Kumar, G. Dwivedi, P. Verma, Parametric performance optimization of three sides roughened solar air heater. Energy Sour. Part A: Recov. Utili. Environ. Effects 42(15), 1–21 (2020)

Chapter 89

Noise Vulnerability Assessment at 78 dB (A) for Kota City Kuldeep, Sohil Sisodiya, and Anil K. Mathur

Abstract Noise pollution due to vehicular traffic is a rapidly growing environmental concern of metropolitan cities all across the world because the quality of life in urban cities and towns is greatly affected by the high noise level. It became a primary source of noise emissions in megacities because two-thirds of the total noise pollution in the urban cities are associated with traffic noise. It is a derivative of industrialization and urbanization. As per WHO, noise is globally recognized as a significant threat for human beings due to several physiological and psychological impacts on human health such as high blood pressure, stress-related disease, sleep disturbances, loss of hearing ability and the harm of productivity. Severe impacts, including loss of memory, frustration, and harmful attacks, cannot be ignored. In this research work, the evolution of traffic noise in Kota city has been studied. Twenty-eight sampling locations are selected to cover the whole city for the estimation of traffic noise levels. Noise data is collected, analysed and further used for noise mapping. Noise maps have been generated with the help of geospatial information system (GIS) to complete the noise vulnerability assessment for Kota city. Noise vulnerability assessment has been done at 78 dB (A). High noise level areas with an exposure time of associated population are identified. Humans living in high noise level areas with an exposure time of 75–100% of the total time are highly susceptible to the adverse effect of traffic noise pollution. Cardiovascular impacts related to traffic noise levels are connected with noise exposure limit and time to explain the noise vulnerability for Kota city. This study reveals the importance of traffic noise reduction policies and strategies for public health. Keywords Noise vulnerability assessment · Traffic noise · Geographic information system (GIS) · Equivalent continuous noise level (Leq)

Kuldeep · S. Sisodiya (B) · A. K. Mathur Department of Civil Engineering, UD, RTU, Kota, Rajasthan 324010, India A. K. Mathur e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1_89

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89.1 Introduction Urbanization and industrialization are increasing in all the developing countries. At the same time, developing countries are also undergoing physical expansion [1, 2]. It provides more excellent opportunities for access to employment, housing and safety, better education, and reduces the expense and time of transportation and communication. The rate of urbanization, industrialization and physical expansion is very high in developed countries in comparison to developed countries. Now, it became the primary cause of various kind of pollution such as noise pollution, water pollution, air pollution. Noise pollution leads to adverse health impacts on human beings and continuously emerging concern for urban dwellers and government officials due to the increased number of vehicles [3, 4]. Undesirable/unwanted level of sound is termed as noise. It disrupts normal activities (i.e. work, sleep and conversation) of humans [5, 6]. When levels of sound exceed the prescribed limits/standards given by regulatory agencies (such as CPCB, and MOEFCC in India), it is described as noise pollution [7]. It is the most underrated environmental problem among all types of pollution because it cannot be seen, smelled or tasted [8]. World Health Organization specified that “noise must be recognized as a major threat to human wellbeing” [9, 10]. MCI stated that there is a direct and deep connection between environmental noise and human health. Noise pollution has a detrimental effect on the lives of many people [11, 12]. The health effects of noise pollution cause short-term and long-term psychological and physical disorders [13, 14]. The role of noise in polluting the environment and its effect on human health is gradually explored. Noise influences hearing ability disrupts sleep, disturbed and disrupts cognitive performance. Besides, epidemiological studies indicate that the incidence of hypertension, myocardial infarction and stroke increases due to noise pollution [10–15, 15–17]. Observation and experimental studies have shown that noise disrupts the structure of night-time sleep, vegetative stimulation. These problems result in increased levels of stress hormones and oxidative stress, which can cause endothelial dysfunction. The heart consequences resulting from noise pollution are mentioned here and emphasize the dependability of noise mitigation policies for public health [3, 9, 18, 19]. The increasing number of vehicles in cities causes uncontrolled noise pollution. Traffic noise causes several physiological and psychological damages to human health, like annoyance and aggression, hypertension, high-stress level, hearing loss, sleep disturbance, interference with speech. Bus and heavy truck traffic have been found to contribute most to noise-induced annoyance [17, 20]. Traffic noise also causes ecological impacts like change in animal behaviour, their spatial distribution, anti-predator behaviour, reproductive success, foraging behaviour, population density and community structure. Traffic noise has also been found to cause depreciation of property value [21]. The increasing number of vehicles has created a severe threat of noise pollution in urban cities. Estimating traffic noise pollution is very difficult. It changes with

