Green Technology for Smart City and Society: Proceedings of GTSCS 2020 9811582173, 9789811582172

Table of contents :
GTSCS Committees
Chief Patron
Patron
Honorary General Chair
General Chairs
General Co-chairs
Program Chairs
Organizing Chairs
International Advisory Committee
National Advisory Committee
Technical Committee
Publicity Chairs
Website Chairs
Registration Chairs
Publication Chairs
Finance Chairs
Sponsorship Chairs
GTSCS Reviewers
Preface
Acknowledgements
About the Conference
Contents
Editors and Contributors
Intelligent Computing in IoT-Enabled Smart Cities: A Systematic Review
1 Introduction
2 Motivations
3 Smart Cities
3.1 IoT-Based Smart City
4 Experiences Throughout World
5 Applications of IoT-Based Smart Cities
5.1 Smart Home
5.2 Smart Surveillance
5.3 Smart Health
5.4 Smart Transportation
5.5 Smart Water and Weather Systems
6 Technologies of IoT-Based Smart Cities
6.1 Big Data Analytics
6.2 Artificial Intelligence
6.3 Machine Learning
6.4 Block Chain
7 Critical Analyses
8 Research Challenges, Limitations and Discussions
9 Conclusion
References
Optimal Design of Fractional Order 2DOF-PID Controller for Frequency Control in Interconnected System
1 Introduction
2 Examined System
3 Architecture of Controllers
4 Grasshopper Optimization Algorithm
5 Results and Analysis
6 Conclusion
References
Electrical Properties of the PVDF-Lead-Free Ceramic-Based Composite Film for Sensor Applications
1 Introduction
2 Processing and Experimental Techniques
3 Results and Discussion
4 Conclusion
References
IoT-Based Voice-Controlled Energy-Efficient Intelligent Traffic and Street Light Monitoring System
1 Introduction
1.1 Motivation
1.2 Contribution to This Work
2 Related Works
3 Components Description
4 Network System Model and Proposed System
4.1 Working Principle
5 Results Analysis
6 Conclusion and Future Scope
Reference
Unsupervised Reduced Deep Convolutional Neural Network of Process Empirical Wavelet Transform Data for Recognition of the Early Stage of Alzheimer’s Disease
1 Introduction
2 Materials and Methods
3 Feature Extraction
4 The Reduced Deep Convolutional Neural Network (RDCNN)
4.1 Convolutional Layer
4.2 Pooling Layer
4.3 Fully Connected Layer
4.4 Softmax Layer
5 Result and Discussion
6 Conclusion
References
Auxiliary Dynamic Damping Loop in the Microgrid for Enhancing Frequency Recovery Rate
1 Introduction
2 System Architecture
3 Design and Configuration of the Auxiliary Dynamic Damping Loop
4 Result and Analysis
5 Conclusion
Appendix
References
Design of Chatbots Using Node-RED
1 Introduction
2 Node-RED
3 Application of Node-RED in Creating the Chatbots
4 Results and Analysis
5 Conclusion
References
Power Factor Correction for Single-Phase Domestic Loads Using Microcontroller and Triac
1 Introduction
2 Literature Review
3 Hardware Description of Proposed System
3.1 Hardware Required
3.2 Software Required
4 Working Algorithm and Proposed System
5 Operation Testing and Results
5.1 Case 1: Resistive Load
5.2 Case 2: Inductive Load
5.3 Case 3: Mixed Load
6 Conclusions
References
Techniques Behind Smart Home Automation System Using NLP and IoT
1 Introduction
2 Related Works
3 ZigBee Network-Based Technology
3.1 ZigBee Topologies
3.2 Structure of ZigBee Protocol
4 IoT Platforms
5 System Architecture, Natural Language Processing Unit, and IoT
6 Conclusion
References
Salp Swarm Optimized PID Controller for Frequency Control of Hybrid Power System with UC and UPFC
1 Introduction
2 HPS Modelling
3 Objective Function
4 PID Controller Design
5 Salp Swarm Algorithm
6 Result and Analysis
6.1 Case 1
6.2 Case 2
6.3 Case 3
7 Conclusion
References
Implementation of Fuzzy Hysteresis Controller for a Three-Phase Photovoltaic Multilevel Inverter
1 Introduction
2 System and Controller Modelling
2.1 The Inverter Output Voltage Modelling
2.2 Small Signal Modelling of the Load
3 System Investigated
4 Controller Structure
4.1 PID Controller
4.2 Fuzzy Logic Controller
4.3 Hysteresis Controller
5 Spider Monkey Optimization (SMO) Technique
6 Result and Discussion
7 Conclusion
Appendix: The System Parameters Are Assigned as Follows
References
Robust Control and Inverter Approach for Power Quality Improvement
1 Introduction
2 Overall System Modeling of SAF
2.1 Working Principle of SAF
3 Proposed Control Scheme
4 Result Analysis
5 Conclusion
References
Economic Analysis of Solar Water Pumping System for Irrigation
1 Introduction
2 Materials Method
3 Working of a Solar Water Pump
3.1 Solar Water Pump
3.2 Cost Analysis of Different Pumping Systems
4 Results and Discussion
5 Conclusion
References
Reliability Assessment and Cost Estimation of a Hybrid Renewable Micro-grid System
1 Introduction
2 Modeling of Hybrid Renewable Micro- Grid System
2.1 Wind Energy System Modeling
2.2 Modeling of Photovoltaic System
2.3 Modeling of Battery Storage System
2.4 Modeling of Diesel Generator
3 Proposed Management Strategy
3.1 Operational Strategy
3.2 Reliability Assessment
3.3 Renewable Factor (RF)
4 Result and Discussion
5 Conclusion
References
An Ontology Based Matchmaking Technique for Cloud Service Discovery and Selection Using Aneka PaaS
1 Introduction
2 Aneka PaaS: An Application Platform for Cloud Computing
3 Proposed Model
3.1 Client Request
3.2 Cloud Service Discovery
3.3 Cloud Service Selection
3.4 Cloud Ontology Triple Store
4 Proposed Matchmaking Technique
5 Implementation Using Aneka PaaS
6 Conclusion and Future Work
References
Proper Harmonic Analysis of Load Current of a Single Phase 5-Level Voltage Source Inverter Using HCC
1 Introduction
2 Multilevel Inverter Topology
2.1 Operation Principle of the Cascade Hybrid Multilevel Inverter ch16thongprasri20115
3 Hysteresis Current Control (HCC) Technique
4 Harmonic Analysis
5 Simulation Results and Analysis
6 Conclusion
References
A New Sparse Array Configuration for Direction of Arrival Estimation
1 Introduction
2 Signal Model with Mutual Coupling
3 Sparse Array Configuration
4 Result Analysis
4.1 Weight Function of Different Arrays
4.2 Estimation Analysis Without Mutual Coupling Effect
4.3 Mutual Coupling Effect on the Estimation
5 Conclusion
References
Forecasting of Net Asset Value of Indian Mutual Funds Using Firefly Algorithm-Based Neural Network Model
1 Introduction
2 Methodologies
2.1 FLANN Model
2.2 Firefly Algorithm (FA)
3 Simulation Study
3.1 Description of Data set
3.2 Training and Testing the Model
4 Conclusion
References
Toward Ultimate Scaling: From FinFETs to Nanosheet Transistors
1 Introduction
2 Simulation Environment
3 Electrical Performance Analysis
4 Comparison of FinFET, NWFET, and NSFET
5 Conclusion
References
Performance of a Free Space Optical Link with ACO-OFDM-Based Signal Transmission Under Beam-Wander-Dominated Atmospheric Turbulence
1 Introduction
2 System Description
2.1 Asymmetrically Clipped Optical OFDM Signal Power
2.2 Free Space Propagation of Optical Gaussian Beam: Intensity and Power:
2.3 Atmospheric Turbulence-Induced Beam-Wander
3 Mathematical Model for Error Performance
4 Numerical Results and Discussion
5 Conclusion
References
A Comparative Analysis of Demand Response on Different Operational Strategies of Battery Energy Storage System for Distribution System
1 Introduction
2 Problem Formulation
3 Solution Methodology
4 Result Discussion
5 Conclusion
References
Design and Analysis of Hands-Free Wireless Charging System
1 Introduction
2 Design Setup of RIC-WPT System and Equivalent Circuit
3 Coil Structure Simulation
4 Conclusion
References
Fault Tolerance Investigation of Solar Photovoltaic Strings Operating Under NOCT
1 Introduction
2 System Description
2.1 Mathematical Modeling of the PV Modules
2.2 NOCT Specification of the PV Module
2.3 Fault Scenarios and Test Benches
3 Results and Discussions
3.1 Module Faults
3.2 Connection Fault (F4)
3.3 Mismatch Fault (F5)
3.4 Bypass Diode Fault
4 Conclusion
References
Frequency Control of an AC Microgrid with Fractional Controller
1 Introduction
2 Proposed Microgrid, Controller and SOS Algorithm
2.1 Microgrid Model
2.2 Fractional Order PID Controller
2.3 Optimization Technique
3 Result and Discussion
3.1 Case-1 Dynamic Behaviour Under Injection of 1% Load Change with Constant Generation of Wind and Solar Power
3.2 Case-2 Dynamic Behaviour Under Variable Solar Power with Constant Wind Power and Load Perturbation
3.3 Case-3 Dynamic Behaviour Under Variable Wind Power with Constant Solar Power and Load Perturbation
3.4 Case-4 Dynamic Behaviour Under Variable Load with Constant Wind Power and Solar Power
3.5 Case-5 Dynamic Behaviour Under Variable Solar Power, Wind Power and Load Perturbation
4 Conclusion
References
Effectiveness of Backpropagation Algorithm in Healthcare Data Classification
1 Introduction
2 Review of Literature
3 Overview of Back Propagation Algorithm
4 Experimental Setup and Result Analysis
5 Conclusion
References
A Review on High-Impedance Ground Fault Detection Techniques in Distribution Networks
1 Introduction
2 Problem Definition
3 Simple Threshold-Based Classifiers for High-Impedance Faults
4 Data-Driven Complex Classifier for High-Impedance Faults
5 Conclusion
References
Voltage Stability Index and Butterfly Optimization Algorithm-Based DG Placement and Sizing in Electrical Power Distribution System
1 Introduction
2 Methodology
2.1 Load Flow Solution
2.2 Derivation of VSI from Distflow Equations
2.3 Problem Formulation
3 Butterfly Optimization Algorithm
4 Results and Discussions
5 Conclusions
References
An Economic Evaluation of the Coordination Between Electric Vehicle Storage and Photovoltaic in Residential Home Under Real-Time Pricing
1 Introduction
2 System Model
2.1 Photovoltaic (PV) System Model
2.2 Electric Vehicle User Behavior
2.3 Load Model
3 Problem Formulation
3.1 Objective Function
3.2 System Operational Constraints
4 Mixed-Integer Linear Programming (MILP)
4.1 MILP Algorithm
5 Result and Analysis
6 Conclusion
References
Particle Filter and Entropy-Based Measure for Tracking of Video Objects
1 Introduction
2 Proposed Scheme
3 Time Motion History of the Object
4 Object Detection
4.1 Weighted Mean Entropy
4.2 Time Motion History
5 Particle Filter-Based Tracking
6 Results and Discussion
7 Conclusions
References
An Automatic Insulin Infusion System Based on the Genetic Algorithm FOPID Control
1 Introduction
2 Problem Formulations
2.1 Structure Overview
2.2 Clinical Support
2.3 Mathematical Model of the Patient
2.4 MID
2.5 Investigation of Patient Activities
3 Control Algorithms
3.1 GA-FOPIDC Design
4 Result and Investigation
4.1 Investigation of Patient Activities with GA-FOPIDC
4.2 Investigation of Stability
4.3 Comparative Investigation
5 Conclusions
References
Design Guidelines for Vector Control of Wound Field Synchronous Motor
1 Introduction
2 Mathematical Modelling of WFSM
3 Reference Currents Determination
4 Design Methodology
5 Results and Discussions
6 Conclusion
Appendix: Wound Field Synchronous Motor (WFSM) Ratings and Parameters
References
Preliminary Study of Magnetic Resonant Coupling Based Wireless Power Transfer System
1 Introduction
2 Equivalent Circuit Diagram of Wireless Power Transfer System
3 Equivalent Circuit and Analysis
4 Results Analysis
5 Conclusion
References
An Optimized Machine Learning-Based Time-Frequency Transform for Protection of Distribution Generation Integrated Microgrid System
1 Introduction
2 Studied System
3 HHT (Hilbert–Huang Transform)
3.1 EMD (Empirical Mode Decomposition)
3.2 Hilbert Spectral Analysis (HSA)
4 Feature Extraction
5 Extreme Learning Machine
5.1 Kernel-Based Extreme Learning Machine (KELM)
5.2 Optimal KELM Parameter
6 Result Analysis
6.1 Case 1: Grid-Connected and Looped Configuration
6.2 Case 2: Grid-Connected and Radial Configuration
7 Discussion
8 Conclusion
Appendix
References
Improving the Performance of AVR System Using Grasshopper Evolutionary Technique
1 Introduction
2 Structure of Controllers
2.1 Conventional PID Controller Structure
2.2 FO-PID Structure
3 Structure of the AVR
4 Overview of Tuning Techniques
4.1 Ziegler and Nichols (ZN) Method
4.2 Nelder–Mead Simplex (NM) Method
4.3 Grasshopper Evolutionary Technique (GET)
5 Simulation Results and Analysis
5.1 Performance Evaluation of PID and FO-PID Controller
5.2 Analysis Through Transient Response
5.3 Analysis Through Stability
6 Conclusion
References
Demand Side Management by PV Integration to Micro-grid Power Distribution System: A Review and Case Study Analysis
1 Introduction
2 Demand Side Management
2.1 Role of DSM in India
2.2 Active Demand Side Management with PV Technology
3 System Description
3.1 Technical Specifications of the Plant
3.2 Basic Schematic Diagram
4 Performance Evaluation
5 Results and Discussion
6 Conclusion and Future Aspects
References
Machine Learning Based Efficiency and Power Estimation of Circular Buffer
1 Introduction
2 Related Work
3 Shift Register to Circular Buffer Optimization
4 Proposed Model
4.1 Motivation
4.2 Overview of Dataset and Models
4.3 Feature Engineering
5 Experimental Results
5.1 Evaluation Methods
5.2 Results and Discussion
6 Conclusion
References
Binary Dragonfly Algorithm-Designed Fuzzy Cascade Controller for AGC of Multi-area Power System with Nonlinearities
1 Introduction
2 System Investigated
3 Fuzzy Cascade Controller
4 Objective Function
5 Proposed Binary Dragonfly Algorithm (BDA)
6 Results and Analysis
6.1 Case 1: Controller Validation
6.2 Case 2: Technique Validation
7 Conclusion
Appendix
References
Verilog Implementation of High-Speed Wallace Tree Multiplier
1 Introduction
2 Background
2.1 7:3 Counter
2.2 5:3 Counter
2.3 Full Adder
2.4 Half Adder
2.5 Ladner –Fischer Adder
3 Conventional and Proposed Wallace Tree Multiplier
4 Result and Simulation
5 Conclusion
References
Multi-objective Optimal Location and Size of Embedded Generation Units and Capacitors Using Metaheuristic Algorithms
1 Introduction
2 Problem Formulation
2.1 Sensitivity Analysis
2.2 Objective Functions
3 Optimization Methods
3.1 Simple Genetic Algorithm (SGA)
3.2 Particle Swarm Optimization (PSO)
3.3 Differential Evolution Algorithm (DEA) [15]
4 Results
4.1 CASE-1: Optimization of Unity Power Factor DGs Only
4.2 CASE-2: Optimization of 0.95 Power Factor DGs Only
4.3 CASE-3: Optimization of DGs Operating at the Optimal Power Factor
4.4 CASE-4: Simultaneous Optimization of UPF DGs and Shunt Capacitors
5 Conclusion
References
Comparative Performance Investigation of Fractional Order and Conventional PID Controller Implemented for Frequency Stability
1 Introduction
2 System Under Study
3 Controller Structure and Objective Function
4 Spotted Hyena Optimization (SHO) Algorithm
5 Simulation Results and Discussion
5.1 Case-1: Transient Analysis of Presided System
5.2 Case-2: Robustness Analysis
6 Conclusion
Appendix
References
Performance Analysis of Solar PV-Based Unified Power Quality Conditioner System for Power Quality Improvement Under Nonlinear Load Condition
1 Introduction
2 UPQC Design
3 Control Strategies for UPQC
3.1 Generation of Reference Current
3.2 Generation of Switching Pulses
3.3 Capacitor Voltage Regulation
4 Solar PV Systems
4.1 Modelling of Solar Panel
4.2 Power Tracking Algorithm for Solar Panel
4.3 Boost Converter
5 Result Analysis
5.1 UPQC Performance Analysis Considering Scenario 1
5.2 UPQC Performance Analysis Considering Scenario 2
5.3 UPQC Performance Analysis Considering Scenario 3
6 Conclusion
References
Distributed Estimation of IIR System's Parameter in Sensor Network Using Block Diffusion LMS
1 Introduction
2 Problem Formulation
3 Determining IIR System's Parameter in Distributed Scenario
3.1 Determining the Parameter of IIR System Using Multihop Communication in Sparse WSN
3.2 Estimation of IIR System's Parameter Using Block LMS for Multihop Diffusion
4 Simulation Results and Discussions
5 Conclusion
References
Krill Herd Algorithm-Tuned IDN-FPI Controller for AGC in Interconnected Reheat Thermal–Wind Power System
1 Introduction
2 Proposed System
3 Proposed Controller
4 Krill Herd Optimization Algorithm
5 Individual Krill Movement:
6 Foraging Motion:
7 Random Diffusion
8 Simulation Result and Discussion
9 Conclusion
Appendix
References
Performance Evaluation of Region-Based Segmentation Algorithms for Brain MR Images
1 Introduction
2 Segmentation by Clustering Approach
2.1 k-Means Clustering
2.2 Fuzzy c-Means Clustering (FCM)
3 Segmentation by Expectation Maximization
4 Quantitative Analysis and Evaluation
4.1 Rand Index
4.2 Variation of Information (VOI)
4.3 Global Consistency Error
5 Results and Discussions
6 Conclusion
Reference
Optimal Allocation of Distributed Generators Using Metaheuristic Algorithms—An Up-to-Date Bibliographic Review
1 Introduction
2 Various Categories of OADG Problem
2.1 Exclusive OADG
2.2 OADG with Shunt Capacitor Allocation
2.3 OADG with Network Reconfiguration
2.4 OADG with D-STATCOM Allocation
2.5 OADG with Energy Storage
3 Review of Approaches and Objectives in OADG Problem
4 Conclusion
References
Quality of Power Enhancement of Distribution Network System Using DSTATCOM in Simulink Tool of MATLAB
1 Introduction
2 System Configuration
3 Control Algorithm
4 Simulation Model of DSTATCOM
5 Simulation Results
6 Conclusion
References
Technical Proposal for Sizing of Equipment and Designing of Solar Photovoltaic System
1 Introduction
2 Survey of Proposed Site
3 Design Methods for 3 kw Solar PV System
4 Mathematical Calculation of 3 KW Implemented Solar PV System
5 Conclusion
References
A Comparative Study of Muscle Artifacts Removal in Single Channel EEG
1 Introduction
2 System Overview
3 Proposed Method
3.1 Block Diagram
3.2 Methods Used
4 Results and Discussion
5 Conclusion and Future Scope
References
Author Index

Citation preview

Lecture Notes in Networks and Systems 151

Renu Sharma · Manohar Mishra · Janmenjoy Nayak · Bighnaraj Naik · Danilo Pelusi   Editors

Green Technology for Smart City and Society Proceedings of GTSCS 2020

Lecture Notes in Networks and Systems Volume 151

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA; Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/15179

Renu Sharma Manohar Mishra Janmenjoy Nayak Bighnaraj Naik Danilo Pelusi •

•

•

•

Editors

Green Technology for Smart City and Society Proceedings of GTSCS 2020

123

Editors Renu Sharma Department of Electrical Engineering Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar, Odisha, India Janmenjoy Nayak Department of Computer Science and Engineering Aditya Institute of Technology and Management (AITAM) Tekkali, Andhra Pradesh, India

Manohar Mishra Department of Electrical and Electronics Engineering Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar, Odisha, India Bighnaraj Naik Department of Computer Application Veer Surendra Sai University of Technology Sambalpur, Odisha, India

Danilo Pelusi Faculty of Communication Science University of Teramo Teramo, Italy

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-981-15-8217-2 ISBN 978-981-15-8218-9 (eBook) https://doi.org/10.1007/978-981-15-8218-9 © 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

GTSCS Committees

Chief Patron Prof. Manoj Ranjan Nayak, President, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Patron Prof. Ashok Kumar Mohapatra, Vice-Chancellor, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Honorary General Chair Prof. Pradipta Kishore Dash, Director, Multidisciplinary research center (MDRC), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

General Chairs Prof. Bijaya Ketan Panigrahi, Professor, Department of Electrical Engineering, IIT, Delhi, India Prof. Pradipta Kumar Nanda, Dean (Research), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Prof. Renu Sharma, Professor, Department of Electrical Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

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GTSCS Committees

General Co-chairs Prof. Sanjeeb Kumar Kar, Professor, Department of Electrical Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Dr. Danilo Pelusi, Assistant Professor, Department of Communication Engineering University of Teramo, Italy Prof. Pravat Kumar Rout, Professor, Department of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Program Chairs Prof. Janmenjoy Nayak, Associate Professor, Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India Prof. Bighnaraj Naik, Assistant Professor, Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Odisha, India Prof. Manoj Kumar Debnath, Assistant Professor, Department of Electrical Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Organizing Chairs Prof. Manohar Mishra, Associate Professor, Department of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Prof. Binod Kumar Sahu, Associate Professor, Department of Electrical Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

International Advisory Committee Prof. Damodar Acharya (Chair), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Prof. Sanjib Kumar Panda, NUS, Singapore Prof. Ehab H. E. Bayoumi, Abu Dhabi Men’s College, UAE Prof. A. Abraham, Machine Intelligence Research Labs, USA Prof. Sanjeevikumar Padmanaban, Aalborg University, Denmark

GTSCS Committees

vii

Dr. Baharat Joyti Ranjan Sahu, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India Prof. Akshay Kumar Rathore, Concordia University, Chicago Prof. Ramazan Bayindir, Gazi University, Turkey Dr. Danilo Pelusi, University of Teramo, Italy Prof. Prasanta Mohapatra, University of California Dr. Ahamed Zobaa, Brunel University, UK Prof. R. C. Bansal, College of Engineering, University of Sharjah

National Advisory Committee Prof. Damodar Acharya (Chair), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Prof. Bidyadhar Subudhi, IIT Goa, India Prof. S. R. Samantray, IIT Bhubaneswar, India Prof. S. K. Kulkarni, AGU, Simla, India Prof. Niva Das, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Dr. Swagatam Das, Indian Statistical Institute, Kolkata, India Prof. Trilochan Panigrahi, NIT Goa, India Prof. C. N. Bhende, IIT Bhubaneswar, India Prof. S. K. Bharadwaj, MNNIT, Madhya Pradesh, India Prof. Srikanta Pattnaik, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Prof. P. K. Sahu, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Technical Committee Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr.