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vehicle speed, type of road geometry and physical conditions. It is more challenging to estimate traffic noise in Indian cities. Cities in India have different traffic conditions such as mixed traffic type, congestion, road conditions and lack of traffic sense [22]. The study related to the estimation of traffic noise levels in Kota city and also identified noise vulnerability on the sampling locations which are under the pollution threat. GIS can be used efficiently for the management of noise pollution with variables such as gathering, weighing, analysing, and presenting spatial and feature information to develop noise maps [21, 23]. Probability Kriging model is used on ArcGIS software to generate noise vulnerability maps. Following assumptions of this model: I (s) = I [Z (s) > ct] = μ1 + ε1(s)

(89.1)

Z (s) = μ2 + ε2(s)

(89.2)

where μ1 and μ2 are constants, and I(s) is a binary variable developed by using a threshold indicator, I(Z(s) > ct). ε1(s) and ε2(s) are random error. Probability kriging strives to do the same thing as indicator kriging, but it uses cokriging in an attempt to do a better job [24].

89.2 Study Area and Research Methodology Kota is a south-west district of Rajasthan (India). It is situated on the bank of Chambal River, and its geographical area is 512 km2 (Forest Survey of India) and seventh largest by population 1,001,694 as per the census of India 2011 [25]. It is 47th most populated city of India. The vehicle population in Kota as of 2017 is 842,886 [26]. Twenty-eight sampling locations were selected for traffic noise assessment in Kota city. These sampling locations are mention in Table 89.1 and marked in Fig. 89.1. The overall research methodology used in research work is shown in Fig. 89.2.

89.3 Observations The observations took during Feb 2019–May 2019 for 96 days. All readings were taken on an hourly basis from 6 am to 10 pm. The sound level meter is held at the height of 5 feet above the road level and 3 m from the curb for taking readings of noise levels. The smallest distance of 20 feet must be kept on the reflective surface from the SLM. The observed equivalent continuous noise level (Leq) for morning hour (6:00 am to 10:00 am), afternoon hours (11:00 am to 3:00 pm), evening hour (5:00 pm to 8:00 pm), and day time (6:00 am to 3:00 pm) at every place is shown in Table 89.2.

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Table 89.1 Sampling locations for noise measurements in Kota city S. No.

Sampling location

S. No.

Sampling location

S. No.

Sampling location

1

MBS Hospital, Nayapura

10

Gobariya Bawdi Circle

19

Antaghar Circle

2

Anantpura, Kota Bypass

11

Sabjimandi

20

I.L. Circle

3

Anantpura, Three way

12

Gumanpura

21

Aerodrome Circle

4

Chambal Garden

13

Borkheda, Three-way

22

Kotri Circle

5

Naya Nohra, Kota Bypass

14

RTU, Kota

23

Dadwara

6

Chambal Industrial Area

15

CAD Circle

24

Keshavpura

7

Dhakad Khedi,

16

Nayapura Circle

25

Talwandi

8

Naya Gaon, Kota Bypass

17

Raipura Circle

26

Jawahar Nagar

9

RICCO Institutional Area, Ranpur

18

RICCO Industrial Area, Ranpur

27

KSTPS

28

IPIA

Fig. 89.1 Study area and sampling locations

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Fig. 89.2 Flow chart of adopted research methodology

89.4 Results and Discussion It is very easy to understand from the observation Table 89.2 that the noise levels in the morning hours lie in between of 66.26 and 80.29 dB (A). Anantpura Bypass, Anantpura (three-way), Naya Nohra (bypass), Borkheda (three-way), Naya Gaon (bypass), Rajasthan Technical University (Kota), Chambal Garden (three-way), Nayapura Circle, Dhakad Khedi, Raipura Circle are the entry points of the Kota city. Heavy traffic was observed in the morning hours. The maximum sound level, 80.29 dB (A), was observed at MBS Hospital, Nayapura. A large number of peoples visit the hospital for a regular check-up and seek medical advice from doctors. The minimum

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Table 89.2 Observed data for each sampling location with population Sampling location

Equivalent noise level, dB (A) Morning hours

Afternoon hours

Evening hours

Day time

Anantpura, Kota Bypass

75.48

79.45

83.17

81.30

Anantpura, Three way

75.76

79.45

83.17

82.83

Raipura Circle

75.98

79.67

82.40

79.76

Naya Nohra, Kota Bypass

75.52

79.45

83.17

76.06

Borkheda, Three-way

76.33

80.13

81.06

79.23

RTU, Kota

76.03

79.67

82.55

72.13

CAD Circle

75.95

79.71

82.51

81.93

Nayapura Circle

76.11

79.63

82.80

83.06

Dhakad Khedi, Kota Bypass

75.52

79.45

83.17

77.96

Naya Gaon, Kota Bypass

76.32

80.01

83.10

71.70

Chambal Garden, Three Way

76.34

80.03

83.05

75.23

Gobariya Bawdi Circle

75.92

79.39

82.86

82.73

Aerodrome Circle

74.54

80.23

84.75

83.33

Kotri Circle

74.69

79.19

82.70

82.26

Sabjimandi

74.35

78.77

82.72

80.90

Gumanpura

74.63

79.19

82.94

81.85

Dadwara

73.97

78.21

82.24

79.52

Keshavpura

73.81

78.09

82.35

79.69

Talwandi

73.83

78.43

82.17

81.05

Jawahar Nagar

73.90

78.46

82.29

79.29

RICCO Institutional Area, Ranpur

66.26

69.98

65.04

69.83

MBS Hospital, Nayapura

80.29

78.20

75.52

80.06

Antaghar Circle

74.21

79.07

82.56

81.92

I.L. Circle

73.71

78.89

82.79

81.44

Kota Super Thermal Power Station

73.82

78.68

81.83

80.16

RICCO Industrial Area, Ranpur

73.80

77.58

81.93

78.71

Chambal Industrial Area

73.93

77.85

82.46

80.51 (continued)

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Table 89.2 (continued) Sampling location Indraprastha Industrial Area

Equivalent noise level, dB (A) Morning hours

Afternoon hours

Evening hours

Day time

73.83

78.21

82.44

81.50

was observed 66.26 dB (A) at RICCO Institutional area, Ranpur. It comes under the silence zone due to the presence of various kind of educational institutes and is situated outside (almost 15 km away) of the Kota city. Hence, traffic activity is shallow in the morning time. The range of noise level in the afternoon was found in between 69.98 and 80.23 dB (A). Gobariya Bawdi Circle, I.L. Circle, Aerodrome Circle, Kotri Circle, and Antaghar Circle are the important places of Kota city. Banks, hospitals, shopping malls, movie theatres, marriage gardens, schools, colleges, government offices, food stores are presented in nearby areas of these locations. Majority of peoples crosses this place to complete their work with their vehicles which is responsible for heavy traffic load on these circles in the afternoon. Aerodrome Circle was the place where the maximum noise level is recorded. It is the heart of Kota city and comes under the category of the commercial zone due to the presence of various types of showroom, marriage gardens, airport, banks, granary, restaurants, etc. The minimum was observed 69.98 dB (A) at RICCO Institutional area, Ranpur (Silence Zone). In the evening, noise levels were recorded in the range of 65.04 and 84.75 dB (A). Anantpura Bypass, Anantpura (three-way), Naya Nohra (bypass), Borkheda (threeway), Naya Gaon (bypass), Rajasthan Technical University (Kota), Chambal Garden (three-way), Nayapura Circle, Dhakad Khedi, Raipura Circle are the exit points of the Kota city. Gobariya Bawdi Circle, I.L. Circle, Aerodrome Circle, Kotri Circle, and Antaghar Circle are the crucial places of Kota city. Banks, hospitals, shopping malls, movie theatres, marriage gardens, schools, colleges, government offices, food stores are presented in nearby areas of these locations. Majority of peoples crosses this place to complete their professional, personal and commercial work. A large number of vehicles activities (public/private/individual) are mainly responsible for heavy traffic load on these circles. Sabjimandi, Gumanpura, Keshavpura, Talwandi, Dadwara and Jawahar Nagar have come under the commercial and residential category of the city. The minimum level of noise was 69.98 dB (A) in the silence zone (RICCO Institutional area, Ranpur). The maximum noise level was 84.75 at Aerodrome Circle as shown in Figs. 89.3 and 89.4. The almost same trend was observed for day time, i.e. maximum [83.33 dB (A)] and minimum levels [69.83 dB (A)] of sound were obtained at Aerodrome Circle and RICCO Institutional area, Ranpur, respectively. Noise level lies in between 69.83 and 83.33 dB (A) for day time. In the second part of study, noise vulnerability assessment is done at 78 dB (A). The reason for selecting 78 dB (A) noise level for the study is vehicular traffic. The standards for vehicles are shown in Table 89.3 given by MOEF. Sound level of