Danilo Pelusi, University of Teramo, Italy Sazia Hasan, BITS-PILANI, Dubai Campus Abhisek Rajan, NIT, Sikkim Mohit Ranjan Panda, CVRCE, Bhubaneswar Shahed Mohammadi, Yandegan University, Tonekabon, Iran Sukanta Kishore Bisoyi, CVRCE, Bhubaneswar G. T. Chandrasekhar, SSCE, Andhra Pradesh Soumya Ranjan Mishra, MVJ College of Engineering, Bengaluru Prakash Kumar Ray, CET Bhubaneswar, BPUT, Bhubaneswar, India Malaya Kumar Nath, NIT, Puducherry Sumit Kushwah, Kamla Nehru Institute of Technology, Uttar Pradesh Trupti Swanakar, SOA, Bhubaneswar

viii

GTSCS Committees

Prof. Jyotir Moy Chatterjee, Asia Pacific University of Technology and Innovation, Nepal Dr. Joymala Moirangthen, NUS, Singapore Dr. K. R. Krishnanand, NUS, Singapore Dr. Sidhartha Panda, VSSUT Engineering College, Burla Dr. N. P. Padhy, IIT Roorkee Prof. M. Nageswara Rao, K. L. University, Vijayawada Prof. P. S. Kulkarni, VNIT, Nagpur Dr. Ahamed Faheem Zobaa, BU, UK Dr. Akhtar Kalam, VU, Australia Dr. Rajesh Kumar Patnaik, GMR, Andhra Pradesh Dr. Krushna Keshab Mohapatra, SOA, Bhubaneswar Dr. Kolla Bhanu Prakash, K. L. University, Vijayawada

Publicity Chairs Prof. Amar Bijaya Nanda, Siksha ‘O’ Anusandhan, Bhubaneswar, India Dr. Basanta Kumar Panigrahi, Siksha ‘O’ Anusandhan, Bhubaneswar, India Mr. Tapas Kumar Mohapatra, Siksha ‘O’ Anusandhan, Bhubaneswar, India Mr. Tanmoya Parida, Siksha ‘O’ Anusandhan, Bhubaneswar, India

Website Chairs Dr. Nakul Charan Sahu, Siksha ‘O’ Anusandhan, Bhubaneswar, India Mr. P. Suresh Kumar, DLBCE, Visakhapatnam Dr. Shubhranshu Mohan Parida, Siksha ‘O’ Anusandhan, Bhubaneswar, India

Registration Chairs Prof. Pradeep Kumar Mohanty, Siksha ‘O’ Anusandhan, Bhubaneswar, India Dr. Satish Choudhury, Siksha ‘O’ Anusandhan, Bhubaneswar, India Mr. Amiya Kumar Naik, Siksha ‘O’ Anusandhan, Bhubaneswar, India Mr. Rasmi Ranjan Panigrahi, GEC, Bhubaneswar, India

GTSCS Committees

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Publication Chairs Dr. Binod Kumar Sahu, Siksha ‘O’ Anusandhan, Bhubaneswar, India Dr. Manohar Mishra, Siksha ‘O’ Anusandhan, Bhubaneswar, India Prof. Renu Sharma, Siksha ‘O’ Anusandhan, Bhubaneswar, India

Finance Chairs Prof. Manas Kumar Malik, Siksha ‘O’ Anusandhan, Bhubaneswar, India Dr. Priyabrata Pattanaik, Siksha ‘O’ Anusandhan, Bhubaneswar, India

Sponsorship Chairs Prof. Ranjan Kumar Mallick, Siksha ‘O’ Anusandhan, Bhubaneswar, India Dr. Sujit Kumar Dash, Siksha ‘O’ Anusandhan, Bhubaneswar, India Dr. Subhendu Pati, Siksha ‘O’ Anusandhan, Bhubaneswar, India Dr. Sasank Choudhury, Siksha ‘O’ Anusandhan, Bhubaneswar, India

GTSCS Reviewers Dr. D. K. Behera, Trident Academy of Technology, Bhubaneswar Dr. M. Sahani, SOA, Bhubaneswar Mr. S. K. Rout, SOA, Bhubaneswar Dr. G. T. Chandrasekhar, SSCE, Srikakulam Dr. P. Vittal, AITAM, Andhra Pradesh Dr. Swagat Kumar Pati, SOA, Bhubaneswar Dr. Pradeep Kumar Mohanty, SOA, Bhubaneswar Dr. Sourav Kumar Bhoi, Parala Maharaja Engineering College (Government of Odisha), Berhampur Mr. B. K. Swain, SOA, Bhubaneswar Mr. Prasant Barik, OUAT, Bhubaneswar Mr. Nimai Patel, Government College of Engineering, Keonjhar Dr. Manohar Mishra, SOA, Bhubaneswar Mr. Sanjeet Kumar Subudhi, NIT, Rourkela Dr. Kumari Kasturi, SOA, Bhubaneswar Prof. Bhaskar Patnaik, Rungta College of Engineering and Technology, Bhilai Ms. Subhashree Subudhi, IIIT, Bhubaneswar Dr. Abhisek Parida, NIT, Rourkela

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Dr. Prakash Chandra Sahoo, SIT, Sambalpur Mr. Shiba Ranjan Paital, IIIT, Bhubaneswar Mr. P. Suresh Kumar, Dr. Lankapalli Bullayya College Mr. Suresh Chandra Moharana, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar Dr. P. M. K. Prasad, Associate Professor, ECE Department, GVP College of Engineering for Women Visakhapatnam, Andhra Pradesh, India Mr. Debasis Mohapatra, PMEC, Berhampur Dr. Nageswara Rao, Velagapudi Ramakrishna Siddhartha Engineering College Dr. S. Albert Alexander, Kongu Engineering College Dr. P. Raman, GMR Institute of Technology, Andhra Pradesh Dr. Ramesh Prusty, VSSUT, Burla Dr. Bighnaraj Naik, VSSUT, Burla Dr. Janmenjoy Nayak, Aditya Institute of Technology and Management, Tekkali, Srikakulam Dr. Abhisek Rajan, NIT Sikkim Dr. Bharat J. R. Sahoo, KNU, South Korea Dr. Malaya Nath, NIT, Tiruchirappalli Dr. Jyotir Moy Chatterjee, Asia Pacific University of Technology and Innovation, Kathmandu, Nepal Dr. Sumit Kushwah, Kamla Nehru Institute of Technology, Uttar Pradesh Dr. B. Surendiran, Associate Dean (Academic), Department of CSE, NIT Puducherry Dr. Renu Sharma, ITER, SOA, Bhubaneswar Dr. Sonali Goel, ITER, SOA, Bhubaneswar Dr. Shakti Prakash Jena, Mechanical Engineering Department, ITER, SOA Deemed to be University Dr. Debabrata Singh, ITER, SOA, Bhubaneswar Mr. Sayantan Sinha, ITER, SOA, Bhubaneswar Dr. D. A. Gadanayak, ITER, SOA, Bhubaneswar Dr. Manoj Kumar Debnath, ITER, SOA, Bhubaneswar Dr. Binod Kumar Sahu, ITER, SOA, Bhubaneswar Dr. Sanjeeb Kumar Kar, ITER, SOA, Bhubaneswar Mr. Priya Ranjan Satpathy, ITER, SOA, Bhubaneswar Mr. Rasmi Ranjan Panigrahi, ITER, SOA, Bhubaneswar Dr. Satish Choudhury, ITER, SOA, Bhubaneswar Ms. Subhadra Sahoo, ITER, SOA, Bhubaneswar Dr. Mukesh Kumar Das, University of Manitoba, Canada Dr. Madhab Tripathy, CET BBSR

Preface

The incessant pursuit for a better, healthier and comfortable life has lead to innovation. Innovation in technologies and their application in real life problems has remained the key driver for a modern society. The present-day innovation in technology and their approach of application in diverse domain of engineering (such as electrical, electronics, mechanical, computer, agriculture and robotics) aims to have a smarter system, such as smart home, smart city and smart society. The innovation in electrical, communication and computing technology is always interrelated. Though, the innovation in one domain will definitely solve the problem associated with the particular problem but it may also help additionally to minimize the problem or may assist to a new innovation belong to other domain. Therefore, continuous research in all these domains along with proper dissemination of the progress or findings is highly important for the development of the global society. The first international conference entitled ‘Green Technology for Smart City and Society’, (GTSCS-2020) is organized by Department of Electrical Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India on 13 and 14 August 2020. The major focus of GTSCS-2020 has concentrated on receiving several innovation thoughts from the global researcher to make the city and society become smarter. In this regards, the contribution of each and every domain of engineering and technological field is essential for the society. Therefore, the ‘call for paper’ has prepared in such a way that it is able to attract more than 120 online submissions from diverged but related field. From these authors contribution, the editors have selected only 48 high-quality submissions based on a rigorous and unbiased peer-review system. The peer-review system has comprised of several knowledgeable researchers with required expertise, the advisory members and programme and technical committee of GTSCS-2020. The decision criteria for the selection of paper directly depend on the major contribution of authors, technicality, novelty and scope of the conference theme. Each phase of the peer-review task has been completed through electronic system. The keynote talk is one of the most important parts of any conference for sharing the research and thoughts in front of researchers and scientist from diverse fields xi

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Preface

and geometry. In this regards, the GTSCS-2020 has given its additional effort to arrange several keynotes address matching to the scope of the conference. Moreover, the presentation of author’s contributions has been arranged with the significance and interdependency of the articles with reference to the basic concept and motivation of the conference. In this regards, we appreciate the authors’ contribution and value the choice that is ‘GTSCS’ for disseminating the output of their research findings. We are also grateful for the help received from the each individual reviewer and the Programme Committee members regarding peer-review process. Bhubaneswar, India Bhubaneswar, India Tekkali, India Sambalpur, India Teramo, Italy

Renu Sharma Manohar Mishra Janmenjoy Nayak Bighnaraj Naik Danilo Pelusi

Acknowledgements

The editors feel privileged to have put forward key proposals and significance of the GTSCS Conference. GTSCS has attracted huge attention of academicians and researchers from all over the globe. The conference provided the most conducive platform to showcase a plethora of original research findings. We were able to have submissions in diverse fields of electrical, electronics, computation engineering and agricultural engineering. Our sincere thanks and gratitude to all the authors who have enriched the conference through their valuable contributions in terms of their time, expertise and research submissions. The pre- and post-conference activities and proceedings would not have had a smooth sailing without the timely and appropriate support and guidance of the national and international advisory committees. We extend our heartfelt thanks to all the members of all these committees. We also extend our profuse thanks to the strong team of reviewers who did a commendable job of a holistic and critical review of all manuscripts and provided the remarks and suggestions so critical in upholding the standard and quality of the conference proceeding. We extend our sincere thanks to all the members of the organizing committee whose tireless effort has made the event a hugely successful one. Our prolific thanks to the editorial members of the Springer Publishing team for shaping up the proceeding in such an innovative and intelligent way. The GTSCS-2020 conference and proceedings receive due acknowledgments before a huge congregation. Finally, our heartfelt thanks to the Management of SOA (Deemed to be University) and faculty members of the Department of Electrical Engineering, ITER, for their continued support and encouragement to make the conference huge success. The editors would also like to thank the Springer Editorial Members for facilitating the publication of the proceedings in Lecture Notes in Network and Systems series.

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About the Conference

A smart city is a place where conventional networks are created with the usage of data, smart and telecommunication methods to enhance its features for the profits of the people. Arrangement of different smart components can create smart cities, where quantity of smart components relies on availability as well as cost of technology. The anticipation of standard of livings has been increased due to major raise of world population in the preceding decades. The employment of smart cities can be useful for the reduction rate of water consumption, energy consumption, city waste, transportation needs, carbon emission, etc. Apart from these, there are many other technologies that have been employed for enhancing the architecture, methodology as well as facilities of smart cities. The first international conference on Green Technology for Smart City and Society (GTSCS-2020) focuses on both theory and applications related to various green technologies such as machine learning, artificial intelligence, deep learning, optimization algorithm, IoT, signal processing and computing. GTSCS, a multidisciplinary conference, organized with the objective of bringing together academicians, scientists, researchers from industry, research scholars and students working in all areas of electrical, electronics, advanced computing and intelligent engineering. The application domain of these green technologies includes smart city, smart infrastructure, smart grid, smart healthcare, smart transportation, smart agricultural, etc. The conference will provide the authors/listeners with opportunities for national/international collaboration and networking among universities/ institutions from India and abroad for promoting research and developing technologies. The aim of this conference is to promote translation of basic research into applied investigation and convert applied investigation into practice. This conference will also create awareness about the importance of basic scientific research in different fields matching with the current trends. The conference also aims at bringing together the researchers, scientists, engineers, industrial professionals and students in this fascinating area of research.

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About the Conference

Nowadays, the multidisciplinary researches have gained a huge attention to fulfil the necessities of smart cities/countries. Therefore, this book may assist the future development/researches by providing several recent innovations on electrical, mechanical, agricultural, computing and communication engineering.

Contents

Intelligent Computing in IoT-Enabled Smart Cities: A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Janmenjoy Nayak, Kanithi Vakula, Paidi Dinesh, Bighnaraj Naik, Subhashree Mohapatra, Tripti Swarnkar, and Manohar Mishra Optimal Design of Fractional Order 2DOF-PID Controller for Frequency Control in Interconnected System . . . . . . . . . . . . . . . . . . Sabita Tripathy, Manoj Kumar Debnath, and Sanjeeb Kumar Kar Electrical Properties of the PVDF-Lead-Free Ceramic-Based Composite Film for Sensor Applications . . . . . . . . . . . . . . . . . . . . . . . . Basanta K. Panigrahi, Varsha Purohit, Vijayeta Pal, Sugato Hajra, Kalyani Mohanta, and S. K. M. Ali

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IoT-Based Voice-Controlled Energy-Efficient Intelligent Traffic and Street Light Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . Anil Kumar Biswal, Debabrata Singh, and Binod Kumar Pattanayak

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Unsupervised Reduced Deep Convolutional Neural Network of Process Empirical Wavelet Transform Data for Recognition of the Early Stage of Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . . . Bhanja Kishor Swain, Susanta Kumar Rout, Mrutyunjaya Sahani, and Renu Sharma

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Auxiliary Dynamic Damping Loop in the Microgrid for Enhancing Frequency Recovery Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pritam Bhowmik and Pravat Kumar Rout

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Design of Chatbots Using Node-RED . . . . . . . . . . . . . . . . . . . . . . . . . . . Siddharth Bhatter, Sayantan Sinha, and Renu Sharma Power Factor Correction for Single-Phase Domestic Loads Using Microcontroller and Triac . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kamran Alam, Lalita Sharma, and Namarta Chopra

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Techniques Behind Smart Home Automation System Using NLP and IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Karisma Mohapatra, Mamata Nayak, and Ajit Kumar Nayak Salp Swarm Optimized PID Controller for Frequency Control of Hybrid Power System with UC and UPFC . . . . . . . . . . . . . . . . . . . . 117 Debidasi Mohanty and Sidhartha Panda Implementation of Fuzzy Hysteresis Controller for a Three-Phase Photovoltaic Multilevel Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Gayatri Mohapatra and Manoj Kumar Debnath Robust Control and Inverter Approach for Power Quality Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Buddhadeva Sahoo, Sangram Keshari Routray, and Pravat Kumar Rout Economic Analysis of Solar Water Pumping System for Irrigation . . . . 157 Sonali Goel and Renu Sharma Reliability Assessment and Cost Estimation of a Hybrid Renewable Micro-grid System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Nakul Charan Sahu, Amar Bijay Nanda, Subrat Bhol, Adarsh Kumar Jena, and Prabhash Rout An Ontology Based Matchmaking Technique for Cloud Service Discovery and Selection Using Aneka PaaS . . . . . . . . . . . . . . . . . . . . . . 183 Manoranjan Parhi, Bhupendra Kumar Gupta, and Binod Kumar Pattanayak Proper Harmonic Analysis of Load Current of a Single Phase 5-Level Voltage Source Inverter Using HCC . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Sushovit Das, Uma Shankar Das, Asish Kumar Sahoo, Shuvangi Mishra, and Tapas Kumar Mohapatra A New Sparse Array Configuration for Direction of Arrival Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 P. Raiguru, A. K. Srivastava, A. Dandpat, and R. K. Mishra Forecasting of Net Asset Value of Indian Mutual Funds Using Firefly Algorithm-Based Neural Network Model . . . . . . . . . . . . . . . . . . . . . . . . 217 Sarbeswara Hota, Sarada Prasanna Pati, and Pranati Satapathy Toward Ultimate Scaling: From FinFETs to Nanosheet Transistors . . . 225 T. P. Dash, E. Mohapatra, Sanghamitra Das, S. Choudhury, and C. K. Maiti Performance of a Free Space Optical Link with ACO-OFDM-Based Signal Transmission Under Beam-Wander-Dominated Atmospheric Turbulence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Sabita Mali and Jayashree Ratnam

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A Comparative Analysis of Demand Response on Different Operational Strategies of Battery Energy Storage System for Distribution System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Sachin Sharma, Khaleequr Rehman Niazi, Kusum Verma, and Tanuj Rawat Design and Analysis of Hands-Free Wireless Charging System . . . . . . . 257 Pradyumna K. Sahoo, Priyansh P. Jena, Ashutosh Patro, Abhijit Roy, Biswaranjan, Swain, Renu Sharma, Durga Prasanna Kar, and Satyanarayan Bhuyan Fault Tolerance Investigation of Solar Photovoltaic Strings Operating Under NOCT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Priya Ranjan Satpathy, Sobhit Panda, Bibekananda Jena, and Renu Sharma Frequency Control of an AC Microgrid with Fractional Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Narendra Kumar Jena, Subhadra Sahoo, Amiya Kumar Naik, Binod Kumar Sahu, and Kanungo B. Mohanty Effectiveness of Backpropagation Algorithm in Healthcare Data Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Ch Chandra Sekhar, Nibedan Panda, B. V. Ramana, B. Maneesha, and S. Vandana A Review on High-Impedance Ground Fault Detection Techniques in Distribution Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Debadatta Amaresh Gadanayak Voltage Stability Index and Butterfly Optimization Algorithm-Based DG Placement and Sizing in Electrical Power Distribution System . . . . 311 Pritish Kumar Mohanty and Deepak Kumar Lal An Economic Evaluation of the Coordination Between Electric Vehicle Storage and Photovoltaic in Residential Home Under Real-Time Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Kumari Kasturi, Sushil Kumar Bhoi, and Manas Ranjan Nayak Particle Filter and Entropy-Based Measure for Tracking of Video Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Jyotiranjan Panda and Pradipta Kumar Nanda An Automatic Insulin Infusion System Based on the Genetic Algorithm FOPID Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 Akshaya Kumar Patra, Anuja Nanda, Bidyadhar Rout, Dillip Kumar Subudhi, and Sanjeeb Kumar Kar Design Guidelines for Vector Control of Wound Field Synchronous Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Ramana Pilla and G. Tulasichandra Sekhar

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Preliminary Study of Magnetic Resonant Coupling Based Wireless Power Transfer System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Hirak Keshari Behera and Durga Prasanna Kar An Optimized Machine Learning-Based Time-Frequency Transform for Protection of Distribution Generation Integrated Microgrid System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 Swetalina Sarangi, Binod Kumar Sahu, and Pravat Kumar Rout Improving the Performance of AVR System Using Grasshopper Evolutionary Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Sunita S. Biswal, D. R. Swain, and Pravat Kumar Rout Demand Side Management by PV Integration to Micro-grid Power Distribution System: A Review and Case Study Analysis . . . . . . . . . . . . 417 Subhasis Panda, Pravat Kumar Rout, and Binod Kumar Sahu Machine Learning Based Efficiency and Power Estimation of Circular Buffer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 Praveen Kumar Yethirajula, Trailokya Nath Sasamal, and Divya Parihar Binary Dragonfly Algorithm-Designed Fuzzy Cascade Controller for AGC of Multi-area Power System with Nonlinearities . . . . . . . . . . . 445 Prakash Chandra Sahu, Subhadra Sahoo, Ramesh Chandra Prusty, and Binod Kumar Sahu Verilog Implementation of High-Speed Wallace Tree Multiplier . . . . . . 457 Sandeep Kumar and Trailokya Nath Sasamal Multi-objective Optimal Location and Size of Embedded Generation Units and Capacitors Using Metaheuristic Algorithms . . . . . . . . . . . . . . 471 Subrat Kumar Dash, Laloo Ranjan Pati, Sivkumar Mishra, and Prashant Kumar Satpathy Comparative Performance Investigation of Fractional Order and Conventional PID Controller Implemented for Frequency Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 B. Begum, Prakash Chandra Sahu, and Binod Kumar Sahu Performance Analysis of Solar PV-Based Unified Power Quality Conditioner System for Power Quality Improvement Under Nonlinear Load Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Sarita Samal, Prasanta Kumar Barik, Tarakanta Jena, and Manoj Kumar Debnath Distributed Estimation of IIR System’s Parameter in Sensor Network Using Block Diffusion LMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 Meera Dash, T. Panigrahi, and Renu Sharma

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Krill Herd Algorithm-Tuned IDN-FPI Controller for AGC in Interconnected Reheat Thermal–Wind Power System . . . . . . . . . . . . 525 Priyambada Satapathy, Jyoti Ranjan Padhi, Manoj Kumar Debnath, Pradeep Ku Mohanty, and Sarita Samal Performance Evaluation of Region-Based Segmentation Algorithms for Brain MR Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539 Tapasmini Sahoo, Rohit Kumar Pradhan, Kunal Kumar Das, and Sibasish Sahu Optimal Allocation of Distributed Generators Using Metaheuristic Algorithms—An Up-to-Date Bibliographic Review . . . . . . . . . . . . . . . . . 553 Subrat Kumar Dash, Sivkumar Mishra, Laloo Ranjan Pati, and Prashant Kumar Satpathy Quality of Power Enhancement of Distribution Network System Using DSTATCOM in Simulink Tool of MATLAB . . . . . . . . . . . . . . . . 563 Jay Prakash Keshri and Harpal Tiwari Technical Proposal for Sizing of Equipment and Designing of Solar Photovoltaic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Manasi Pattnaik, Harish Sharma, Manoj Badoni, Yogesh Tatte, and Manoj Kumar Debnath A Comparative Study of Muscle Artifacts Removal in Single Channel EEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 Binapani Pal and Karmila Soren Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595

Editors and Contributors

About the Editors Dr. Renu Sharma has received her Ph.D. in Electrical Engineering from SOA University, Bhubaneswar in 2014. At present, she has more than 20 years of teaching experience. She is a Senior Member of IEEE, Member IEEE and Life member ISTE, Life member ISSE. Her research areas are Soft Computing, Solar Photovoltaic systems, Power system Scheduling, and Wireless Sensor Networks. She has published more than 60 research papers in various reputed peer reviewed International Journals, Conferences and Book Chapters. Dr. Manohar Mishra has completed his Ph.D. in Electrical Engineering in 2017. He has published more than 30 research papers in various reputed peer reviewed International Journals. His area of interest includes power system analysis, power system protection, signal processing, power quality and Micro-grid system. Currently, he is serving as Guest editor in different international journals with repute publishers like Springer and Inderscience. Dr. Janmenjoy Nayak has received his Ph.D. in Computer Science and Engineering in 2016 from VSSUT, Burla. He has published more than 80 research papers in various reputed publishers. His area of interest includes data mining, nature inspired algorithms and soft computing. He has edited Six books and serving as Guest editor in different international journals with repute publishers like Springer, Elsivier and Inderscience. His area of interest includes Data Mining, Computational Intelligence, Soft Computing and its applications. Dr. Bighnaraj Naik has received his Ph.D. in Computer Science and Engineering in 2016 from VSSUT, Burla. He has published more than 70 research papers in various reputed peer reviewed International Journals, Conferences and Book Chapters. He has edited Six books from various publishers such as Elsevier, Springer and IGI Global. His area of interest includes Data Mining, Computational

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Intelligence, Soft Computing and its applications. Currently, he is serving as Guest editor in different international journals with repute publishers like Springer, Elsivier and Inderscience. Dr. Danilo Pelusi has received the Ph.D. degree in Computational Astrophysics from the University of Teramo, Italy. He is an Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Access, International Journal of Machine Learning and Cybernetics (Springer) and Array (Elsevier). His research interests include Fuzzy Logic, Neural Networks, Information Theory and Evolutionary Algorithms.