Fig. 89.3 Noise Level Maps for morning, afternoon and evening for the city

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Fig. 89.4 Noise Level Maps for day time for the city Table 89.3 CPCB standards for vehicles

Type of vehicles

Noise limits dB (A)

Two-wheeler • Displacement up to 80 cm3

75

• Displacement more than 80 cm3 but up to 175 cm3

77

• Displacement more than 175 cm3

80

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78 dB (A) is commonly generated by the two-wheelers. The following noise limits for vehicles shall be applicable from 1st January 2003.

89.5 Noise Vulnerability Assessment for Kota City at 78 dB (A) MBS Hospital (Nayapura), Naya Gaon Kota Bypass, Chambal Garden, Rajasthan Technical University, RICCO Institutional Area, Ranpur, and Naya Nohra (bypass) are the areas which are less vulnerable to the noise level of 78 dB (A) because the population of these areas is exposed to this level of noise for 0–25% time in a day of total time. Hence, human beings living in nearby areas of these sampling locations are very less affected by the adverse impacts of high noise levels. Dhakad Khedi is the place where the noise level of 78 dB (A) mostly lies between 25 and 50% time in a day, and the residents of these areas are more susceptible to the adverse effects on noise pollution. Anantpura (bypass), Anantpura (three-way), Gobariya Bawdi Circle, Sabjimandi, Gumanpura, Chambal Industrial Area, Borkheda (three-way), CAD Circle, Nayapura Circle, Raipura Circle, RICCO Industrial Area (Ranpur), Antaghar Circle, I.L. Circle, Aerodrome Circle, Kotri Circle, Dadwara, Keshavpura, Talwandi, Jawahar Nagar, Kota Super Thermal Power Station, and Indraprastha Industrial Area are the locations where peoples are highly vulnerable to severe impacts of noise pollution because 78 dB (A) of noise level is maintained almost 75–100% of the time. Noise vulnerability assessment/analysis for 78 dB (A) is shown in following Fig. 89.5.

89.6 Conclusion Traffic noise is an environmental pollutant which has many adverse health impacts on human beings. It causes physical and/or mental fatigue. High frequency or ultrasonic sound can affect the semicircular canals of the internal ear and cause nausea and dizziness. High-level noise gives pain to the ear and diminishes the power of hearing. It increases the secretion of adrenaline, the hormone of flight and fright. It causes headache, stress, deafness, high blood pressure, constriction of the blood vessel and emotional upsets. It is also responsible for the dilation of the pupil of the eye. As per WHO guidelines noise disturbs sleep, interferes with communication system due to lack of concentration.[27, 28] Peoples working in the industrial areas with high noise level lead to reduced work efficiency, reduced work rate and higher chances for accidents. Many studies reveal that exposure to high noise level [50–75 dB (A)] for 16 h leads to increased cardiovascular diseases to humans such as arterial hypertension,

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Fig. 89.5 Noise vulnerability assessment for 78 dB (A)

myocardial infarction and stroke. It can be concluded that noise levels range between 65.75 to 88.06 dB (A) for 90% of the stations. Almost 75% of the sampling locations are vulnerable to the harmful effect of noise pollution for the noise level of 78 dB (A). These data also indicate that the prolonged exposure to high noise level [60– 83 dB (A)] may contribute significantly towards noise originated diseases which may encompass the residents of the vulnerable areas prone to noise pollution. Vulnerable or susceptible groups are childs, the old persons, the chronically ill and people who have a hearing impairment. It would help the regulatory authorities, stakeholders and municipal corporation of Kota for better traffic control and management, and working towards reducing vulnerability prone to noise pollution.

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Author Index

A Abbas, Ali, 1099 Abhinaya Srinivas, B., 805 Aggarwal, Shivansh, 679 Agrawal, Abhishek, 163 Ahmed, Siraj, 339 Amarnath, Anumula, 463 Ananth, D. V. N., 523

B Baranwal, Naimish Kumar, 127 Baredar, Prashant, 269, 339, 759 Barhate, Sujit Sopan, 649 Barve, Akhilesh, 641 Basak, Souryadeep, 1011 Behura, Arun K., 69 Bhagoria, J. L., 339, 453 Bhatia, Aviruch, 1011 Bhatia, Rashmi, 1035 Bhatt, Jignesh, 563 Bhawarker, Yogesh, 589 Bhowmick, Dibyendu, 201 Bhukesh, Sanjeev Kumar, 269, 307 Bhukya, Rajendra Naik, 57 Boddapati, Venkatesh, 361 Boopalan, C., 777

C Chakraborty, Kunal, 171 Chaudhary, Rubina, 921 Chaurasiya, Prem Kumar, 759 Chhalotre, Sanjay, 759 Chikhalikar, Mandar, 403 Choubey, Abhishek, 843

Choubey, Anurag, 941 Choubey, Shruti Bhargava, 843 Choudhary, Deepak, 501 Choudri, R. V., 759 D Daniel, Chris, 177 Daniel, Arul S., 361 Das, Swarup Kumar, 963 Debnath, Suman, 1137 Deepika, Kumari, 101 Deepika, N. M., 887 Dhingra, Madhavi, 737 Divakar, Monika, 551 Dwivedi, Gaurav, 23, 69, 1127 Dwivedi, Nidhi, 85 Dwivedi, Rashmi, 759 G Gangil, Brijesh, 375 Gannamani, Deepuphanindra, 141 Garg, Rachana, 869 Gaur, Ambar, 23 Gautam, Atul, 339, 453 Gidwani, Kavita, 403 Godara, S. S., 1127 Gohil, Pankaj P., 1 Goswami, Shlok, 679 Gugulothu, S. K., 387 Guleria, Ajay, 1035 Gupta, Rohit, 269 H Haq, Shamsul, 85

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. V. Baredar et al. (eds.), Advances in Clean Energy Technologies, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-16-0235-1

1161

1162 Harish, V. S. K. V., 563, 605, 899, 931

J Jadon, Rakesh Singh, 737 Jain, S. C., 737 Jain, Siddharth, 375, 855, 1113 Janardhan, Kavali, 429, 793, 1051 Jani, Hardik, 805 Jani, Omkar, 563 Jayanthi, N., 887 Joshi, Renu, 235

K Kad, Sandeep, 1067 Kale, Manish, 589 Kamble, Rushikesh, 403, 577 Kankale, Ravishankar S., 819 Karneddi, Harish, 429 Karthik, Akkenaguntla, 463 Katdare, Prakash, 589 Kaur, Rajbir, 475, 487, 1059 Kaur, Rupinder, 475 Khandelwal, Achala, 833 Khare, Amit Prem, 85 Khare, Ruchi, 913 Khatri, Rahul, 679 Kirar, Mukesh K., 153 Koushik, C. S. N., 843 Krishna, D. S. G., 211 Kudal, Mohit, 1127 Kuldeep, 1001, 1147 Kulkarni, R. D., 627 Kumar, Ajay, 693 Kumar, Aman, 69 Kumar, Anuj, 141 Kumar, Arun, 605 Kumar, Hitesh, 221, 235, 253, 589, 941, 973 Kumar, Mukesh, 951, 1137 Kumar, Rahul, 221, 235, 253, 589, 973 Kumar, Ravi, 1099 Kumar, Sajjan, 963 Kumar, Vikas, 1137 Kumar, Vivek, 307

M Mahajan, Priya, 869 Mali, Vima, 987 Maniraj, M., 1113 Manohar Reddy, T. M., 463 Markapuram, Srinivasa Rao, 403 Mathur, Anil K., 1001, 1025, 1147