Contributors Kamran Alam Amritsar College of Engineering and Technology, Amritsar, India S. K. M. Ali Electronics and Telecommunication Engineering, Trident Academy of Technology, Bhubaneswar, India Manoj Badoni Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, India Prasanta Kumar Barik Department of Mechanical and Electrical Engineering, CAET, OUAT, Bhubaneswar, India B. Begum Department of Electrical Engineering, IITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Hirak Keshari Behera ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Siddharth Bhatter Karkhana Makerspace, Bhubaneswar, Odisha, India Sushil Kumar Bhoi Department of Electrical Engineering, Government College of Engineering, Kalahandi, Bhawanipatna, Odisha, India Subrat Bhol Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Pritam Bhowmik Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Satyanarayan Bhuyan Department of Electronics and Communication Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Anil Kumar Biswal Department of Computer Science & Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Editors and Contributors

xxv

Sunita S. Biswal Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Biswaranjan Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Ch Chandra Sekhar Department of Information Technology, Aditya Institute of Technology and Management (AITAM), Tekkali, K Kotturu, Andhra Pradesh, India Namarta Chopra Amritsar College of Engineering and Technology, Amritsar, India S. Choudhury Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India A. Dandpat Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Kunal Kumar Das ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Sanghamitra Das Department of Electronics and Communication Engineering, Silicon Institute of Technology, Bhubaneswar, India Sushovit Das ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Uma Shankar Das ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Meera Dash Department of ECE, ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Subrat Kumar Dash Government Bhawanipatna, Odisha, India

College

of

Engineering,

Kalahandi,

T. P. Dash Department of Electronics and Communication Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Manoj Kumar Debnath ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India; Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Paidi Dinesh Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India Debadatta Amaresh Gadanayak ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

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Editors and Contributors

Sonali Goel Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Bhupendra Kumar Gupta Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Sugato Hajra Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Sarbeswara Hota Department of Computer Application, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Adarsh Kumar Jena Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Bibekananda Jena Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Narendra Kumar Jena Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Priyansh P. Jena Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Tarakanta Jena Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Durga Prasanna Kar Department of Electronics and Communication Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Sanjeeb Kumar Kar Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Kumari Kasturi Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Jay Prakash Keshri Malaviya National Institute of Technology, Jaipur, India Sandeep Kumar School of VLSI and Embedded System Design, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana, India Deepak Kumar Lal Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India C. K. Maiti Department of Electronics and Communication Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Sabita Mali ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Editors and Contributors

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B. Maneesha Department of Information Technology, Aditya Institute of Technology and Management (AITAM), Tekkali, K Kotturu, Andhra Pradesh, India Manohar Mishra Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India R. K. Mishra Department of Electronics Science and Technology, Berhampur University, Berhampur, Odisha, India Shuvangi Mishra ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Sivkumar Mishra Centre for Advanced Post Graduate Studies, BPUT, Rourkela, Odisha, India Kalyani Mohanta Department of Ceramic Engineering, IIT, BHU, Varanasi, India Debidasi Mohanty Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India Kanungo B. Mohanty Department of Electrical Engineering, National Institute of Technology, Rourkela, Rourkela, Odisha, India Pradeep Ku Mohanty ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Pritish Kumar Mohanty Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India E. Mohapatra Department of Electronics and Communication Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Gayatri Mohapatra ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Karisma Mohapatra Department of Computer Science & Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Subhashree Mohapatra Department of Computer Applications, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Tapas Kumar Mohapatra ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Amiya Kumar Naik Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Bighnaraj Naik Department of Computer Applications, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

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Editors and Contributors

Amar Bijay Nanda Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Anuja Nanda Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Pradipta Kumar Nanda Image and Video Analysis Lab, Department of Electronics and Communication Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Ajit Kumar Nayak Department of Computer Science & Information Technology, ITER, Siksha O Anusandhan (Deemed to be University), Bhubanwswar, Odisha, India Janmenjoy Nayak Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Tekkali, K Kotturu, Andhra Pradesh, India Mamata Nayak Department of Computer Science & Information Technology, ITER, Siksha O Anusandhan (Deemed to be University), Bhubanwswar, Odisha, India Manas Ranjan Nayak Department of Electrical Engineering, CAPGS, Biju Patnaik University of Technology, Rourkela, India Khaleequr Rehman Niazi Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India Jyoti Ranjan Padhi ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Binapani Pal Department of Instrumentation and Electronics Engineering, College of Engineering and Technology, Bhubaneswar, Bhubaneswar, Odisha, India Vijayeta Pal Department of Materials Science and Technology, IIT, BHU, Varanasi, India Jyotiranjan Panda Image and Video Analysis Lab, Department of Electronics and Communication Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Nibedan Panda Department of Information Technology, Aditya Institute of Technology and Management (AITAM), Tekkali, K Kotturu, Andhra Pradesh, India Sidhartha Panda Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

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Sobhit Panda Department of Instrumentation and Electronics Engineering, College of Engineering and Technology, Bhubaneswar, Bhubaneswar, Odisha, India Subhasis Panda Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Basanta K. Panigrahi Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India T. Panigrahi Department of ECE, NIT Goa, Veling, Goa, India Manoranjan Parhi Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Divya Parihar CSD Calypto—PowerPro, Mentor Graphics Corporation, Noida, Uttar Pradesh, India Laloo Ranjan Pati Centre for Advanced Post Graduate Studies, BPUT, Rourkela, Odisha, India Sarada Prasanna Pati Department of Computer Science and Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Akshaya Kumar Patra Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Ashutosh Patro Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Binod Kumar Pattanayak Department of Computer Science and Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Manasi Pattnaik Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, India Ramana Pilla Department of Electrical and Electronics Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India Rohit Kumar Pradhan ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Ramesh Chandra Prusty Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India Varsha Purohit Department of Physics, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India P. Raiguru Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

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Editors and Contributors

B. V. Ramana Department of Information Technology, Aditya Institute of Technology and Management (AITAM), Tekkali, K Kotturu, Andhra Pradesh, India Jayashree Ratnam ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Tanuj Rawat Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India Bidyadhar Rout Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India Prabhash Rout Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Pravat Kumar Rout Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Susanta Kumar Rout ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Sangram Keshari Routray Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Abhijit Roy Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Mrutyunjaya Sahani ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Asish Kumar Sahoo ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Buddhadeva Sahoo Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Pradyumna K. Sahoo Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Subhadra Sahoo Department of Electrical Engineering, IITER, Siksha O Anusandhan (Deemed to be University), Sambalpur, Bhubaneswar, Odisha, India Tapasmini Sahoo ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Binod Kumar Sahu Department of Electrical Engineering, IITER, Siksha O Anusandhan (Deemed to be University), Sambalpur, Bhubaneswar, Odisha, India Nakul Charan Sahu Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Editors and Contributors

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Prakash Chandra Sahu Department of Electrical Engineering, Silicon Institute of Technology, Sambalpur, Odisha, India Sibasish Sahu ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Sarita Samal School of Electrical Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India Swetalina Sarangi Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Trailokya Nath Sasamal Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana, India Pranati Satapathy Department of Computer Science and Applications, Utkal University, Bhubaneswar, Odisha, India Priyambada Satapathy ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Prashant Kumar Satpathy College of Bhubaneswar, Bhubaneswar, Odisha, India

Engineering

and

Technology,

Priya Ranjan Satpathy Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Harish Sharma TUV-SUD, Asia, Gurgaon, India Lalita Sharma Amritsar College of Engineering and Technology, Amritsar, India Renu Sharma Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Sachin Sharma Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India Debabrata Singh Department of Computer Science & Information Technology, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Sayantan Sinha Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Karmila Soren Department of Instrumentation and Electronics Engineering, College of Engineering and Technology, Bhubaneswar, Bhubaneswar, Odisha, India A. K. Srivastava Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

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Editors and Contributors

Dillip Kumar Subudhi Department of Computer Science & Information Technology, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Swain Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Bhanja Kishor Swain ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India D. R. Swain Department of Electrical Engineering, College of Engineering and Technology, Bhubaneswar, Bhubaneswar, Odisha, India Tripti Swarnkar Department of Computer Applications, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India Yogesh Tatte Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, India Harpal Tiwari Malaviya National Institute of Technology, Jaipur, India Sabita Tripathy ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India G. Tulasichandra Sekhar Department of Electrical and Electronics Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India Kanithi Vakula Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India S. Vandana Department of Information Technology, Aditya Institute of Technology and Management (AITAM), Tekkali, K Kotturu, Andhra Pradesh, India Kusum Verma Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India Praveen Kumar Yethirajula School of VLSI Design and Embedded System, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana, India

Intelligent Computing in IoT-Enabled Smart Cities: A Systematic Review Janmenjoy Nayak, Kanithi Vakula, Paidi Dinesh, Bighnaraj Naik, Subhashree Mohapatra, Tripti Swarnkar, and Manohar Mishra

Abstract A smart city is an enhanced urban area that shines in the field of governance, economy, social capital, people and life through strong human capital. It is a novel advance to raise the effectiveness, decrease the expenses, control the difficulty of city life and enhance the standard of living of the people. These applications of smart cities hold the upcoming vision of cities, which intends at developing the information and communication technology (ICT) infrastructure, i.e., Internet of things (IoT) for worth added service delivery. Because of these raising improvements in sophisticated digital technologies, smart cities have been set with several electronic devices with the usage of IoT. In this paper, a detailed investigation has been J. Nayak Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Tekkali, K Kotturu 532201, Andhra Pradesh, India e-mail: [email protected] K. Vakula · P. Dinesh Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh 532410, India e-mail: [email protected] P. Dinesh e-mail: [email protected] B. Naik Department of Computer Applications, Veer Surendra Sai University of Technology, Burla, Sambalpur 768018, Odisha, India e-mail: [email protected] S. Mohapatra · T. Swarnkar Department of Computer Applications, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] T. Swarnkar e-mail: [email protected] M. Mishra (B) Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_1

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conducted on the significance, usage levels as well as advancements of IoT-based smart cities. Also, few challenging issues along with the limitations of IoT-based smart cities are realized. Also, this paper aims to inspire and motivate researchers to innovate novel solutions for smart cities with the help of IoT technology. Keywords Smart city · IoT · Digital technology · Intelligent computing

1 Introduction The Internet of things (IOT) is a classification of interconnected computing devices, automatic and digital machines that are presented with unique identifiers (UIDs) and the capability to transmit information over a network with no requirement of humanto-human or human-to-computer interaction [1]. IOT presents various profits to the organizations such as monitoring of complete business profits, improving customer experiences, improve employee productivity, save time and money and many more. Progressively, more organizations in a range of industries are utilizing IoT to work more proficiently, better understand clients to transport improved customer service, advance decision-making and enlarge the value of the business. The wide set of applications for IoT devices [2] is often divided into many applications such as smart home [3], smart city [4], medical and health care [5], transportation [6], building and home automation [7], manufacturing [8], agriculture [9], energy management [10], environmental monitoring [11] as well as military applications [12]. IOT assists people to survive and work smarter as well as to get complete control over their lives. Since the world’s population gradually grows to be larger, it is obvious that urban areas will undergo through a lot of transformations. Half of the population has been living in urban areas and it is expected that there is a chance of increasing this population rate to the more than half of earth’s population in upcoming years. The amount of citizens existing in these megalopolises sets a huge damage on the environment and wealth. Due to this reason, politicians as well as scientists have initiated to develop strategies about renovating urban areas into smart cities respectively. Nowadays, cities are seeing extraordinary development, getting foremost challenges as they seek to stay healthy, sustainable and secure places for people to exist and work. The idea of the smart city has appeared for excellent motive. Primarily, because the bulk of novel and innovative works are being produced in urban fields, the development of such areas is increasing [13]. Secondarily, to improve the educational openings for their family, a huge number of families in countryside regions are migrating to urban areas. A smart city is a difficult ecosystem division illustrated by the exhaustive use of information and communications technologies (ICT), planning to construct cities with adequate facilities and sustainable as well as exclusive places for entrepreneurship [14].

Intelligent Computing in IoT-Enabled Smart Cities…

3

Since IoT stands for its huge ecosystem of devices as well as sensors that are associated over one network, it provides novel prospects for cities to utilize information to manage traffic and build better usage of transportation to keep citizens secure. The attractive IoT services are enabling smart city proposals throughout the world [15]. These services are renovating cities by enhancing transportation systems, decreasing traffic overcrowding, offering and civilizing the quality of human life. As an outcome, IoT is altering cities by improving infrastructure, generating more efficient and cost-effective public services and enhancing citizens’ safety [16]. In order to attain the complete potential of IoT, smart city contributors identified that cities should not only have a smart city feature but also should carry secure and scalable IoT solutions that contain well-organized IoT systems. In this research, we presented a detailed investigation on the significance of IoT for smart cities. We have also described the perspectives of IoT for the improvement of smartness (with latest technology and solutions) of a city. The sections of this paper have been segmented in the following manner. Section 2 described the motivation behind IoT in smart cities. In Sect. 3, we have deeply explained about smart cities and how IoT has been used in smart cities. In Sect. 4, we have mentioned the experiences over the world by using IoT in smart cities. Section 5 presents the several applications of IoT in smart cities. Some advanced technologies that are used along with IoT for smart city have been mentioned in Sect. 6. A critical analysis on the IoT-based smart cities and its advancements, usage levels, countries where smart cities have developed along with the list of smart cities and many more have been described briefly in Sect. 7. Few research challenges of IoT in smart cities are mentioned in Sect. 8. Concluding remarks along with future goals have been realized in Sect. 9.

2 Motivations Challenges such as social security, environmental checking, financial growth, jobs creation and control are being faced by many cities in the today’s world. Due to these challenges, Internet has become the essential part of upcoming planning. Since cities raise and enlarge, novel and smart results are playing vital role for raising operational efficiencies, enhancing productivity and decreasing management costs [16]. Digital urbanism is rapidly gaining desirability and attention of inventors, public services and transportation organizations due to the profits of IoT that are faced by major cities. People have slowly furnished their houses with the usage of IoT devices like TV as well as Internet boxes, respectively. Generally, a smart city is equipped with various electronic components used by many applications that include: sensors for transportation and street cameras for observation systems, etc. This can also spread the convention of individual mobile devices [4]. Some of the major aspects of smart city are shown in Fig. 1. The major motivation behind the usage of IoT in smart city includes the followings. Several features of smart city will affect the people’s life such as security, transportation by using IoT. On the other side, it plays a crucial role in decision-making such as

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Fig. 1 Prospects of smart city

remote monitoring, energy saving, required infrastructure. Based on this, IoT helps to present more effective, safe and economic operation of the system that is based on several characteristics like reliability levels, economic considerations, etc.

3 Smart Cities Over the past decades, improvement of software as well as hardware design has made a huge development of information and communication technologies (ICTs) [17]. The usage of ICT in cities with different forms for various city actions has directed to the rising efficiency of city procedures and these are labeled with many terms such as digital city, telicity, flexicity, wired city, electronic city, cyberville, smart city. Smart city is the major concept amid the used labels, because it includes other labels used for cities. A smart city is a place where conventional networks are created with the usage of data, smart and telecommunication methods to enhance its features for the profits of the people. Arrangement of different smart components can create smart cities, where quantity of smart components relies on availability as well as cost of technology. Anticipation of standard of livings has been increased due to major raise of world population in the preceding decades. It is guessing that more than sixty percent of population will exist in urban areas by the upcoming years. Currently, 75% of world’s energies and assets have been consumed by cities that direct to the production of eighty percent of greenhouse gases. Therefore, there is a serious pessimistic impact on the surrounding after the subsequent two decades. This builds the idea of smart cities as essential criteria to change the lifestyle. If smart cities

Intelligent Computing in IoT-Enabled Smart Cities…

5

employed once, then automatically there will be reduction rate of water consumption, energy consumption, city waste, transportation needs, carbon emission, etc. Apart from these, there are many other technologies that have been employed for enhancing the architecture, methodology as well as facilities of smart cities. As our research work mainly focuses on the IoT-based architectures as well as advancements of smart cities, this section mainly focuses on the overview of smart city and IoT-based smart city.

3.1 IoT-Based Smart City IoT is the method of concatenating the diverse devices all at once beyond the Internet. There is a need in the usage as well as development of several devices in homes along with cities to be connected with Internet for speeding up the daily activities. For making these devices linked to internet, many sensors were deployed at various surroundings for gathering and analyzing the data for using it efficiently. The critical objective is to obtain several systems such as smart home system (SHS), smart parking system (SPS), weather systems (WeS), water systems (WaS), vehicular traffic system (VTS), surveillance system (SS). SHS focuses on monitoring of data from sensors for measuring the temperature as well as smoke and its levels. The same methodology can be deployed for detecting the fire in a SC. Likewise, for detecting the fire, electricity as well as consumption of gas, this SHS will be helpful. Also, pollution monitor is being used in healthcare field. SPS is helpful to check the moving cars from the parking zones. The data obtained from SPS will be helpful to the people of SCs for finding out the nearest parking zones and this system also avoids the usage of fuel in vehicles. Furthermore, WeS as well as WaS is helpful in increasing the SCs performance by affording the data of weather such as temperature level, humidity level. This can be achieved by employing the sensors in reservoirs of water. For detecting the floods in advance due to heavy rains, rain sensors also need to be placed. This is also used in predicting the water need to the people in SCs. Moreover, VTS will help in analyzing the real-time traffic and gathers the traffic data throughout the SC. This gathered data will benefit the government for getting the traffic data such as blocks in traffic, accident information. Also, VTS will reduce the usage levels of fuel and levels of pollution. Most SCs gather data with the help of (GPRS), by placing sensors in the vehicles, etc. The information will be gathered if any vehicle met with an accident and an alert will be sent to local authorities, local hospitals as well as local police, etc. It cannot be overstate to say that a city will never become smart if it is fulfilled with unhealthy people. Therefore, it is important to check each and every citizen health conditions for a greater solution. This can be achieved by employing IoT devices as well as sensors. For this, pollution control is necessary and people must not roam outside in a high level pollutant environment. In order to predict the pollution levels in environment, some IoT devices along with sensors can also be employed. This can be done by detecting dangerous gases through sensors. Also, there is a need of providing security to the types of data such as accessed data

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and stored data through the use of SS. There is a rapid enhancement in technology of IoT at several applications. Moreover, IoT has been extensively used for public daily needs such as water, transport. [18, 19]. With the help of IoT, people’s lifestyle becomes more comfort in terms of economics, transport and many more. Moreover, SCs that were already developed throughout the world have been depicted in Table 1. Table 1 Developed smart cities all over the world S. No.

Name of the city

Location of city

Enhancement/Experience

References

1

Amsterdam

Netherlands

Enhancement of security, reduction of traffic

[20]

2

Barcelona

Spain

Smart traffic, sensor-based technology

[21]

3

Stockholm

Sweden

Implemented fiber optic network (FON)

[22]

4

Santa Cruz

California

Criminal data analysis

[21]

5

Songdo

Korea

Automation in buildings

[23]

6

PlanIT Valley

Portugal

Employment of 100,000,000 sensors

[24]

7

Fujisawa

Japan

Reduction of carbon footprints (70%)

8

Groening

The Netherlands

Smart transport system

9

Norfolk

England

Development in data sharing system of municipality

10

Vienna

Austria

Securing climatic conditions

11

Santander

Spain

SPS

12

IBM SC

Belgium, Brazil, Italy, Saudi Arabia, Spain, USA, etc.

Enhancement of structure of cities

[25]

13

Santander (Project)

Germany, Serbia, Spain, UK

Proper utilization of IoT platform

[26, 27]

14

Barcelona

Spain

Enhancement in network management with the help of smart sensors

[28]

15

Manchester, Turin

UK, Italy

Reduction of changes in [29] climate using smart sensors

16

Uppsala

Sweden

Smart traffic, pollution reduction

17

Padova

Italy

Smart lighting as well as [31] pollution reduction with the help of wireless sensors

[30]

Intelligent Computing in IoT-Enabled Smart Cities…

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4 Experiences Throughout World IoT is one of the main efficient tools on the US interests based on the report of US national intelligence council [32]. Number of interconnected devices are majorly growing day by day. With increased study and development of smart cities, more chances will come to allocate information and find out from best practices over the world. A few experimental examples over the realistic occurrences of smart cities are summarized as below. A survey [20] has been conducted in Amsterdam city where, Amsterdam has changed itself into a laboratory of improvement for sustainable urban growth. Several experiences by using IoT have proved its efficiency in reducing vehicle traffic, decreasing energy saving technologies and increasing the level of safety. In [33], study has been conducted in the city of Barcelona, where the approach of Barcelona responds to the limitations of the city that are facing regarding their place, people, private and public administration, respectively. It was found that execution of smart traffic and sensor technology are some of the experiences. Baxter [34] has surveyed in the Santa Cruz city for executing a predictive policing program by the Santa Cruz police department. Their experiences proved its efficiency in investigating the information of crimes to predict the requirements of police and exploit the existence of police in the necessary places. A survey has been made in [24], where many cities have produced outcome as: exploitation of 100,000,000 sensors in the PlanIT Valley, Portugal city, enhancement of open transport system in the Groening, The Netherlands city, reduce carbon foot print by 70% in Fujisawa, Japan city, development of data rescue and collection service in the city of England and raising energy efficacy and climate safety in the city of Austria, respectively. All these are the strategies of cities related to smart city.

5 Applications of IoT-Based Smart Cities Many applications were urbanized for SCs which makes use of ICTs [14]. In this section, some applications developed for SCs are summarized and explained in a brief way. Almost, all the applications which make use of IoT for SCs have been covered (see Fig. 2).

5.1 Smart Home One of the appliances of SC is smart home. Smart homes (SmH) are monitored by consuming the data created with the help of sensors place at homes [35]. IoT technology is employed with smart homes as well as its applications such as smart televisions, smart lighting. The sensors will be placed at every application of smart home which helps in monitoring of conditions at home such as pollution, temperature,

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Fig. 2 Various applications of smart city

even the owner is not at home [36]. Similarly, this smart home data can be used for security purposes. Furthermore, in order to shape a smart community (SMC), neighbor area network (NAN) will be used [37].

5.1.1

Smart Lighting

Smart lighting (SL) is one of the enhanced applications for SCs [38]. In order to fulfill the requirements and make use of proper light assets, sensor-based technology has been achieved and used [39]. It is one of the advanced methods for reducing the usage levels of energy in a city which demands for obtaining the flexible street lighting system (SLS) [27].

5.2 Smart Surveillance One of the key factors that need to be noticed in SC is security. SC should be secured in all aspects for preventing crimes by monitoring real-time data. Rathore et al. [40] have developed a novel methodology for enhancing the security measures. Conventional closed-circuit television (CCTV) systems offer efficient structure for smart surveillance. Conversely, CCTVs lack in processing of data as they are connected with a video recorder (VR). As these equipments are maintained and accessed by human interaction, there may be a chance of error occurrence. People’s everyday moments will be observed with the help of these CCTVs at many places such as footpaths, airports, railway stations, public as well as private places even at night time using infrared cameras [41]. These systems can also identify the tasks carried by the people for finding out some offensive objects [42]. Some special surveillance

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devices have the capability of identifying the vehicles traveling in wrong way with the help of motion detection algorithms [43].

5.3 Smart Health In the field of health care (HC), there is an effective significance of IoT. Some of the appliances include patient data monitoring, detecting as well as sensing. Using smart health technology, some daily health needs blood, ambulance, etc. can be easily monitored. It can also be used for preventing the mistakes like giving wrong content of drug doses to patients [44]. This smart health technology will help in preventing the human mistakes of data collection, etc. With the incorporation of bio-signal monitoring (BSM), the condition of patients can be examined through the use of wireless techniques [45].

5.4 Smart Transportation Smart transportation (ST) helps in reducing some of the problems of traffic such as traffic congestion. With the help of ST, parking spots can be designed and the traffic data should be used for identifying the entrance time at destinations [46]. IoT can be helpful for the monitoring of traffic conflicts like accident information, traffic congestion, etc. Conversely, many camera-enabled traffic monitoring systems (TMS) are implemented in several cities. However, they have to be enhanced for accessing more data. Traffic monitoring (TM) can be done with the help of GPS, acoustic sensors, etc. [47]. This monitored data can be helpful for the people to follow traffic in a proper fashion and many more [38].

5.4.1

Smart Parking System

Smart parking system enables the life easier as well as comfortable to people living in SCs. Each and every vehicle traveling over a city is traceable with the help of smart parking [48]. Therefore, parking spots need to be designed having the capability of allowing more vehicles [49]. Further, there is a need to shape parking spots which can be able to park more number of vehicles such as cars, motor cycles. [50]. This can be worked with the help of sensors, placed at road and LED displays should be placed on roads in order to find out the best parking spots [51]. This will help in the reduction of congestion of traffic. Furthermore, with the help of technologies such as RFID as well as NFC, better services can be offered to people in SCs [38].

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5.5 Smart Water and Weather Systems Smart weather system (SWeS) has been developed with the help of smart sensors for supplying the data of weather conditions such as temperature, humidity [52]. Similarly, smart water system (SWaS) has also been employed for controlling the proper usage of water facilities throughout the SC. However, statistical methodologies are lacking in detecting the leakage of pipe lines, etc. To overcome this conflict, IoT technology has been used by placing sensors at various levels of SWaS. This IoT-based technologies will be helpful for the detection of errors at SWaS of rivers, reservoirs, etc. With the proper utilization of services of IoT, a SC is able to control the water wastage as well as sewer overflow. Moreover, with the help of this technology, the water levels of ground as well as rivers etc. can also be detected which helps in preventing the water floods.

5.5.1

Noise Monitoring

Noise monitoring (NM) is one of the appliances of ST which can be helpful in identifying the diverse noise resources with the help of real-time tracking as well as evaluation of data for detecting the infectivity targets in resolute regions [38]. This NM will help in the prevention of noise pollution.

6 Technologies of IoT-Based Smart Cities In this section, the technologies that have been used for resolving several problems of IoT-enabled SCs are summarized and explained in a brief way. Various technologies such as RFID [55], LWPAN [56], NFC [24], ZigBee [24], 6LoWPAN [38], WSN [57] and Dash7 [58] are utilized along with IoT for several vicinities of SCs and they have been depicted in Fig. 3. Further, some other benchmark technologies such as big data analytics, artificial intelligence, machine learning and block chain used for the technological enhancement are described in the Sects. 6.1, 6.2, 6.3 and 6.4, respectively.

6.1 Big Data Analytics Rathore et al. [40] have developed a modern approach for SCs construction as well as arrangement based on IoT with big data analytics (BDA). The authors made a comparative analysis on big data utilized IoT technologies as well as its applications by considering the datasets such as vehicular traffic system, parking lot system, smart home systems and smart parking. Performance test for the proposed system

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Fig. 3 Diverse technolgies used for IoT-based SCs

has been made in terms of throughput as well as time of processing. Based on the experimental results, the authors have claimed that the proposed architecture yields better performance with complete robustness. Romero et al. [53] have proposed a novel analysis on the enhancement of IoT-based SCs using BDA as information management(IM). The authors have contributed an overview for the identification of diverse architectures of smart cities along with its applications. Their analysis described the significance of big data (BD) for IoT-enabled SCs. Tonjes et al. [54] have developed a modern method with the help of BDA to overcome the specific conflict of interpretation as well as processing of data in smart city. The authors have designed a framework named city pulse. Many technologies as well as solutions to allow the proposed architecture have also been addressed. Finally, the authors have claimed that city pulse worked as reliable, robust as well as efficient framework for overcoming the mentioned problem. Some other BDA applications have been mentioned in Table 2.

6.2 Artificial Intelligence Guo et al. [62] have developed a modern methodology for IoT with the help of artificial intelligence (AI). The proposed strategy was named as artificial intelligencebased semantic IoT (AISIoT). The authors mainly focus on utilizing AI for supple devices of IoT and stated that the proposed architecture can be used to reduce the heterogeneity of smart cities. Also, challenges, issues as well as opportunities are

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Table 2 Application of BDA on IoT-based smart city S. No.

Technology employed

Problem type

Datasets used

Result obtained

References

1

BDA

Management of BD

SmartSantander

The research work [59] showed the significance of BD generated by the incorporation of IoT in Santander

2

BDA

Designing of IoT-based system for SC

Vehicles, road sensors, smart car parks, smart home

The proposed [60] architecture outperformed for processing the BD datasets in terms of throughput as well as time of processing

3

BDA

Enhancement of IoT-based SC

Pollution, city traffic, water usage, etc.

Proposed [40] architecture yields reliable outcomes

4

BDA

RT-STMS (Real-Time Smart Traffic Management System)

–

Reliable outcomes [61] were observed for the managing the real-time data traffic

addressed in the paper. Finally, they have claimed that AISIoT can be helpful for providing the robust results for overcoming the conflicts of SCs. Soomro et al. [63] have proposed a novel method for reducing and overcoming the problem of congestion in traffic of SCs. For this, the authors have utilized AI algorithm named A* [64]. IoT devices in proposed architecture are equipped with electric poles, traffic signs, etc. Reliable outcomes are yielded to overcome the conflict of the proposed SC traffic congestion management system with the incorporation of robust AI algorithm.

6.3 Machine Learning Alrashdi et al. [65] have developed modern method named random forest algorithm (RFA) for the identification of anomaly detection (AD) in IoT devices of SCs with the help of machine learning (ML) strategies. In order to evaluate the performance of proposed architecture, a dataset named UNSW-NB 15 has been utilized. Precision, recall, F1 are used as evaluation metrics. Reliable outcomes are obtained for detecting the cyber attacks in the devices of IoT for SCs with the help of proposed architecture. Devi and Neetha [66] have developed a modern strategy for predicting the congestion of traffic in IoT-based SCs. ML algorithm named logistic regression (LR) has been

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employed for controlling the data flow. Precision as well as accuracy, etc. are used as performance metrics and a comparison has been made with other benchmark algorithms in literatures such as MLP, SVM, RF and DT. Higher performance has been obtained for controlling the data congestion with proposed method with an accuracy of 99.9%.

6.4 Block Chain In the year 2019, Rahman et al. [67] have proposed a block chain (BC) as well as IoT-based framework for the distribution of wealth services in SC. Several nodes of BC at the edge of mobile are deployed and Amazon Web Services (AWS) platform has been utilized to instantiate the nodes. Crypto-currency named Ethereum has been influenced and RESTful structure has been employed for reading and writing data of IoT to the proposed framework. Finally, the authors have claimed that their proposed architecture will be helpful in providing the quality economy services in SC.

7 Critical Analyses In this paper, a detailed investigation has been conducted on IoT-based SCs with its advancements, applications as well as limitations. A keyword search has been performed in benchmark database named google scholar. An inclusion as well as exclusion strategies has also been applied for investigation purposes. This strategy has been depicted in Fig. 4. As per the investigation conducted, it was observed that IoT became an efficient technology and played a vital role for smart cities. It is noticed that many factors are taken into consideration as an inspiration for the development as well as enhancement of smart cities. Factors such as smart security, smart building, smart education, smart technology, smart infrastructure, smart health care, smart energy, smart mobility as well as smart citizens are observed as the sources of inspiration for smart cities. It is also noticeable that some countries such as India, Netherlands, Spain, Sweden, California, Korea, Portugal, Japan, Germany, Italy, Saudi Arabia, Belgium, Brazil and England have developed smart cities Fig. 5. It is also noticed that SCs such as Bhubaneswar, Ahmadabad, Pune, Visakhapatnam, Kochi [68] Amsterdam, Barcelona, Stock Holm, Santa Cruz, Sondo, PlanIT Valley, Fujisawa, Groening, Norfolk, Vienna, Santander, Manchester and Padova are some of the already developed SCs throughout the world. The list of these cities has been mentioned in Fig. 6. Based on the key word search, many papers are found. But, only some papers which are related to the study have been taken into consideration. As per the paper count, a detailed analysis has been conducted and it is worth able to notice that appliances of SCs such as smart surveillance, smart home, smart transportation, smart weather system, smart water system as well as smart health are found. This evident the importance of IoT-based SCs from the past decades in

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Fig. 4 Paper extraction strategy applied for the study

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% of List of Countries having smart cities over the world India Brazil Saudi Arabia

Netherlands England Korea

8%

Spain California Portugal

9%

8% 9%

8%

9%

8%

9%

8%

Sweden Belgium Japan

8% 8% 8%

Fig. 5 List of countries contains smart cities

% of List of Smart Cities throughout the world

6%

6%

6% 6% 6% 6%

6%

6%

6% 6% 6% 6% 6%

6% 6% 6% 6% 6%

Amsterdam Barcelona Stockholm Santa Cruz Sondo PlanIT valley Fujisawa Groening Norfolk Vienna Santander Manchester Padova Bhubaneswar Ahmadabad Pune Visakhapatnam Kochi

Fig. 6 List of SCs throughout the world

various appliances. The analysis on a scale of 1–100 for the appliances already used in the literature has been depicted in Fig. 7. It is also observed that diverse technologies such as RFID, LWPAN, NFC, ZigBee, 6LoWPAN, WSN, Dash7, BDA, BC, AI as well as ML are developed along with IoT for SCs. The analysis (on a scale of 1–100) on the technologies has been depicted in Fig. 8. Moreover, maximum papers for IoT-based SCs have been used and outlined in this research.

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Analysis on the % of types of Applications of SCs Smart Home 27%

20%

Smart Health Smart Water System Smart Wetaher System

13%

13% 13%

7%

7%

Smart Surveillance Smart Transport System Others

Fig. 7 Usage levels of applications in IoT-based SCs

Analysis on the % of types of technologies used with IoT based SCs 6% Block Chain 6% Artificial Intelligence 30% Machine Learning Big Data Analytics 12% Others 46% Fig. 8 Usage levels of several technologies with IoT-based SCs

8 Research Challenges, Limitations and Discussions In this study, a detailed analysis has been conducted on the advancements, usage levels as well technologies that deals with the IoT-based SCs. Many strategies have been taken into consideration such as paper collection, separating the papers related to the study. Based on the conducted analysis, it is observed that various limitations are raised by the several applications of IoT-based smart cities. These limitations include security, privacy, heterogeneity, reliability, large scale, big data and sensor networks. These challenges are mentioned in Fig. 9. Security and privacy issues can be taken place when there will be an assortment of entire data in a general platform that affects to several attacks and exposed to significant vulnerabilities [52]. Heterogeneity issue progressed with eminent solutions in which entire system is joined to the specific application context. Reliability problems are caused by IoTbased methods for taking care of mobility and interconnection issues [69]. Largescale data required suitable storage and computational capability, as it assembled at

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Fig. 9 Challenges of IoT-based smart cities

high charges that directs to the standard confront [21]. IoT is a service that generally considered through the user provided information. Candidates are faced with enough motivation to concentrate an individual scenario and data gathering which becomes a major challenge in local and social aspects, respectively [70]. In big data, it is necessary to pay an attention to storage of data, transferring the data, recollect and examine the huge amount of generated data by billions of devices [52]. In order to allow the IoT as a notable technology, sensor network has been considered [71] that can give ability of computing, gathering and understanding environmental indexes. Kim and Shcherbakova [72] have mentioned some challenges of IOT-based smart cities including framework barrier, customer’s and provider barriers with a limitations such as lack of communication limit and knowledge, no potential in saving money and no proper availability of equipment for customers. All these are the present challenges that are facing by many researchers for improving the IoT-based smart cities. There is a need for the advancement of transport system with the help of smart transport card which is rechargeable. This can be achieved with the incorporation of Near Field Communication (NFC) technology for the transmission of data from machine to card and vice versa. This card can be used at parking spots, toll plazas, etc. while traveling. Any vehicle owner can use this card and the transport agencies will be issued some equipment for the authorization of vehicles and its owners. An

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advanced technology also needs to be developed for smart tourism. This technology will help the tourists, visited from all over the world. The technology should give the user information about all the tourist places of a particular region or city. This system can be achieved with the help of QR codes. Moreover, many more problems need to be explored. Still, there is a need that many innovations yet to be explored using IoT technology on SCs for solving those types of problems which have not been applied.

9 Conclusion It is expected that IoT is broadly used to connect billions of devices in the coming future. In order to comprehend the idea of a smart city, the technologies of IoT are playing the most crucial elements in moving ahead the complete strategy of a smart city. It is indisputable to consider the significance of how novel technologies as well as perceptions like IoT can benefit SCs. This study provides a deep review on the need and role of IoT in improvement of SCs. The major reason behind this research is to survey the previous experience throughout the world and motivate for the further effective usage of IoT in smart cities. We mentioned several technologies that are used in IoT-based smart cities along with the effective incentives. As the success of IoT can facilitate the number of opportunities for smart cities, the foremost study on IoT in smart cities is uttered and many applications are also analyzed. Similarly, the future challenges and limitations from employing the IoT-based smart cities are summarized accordingly. The future growth of IoT will witness some more advanced technologies for the development of smart city, which will enable the user to maintain a simple yet interactive lifestyle.

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Optimal Design of Fractional Order 2DOF-PID Controller for Frequency Control in Interconnected System Sabita Tripathy, Manoj Kumar Debnath, and Sanjeeb Kumar Kar

Abstract Due to increasing load demand in interconnected system, load frequency control (LFC) is implemented to maintain power quality in terms of frequency regulation. In this work, fractional order two degree of freedom PID (2DOF-FOPID) controller is implemented in a unified power system to regulate frequency abnormality due to abrupt load variation. The examined system includes a geothermal power plant (GTPP), conventional steam power plant (SPP), and dish-sterling solar thermal system (DSTS) in each control area. Considering ITAE as fitness function, grasshopper optimization algorithm (GOA) is utilized to find the finest gains of 2DOF-FOPID controllers during abrupt load perturbation (0.05 p.u.) in control area 2. With consideration of system response evaluative factors (undershoots, settling time, and overshoots), the supremacy of the 2DOF-FOPID controller is established over 2DOF-PID controller and PID controller. By amplifying the load conditions of the control area 2, the robustness of the 2DOF-FOPID controllers is verified. Keywords Fractional order controller · Grasshopper optimization algorithm · 2DOF-PID controller · Load frequency control

1 Introduction In unified power system, it is an essential factor to have a proper balance between demand and generation in order to maintain constant frequency. To avoid loss of synchronization, it is required to have constant frequency in unified system. LFC serves a prime role in this context. In order to control the frequency to its required value, LFC is used and it supports in transforming the power in the planned value. S. Tripathy (B) · M. K. Debnath · S. K. Kar ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] M. K. Debnath e-mail: [email protected] S. K. Kar e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_2

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Proper balance must be maintained between the generated power in total with the amount of load demanded in a specified region so that operating of the power plants which are interconnected between each other shall be proper [1, 2]. Over few past years, various literature reviews are carried out on this concerned area and many researchers recommended different methodologies to overcome LFC issues. Multiple control strategies have been conveyed in article [3] regarding automatic generation control in unified system. Conventional controllers tuned by bacteria foraging technique were applied to solve LFC in paper [4]. A novel decentralized control scheme [5] was developed to solve LFC in multi-area unified system. Reheat turbine-type thermal system was included by many researchers for frequency regulation in unified system like hydro-thermal [6] and thermal-thermal [7]-type power system. Due to simple and robust operation, PID controllers [8, 9] were applied by most of the researchers for frequency control in unified system. Sahu et al. [10] used 2-DOF-PID controller tuned by differential evolution for frequency regulation in a system with nonlinearity in the form of governor dead-band. Later, PID controller based on fuzzy logic [11] was employed for frequency control in power system incorporating superconducting magnetic energy storage units. Sahu et al. [12] incorporated Fuzzy-PID controller tuned by hybrid DEPSO technique to control frequency in multiple power systems. Fuzzy logic-based PI/PID controller optimized by DEPS technique was described in paper [13] for LFC in multi-area model. Fractional calculus-based controllers were implemented in article [14] for LFC applying chaotic optimization technique. Sine–cosine algorithm (SCA) was used in LFC application for tuning 2DOF-PID controller in hybrid generation-type power system [15]. Novel hybrid-type PID-Fuzzy-PID controller [16] was incorporated in power system for LFC using modified GWO algorithm. Electric vehicle aggregators were considered in paper [17] for LFC in multi-generation system using hybrid modified algorithm. A recent bio-inspired optimization process known as grasshopper optimization algorithm was introduced by Mirjalili [18] for solving many engineering optimization problem. Many researchers proposed different methodologies for overcoming LFC challenges using conventional-type controllers. But in this research work, fractional order dual loop controller (2DOF-FOPID) is recommended to support load frequency control in hybrid generation-type interconnected power system.

2 Examined System As deliberated in Fig. 1, a two control area system is scrutinized in this paper for frequency regulation. Each control of this unified system consists of three generating sources namely GTPP, DSTS, and SPP. 3.6% of governor deadband (GDB) and 3% of generation rate constraint (GRC) are implemented in the solar power plant equipped with reheat-type turbine. The rated values of all the parameters considered in the examined model are taken from published article [15]. Two distinct controllers are employed in each control area for achieving load frequency control. Three categories

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Fig. 1 MATLAB–Simulink block diagram of two-area hybrid source power system

of controllers namely PID, 2DOF-PID, and 2DOF-FOPID controllers are considered for LFC in the unified system with the application of sudden load perturbation 0.05 p.u. in control area 2. The finest coefficients of the implemented controllers are attained with the help of GOA technique. Integral time absolute error or ITAE is taken as fitness function in the optimization or tuning process. Area of controller (ACE) acts as actuating signal for the applied controllers in the model. Mathematically ACE is described as below. ACE1 = P12 + B1 ω1

(1)

ACE2 = P21 + B2 ω2

(2)

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Fig. 2 Internal architecture of 2DOF-PID controller

3 Architecture of Controllers The conventional PID controllers implemented in the examined model contain only one closed loop for control mechanism. But the 2DOF-PID controller contains dual closed loop for accomplishing control mechanism. The number of closed loops implemented in the controller for accomplishing control mechanism is known as degree of freedom. The purpose of implementing dual control loop in the 2DOF-PID controller is to accelerate control process. Figure 2 depicts the internal architecture of the 2DOF-PID controller. This dual loop controller contains C D , C I &C P as derivative, integral, and proportional coefficients.DW and P W stand for derivative and proportional weight, respectively. The reference input signal is depicted by R(s), the feedback signal is depicted by Y (s), and U(s) is the controller output signal. Based on fractional calculus, 2DOF-PID controller is modified to develop 2DOF-FOPID controller. Figure 3 depicts the internal architecture of the 2DOF-FOPID controller. Here, the PID section is modified with fractional order PID controller. λ and μ represent fractional coefficients of integral and derivative sections. All types of controller gains are tuned with GOA technique.

4 Grasshopper Optimization Algorithm Based on the movement of grasshoppers, grasshopper optimization algorithm (GOA) [18] is developed to produce many engineering optimization problem. It generally mimics the environment of grasshoppers. Each solution candidate is represented by a grasshopper in the population. Due to its efficient optimization technique, we have applied this process to get the finest gains of different controllers to solve

Optimal Design of Fractional Order 2DOF-PID Controller …

27

Fig. 3 Internal architecture of 2DOF-FOPID controller

LFC problem in unified system. GOA successfully maintains the balance between exploration and exploitation to achieve optimum value. Generally, three terms indicate the nature of grasshoppers in the population. These are social interaction, gravitational force, and wind force. Accordingly, the position of a particular grasshopper (let Px ) is calculated as follows. Px = Sx + Wx + G x

(3)

In the above expression, the position of the xth grasshopper is denoted by Px . Sx ,Wx and G x , respectively, indicate the social interaction, the wind force, and gravity force of the xth grasshopper. The social interaction factor Sx is obtained as below. Sx =

N 

∧

s(dx y ). dx y

(4)

y=1 x= y

The following expressions are followed to evaluate Eq. (8). s(r ) = f e

−r l

− e−r

(5)

  dx y =  Py − Px 

(6)

(Py − Px ) dx y

(7)

∧

dx y =

The wind force factor Wx is obtained as below:

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S. Tripathy et al. ∧

Wx = u ew

(8)

The gravity force factor G x is obtained as below: ∧

G x = −g e

(9)

g

Equation (3) can be evaluated using Eqs. (4)–(9), as below: N   Py − Px  ∧ ∧ Px = s  Py − Px  − g e +u ew g d xy y=1

(10)

y=x

Equation (3) can be simplified if we include different assumption as follows. ⎛

⎞ ∧    P − P − Lb U b y x y d d ⎠ + Td .S  Pyd − Pxd  Pxd = c⎝ c. 2 dx yY y=1 N 

(11)

Here,

c max −c min c = c max −l L

 (12)

Equation (11) is followed to update the population of GOA technique.