Author Index Maurya, Sushil Kumar, 973 Mayuri, K., 887 Mishra, Krishna Kant, 951 Mishra, Manvi, 869 Mishra, Shri Krishna, 221, 235, 253, 589, 973 Mistry, Prani R., 721 Mittal, Arvind, 429, 627, 793, 1051 Mondal, Ashoke, 281 Mude, Shoban, 57 Mudhalwadkar, Rohini, 649 Muhmood, Luckman, 13, 45 Mukherjee, Sanchita, 171 Mustafa Kamal, Md., 1099 N Nagababu, Garlapati, 805 Nagashree, A. N., 551 Nagori, Ankur, 921 Navada, Neha R., 551 Nema, Pragya, 833 Nema, R. K., 617 Nishwitha, G., 887 O Ohja, Amit, 627 Ojha, Amit, 429, 617 P Pahuja, Roop, 501 Paliwal, Priyanka, 153 Pamujula, Madhusudhan, 627 Pandey, Suyasha, 23 Pandit, Purnima, 721 Panwar, Payal, 1025 Paraskar, Sudhir R., 819 Pathariya, Anoop Kumar, 221 Pati, Swayamsidha, 23 Paul, Samrat, 171 Pavan Kumar, A. V., 463 Pavan Kumar, Y. V., 1081 Pawar, Siddhesh C., 13 Poonia, Surendra, 189 Praneeth, T. R. S., 1081 Prasad, Vishnu, 1099 R Radhika, G., 707 Rafiuzzama, S., 387 Raghavendiran, T. A., 777 Rai, Priyanka, 577

Author Index Rajak, Upendra, 759 Rajendran, Karnan, 443 Rajkumar, Sujatha, 443 Ramachander, J., 387 Ramireddy, Karthik, 1081 Rao, Markapuram Srinivasa, 307 Ravi Kumar, D., 707 Ravi, Ravikant, 951, 1137 Rawat, Mohan, 293 Rawat, Pooja, 323 Reddy, Banka Sai, 463 Rihan, Mohd, 745 Roy, Pranjit Kumar, 281 S Sachan, Shailu, 115 Saha, Pradip Kumar, 281 Saha, Shilpi, 201 Saini, Amol, 951, 1137 Sai Rakshitha, T., 211 Saiyed, Altafhusen, 1 Salwan, Kunal, 951, 1137 Samantaray, Deviprasad, 23 Sant, Amit Vilas, 605, 899 Saravanan, V., 777 Sarviya, R. M., 101 Sasi Kumar, G., 707 Sasidharan, Chandana, 931 Sastry, Ravikiran G., 387 Saxena, Nishant, 235 Saxena, Rachit, 221, 253 Saxena, Sonal, 253 Shah, Spandan, 69 Shanker, Saket, 641 Sharma, Hritika, 641 Sharma, Kapil Dev, 855 Sharma, Mahendra Pal, 951 Sharma, Meeta, 177 Sharma, Pramod Kumar, 339 Sharma, Pritee, 663 Sharma, Rohini, 1035 Shaw, Rahul, 963 Shiva Prasad, E., 211 Shrotri, Varun, 13, 45 Shukla, Anoop Kumar, 177 Shyam, Radhey, 793, 1051 Shylashree, H. B., 551 Singh, A. K., 189 Singhal, Mukesh Kumar, 127 Singh, Anna Raj, 1113

1163 Singh, Bharat, 375 Singh, Digvijay, 189, 351 Singh, Jasdeep, 1067 Singh, Jitendra Pratap, 693 Singh Kachhwaha, Surendra, 805 Singh, Kiran Deep, 475 Singh, Payal P., 721 Singh, Prabh Deep, 475, 487, 1059, 1067 Singh, R. N., 293 Singh, Sanjeev, 429 Singh, S. P., 189, 293, 351, 921 Singh, Tejinderdeep, 1059 Sirsa, Aditya, 429 Sisodiya, Sohil, 1001, 1025, 1147 Sneha, G., 57 Soni, Archana, 307, 323, 577 Sood, Divyanshu, 163 Sravani, K., 211 Sufyan, Marwan Ahmed Abdullah, 745 Sujatha, R., 415 Sumanth, J. V. A. R., 1081 Suravarapu, Lakshman, 913 Suresh, Sailesh, 443 Sutar, Sagar Balkrishna, 339 Swarnkar, Pankaj, 115, 627

T Tatajee, D. A., 523 Thatipelli, Prathyusha, 415 Tikar, Poonam P., 819 Tiwari, Ankit, 663 Tiwari, Bhupen, 403 Tripathi, Brijesh, 987

V Vasnani, Himanshu, 973 Verma, Shrey, 1127 Verma, Tikendra Nath, 1127 Vishnu, K., 617

W Wankhede, Sumeet K., 153 Warudkar, Vilas, 339, 453

Z Zuhaib, Mohd, 745