5 Results and Analysis MATLAB 2016 Simulink software is followed to model the two-area unified system to examine the LFC issues due to sudden load disturbances in control areas. An abrupt load perturbation of 0.05 p.u. is applied in control area 2, and the gains of all controllers (2DOF-PID, 2DOF-FOPID, and PID) are tuned by GOA with the help of following evaluative fitness function (integral time absolute error). ITAE =

(| f 1 | + | f 2 | + |ptie |).t.dt

(13)

The optimum gains of the aforesaid controllers are tabulated in Table 1. With these finest gains, the system responses ( f 1 , f 2 , Ptie ) are observed as depicted in Figs. 4, 5 and 6. The oscillations of frequencies in control area 1 and control area 2 are exposed in Figs. 4, 5 and 6 and the interline power between control area are exposed in Fig. 6. The performance comparisons of different controllers are tabulated in Table 2 in terms of overshoot, settling time, and undershoot. Figures 4, 5 and 6 and

Optimal Design of Fractional Order 2DOF-PID Controller …

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Table 1 Finest gains of different controllers obtained using GOA Controller parameters

Area 1

Area 2

PID

2DOF-PID 2DOF-FOPID PID

PW

NA

4.5421

2.2478

NA

4.5178

DW

NA

2.2014

0.0147

NA

3.6871

4.1361

CP

1.3258 0.3241

0.8754

1.0378 1.4524

1.0512

CI

2.0000 1.9785

1.8759

0.7845 0.0100

0.0124

CD

0.5211 0.1748

0.6412

0.6742 0.7418

1.1879

μ

NA

NA

0.81

NA

NA

0.78

λ

NA

NA

0.94

NA

NA

0.85

Fig. 4 Oscillations of frequency in control area 1

Fig. 5 Oscillations of frequency in control area 2

2DOF-PID 2DOF-FOPID 3.1013

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Fig. 6 Oscillations of interline power between control area 1 and control area 2

Table 2 Response evaluative indices obtained using different control techniques Abnormalities

Time-domain factors

f 1

T s (s)

2.2791

1.0751

1.0268

Overshoot (Osh × 10−3 )

0.3277

0.3188

0.0965

−0.0270

−0.0189

3.5324

3.4531

Undershoots  f2

T s (s) Overshoot (Osh

× 10−3 )

Undershoots Ptie

T s (s) Overshoot (Osh Undershoots

× 10−3 )

PID

2DOF-PID

0.2646

0.0300

−0.0153

−0.0062

3.0380

3.0558

0.0242

0.0105

−0.0067

−0.0040

2DOF-FOPID

−0.0128 3.3461 0.0300 −0.0052 3.0458 0 −0.0034

Table 2 jointly prove the dominance of recommended 2DOF-FOPID controller over 2DOF-PID and PID controllers. 2DOF-FOPID controller settles the oscillations of frequencies and interlines power faster than other two types of controllers. The controllers are said to be robust if it can handle the frequency deviations during uncertain load variations. In order to verify the robustness of projected 2DOFFOPID controller, the loading in control area 2 is amplified suddenly by 10% and the responses ( f 1 ,  f 2 , Ptie ) of the model are observed. Under this uncertain loading condition, the oscillations of frequencies in control area 1 and control are 2 are exposed in Figs. 7 and 8 and the interline power between control area are exposed in Fig. 9. Figures 7 and 9 justify that the projected 2DOF-FOPID controller holds the least values of the undershoot, settling time, and peak overshoot and hence more robust as compared to 2DOF-PID and PID controller.

Optimal Design of Fractional Order 2DOF-PID Controller … Fig. 7 Oscillations of frequency in control area 1 with modified loading

Fig. 8 Oscillations of frequency in control area 2 with modified loading

Fig. 9 Interline power oscillations with modified loading

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6 Conclusion In this research analysis, fractional order-based 2DOF-PID controller is recommended for frequency regulation in hybrid generation two-area power system. The dominance of 2DOF-FOPID controller is proved over 2DOF-PID and PID controller. Geothermal power plant (GTPP), conventional steam power plant (SPP), and dishsterling solar thermal system (DSTS) are placed in each control area of the unified system. GOA technique is successfully applied to get the finest gain of the implemented controllers considering ITAE as evaluative function. During abrupt load perturbation in control area 2, the projected 2DOF-FOPID controller is found to perform better as compared to 2DOF-PID and PID controller in terms of overshoot, settling time, and undershoot. Finally, the robustness of the projected controller is also verified by suddenly amplifying the loading of control area 2.

References 1. Fosha CE, Elgerd OI (1970) The megawatt-frequency control problem: a new approach via optimal control theory. IEEE Trans Power Apparatus Syst 4:563–577 2. Kundur P, Balu NJ, Lauby MG (1994) Power system stability and control, vol 7. McGraw-hill, New York 3. Kumar P, Kothari DP (2005) Recent philosophies of automatic generation control strategies in power systems. IEEE Trans Power Syst 20(1):346–357 4. Nanda J, Sukumar M, Saikia LC (2009) Maiden application of bacterial foraging-based optimization technique in multiarea automatic generation control. IEEE Trans Power Syst 24(2):602–609 5. Alrifai MT, Hassan MF, Zribi M (2011) Decentralized load frequency controller for a multi-area interconnected power system. Int J Electr Power Energy Syst 33(2):198–209 6. Prakash S, Sinha SK (2011) Load frequency control of three area interconnected hydro-thermal reheat power system using artificial intelligence and PI controllers. Int J Eng Sci Technol 4(1):23–37 7. Gozde H, Cengiz Taplamacioglu M, Kocaarslan I (2012) Comparative performance analysis of artificial bee colony algorithm in automatic generation control for interconnected reheat thermal power system. Int J Electr Power Energy Syst 42(1):167–178 8. Shabani H, Vahidi B, Ebrahimpour M (2013) A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems. ISA Trans 52(1):88–95 9. Ali ES, Abd-Elazim SM (2013) BFOA based design of PID controller for two area Load Frequency Control with nonlinearities. Int J Electr Power Energy Syst 51:224–231 10. Sahu RK, Panda S, Rout UK (2013) DE optimized parallel 2-DOF PID controller for load frequency control of power system with governor dead-band nonlinearity. Int J Electr Power Energy Syst 49:19–33 11. Pothiya S, Ngamroo I (2008) Optimal fuzzy logic-based PID controller for load–frequency control including superconducting magnetic energy storage units. Energy Convers Manage 49(10):2833–2838 12. Sahu BK, Pati S, Panda S (2014) Hybrid differential evolution particle swarm optimisation optimised fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system. IET Gener Transm Distrib 8(11): 1789–1800 13. Sahu RK, Panda S, Yegireddy NK (2014) A novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systems. J Process Control 24(10):1596–1608

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14. Pan I, Das S (2015) Fractional-order load-frequency control of interconnected power systems using chaotic multi-objective optimization. Appl Soft Comput 29:328–344 15. Patel NC, Sahu BK, Debnath MK (2020) Application of SCA-Based two degrees of freedom PID controller for AGC study. In: computational intelligence in pattern recognition. Springer, Singapore, pp 969–980 16. Debnath MK, Jena T, Sanyal SK (2019) Frequency control analysis with PID-fuzzy-PID hybrid controller tuned by modified GWO technique. Int Trans Electr Energy Syst 29(10:e12074 17. Patel NC, Sahu BK, Debnath MK (2019) Automatic generation control analysis of power system with nonlinearities and electric vehicle aggregators with time-varying delay implementing a novel control strategy. Turkish J Electr Eng Comput Sci 27(4):3040–3054 18. Mirjalili SZ et al (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48(4):805–820

Electrical Properties of the PVDF-Lead-Free Ceramic-Based Composite Film for Sensor Applications Basanta K. Panigrahi, Varsha Purohit, Vijayeta Pal, Sugato Hajra, Kalyani Mohanta, and S. K. M. Ali

Abstract The ceramic-polymer composites were prepared by mixing 0.7Bi(Fe0.98 Ga0.02 )O3 -0.3BaTiO3 (BFBTO) and poly(vinylidene fluoride) (PVDF), taken at various weight ratios 5, 10, and 15% processed by solvent-casting technique. The X-ray diffraction (XRD) spectra depict combined phases (α−, β− and γ −) of the PVDF and tetragonal phase of BFBTO. The surface morphology was examined by scanning electron micrograph showing the spread of lead-free BFBTO filler on matrix PVDF. The rise in filler content, in the PVDF matrix, enhances dielectric constant and remnant polarization. The Nyquist plot suggests the contribution of grain and grain boundary effect. The composite films shall be useful for various self-power sensors. Keywords PVDF · XRD · Nyquist plot · Dielectric constant B. K. Panigrahi (B) · S. Hajra Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] S. Hajra e-mail: [email protected] V. Purohit Department of Physics, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] V. Pal Department of Materials Science and Technology, IIT, BHU, Varanasi 221005, India e-mail: [email protected] K. Mohanta Department of Ceramic Engineering, IIT, BHU, Varanasi 221005, India e-mail: [email protected] S. K. M. Ali Electronics and Telecommunication Engineering, Trident Academy of Technology, Bhubaneswar 751024, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_3

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1 Introduction The sensors industry has been widely explored because of its inclusion in several applications [1–3]. So, the deployment of sensor networks enables uninterrupted structural integrity monitoring of an aircraft, providing crucial information on operation condition, deformation, and potential damage to the structure. There is a global alert of several lead-based pollution and toxicity which bars the usage of lead in future electronics. Therefore, the quest has been generated to develop multiferroic materials having several ferroic orders like ferroelectricity, ferroelasticity, and ferromagnetism serving toward device applications such as sensors, memories, and energy harvesters [4]. Bismuth ferrite (BiFeO3 , BFO) have high polarization and chemical stability [5]. BFO-BaTiO3 based lead-free solid solutions bear a high T c (i.e., 580 °C) with superior electrical properties. The ceramic materials are brittle which hinders the application of the ceramic fillers in flexible device design, so researchers develop a method of using polymer-ceramic composites comprising excellent flexibility. The ferroelectric PVDF matrix as polymer has better piezoelectric properties and can be fabricated by several synthesis methods. The PVDF comprises of five phases, such as α, β, γ , δ, ε with various phase structures like based on trans (T) and gauche (G); TGTG’ conformations for α and δ, while T3GT3G’ conformations for γ and ε, and highly electroactive β phase has TTTT [6]. Fan et al. synthesized the graphene/PVDF composites and reported its dielectric properties [7]. Dash et al. studied the electrical properties of PVDF/BFO films [8]. The PVDF-based composite films (CF) are used for various self-power sensors application via the piezoelectric and triboelectric effect. The piezoelectric energy harvesting uses mechanical stress to generate the electrical output. The piezoelectric nanogenerator made of PVDF-ceramic composite films is widely used, and the polarization in such polymer composite films upon mechanical stress or bending is responsible for the generation of electrical output leading to powering up of several self-power sensors [9, 10]. The triboelectric nanogenerator uses the polymer-ceramic CF as one triboelectric layer having a different work function as respect to the opposite triboelectric layer. Whenever there is friction or rubbing between the two triboelectric layers due to contact electrification and electrostatic induction, the electrical energy is produced which is also responsible for the powering of the self-power sensors [11, 12]. So, polymer-ceramic CF is widely used for the powering of self-powered sensors. In this study, the cost-effective synthesis of polymer-ceramic CF was performed. Further, the structural and electrical properties were correlated. It is predicted that these films are flexible and shall serve in various device applications like sensors.

2 Processing and Experimental Techniques The BFBTO ceramic powder was fabricated using a conventional solid-state reaction. The various oxides and carbonates of high purity were consumed and measured

Electrical Properties of the PVDF-Lead-Free …

37

carefully in the digital weighing machine according to the required ratio. Further, it was mixed in an agate mortar with methyl alcohol for 2 h. The homogenous mixture was calcined in a muffle furnace at 940 °C for 4 h with a heating rate of 5 °C/min. After calcination, it was allowed to reach room temperature, and further, the obtained product was made fine powder for experimental procedure or synthesis of PVDF-ceramic CF. The solution casting route is followed to synthesis the PVDF-BFBTO films (5, 10, 15 wt%). Initially, 2 g of commercial PVDF pellets (M/S Sigma Aldrich) were put into 20 ml of N-methyl pyrrolidone (NMP) solvent and kept for stirring in magnetic stirrer for 1 h. The transparent PVDF solution appears as the pellet dissolves in the NMP solvent. Further, the precise amount of BFBTO ceramic powder (5, 10, 15 wt%) added in PVDF solution and stirring continued till 60 min. Further, a glass Petri dish was employed to pour the solution and dried in an oven at 70 °C for 12 h to eliminate excess solvent. Finally, the opaque films were peeled off from the glass plate. The systematic procedure of the fabrication of polymer-ceramic composite is given in Fig. 1. The crystalline structure of synthesized BFBTO ceramics, PVDF, and composite films were measured using the X-ray diffractometer (Rigaku Smart lab, Japan) operated at 40 kV/10 mA at room temperature. The surface morphological of composite films is characterized using an FE-SEM (M/S Zeiss EVO). The electrical properties of the composite films were analyzed using phase-sensitive LCR meter (N4L, UK) at room temperature. The ferroelectric loop was obtained using an advance loop tracer (M/S Marine India). For the electric and ferroelectric measurements, the samples were painted with highly pure silver paste on the opposite side making a metal–insulator–metal format.

Fig. 1 Systematic steps for preparing the polymer-ceramic composite

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3 Results and Discussion The structural analysis of the ceramic, pure PVDF, PVDF-BFBTO composites is given in Fig. 2a–e. Figure 2a presents the filler XRD which depicts that it crystallizes at tetragonal symmetry at room temperature. The POWDMULT software accomplishes the lattice parameters to be: a = 3.990 (Å), c = 3.986 (Å), Vol = 63.49 (Å)3 and c/a = 0.9990. A small intensity, non-perovskite phase (Bi25 FeO39 and Bi2 Fe4 O9 ) signified by #, * were seen which is consistent with the previous report. Figure 2b depicts the XRD pattern of pure PVDF films suggesting the presence of two peaks at various Braggs angle 2θ ~ 19.9 and 20.3 similar to α (110) and β (200) planes, respectively [9]. The peaks at 36.7 (101), 38.1 (200), and 38.6 (211) correspond to the β−, α−, γ − phases of PVDF, respectively [10]. Figure 2c–e depicts the polymer-ceramic composites having combined phases of PVDF and BFBTO. Figure 3a–c presents the surface micrographs of polymer-ceramic composites shedding light upon the microstructure and the diffusion of the BFBTO filler along with the PVDF matrix. It clearly shows no powder agglomeration occurred, and the ceramic particles are well distributed over polymer matrix. Figure 4 presents the dielectric constant and loss factor at different frequencies at room temperature. The defects, domains, and grain size influence the dielectric properties [11]. The dielectric constant of pure PVDF is 10 at 100 Hz as per the

Fig. 2 X-ray diffraction spectra for a BFBTO, b PVDF, c, d, e polymer-ceramic composites

Electrical Properties of the PVDF-Lead-Free …

39

Fig. 3 Surface micrograph of a PVDF-5BFBTO, b PVDF-10BFBTO, c PVDF-15BFBTO

Fig. 4 Dielectric constant and loss factor at different frequencies at room temperature

reported results. So, the dielectric constant increases when the filler is added at different wt% in PVDF. It is seen that at lower frequency (1 kHz) the dielectric constant of PVDF-5BFBTO, PVDF-10BFBTO, PVDF-15BFBTO is noted to be 15, 16.8, 20, respectively. The dielectric constant rises at low frequency due to the contributions of various interfacial polarization [12]. The dielectric loss has a similar trend like dielectric constant. The increase in dielectric loss in lower frequency is related to temperature-dependent charge carriers and unknown defects [13]. The complex impedance spectroscopy is helpful to reveal the correlation between the electrical and microstructural properties of the material. It also sheds light on various contributions to the relaxation process such as micrograins, grain boundary, and electrode interfaces [14–16]. Figure 5 illustrates the Nyquist plots for all the compositions of PVDF-BFBTO composites at room temperature. In the case of homogeneous materials, a semicircle with center on the real Z-axis represents single relaxation phenomena and can be fitted by parallel RC circuit which corresponds to Debye-type behavior. Figure 5 suggests that the semicircular arc is depressed illustrating inhomogeneity. The depressed circle suggests the shift from ideal Debye condition adding constant phase element (Q) to the parallel RC network [17]. It suggests that for all composites films, there is a contribution of grain and grain boundary. Figure 6a–c presents the P-E loop which gives an insight toward the ferroelectric

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Fig. 5 Nyquist plot of polymer-ceramic composites at room temperature and equivalent circuit model (insert)

Fig. 6 Ferroelectric (P-E) loop of various PVDF-5, 10, 15 BFBTO

nature of the prepared CF. It shows the remnant polarization and coercive field, and the loop area reduces with the increase in filler doping into the PVDF matrix. This is maybe due to the formation of oxygen vacancies for charge neutralization, promoting high conduction.

4 Conclusion The BFBTO introduced to ferroelectric PVDF leads to the formation of flexible composite films by a simple solution casting route. The structural, morphological, dielectric, and ferroelectric properties of the composite films were examined in detail. The in-depth study confirms that the PVDF-15 BFBTO has a better dielectric, and ferroelectric properties than other composites and suitability to use as sensor applications.

Electrical Properties of the PVDF-Lead-Free …

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References 1. Chiu Y-Y, Lin W-Y, Wang H-Y, Huang S-B, Wu M-H (2013) Sens Actuators A Phys 189:328– 334 2. Teka A, Bairagi S, Shahadat M, Joshi M, Ahammad SZ, Wazed Ali S (2018) Polym Adv Tech 29:2537–2544 3. Cho JY, Jeong S, Jabbar H, Song Y, Ahn JH, Kim JH, Jung HJ, Yoo HH, Sung TH (2016) Sens Actuators A 250:210–218 4. Khomskii D (2009) Physics 2:20 5. Wu J Fan Z, Xiao D, Zhu J, Wang J (2016) Prog Mater Sci 84:335–402 6. Wang S, Liu L, Zeng Y, Zhou B, Teng K, Ma M, Chen L, Xu Z (2015) J Adhes Sci Technol 29:678–690 7. Fan P, Wang L, Yang JT, Chen F, Zhong MQ (2012) Nanotechnology 23:8 8. Dash S, Choudhary RNP, Goswami MN (2017) J Alloy Compd 715:29–36 9. Chinya I, Sasmal A, Sen S (2020) J Alloy Compd 815:152312 10. Patra A, Pal A, Sen S (2018) Ceram Int 44:11196–11203 11. Wu Y, Qu J, Daoud WA, Wang L, Qi T (2019) J Mater Chem A 7:13347–13355 12. Sriphan S, Charoonsuk T, Maluangnont T, Vittayakorn N (2019) ACS Appl Energy Mater 2:3840–3850 13. Damaraju SM, Wu S, Jaffe M, Arinzeh TL (2013) Biomed Mater 8:045007–045017 14. Hajra S, Sahoo S, Choudhary RNP (2019) J Polym Res 26:14 15. Bassiri-Gharb N, Fujii I, Hong E, Trolier-McKinstry S, Taylor DV, Damjanovic D (2007) J Electroceram 19:49–67 16. Hajra S, Sahu M, Panigrahi BK, Choudhary RNP (2019) Pramana 93:48 17. Hajra S, Tripathy A, Panigrahi BK, Choudhary RNP (2019) Mater Res Expr 6:076304

IoT-Based Voice-Controlled Energy-Efficient Intelligent Traffic and Street Light Monitoring System Anil Kumar Biswal, Debabrata Singh, and Binod Kumar Pattanayak

Abstract Traffic congestion and street light monitoring is a vital problem in all part of the cities of India along with other areas of countries. This congestion is formed due to poor management of light signals as well as law enforcement technique. In this paper, we present an idea to build up a voice-controlled smart traffic (VCST) based on Arduino UNO where the light system can be monitored by the implementation of specific voice commands and traffic-flow-based smart (LED) street light. It offers energy optimization methods, low-cost, highly reliable, and user friendly architecture in smart grid architecture with a low-power ZigBee network. Generally, in this proposed system, we use simple voice commands like red, green, yellow, and stop which are used to operate the system. The voice command is forwarded by using an Android application to the Bluetooth module, which successfully differentiates between pre-stored commands and identifies them exactly to control the street light and traffic light system. We propose a smart traffic system based on Arduino UNO that evaluates to change light signals according to the voice message. Keywords Arduino UNO · Voice control · Bluetooth module · Smart traffic · IoT

1 Introduction Nowadays, traffic congestion is a major issue in many cities across the world that affects the transportation system. The traffic congestion is mainly caused by delays in traffic light signals [1]. At the time of emergency, vehicles like ambulance needs A. K. Biswal · B. K. Pattanayak Department of Computer Science & Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] B. K. Pattanayak e-mail: [email protected] D. Singh (B) Department of Computer Science & Information Technology, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_4

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to give first priority, but it is not possible due to heavy congestion. So, automation makes the system very simple by implementing IoT devices which enables us to connect and exchange data [2]. This project gives an easy and flexible way to control and work on light signals. This system is related to a voice recognition module to recognize the command for monitoring the traffic light signals like red, yellow, and green [3]. When the voice recognition module is connected, then it forwards the message to Arduino. But, the Arduino is interfaced with LEDs to monitoring it [4, 5]. The test framework of the voice-controlled smart traffic system is depicted as in Fig. 1. In the upcoming level of intelligence, the technique is introduced as smart street lighting with commutable traffic control. The smart-networked street-lighting system is using LED which has added some valuable features of adaptable dimming, power of occupancy, and optimal luminaries. Due to these, features are efficiently used to reduce energy consumption as well as provide an environment for long life usability and less maintenance cost in it. Thus, an intelligent luminaries system is constructed by exploiting the integration of smart sensors and some monitoring modules with the robust connectivity architecture in smart street lighting as well as in traffic as shown in Fig. 2.

Fig. 1 Test framework of voice-controlled smart traffic system

IoT-Based Voice-Controlled Energy-Efficient Intelligent …

45

Fig. 2 Abstract level of three-layer architecture of smart lighting system through IoT

1.1 Motivation In the modern generation, traffic congestion is the main issue, which is greatly affected by emergency vehicles like ambulance, fire brigade, and others. The old traffic system and smart street lights were much delayed in nature because it is preprogrammed to control overall signal management. Thus, the traffic lights and smart street lights need to smartly control via sending voice commands to avoid the cause of instant congestion. The implementation of this IoT-based system includes swiftness in handling traffic lights at any urgent time through voice commands.

1.2 Contribution to This Work This purposed system is designed by using a mobile voice recognizer Android application (Arduino Voice Control), Bluetooth module (HC-05), Arduino UNO board, jumper wire, and three-colored LEDs like red, green, and yellow. The important contribution of this paper is not only to regulate the brightness of smart LED lights by changing diurnal traffic volume but also to experimentally implement adaptive control. So, it can be optimally maintained different periodic energy levels of daylight timing and to control the minimum reduction of the emissions level. As well, the future generations of green or smart cities are associated with smart street light which is integrated with the intelligent grid environment with demand response (DR) and advanced metering information (AMI). The rest of the paper is organized as follows: Sect. 2 illustrates some related works. Sect. 3 contains a description of various components. We explain the proposed system in Sect. 4, and the working principle is described in Sect. 5. Section 6 analyzes the results that we got, and in the next section, we conclude our paper with future scope.

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2 Related Works Most of the recent systems only have the provision of altering the traffic light signals automatically using many technologies like PIC microcontroller [6], XBee wireless device, RFID, IR sensors [7], video and image processing [8, 9]. So, these kinds of systems are required very high cost as well as more power to operate. The existing system do not use voice commands to interact with the traffic light, which normally includes video or image monitoring and traffic density calculation. Maddileti et al. [10] built a voice-controlled car through Bluetooth module using Android applications. Using image processing, it can help to become aware of shade and the obstacles, and the voice command is analyzed by the microcontroller to execute the task. This application is very effective in designing a vehicular-type wheelchair for handicapped within limited cost. Chavan et al. [11] provide a design of a voice-controlled machinery in the agricultural field using Raspberry Pi. The Android voice commands are transmitted to control agricultural machinery via WiFi module with Raspberry Pi 3. It can facilitate to manage the irrigation process through voice command like “on the motor” and “off the motor.” This system is avoiding human negligence and also minimizing time effort. Mporas et al. [12] proposed an automatic speech recognition system for home appliances control through voice recognizer application. This system allows operating various home appliances like a TV set, mobile phones, as well as other electronic gadgets bypassing voice commands. Due to this application, there is no need for extra human manual effort and devices for any type of operation. Halder et al. [13] developed an artificially intelligent home automation system based on Arduino as the master controller with speech recognition and graphical user interface. The implementation of artificial intelligence is making whole process automation with the addition of security in it. This system can be used to securely handle home appliances from a remote distance in case of an emergency. Mostaque et al. [14] presented a design of low-cost Arduino-based voice-controlled pick and drop service with movable robotic arm using a sonar sensor and micro servo. This purposed system can easily pick and drop any kind of real-world object by sending voice commands. When it can receive a voice signal pick and drop, this process works via the sonar sensor. Munteanu et al. [15] designed a voice-controlled smart assistive device for visually impaired individuals using Zigbee devices and microcontrollers. This model is based on a smart assistive system which can help to visually impaired or blind people, but it connects via voice command. When objects are found on the road or walking path, obstacles through accidents can be avoided by the functioning of the ultrasonic sensor. Many current systems process under Vehicular Ad-Hoc Network (VANET) which is based on the mobile agent controller that runs a congestion control algorithm to uniformly manage the traffic signals by avoiding the congestion at any traffic zone [16, 6].

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3 Components Description To save energy, this system is designed by the proper implementation of smart LED lights. So, the smart street light system and traffic system are associated with luminaries to control and monitor its performance through various sensors, actuators IoT modules, where these are handled by the wireless network [17]. Then, the proposed system consists of major type components which are described below: A. Android application In order to recognize a voice message, an Android application that is the Android voice control module, can be used. The microphone is a hardware component that can change analog input to digital output [18]. B. Arduino UNO R3 controller It is a simple integrated development environment (IDE) and open source microcontroller which is programmed by writing programs for Arduino in C or C++ language [19]. After installed and programmed Arduino which is ready to read a different type of inputs like lights on a sensor, a finger on a button, publishing something online. But this project, the Arduino UNO board was used as voice recognition module. C. Bluetooth module (HC-05) It is a popular communication device in short range which is used on cell phones, computers, headphones, and other devices. Bluetooth devices use short distance communication frequency within 2.4–2.5 GHz [7]. It provides a channel between voice recognition modules to Arduino [20, 21]. D. Light Sensor This sensor consists of a light-dependent resistor (LDR) and photodiode that measuring the output signal of the intensity of light. It ranges in frequency from infrared to ultraviolet light spectrum. The zones of the light lamp regularly update the varying brightness level of the smart LED through a light sensor [22]. Due to this data, making aware the remote user of the fluorescence level is controlled by the user input level to adjust its power and conserving energy level on a proper plan basis. E. Temperature Sensor The temperature sensor is usually a resistance temperature detector (RTD) that collects data about the temperature of the light lamp which is fixed with a printed circuit board (PCB). When the safe point of temperature exceeds, this information is wirelessly transferred to the supervisor to maintain real-time server. F. Power Metering Sensor The regular monitoring of the power utilization and temperature record of lights is properly observed through the power metering sensor. It is also indicating power failure due to any crash as well as a proper diagnostic feature of power monitoring. The setup mechanism is so faster and easy to control over a long distance or through satellite. G. ZigBee Module

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Fig. 3 Components of proposed system

This module uses of radio frequencies like 2.4 GHz that can communicate between some devices. It is used open communication standard to wirelessly through IP layer that is known as IEEE 802.14.4. The development of the proposed system is built on the IoT modules and sensors as shown in Fig. 3.

4 Network System Model and Proposed System In the proposed network, the system is used on the ZigBee level computerized outline to design LED lights with the help of a connectionless mesh network. But it is based on two standard layers declared as physical (PHY) and medium access control (MAC) in IEEE 802.15.4 protocol. The network voluminous is specially implemented for monitoring remote control and potentiality of response timing through designing a graphical user interface (GUI) for a proper basis of the energy management system (EMS) in use of TCP/IP protocol as represented in Fig. 4. In this network model, the ZigBee gateway is remotely noticed and controlled the aspects of smart loads. So this node provides a connection interface across a ZigBee network to any other network as well as carry out protocol conversion in it. However, the interconnectivity between different network categories is provided a channel for message conversion

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ZigBee Node

ZigBee Gateway

Application Layer

Application Services ZigBee Gateway, Protocols

Transport Layer

TCP

Network Layer MAC Layer Physical Layer

Cloud

App Support/Routing Network Access

IP

Computing

Eth ern et

Wi Fi

MAC PHY/PHY Radio

Fig. 4 Network system model

into the mesh network of ZigBee and TCP/IP networks through the architecture of ZigBee gateway. This network model is configuring the ZigBee node by using of smart LED lights as a router of ZigBee, which makes the whole system trustable and powerful by meshing together various ZigBee nodes. Here, the message translation of ZigBee gateway in two basic formats: 1. Obtains data records from use sensors the ZigBee packet converts to packet format of the Internet before transmitting messages through the Internet. 2. The incoming message of gateway manages the conversion of the ZigBee packet set out via distant users from the distinct stations of LED lights or lamps. The objective of the project also shows that it is flexible to successfully control real-world objects through voice commands, which avoids the need to manually click operation within the system as depicted in Fig. 5. The main purpose of this architecture can be focused on Bluetooth connection interface which can construct the system at a low cost. The user first needs to send voice command through the voice app, that is smartly operating the system.

4.1 Working Principle Voice app interprets the commands as well as the program implements Google’s voice recognition software to convert voice signal to text, then it will be sent through a component of Bluetooth to the receiver section. The main components are the Arduino UNO R3 and HC-05 Bluetooth [23]. The Arduino UNO board has serial communication connectivity with SPI, 12C, and UART, which will operate at 16 MHz

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Fig. 5 Proposed system for smart street lighting and traffic

Table 1 Voice command actions

Voice command

Action

Red

Enable red light signal (LED)

Green

Enable green light signal (LED)

Yellow

Enable yellow light signal (LED)

Stop

Disable all signals

clock speed. The voice command signals are forwarded via Bluetooth with the voice controller app [20]. Table 1 shows the voice commands controlled to traffic signals. All commands are received on the traffics system by connecting with the Bluetooth module setup on digital I/O pins of 0 and 1 of the Arduino board. Similarly, the Arduino digital I/O pins 6, 7, and 8 are programmed as output pins in this architecture. This smart traffic system circuitry is activated by the usage of a 9 V rechargeable battery attached to it [22]. The Arduino UNO R3 programmed is presented in Fig. 6a and voice-controlled traffic system is depicted in Fig. 6b. The Bluetooth module (HC-05) is successfully paired and then runs the voice controller app on the mobile phone. When it is opened, voice commands are accepted through a voice app, which translates into textual form and sends it to Bluetooth. In receiving end, Arduino UNO is programmed in a specific order that is supposed to run the received text. If that text is matched, then it smartly controls traffic signals as well as the street signals. Whenever any unavoidable situation (slow in the process) occurred at that time, need to shut down (close) or restart the process that mentions in the flowchart.

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Fig. 6 a Arduino UNO R3 programmed section of voice-controlled traffic system and b flowchart of voice-controlled traffic system

5 Results Analysis Voice-controlled smart traffic light system is connected with the Bluetooth module after that command sends to the Arduino control unit. The voice command can be forwarded as input via Google’s speech recognition app to manage the system. Figure 7a shows the Bluetooth is successfully paired to the Bluetooth module. When the voice command like red is passed to Arduino via voice recognizer app, the command is processed in the microcontroller unit of the Arduino board as depicted in Fig. 7b. Finally, the signal is matched then the red light LED will be enabled. If the red signal active, all vehicles should have to wait for the ready signal. Similarly, after receiving the yellow voice command, the system is enabled the yellow LED instead of the red of traffic signal. That means it warns to ready but waits for the green signal which is depicted in Fig. 8a. Finally, receiving the green voice command is allowed to go all vehicles to their destination without any other signal. So, all vehicles should wait for this signal for movement and proceed further, which is depicted in Fig. 8b. This purposed system is fully deactivated after receiving the stop

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Fig. 7 a System status connected and b voice-controlled smart traffic light system (received ‘Red’ command)

Fig. 8 a Voice-controlled smart traffic light system (received ‘Yellow’ command) and b voicecontrolled smart traffic light system (received ‘Green’ command)

voice command through a mobile voice recognizer application. Voice-controlled smart traffic light system (received ‘Stop’ command) stops its operation after the command executed.

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6 Conclusion and Future Scope The integration of voice recognition in this project, it was specially designed for a smart city to successful automation of traffic light manipulation process. So, this work vitally focuses on the Smart Traffic management system using voice commands that will remove the drawbacks of the existing system such as dependency on environment circumstances as well as implementation cost. It aims at an effective way to manage traffic congestion and very cost effective than the existing processes. This system provides an environment to transmit commands to automate the whole process without the need to purchases any other expensive gadgets. In this purposed work, we used a limited power source, and energy sources can be replaced by the implementation of solar cells. This system was covered within a short range due to the use of Bluetooth or ZigBee gadget (range 100 m), but it may be linked through long-distance coverage module. The proposed system can be enhanced by implementing any powerful communication network to fast processing of commands. Further work might show that improving security by advanced authentication schemes, with the help of a machine learning algorithm.

Reference 1. Zhang T, Lv C, Li L, Chen S, Liu S, Wang C, Su B (2013) Plasma miR-126 is a potential biomarker for early prediction of type 2 diabetes mellitus in susceptible individuals. BioMed Res Int 2. Systematics, Cambridge. Traffic congestion and reliability: Linking solutions to problems. No. FHWA-HOP-05-004. United States. Federal Highway Administration, 2004 3. Bayani M, Segura A, Alvarado M, Loaiza M (2018) IoT-based library automation and monitoring system: developing an implementation framework of implementation. E-Ciencias de la Información 8(1):83–100 4. Ghazal B et al (2016) Smart traffic light control system. In: 2016 third international conference on electrical, electronics, computer engineering and their applications (EECEA). IEEE 5. Linganagouda R, Raju P, Patil A (2016) Automatic intelligent traffic control system. Int J Adv Res Electr Electron Instrument Eng 5(7): 5902–5906 6. Rath M, Pati B, Pattanayak BK (2019) Mobile agent-based improved traffic control system in VANET. In: Integrated intelligent computing, communication and security. Springer, Singapore, pp 261–269 7. Javaid S et al (2018) Smart traffic management system using internet of things. In: 2018 20th international conference on advanced communication technology (ICACT). IEEE 8. Bhole PR et al Voice command based robotic vehicle control. IJRASET. ISSN: 2321-9653 9. Rath M (2018) Smart traffic management system for traffic control using automated mechanical and electronic devices. IOP Conf Ser Mater Sci Eng 377(1) 10. Maddileti T, Jammigumpula M, Jagadish Kumar H, Sai Sashank KV (2019) Voice controlled car using Aurduino and Bluetooth module. Int J Eng Adv Technol IJEAT 9(2):1062–1065 11. Chavan B, Jadhav D, Atar S, Kadam S (2019) Voice controlled machineries in agricultural field using Raspberry Pi 12. Mporas I, Ganchev T, Kostoulas T, Kermanidis K, Fakotakis N (2009) Automatic speech recognition system for home appliances control. In: 2009 13th Panhellenic conference on informatics. IEEE, pp 114–117

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13. Halder R, Sengupta S, Ghosh S, Kundu D (2016) Artificially intelligent home automation system based on Arduino as the master controller. Int J Eng Sci IJES 5(2):41–45 14. Mostaque SK, Karmakar B (2016) Low cost arduino based voice controlled pick and drop service with movable robotic arm. Euro J Eng Res Sci 1(5):29–33 15. Munteanu D, Ionel R (2016) Voice-controlled smart assistive device for visually impaired individuals. In: 2016 12th IEEE international symposium on electronics and telecommunications (ISETC). IEEE, pp 186–190 16. Hasan MM et al (2014) Smart traffic control system with application of image processing techniques. Int Conf Inform Electron Vision ICIEV IEEE 17. Priyadarshini SB, Singh D, Panda M (2019) A comparative study of redundant data minimization and event coverage in wireless multimedia sensor networks (WMSNs). In: International conference on applied machine learning (ICAML). IEEE, pp 252–257 18. Mohanty NP, Singh D, Hota A, Kumar S (2019) Cultivation of cash crops under automated greenhouse using internet of things (IoT). In: 2019 International conference on communication and signal processing (ICCSP). IEEE, pp 0235–0239 19. Neha S, Parvez M, Fatima NM, Marturkar R (2017) Arduino based voice controlled home appliances using Bluetooth 20. Megalingam RA et al (2011) Smart traffic controller using wireless sensor network for dynamic traffic routing and over speed detection. 2011 IEEE global humanitarian technology conference. IEEE 21. Rai N et al (2016) Bluetooth remote controlled car using Arduino. Int J Eng Trends Technol 33:381–384 22. Kanungo A, Sharma A, Singla C (2014) Smart traffic lights switching and traffic density calculation uses video processing. In: 2014 recent advances in engineering and computational sciences (RAECS). IEEE 23. Khan AU, Ratha BK ()Time series prediction QoS routing in software defined vehicular ad-hoc network. In: 2015 international conference on man and machine interfacing (MAMI). IEEE, pp 1–6

Unsupervised Reduced Deep Convolutional Neural Network of Process Empirical Wavelet Transform Data for Recognition of the Early Stage of Alzheimer’s Disease Bhanja Kishor Swain, Susanta Kumar Rout, Mrutyunjaya Sahani, and Renu Sharma Abstract The diagnosis of Alzheimer’s disease (AD) at the prodromal state has been a challenge in the field of healthcare. In this work, the classification of AD, mild cognitive impairment (MCI), and healthy controls (HC) is proposed on the basis of empirical wavelet transform (EWT) and a novel reduced deep convolutional neural network (RDCNN) by using the structural magnetic resonance imaging (MRI) scans. The multi-resolution analysis of the MRI scans are performed by decomposing the digital images by applying the two-dimensional empirical wavelet transform (2D-EWT). The architecture of the proposed RDCNN classifier is developed for automatic extraction of discriminative features from the input magnetic resonance imaging scans. The performance of the classification of AD, MCI, and HS of EWTRDCNN and RDCNN are compared on the basis of the classification accuracy and learning speed taking the MRI scans as input. The performance of the proposed 2D-EWT-RDCNN classifier in terms of minimum cross-entropy loss and superior classification accuracy is better than RDCNN both in noisy and noise-free conditions. Keywords Alzheimer’s disease (AD) · Empirical wavelet transform (EWT) · Magnetic resonance imaging (MRI) · Reduced deep convolutional neural network (RDCNN)

B. K. Swain (B) · S. K. Rout · M. Sahani · R. Sharma ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] S. K. Rout e-mail: [email protected] M. Sahani e-mail: [email protected] R. Sharma e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_5

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1 Introduction The development of medical science and the discovery of many healthcare facilities have reduced the mortality rate of human beings worldwide resulting in the long life of people in most of the countries and as a consequence a significant increase in the aging population. Furthermore, as a significant portion of population have elderly people, they are at a high risk of developing dementia. Recently, a study reveals that around 47 million of people in different countries have been living with dementia, and this number may increase to approximately 131 million by 2050 [1]. The most common form of dementia usually observed in case of elderly people is Alzheimer’s disease (AD) which causes permanent damage to the brain cells. AD is an irreversible, progressive neurological brain disorder which affects the usual function of brain cells causing partly or full memory loss and thinking ability of a patient resulting in the human cognitive decline [2]. Although it is not clearly known about the reason of AD, the pathological changes such as the accumulation of amyloid plaques and neurofibrillary tangles in the brain and the loss of cortical neurons and synapses are the main reason of AD [3]. The AD patient at the initial stage of the disease loses the memory partly and that becomes worse with respect to time causing the patient to be completely dependent on the care taker. It becomes a difficult job for a care taker to take care of the AD patients, and the cost of the treatment is also very high. When the AD progresses to its next level, the problem compounds and the heart functionality and breathing gets affected adversely because of improper function of brain and finally that leads to the death of the patient. Therefore, it is very important to detect the AD at its early stage, so that proper care can be taken and the speed with which the disease progresses can also be managed and slowed down. Computer-aided detection methods are very helpful in the diagnosis of AD which avoids the cumbersome method of manual detection and also faster and accurate than the primitive methods. Unlike computer tomography (CT), the magnetic resonance imaging (MRI) provides higher quality structural information about the brain, and numerous scientists have demonstrated enthusiasm for examining the MRI information of brain. It gives pervasive fragile tissue differentiation, high spatial resolution, and better contrast and can even recognize little anomalies in the brain [4]. Moreover, the investigative usage of MRI has been colossally improved due to the automated and accurate naming of MRI scans, which plays out a noteworthy activity in recognizing AD patients from healthy controls (HC) [5]. In the literature, AD diagnosis at its various stages using structural magnetic resonance imaging has proved its effectiveness because of the capability of MRI to present the visualization of brain anatomy [6]. Most of the studies are based on the extensive analysis of the cross-sectional MRI data from one single time point. These analyses are mainly associated with the features extracted based on gray matter [7], cortical thickness [8], and shape and volume measurement of hippocampus [9, 10]. Although the aforementioned methods of analyzing structural abnormality of brain produce good results, but they depend upon the regional measurement of brain anatomy of a predefined region of interest (ROI), and the disadvantage of ROI method

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is that the prior knowledge of abnormal regions in the brain is required, which may not be available always. Recently, a few researchers studied the longitudinal MRI data and concluded that the analysis of spatial structural abnormalities and the longitudinal variations of tissues provide good result in the diagnosis of AD [11–13]. However, the longitudinal data-based analysis is time consuming as it requires nonlinear registration and tissue segmentation. Moreover, the longitudinal data may not cover all the morphological abnormalities of the brain and also show inconsistency of scans across subjects. Many researchers are focusing on the group comparison methods [14, 15] to automatically differentiate the anatomical aberration between a healthy control (HC) and an AD patient. For instance, voxel-based morphometry (VBM) [16] method is used to identify the group difference by warping the MRI scans to the template image using a nonlinear registration method. After the registration of individual scans, VBM method is used to compare the brain scans on a voxel basis. Other than VBM, several brain morphometric analysis methods are developed within the spatial normalization networks to classify the AD patients from the healthy controls. Deformation-based morphometry (DBM) [17] is used to identify relative shape differences between group of individual brain scans [18, 19]. TBM methods identify the difference of brain structures by utilizing the Jacobian of deformation fields. For instance, the classification of AD and MCI patients is performed by using TBM on the basis of brain atrophy in MRI scans [20]. More recently, researchers are using advanced signal processing techniques to extract meaningful features from the MRI scans and are using various neural networkbased classifiers to categorize the AD patients from the healthy controls. Lahmiri et al. [21] applied multi-resolution techniques such as discrete wavelet transforms (DWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD) to extract fractal features and trained an SVM with the extracted features to categorize the normal controls from the patients subjected to AD. In [22], the authors have used DWT to extract the meaningful features and trained a reduced deep convolutional neural network (RDCNN) to classify the MCI patients from healthy controls and achieved significantly competent results. In this paper, first, the multi-class classification of AD, MCI, and HC are performed by a novel reduced deep convolutional neural network (RDCNN) by automatically extracting the discriminative features from the brain MRI scans. Second, twodimensional empirical wavelet transform-based reduced deep convolutional neural network (2D-EWT-RDCNN) is proposed in which the multi-resolution analysis of the MRI scans is performed by decomposing the digital images by applying the empirical wavelet transform. The performance of the classification of AD, MCI, and HS of 2D-EWT-RDCNN and RDCNN is compared on the basis of the classification accuracy and learning speed by considering the scanned digital images of brain as input. Finally, the performance of the proposed 2D-EWT-RDCNN classifier in terms of minimum cross-entropy loss and superior classification accuracy is proved to be better than RDCNN method both in noisy and noise-free conditions. The organization of the remaining part of the article is as follows, a brief explanation of the dataset is mentioned in Sect. 2, Sect. 3 introduces the mathematical base

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of two-dimensional EWT, the proposed RDCNN classifier is presented in Sect. 4, discussion of the results obtained are in Sect. 5, and Sect. 6 concludes the article.

2 Materials and Methods Alzheimer’s disease has different stages of its severity, and they are most frequently represented by the clinical dementia rating (CDR) with individual numerical values for normal control (NC) subjects otherwise called as healthy controls (HC), very mild, mild, moderate, and severe stage of Alzheimer’s disease. The CDR score for the aforementioned stages are 0, 0.5, 1, 2, 3 for HC, very mild, mild, moderate, and severe, respectively, which indicates a higher numerical value for more severity of the disease. Similarly, mini-mental state examination (MMSE) score is another parameter to check the cognitive impairment of a subject, and it has a maximum value of 30. This examination is not used for diagnosis, but it indicates the presence of cognitive impairment such as dementia. The answers of the subject are evaluated out of 30, a score between 25 and 30 is considered as normal, 21–24 belongs to mild AD, 10–20 belongs to moderate AD, and a score of less than 10 indicates the severe impairment stage of a subject. In this work, the MRI scans used for analyzing the proposed algorithms are collected from an openly available dataset called as Open Access Series of Imaging Studies (OASIS) from https://www.oasis-brains.org. The OASIS dataset contains a compilation of MRI scans, and it is openly available to study on the Alzheimer’s disease by scientists and researchers. The OASIS dataset has three types of data, namely OASIS1, OASIS2, and OASIS3, and in this paper, the cross-sectional brain MRI scans of OASIS1 dataset is used. This dataset contains T1 weighted single scans of 416 subjects within an age group of 16–96. In our proposed work, 196 samples are selected containing 98, 70, 28 samples of different classes such as normal control, very mild cognitive impairment, and MCI, respectively. Figure 1 depicts the visual difference between the cross-sectional MRI scans of two classes, MCI and the normal control. Table 1 represents the statistical information of the selected 196 subjects.

3 Feature Extraction Many researchers have considered the discrete wavelet transform (DWT) as a very useful tool to analyze the non-stationary and complicated signals [23, 24], but the empirical wavelet transform (EWT) combines the usefulness of Fourier analysis and the wavelet theory with a rigorous theorical background for a better consistent decomposition of non-stationary and complicated signals. The input MRI scans I (t) are decomposed to extract different frequency content information by using adaptive wavelet filter bank. The Fourier transform is applied to each column (or row) in orthonormal basis to compute the mean Fourier spectrum

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Fig. 1 Selected cross-sectional MRI scan of a healthy control and b MCI

Table 1 Statistical data of selected 196 subjects

Factors

Healthy controls

Very mild cognitive impairment

Mild cognitive impairment

No. of patients

98

70

28

Age

75.91 ± 8.98

74.87 ± 7.64

77.75 ± 6.99

CDR

0

0.5

1

MMSE

28.95 ± 1.20

27.28 ± 1.71

21.67 ± 3.75

Gender (M/F)

26/72

29/41

9/19

I (ω) in a range [0, π ]. The Fourier support of I (ω) is used to find the center frequency based on N number of maxima within the compact frequency support of width 2τn . The boundary detection method is used to obtain N contiguous segments   to build the wavelet filter bank with boundary frequencies [0, 1 ], [1 , 2 ], . . .  N −1, π . The scaling function Sn (ω) and wavelet function Wn (ω) are defined as ⎧ ⎪ ⎨ 1,  if |ω| ≤ ωn − τn π 1 Sn (ω) = cos 2 α( 2τn (|ω| − ωn + τn )) , if ωn − τn ≤ |ω| ≤ ωn + τn ⎪ ⎩ 0, otherwise

⎧ 1,  ⎪ ⎪ ⎪ ⎪ ⎨ cos π α( 1 (|ω| − ωn+1 + τn+1 ,  2 2τn+1 Wn (ω) = π α( 1 (|ω| − ω + τ )) , ⎪ sin ⎪ n n ⎪ 2 2τn ⎪ ⎩ 0,

(1)



if ωn + τn ≤ ω ≤ ωn+1 − τn+1

if ωn+1 − τn+1 ≤ |ω| ≤ ωn+1 + τn+1

if ωn − τn ≤ |ω| ≤ ωn + τn otherwise

(2)

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The arbitrary function Sn (ω) in the tight form is defined as  α(x) =

0, if I ≤ 0 and α(I ) + α(1 − I ) = 1∀I ∈ [0, 1] 1, if I > 1

(3)

The detail coefficients D(n, t) are computed based on the inner product of scaling coloum function Sn (ω) and Snrow (ω) as follows D(n, t) = I, Sn  =

I (τ ).Sn (τ − t)dτ

(4)

Similarly, the approximate coefficients A(0, t) are computed by the inner product coloum of wavelet function Wn (ω) and Wnrow (ω) as follows A(0, t) = I, ωn  =

I (τ ).ωn (τ − t)dτ

(5)

So, total (Nc +1) with (N R +1) dimensions of images as BBLIMFs are generated as shown in Fig. 2, using 2D-EWT for importing to RDCNN as inputs.

4 The Reduced Deep Convolutional Neural Network (RDCNN) The artificial neural networks (ANN) have been consistently showing unassailable performances in the various fields of classification and pattern recognition. Neural networks have the ability to learn from the examples and develop the learning ability to apply in predicting the output. The loss of data does not significantly affect the output of the neural network as the network can perform its task if some information is missing or even a neuron is not responding. Neural network is applied in various fields such as image processing, speech processing, and forecasting. Although the artificial neural network is producing satisfactory results in the field of classification and pattern recognition but especially for two dimensional data such as digital images the artificial neural network is receptive to shift variation and translation. The deep convolutional neural network solves the problem of shift variation and translation by extracting the powerful features from the input digital images by the application of several filters of different sizes. The architecture of the proposed RDCNN classifier is consisting of four important layers: (1) convolutional layer, (2) pooling layer, (3) fully connected layer, and (4) softmax layer.

Unsupervised Reduced Deep Convolutional Neural Network …

(a) MCI sample

(b) BBLIMF 1

(c) BBLIMF 2

(d) BBLIMF 3

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Fig. 2 Three BBLIMFs of the 2D-EWT of a selected MCI sample

4.1 Convolutional Layer This is the most important portion of the proposed RDCNN which is responsible for feature extraction. In this layer, the powerful features from the input MRI scan are extracted automatically, and the convolution operation is performed by sliding the preselected filters over the input MRI scans. The convolution operation results in the feature map [25] and Eq. 6 mathematically defines its output. Cm =

N −1

n=0

f n km−n

(6)

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where Cm represents the computed m th output element, k represents the signal, and f stands for the filters. A common concern of a convolutional neural network is to reduce nonlinearity and to increase the speed of learning. To expedite the process of learning with reduced nonlinearity, rectified linear unit (ReLU) activation function is used. The thresholding operation is performed on the values of feature map with the help of rectified linear unit activation function. Equation 7 defines the ReLU function with an indication of the thresholding operation  f (r ) =

R; r ≥ 0 0; Otherwise

(7)

The over fitting problem is addressed by employing the batch normalization operation on the output of ReLU activation function.

4.2 Pooling Layer To extract the meaningful features, the data redundancy must be taken care of so that the burden on the network will be less. Therefore, pooling operation is performed after one set of convolution operation to minimize the amount of required resources. The proposed architecture of RDCNN employs the max pooling operation to preserve the meaningful and discriminative features by reducing the dimension of the activation map.

4.3 Fully Connected Layer Fully connected layer has all its neurons between two consecutive layers and are connected with each other. The normalized discriminative features are fed to the fully connected layer, and the output equation of a fully connected layer is given as follows.

w ji y j + bi (8) xi = j

where w and b are the weights and biases vectors, x is the output of current layer, and y is the output of previous layer.

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4.4 Softmax Layer The output of last fully connected layer is fed to a softmax classifier which uses the probability density function to predict the class of an input signal. The prediction value of softmax function lies within zero to one. Equation 9 is the mathematical representation of softmax activation function. e xi Pi = k xi me

(9)

where the prediction of an input xi to a particular class is represented by Pi .

5 Result and Discussion A two-dimensional empirical wavelet transforms (2D-EWT)-based reduced deep convolutional neural network (RDCNN) is used to classify AD patients from MCI, very mild AD, and healthy controls (HC). The reported research work is performed in MATLAB/Simulink software environment using a processor of Intel core (R) with a RAM of 16 GB and with a processor speed of 2.6 GHz, and verified by using the OASIS1 dataset for the automatic classification Alzheimer’s disease subjects. The proposed classifier automatically extracts the discriminative features from the input MRI scans of the subjects, and it is presented with details in Table 2. The proposed architecture of RDCNN is shown in Fig. 3, where each set of convolution layer is consisting of convolution, ReLU, and batch normalization operations followed by two-stride controlled max pooling operation. Three sets of convolution layers are followed by a softmax layer based on probability function to categorize MCI, very mild AD, and HC. To address the challenge of overfitting, each set of convolution layer is followed by max pooling layer. In the RDCNN method, the MRI scans of OASIS1 dataset are directly given as input, and the proposed network is trained with minimum training loss and maximum classification accuracy as shown in Fig. 4. Further in 2D-EWT based RDCNN to enhance the performance of the classifier for the proper recognition of MCI, very mild AD, and HC, the BBLIMFs of the MRI scans are fed to the RDCNN. In both the proposed methods for training and testing purpose, 70% and 30% of the sample are selected, respectively. It is observed that the total run time for 2D-EWT-RDCNN is more in comparison with RDCNN, but better classification accuracy, minimum training and testing loss, and anti-noise performance are the major advantages of EWT-RDCNN method over RDCNN method. The performance comparison of both the methods are presented in table 3. Finally, Table 3 provides the necessary information about the robustness of the proposed 2D-EWT-RDCNN in classifying the prodromal stage of AD, called as MCI.

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Table 2 Detailed architecture of RDCNN Layer

Name

Output nodes

0–1

Convolution

[204 × 172 × 1] × 5

Filter size

Stride

1–2

Rectified linear unit

[204 × 172 × 1] × 5

1

1

2–3

Batch normalization

[204 × 172 × 1] × 5

10

10

3–4

Max pooling

[102 × 136 × 1] × 5

2

2

4–5

Convolution

[93 × 127 × 1] × 10

10

1

5–6

Rectified linear unit

[93 × 127 × 1] × 10

1

1

6–7

Batch normalization

[93 × 127 × 1] × 10

10

10

7–8

Max pooling

[47 × 64 × 1] × 10

2

2

8–9

Convolution

[33 × 50 × 1] × 15

15

1

9–10

Rectified linear unit

[33 × 50 × 1] × 15

1

1

10–11

Batch normalization

[33 × 50 × 1] × 15

10

10

11–12

Max pooling

[17 × 25 × 1] × 15

2

2

12–13

Fully connected

6375 × 1

0

0

13–14

Fully connected

3000 × 1

14–15

Fully connected

1000 × 1

15–16

Fully connected

100 × 1

0

0

16–17

Fully connected

3×1

0

0

5

0 0

1

0 0

Fig. 3 Architecture of the proposed unsupervised RDCNN classifier

6 Conclusion The multi-class classification of MCI, very mild AD, and HC are performed by automatically extracting the powerful features from the MRI scans using the proposed RDCNN classifier. The multi-resolution analysis of the MRI scans are performed by

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65

Fig. 4 Performance of the proposed RDCNN classifier

Table 3 Performance comparison of the proposed methods in noisy and noise-free conditions Proposed model

Noise-free condition

SNR values 20 dB

30 dB

40 dB

RDCNN

95.2

90.3

92.1

94.2

2D-EWT-RDCNN

98.9

96.2

97.4

97.9

decomposing the digital images by applying the empirical wavelet transform. The extracted discriminative features are fed to the softmax layer to classify the MRI scans using the probability density function. The performance of the classification of MCI, very mild AD, and HS of 2D-EWT-RDCNN and RDCNN is compared by considering the noisy as well as noise-free input MRI scans in terms of learning rate and classification accuracy. Finally, in terms of classification accuracy and learning speed, the 2D-EWT-RDCNN method outperforms the RDCNN method both in noisefree and noisy conditions.

References 1. Morrison B, Phillips BN, Jones JE, Przybelski R, Huck G (2019) The impact of risk and resistance factors on quality of life in caregivers of individuals with dementia. Clin Gerontol:1– 13 2. Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R, Feng D, Fulham MJ (2014) Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans Biomed Eng 62(4):1132–1140

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3. Kang Y, Escudero J, Shin D, Ifeachor E, Marmarelis V (2015) Principal dynamic mode analysis of EEG data for assisting the diagnosis of Alzheimer’s disease. IEEE J Trans Eng Health Med 3:1–10 4. Mahanand BS, Suresh S, Sundararajan N, Aswatha Kumar M (2012) Identification of brain regions responsible for Alzheimer’s disease using a self-adaptive resource allocation network. Neural Netw 32:313–322 5. Jeurissen B, Leemans A, Sijbers J (2014) Automated correction of improperly rotated diffusion gradient orientations in diffusion weighted MRI. Med Image Anal 18(7):953–962 6. Frisoni GB, Fox NC, Jack CR, Scheltens P, Thompson PM (2010) The clinical use of structural MRI in Alzheimer disease. Nature Rev Neurol 6(2):67–77 7. Liu M, Zhang J, Yap P-T, Shen D (2017) View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data. Med Image Anal 36:123–134 8. Querbes O, Aubry F, Pariente J, Lotterie J-A, Démonet J-F, Duret V, Puel M et al (2009) Early diagnosis of Alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain 132(8):2036–2047 9. Gerardin E, Chételat G, Chupin M, Cuingnet R, Desgranges B, Kim H-S, Niethammer M et al (2009) Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage 47(4):1476–1486 10. Liu F, Zhou L, Shen C, Yin J (2013) Multiple kernel learning in the primal for multimodal Alzheimer’s disease classification. IEEE J Biomed Health Inform 18(3):984–990 11. Yau W-YW, Tudorascu DL, McDade EM, Ikonomovic S, James JA, Minhas D, Mowrey W et al (2015) Longitudinal assessment of neuroimaging and clinical markers in autosomal dominant Alzheimer’s disease: a prospective cohort study. Lancet Neurol 14(8):804–813 12. Landin-Romero R, Kumfor F, Leyton CE, Irish M, Hodges JR, Piguet O (2017) Disease-specific patterns of cortical and subcortical degeneration in a longitudinal study of Alzheimer’s disease and behavioural-variant frontotemporal dementia. Neuroimage 151:72–80 13. Grassi M, Loewenstein DA, Caldirola D, Schruers K, Duara R, Perna G (2019) A clinicallytranslatable machine learning algorithm for the prediction of Alzheimer’s disease conversion: further evidence of its accuracy via a transfer learning approach. Int Psychogeriatr 31(7):937– 945 14. Hinrichs C, Singh V, Mukherjee L, Guofan Xu, Chung MK, Johnson SC, Initiative ADN (2009) Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage 48(1):138–149 15. Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert M-O, Chupin M, Benali H, Colliot O, and Alzheimer’s Disease Neuroimaging Initiative (2011) Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56(2):766–781 16. Ashburner J, Friston KJ (2001) Why voxel-based morphometry should be used. Neuroimage 14(6):1238–1243 17. Gaser C, Nenadic I, Buchsbaum BR, Hazlett EA, Buchsbaum MS (2001) Deformation-based morphometry and its relation to conventional volumetry of brain lateral ventricles in MRI. NeuroImage 13(6):1140–1145 18. Teipel SJ, Born C, Ewers M, Bokde ALW, Reiser MF, Möller H-J, Hampel H (2007) Multivariate deformation-based analysis of brain atrophy to predict Alzheimer’s disease in mild cognitive impairment. Neuroimage 38(1):13–24 19. Lau JC, Lerch JP, Sled JG, Mark Henkelman R, Evans AC, Bedell BJ (2008) Longitudinal neuroanatomical changes determined by deformation-based morphometry in a mouse model of Alzheimer’s disease. Neuroimage 42(1):19–27 20. Hua X, Hibar DP, Ching CRK, Boyle CP, Rajagopalan P, Gutman BA, Leow AD et al (2013) Unbiased tensor-based morphometry: improved robustness and sample size estimates for Alzheimer’s disease clinical trials. Neuroimage 66:648–661 21. Lahmiri S (2016) Image characterization by fractal descriptors in variational mode decomposition domain: application to brain magnetic resonance. Phys A 456:235–243

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22. Swain BK, Sahani M, Sharma R (2020) Automatic recognition of the early stage of Alzheimer’s disease based on discrete wavelet transform and reduced deep convolutional neural network. In: Innovation in Electrical Power Engineering, Communication, and Computing Technology. Springer, Singapore, pp 531–542 23. Sahani M (2019) Detection and classification of power quality events using empirical wavelet transform and error minimised extreme learning machine. Int J Power Energy Convers 10(4) 24. Mishra M, Rout PK, Routray P (2015) High impedance fault detection in radial distribution system using wavelet transform. In: Annual IEEE India conference (INDICON). IEEE, pp 1–6 25. Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imag 35(5):1299–1312

Auxiliary Dynamic Damping Loop in the Microgrid for Enhancing Frequency Recovery Rate Pritam Bhowmik

and Pravat Kumar Rout

Abstract With the increasing penetration rate of the renewable energy sources in the microgrid, the state-of-stability (SoS) in the system is decreasing. The deep-down issue is rooted in the loss of inertia. Eventually, in recent time, the research in the field of virtual inertia emulation schemes has gained a lot of momentum. The virtual inertia can potentially improve the SoS in the microgrid. However, another major concern in the static generator-based microgrid, the frequency recovery rate, cannot be improved through virtual inertial support. Therefore, concerned with this issue, an auxiliary dynamic damping loop (ADDL) has been introduced in the study to work synchronously with the existing virtual inertial loop. The performance of the ADDL-based scheme has been evaluated in the ARM Cortex A-72 processor-driven prototype hardware model and found a significant improvement. Keywords Microgrid · Frequency recovery · Virtual inertia · Virtual damping · Fuzzy · HDL coding · ARM cortex

1 Introduction The concept of microgrid was first evolved to integrate multiple numbers of smallcapacity-distributed generators nearer to the distributed local loads in a closed-loop circuit. The kind of distributed generators considered in a microgrid is mostly renewable in nature [1]. Moreover, most of the renewable power generators are static in nature [2]. Therefore, these kinds of sources do not offer any rotational inertia to the network. Moreover, having converter integrated topology, the only potential source of rotational inertia, the wind generators also lack to provide any kind of inertial P. Bhowmik (B) Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] P. K. Rout Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_6

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support to the network [3]. As a result, the microgrid is considered as a non-inertial system. From the conceptualization period of the grid, alternators are used to operate in parallel to share the total load demand. An alternator can characteristically operate in parallel and share power according to its leakage reactance without demanding any reference signal [4]. Electrically, the amount of power sharing can be regulated in an alternator by regulating the leakage reactance externally [5]. Furthermore, by regulating the governor setting, the input mechanical power to the alternator can be adjusted. Thus, the amount of power sharing can be regulated precisely [6]. However, to achieve satisfactory parallel operation of converters, conveniently, individual powerreference signals are required [7]. As the response time of any converter is much faster than an alternator, any parallel converter-based topology demands high-speed computation of reference power in the real time. Conventionally, the reference power used to be computed based upon the relative measurements. The relative measurement-based approach demands for high-speed communication among the converters present in a network [8]. The establishment cost of any high-speed communication channel is huge. As a result, the capitalization cost of the microgrid becomes significantly large. On the other hand, the system awake time becomes significantly subjected to communication failure [9]. As a substitute, the concept of static droop has been developed which can perfectly emulate the characteristics of a mechanical governor droop [10]. Fundamentally, in static droop-based approach, the reference power signal is generated computing the locally measured signals. Therefore, in this topology, the parallel operation of a converter becomes independent of global measurements [11]. Thus, the static droop-based topology rides through the hazard involved in the communicationbased topologies and potentially offers low-capital-cost-based microgrid with superior awake time. In the article [12], the authors have compared the performance of majorly three categories of the static droop, i.e., power–frequency, power–voltage, virtual impedance-based droopings. The integration of the distributed static compensator through power–frequency droop has been realized in the article [9]. As a powerdense storage element, the supercapacitor has been regulated locally in a microgrid through power-voltage-based drooping concept in [1]. The effect of unequal line impedance on the drooping scheme has been resolved through establishing the concept of virtual impedance-based modified droop has been introduced in the article [11]. In the aforesaid article, the power-sharing between two topologically diverged photovoltaic units has been realized through the same concept. Furthermore, on modifying the principle of static droop, the state-of-charge management scheme has been developed in an article [2, 5]. Extending the study further, the issues related to the transient power imbalance has been resolved by undertaking one static-droop integrated flywheel-based high power-dense storage unit in the article [13]. However, the static droop-based principle starts facing technical challenges with the fast penetrating rate of renewable sources in the microgrid. Being mostly static generators, renewable sources dramatically reduce the short-circuit capacitance of

Auxiliary Dynamic Damping Loop …

71

the microgrid. Therefore, the microgrid becomes highly sensitive toward any uncertainty in the network. The deep-rooted problem lies behind the decaying short-circuit capacitance in the loss of rotational inertia in the microgrid [14]. Eventually, the renewable generation-dominated conventional microgrid is often referred to as noninertial network (N-IN). Though the well-established static droop-based approach partially offers rotational inertia in the network from the wind generator, but, the transient improvement through this inertia is negligible [15]. As a result, the concept of virtual inertia has earned the momentum in the field of the research of the microgrid. According to the principle of virtual inertia, the instantaneous power-reference signal (IPRS) is generated from computing the stateof-stability (SoS) of the microgrid [15]. The SoS is a complex term and can be determined by measuring some locally available system parameters like voltage, frequency, rate of change of voltage (RoCoV), and rate of change of frequency (RoCoF) [13]. It had been observed a decade-back that the frequency is the most suitable measurable parameter for computing the SoS [16]. Upon the successful computation of the IPRS, a high-speed converter and the power-dense storage system are another two requirements for establishing the virtual inertial-support (VI-S) system. However, the VI-S system cannot completely emulate the characteristics of a physical synchronous generator without the suitable damping assured [17]. Therefore, it is worth to be mentioned here that the VI-S system is incomplete without an auxiliary damping control loop. It is often observed in any electrical network, relatively low damping can ensure fast frequency recovery (FFR) while compromised by the low SoS. Therefore, computing optimal damping in real-time is critically important. Therefore, to satisfy two major objectives such as FFR and SoS in the microgrid, the study has put an effort to compute the optimal amount of damping in the realtime which will operate as an auxiliary control loop along with the VI-S system. The proposed technique has been referred to as auxiliary dynamic damping loop (ADDL) later in the study. In the proposed ADDL-based technique, the amount of damping has been computed through an online fuzzy interference system (FIS) taking the locally measured signal, frequency. The performance of the proposed ADDL-based scheme has been evaluated concerning the VI-S system and the N-IN system in the simulated environment, and the proposed scheme is found to be outstanding. Furthermore, an ARM Cortex-A72 processor-driven prototype hardware test setup has been developed to validate the proposed scheme in the real time. The performance of the ADDL-based scheme has been observed satisfactory in the real time.

2 System Architecture In the undertaken test system, as a static generator, a photovoltaic unit has been considered. The effect of uncertainties introduced from the climate change has been superimposed in the said unit. As a potential source of kinetic and electrical energy, a wind turbine-based micro-generation unit has been incorporated into the system. However, there is no such scheme that has been considered in the study to avail

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the kinetic energy from the wind generator. Furthermore, a diesel generator superimposed with the effect of generation rate constraint (GRC) and the effect of area control error (ACE) has been taken into consideration. The spinning mass in the large area network-based grid is the major source of natural inertia and the damping [18]. Similarly, in the alternator-based small network, rotational mass is the inherent source of inertia. The statement can be mathematically represented as: δf =

δ Palt − δ Pload 2Hsys s + Dsys

(1)

where the system inertia and the damping coefficient have been expressed in terms of H_sys and D_sys, respectively. For the improvement of SoS in the microgrid, majorly two control principle had earned the attention in the last few decades. Those two control principles were the primary control loop and the secondary control loop. The secondary control loop act upon ensuring the steady-state stability of the network. The reference power in the secondary control loop is computed from the slow pulses of multi-area measurements made available through small bandwidth-based communication network. The major form of the primary control loops is the droop-based scheme where local measurements are used to generate reference signals. The primary control loop partially helps to improve the FFR. However, none of the mentioned control loops improves the SoS of the network. Eventually, the inertial control loop comes into the picture. Here, knowing the information regarding the apparent power, the system inertia of any network can be mathematically represented as:  Hsys =

i (Halt (i)Salt (i))

Ssys

(2)

From the above analysis, the considered test system has been designed in the form of transfer functions in the simulated environment. The schematic representation of the test model is given as in Fig. 1. According to the schematic test system illustrated in Fig. 1, the deviation of frequency in the network can be mathematically represented as: δf =

δ Pwind + δ Palt + δ Ppv + δ Pinertial − δ Pdemand 2Hsys s + Dsys

(3)

The power delivered from the alternator can be expressed by δ Palt =

where

δ Pgen 1 + sTgen

(4)

Auxiliary Dynamic Damping Loop …

73

ADDL

Fig. 1 Schematic of test system simulated

δ Pgen

  1 δf = ACE − R 1 + sTgov

(5)

here, ACE =

k ∗ β ∗ δf s

(6)

In a similar manner, the power delivered from the photovoltaic unit and the wind generation unit are self-explanatory and thus avoided mathematical representation of the same in the manuscript. The time constants and other relative parameters have been given in Appendix.

3 Design and Configuration of the Auxiliary Dynamic Damping Loop A physical non-static generation unit inherently provides the inertial support to improve the FFR response of an electrical network. Moreover, a physical non-static generator works as a spinning reserve and offers natural damping to improve the SoS of the system. It is worth to be understood that the inertial support and the damping to the network are practically realized through injecting a large amount of instantaneous real power into the network. Therefore, the equivalent virtual platform should have that capability of injecting a large amount of instantaneous power. A highly powerdense storage element like supercapacitor can easily satisfy this condition. However, the computation of the IPRS has to be very accurate to ensure the improved FFR and SoS response in the microgrid. In the prosed system, the inertial power has been computed from the local derivative measurement of the frequency deflection in the system. Furthermore, the damping power is computed through an online FIS. The

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IPRS

Fig. 2 Schematic representation of the ADDL-based principle

summative power response of the inertia and the damping is referred to as the IPRS in the study.

δ Pinertial =

  {(δ f )Jinertial s} + (δ f ) D˜ inertial 1 + Tinertial s

(7)

Based upon (7), the schematic illustration of the proposed system has been made available in Fig. 2. In the study, a simple Mamdani-based interference system has been taken as the FIS. The center-of-area-based technique has been used for the defuzzification purpose.

4 Result and Analysis The performance of the proposed ADDL scheme has been compared against the conventional VI-S and the conventional NI–N scheme in regards to dynamically fluctuating demand and generation power. Introduced uncertainties in the form of disturbances and varying power in the test system have been illustrated in Fig. 3. It is worth to be observed in Fig. 3 that reflecting the possibilities of drastic change in solar irradiance, photovoltaic power slew has been considered quite a high in respect to the wind power. Therefore, the standard deviation in the photovoltaic power remains 38.24% more than the wind power and 46.19% more than the load power. The undertaken particular situation makes the test environment suitable for the evaluation of the proposed scheme. The stability of a system is a relative term, and generally, it is expressed through ambiguous statements. However, the study has put an effort to express the SoS of the system in terms of relative percentage through computing the center-of-area (CoA) of the Nyquist response. The acquired Nyquist responses for the NI–N, VI-S, and the ADDL scheme have been illustrated in Fig. 4a–c, respectively. Simply, from the visual observation in Fig. 4a, it can be understood that the maximum portion of the response obtained from the NI–N scheme lies outside the minimum stability margin and thus fails to ensure an acceptable value of the SoS. Statistically, it can be stated that the percentage of the SoS is improved through the conventional VI-S scheme

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75

Fig. 3 Introduced disturbances

by 83.61% in comparison with the NI–N scheme. On the other hand, the proposed ADDL-based emulation scheme improves the SoS by 98.27% over the NI–N based system. The improvement in the SoS through the proposed ADDL-based scheme is remarkably significant in this scenario. Furthermore, the improvement in the FFR in the microgrid can be evaluated from the Nyquist response by obtaining the statistics related to the input-to-output (I/O) delay. It can be observed from Fig. 4a that the NI–N based scheme retains approximately the I/O delay of 23 cycles. However, the conventional VI-S based scheme retains the delay of 8.5 cycles (see Fig. 4b). On the other hand, it can be observed in Fig. 4c that the proposed ADDL-based scheme improves the FFR response of the microgrid by 37.65% over the conventional VI-S scheme, maintaining the I/O delay only within 5.3 cycles. Finally, the acquired frequency response from the simulated test system has been shown in Fig. 5 to analyze the improvement in the FFR and the SoS of the microgrid through the ADDL-based emulation scheme. It can be observed from Fig. 5 that the peak overshoot and the peak undershoot of the frequency have been reduced by 42.98% and 45.86% in respect to the VI-S based scheme, respectively. The mean deflection in the frequency, measured in respect to the standard deviation, has been improved by 70.42% in regards to the VI-S scheme. For validating the ADDL-based technique in the real time, an ARM Cortex A-72 processor-driven prototype of the microgrid test bench has been developed as shown in Fig. 6. From analyzing the mean standard deviation, the accuracy of the prototype has been found to be as 72.41% with respect to the simulation. Obtained real-time response from the digital storage oscilloscope has been shown in Fig. 7. It can be observed from Fig. 7 that the proposed ADDL-based scheme minimizes the peak overshoot and the peak undershoot of the frequency in the real time by 39.48% and 37.65%, respectively, in comparison with the conventional VI-S based scheme.

76 Fig. 4 Assessment of SoS a NI–N, b VI-S, c ADDL

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Fig. 5 Deflection in frequency response

Fig. 6 Prototype model

5 Conclusion In the study, to improve the FFR and the SoS response of the microgrid, the novel ADDL-based virtual damping has been introduced as an auxiliary control loop along with the existing virtual inertial loop. The amount of optimal damping has been computed through an online FIS. The performance of the novel ADDL-based technique has been evaluated in a prototype hardware platform. It has been found in the

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Fig. 7 Real-time response of the system

study that the ADDL-based online optimal damping scheme can effectively minimize the peak overshoot and the peak undershoot of the frequency by 39.48% and 37.65%, respectively, with respect to the conventional VI-S scheme in the real time. Acknowledgements This work is supported by the Council of Scientific and Industrial Research, Govt. of India, under the acknowledgement number 143232/2K18/1 (File#09/969(0008)/2019EMR-I).

Appendix

Parameter

Value

Parameter

Value

Hsys (pu − s) Dsys , β(pu − s)

0.0692

Tgov (s)

0.28

0.019, 0.49

Tgen (s)

0.64

Tturbine (s)

0.21

Ki

0.054

Tarray (s)

0.59

Jinertial (s)

2.95

References 1. Choudhury S, Dash TP, Bhowmik P, Rout PK (2018) A novel control approach based on hybrid fuzzy logic and seeker optimization for optimal energy management between micro-sources and supercapacitor in an islanded Microgrid. J King Saud Univ Eng Sci 2. Bhowmik P, Chandak S, Rout PK (2018) State of charge and state of power management among the energy storage systems by the fuzzy tuned dynamic exponent and the dynamic PI controller. J Energy Storage 19:348–363

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3. Bhowmik P, Chandak S, Rout PK (2019a) Frequency superimposed energy bifurcation technology for a hybrid microgrid. Sustain Cities Soc 45:607–618 4. Kerdphol T, Rahman FS, Mitani Y, Hongesombut K, Küfeo˘glu S (2017) Virtual inertia control-based model predictive control for microgrid frequency stabilization considering high renewable energy integration. Sustainability 9(5):1–21 5. Bhowmik P, Chandak S, Rout PK (2019b) State of charge and state of power management in a hybrid energy storage system by the self-tuned dynamic exponent and the fuzzy-based dynamic PI controller. Int Trans Electr Energy Syst 29(5):e2848 6. Chandak S, Bhowmik P, Mishra M, Rout PK (2018) Autonomous microgrid operation subsequent to an anti-islanding scheme. Sustain Cities Soc 39:430–448 7. Bhowmik P, Chandak S, Rout PK (2019c) State of charge and state of power management of the hybrid energy storage system in an architecture of microgrid. J Renew Sustain Energy 11(1):014103 8. Choudhury S, Bhowmik P, Rout PK (2018a) Economic load sharing in a D-STATCOM integrated islanded Microgrid based on fuzzy logic and seeker optimization approach. Sustain Cities Soc 37:57–69 9. Chandak S, Bhowmik P, Rout P (2019) Load shedding strategy coordinated with the storage device and D-STATCOM to enhance the microgrid stability. Prot Control Mod Power Syst 10. Beck HP, Hesse R (2007) Virtual synchronous machine. In: 2007 9th international conference on electrical power quality and utilisation, EPQU 11. Choudhury S, Bhowmik P, Rout PK (2017) Seeker optimization approach to dynamic pi based virtual impedance drooping for economic load sharing between PV and SOFC in an islanded microgrid. Sustain Cities Soc 12. Choudhury S, Bhowmik P, Rout PK (2018b) Robust dynamic fuzzy-based enhanced VPD/FQB controller for load sharing in microgrid with distributed generators. Electr Eng 100(4):2457– 2472 13. Chandak S, Bhowmik P, Rout P (2019) A robust power balancing scheme for the grid-forming microgrid. IET Renew Power Gener 14. Bhowmik P, Rout PK (2019) Frequency superimposed robust coordinated control in a hybrid microgrid. Sustain Cities Soc:101791 15. Bhowmik P, Rout P (2019, May) Emulation of virtual inertia with the dynamic virtual damping in microgrids. In 2019 international conference on applied machine learning (ICAML). IEEE, pp 130–133 16. Bhowmik P, Rout P (2019) Establishment of an auxiliary virtual damping loop for the superior inertial response in the microgrid. IET Smart Grid 17. Chandak S, Bhowmik P, Rout P (2019) Dual-stage cascaded control to resynchronize an isolated microgrid with the utility. IET Renew Power Gener 18. Rakhshani E, Rodriguez P (2017) Inertia Emulation in AC/DC interconnected power systems using derivative technique considering frequency measurement effects. IEEE Trans Power Syst 32(5):3338–3351

Design of Chatbots Using Node-RED Siddharth Bhatter, Sayantan Sinha , and Renu Sharma

Abstract Over the past decades, there has been a tremendous technological advancement in the field of human computer speech system. Emerging as an effective medium for computer interaction, the last decade has witnessed the growth of various speechbased search engines like Siri, Google Chrome, and Cortana. Use of various language processing techniques like NLTK which mainly works in Python language has been done in speech analysis and design of an intelligent response engine to generate responses as close as human nature. The last couple of years has seen a growth, although in the initial phase, of conversational agents based on text messages. They basically suffer from a low usage factor as more than 85% of the Internet population do not rely and use the conversational agents (CAs). In spite of this shortcoming, proper investigation of the usage pattern of CA users can pave the path for the future development of the chatbots. This paper briefly discusses the primary stage of designing a chatbot with the help of Node-RED, a lightweight platform for the application of Internet of things. Keywords Node-RED · Chatbot · IoT · Data flow · Cuis

1 Introduction The recent years have seen the growth of human computer interaction also termed as conversational user interface (CUI). The main characteristic of a CUI is to enable communication between different computers using linguistic techniques in speech S. Bhatter (B) Karkhana Makerspace, Bhubaneswar, Odisha 751030, India e-mail: [email protected] S. Sinha · R. Sharma Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] R. Sharma e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_7

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or normal textual mode. The most common type of CUI is chatbots. Briefly chatbots can be defined as systems that have the capability to execute various actions in the aid of computers. This in turn reduces the load on the human beings and decreases the process of continuous engagement with the computers. This basically works on two strategies. The first strategy has no requirement of knowledge for the user to communicate with the CUIs. This means that the CUIs make use of a set of languages that are commonly in use and is interactive in nature. The second strategy is the huge knowledge base that is being shared with the computer and the users. This is beneficial in addressing any particular set of problem and the solution is mainly found by negotiation upon the knowledge base shared. The solution is reached without any stress on the either side. For the last few years, different types of chatbots were put into development. Chatbots basically date back to 50 years. In the 1960s, it was Turin and his experiment [1] that first proclaimed the emergence of an intelligence in spite of being brought down by some researchers due to the lack of sufficient conditions for the demonstration of the intelligence. Later, it was in 1966 when Weizenbaum developed his intelligence ELIZA [2] which was capable of simulating human behavior through simple data matching between the user data and the stored repositories. Later on discoveries like PARRY is developed by Colby in 1999 [3] and CONVERSE is developed by Batacharia in 1999 [4]. There are a large number of domains in which chatbots have been significantly found in application. A broad literature survey has been done on the application and detailed conceptual analysis on CUIs. The presence of chatbots in modern society is an inevitable phenomenon. They have become the essential section of everything in application that might range from a personal assistants that some in mobile phones to telephonic technical support and also effectively used in health sector [5]. It was estimated that in 2015, the global chatbot market would reach 113 million US dollar and by 2024 it has been estimated that the global chatbot market will reach as high as 994.5 million US dollar [6]. By a report presented by Maruti Tech laboratories [7], it was estimated that in 2016, more than 60% of the smartphone users will take the aid of chatbot-powered messaging applications. Gartner and Olsen in 2016 [8] estimated that by 2020 there will be a minimum of 30% of the total net browsing activities that will be done in the absence of a screen. More than 50% of the Internet searches will be powered by voices and nearly 80% of the enterprises will enrich relationships among themselves without human intervention. The sudden increase in the involvement of the chatbots is mainly due to the four major reasons. The primary advantage being the total replacement of human assistants all over the world by chatbots has led to less error rate and more reliability. A report by Juniper [9] has suggested that the interaction with a customer via chatbots might save them an amount of 0.70 US dollar as compared to earlier methods of human intervention. The second point of advantage is that unlike human interaction there is no time constraint. The chatbots are available all around the day. For an instance, the famous hotel chain, THE MARRIOT, has taken the aid of chatbot interaction in 2017 for hotel booking services. The third point of advantage being that the chatbots can easily predict the queries asked by the users and answer them proactively. In case of human interaction, the search operations were mainly done by the human itself. This

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would make the process erroneous as well as inefficient. On the other hand, the chatbots exactly understand the users’ need and give them the exact information without any much hastle from the users side. A report according to Howlett in 2017 [10] has made it evident that the chatbots address the human users personally and make them feel friendly. The fourth point of advantage is that the chatbots are of great use in case of business analysis. This is because the chatbots make use of the conversation of users with them as a medium of analysis and understand the customer requirement better. In spite of all these disadvantages, it can be mentioned that the chatbots are still in their juvenile mode of operation, and according to chatbots magazine [11], it was predicted that in the year 2018, chatbots to stretch its hand to varied field of applications. The proposed paper has made an attempt to design chatbot in Node-RED platform. The Node-RED has been designed in Node.js platform with data from the IBM Watson cloud provider.

2 Node-RED Node-RED can be defined as an open-source tool for model development that basically works on the flow-based technique. This is very effective tool for the integration of IoT devices, application program interfaces (APIs), and some of the recent technologies as developed by IBM [12]. This is basically a free tool that has been developed in a JavaScript-based platform called Node.js. This is a virtual flow editor which is complete browser operated. The Node-RED platform comprises various nodes and each node is symbolized by a particular icon. The entire Node.js platform has two modus operandi mainly the drag-and-drop mode and the JavaScript coding mode. The Node-RED enables the developers to create data flow for various processes by inserting, extracting, and processing nodes. This platform also helps in control aspects and security aspect by sending various alerts [12]. The underlying principle behind the operation of Node-RED is the ability to bring together various Web services and to design nodes according to the needs to perform various operations like data transmission between various sensors via electronic mail and social networking services like Twitter. The Node-RED is also known to perform complex system analysis. The entire Node-RED is subdivided into the three following basic components: The first component is termed as the flow panel. It contains all the different types of nodes that can be used for the creation of a particular flow diagram. The second component is the flow panel. This consists of all the flow mechanisms needed to create a particular flow which even involves in writing some JavaScript coding and introducing json files. The third component can be called the Info and Debug panel. After the drafting of a particular data flow technique, the debug option gives us to ensure that the flow constructed is technically correct.

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3 Application of Node-RED in Creating the Chatbots Node-RED basically developed by IBM effectively integrates any hardware and APIs for the effective data flow management. Node-RED is basically an IoT development platform which has its local server. But for complex operation like speech recognition, signal processing, and all the other applications under natural language processing (NLP), it is necessary that the data are stored in a high-speed cloud server and Node-RED has access to it at each time. In this case, we have used the Watson cloud developed by IBM. It is a cloud server which is compatible with Node-RED platform and can be used for a variety of applications.

4 Results and Analysis The creation of chatbot has been described in the following steps: Step 1 The first step deals with the installation of Node.js in the system and we are working and then the installation of Node-RED in the system. This can be done using the following code section: npm install -g --unsafe-perm node-red. The above code when run in command prompt automatically installs the NodeRED platform to the system. Step 2

This step involves opening of the new tab in Node-RED and importing of the clipboard. Figure 1 is a snapshot of the code that must be imported in

Fig. 1 Snippet of the code that is to be imported

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the clipboard. After the import has been done, we have the Ok Watson flow in our flow editor. Figure 2 denotes the Ok Watson editor. This step involves inclusion of some sample input strings. The following diagram Fig. 3 depicts the sample input strings. The next step as shown in Fig. 4 involves the passing of text to the tone analyzer section.

Fig. 2 Ok Watson flow editor

Fig. 3 Sample input strings for Watson editor

Fig. 4 Passage of text to tone analyzer section of the Node-RED

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Fig. 5 Setting the microphone as the output device

Step 6

This step as shown in Fig. 5 sets the microphone as the output device and involves the speech to text node block. Step 7 The next step gives us a choice to set the speech to text node block to our preferred language. We have to ensure that the speech to text node block should also pass the data to the tone analyzer block. Step 8 This step configures the tone analyzer block as shown in Fig. 6. Step 9 In this step like shown in Fig. 7, we add emotion to context function node. The script in this function node determines the highest tone score and sets it to msg fields that the assistant node will look for. Figure 8 depicts the flow diagram for adding emotion to context. Step 10 Along with the tone analyzer nodes, assistant nodes are also to be linked and configured as shown in Fig. 9. Step 11 After the entire flow has been done, the flow is to be deployed. The proper flow diagram is shown in Fig. 10. The final Node-RED data flow diagram can be depicted as.

Fig. 6 Configuration of the tone analyzer block

Design of Chatbots Using Node-RED

Fig. 7 Adding emotion to context functioning node

Fig. 8 Node-RED pathway for adding emotion to context

Fig. 9 Linkage and configuration of the assistant nodes

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Fig. 10 Final data flow diagram

5 Conclusion As discussed in the previous section, the concept of chatbot is still in a juvenile stage and an attempt has been made here to design a chatbot using Node-RED and data are being accessed from IBM Watson cloud. The future prospect of research indicates that the chatbot designed above can be integrated with machine learning for more reliable human-to-computer interaction devices. On an advanced stage, proper integration of chatbot with Web sites and mobile applications can also be done. This can find great application in the designing of assistant robots which can be used for educational purposes and also for advanced applications like space missions and augmented reality areas.

References 1. McDonnell M, Baxter D (2019) Chatbots and gender stereotyping. Interact Comput 31(2):116– 121 2. Pruijt H (2006) Social interaction with computers: an interpretation of Weizenbaum’s ELIZA and her heritage. Soc Sci Comput Rev 24(4):516–523 3. Colby KM (1967) Computer simulation of change in personal belief systems. Behav Sci 12(3):248–253 4. Batacharia B, Levy D, Catizone R, Krotov A, Wilks Y (1999) CONVERSE: a conversational companion. Mach Convers. Springer, Boston, MA, pp 205–215 5. Zamora J (2017) Rise of the chatbots: finding a place for artificial intelligence in India and US. In: Proceedings of the 22nd international conference on intelligent user interfaces companion, pp 109–112 6. Nili A, Barros A, Tate M (2019) The public sector can teach us a lot about digitizing customer service. MIT Sloan Manage Rev 60(2):84–87

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7. Brandtzaeg PB, Følstad A (2017) Why people use chatbots. In: International conference on internet science. Springer, Cham 8. Morgan B (2019) The customer of the future: 10 guiding principles for winning tomorrow’s business. HarperCollins Leadership 9. Sharma G, Anantaram C, Ghosh H (2009) The role of semantics in next-generation online virtual world-based retail store. In: International conference on facets of virtual environments. Springer, Berlin, Heidelberg, pp 91–105 10. Howlett N (2017) How machine learning is developing to get more insight from complex voice-of-customer data. Appl Market Analytics 3(3):250–254 11. Androutsopoulou A, Karacapilidis N, Loukis E, Charalabidis Y (2019) Transforming the communication between citizens and government through AI-guided chatbots. Gov Inf Q 36(2):358–367 12. Leki´c M, Gardaševi´c G (2018) IoT sensor integration to Node-RED platform. In: 2018 17th international symposium INFOTEH-JAHORINA (INFOTEH). IEEE, pp 1–5

Power Factor Correction for Single-Phase Domestic Loads Using Microcontroller and Triac Kamran Alam, Lalita Sharma, and Namarta Chopra

Abstract Electrical power bears an important role in the production of industrial economy. Power factor might be poor due to the presence inductive components in the form of loads such as welding machines, induction motors, voltage regulators power transformers, induction furnaces, and choke coils. It can lead to drastic loss with poor power factor which reduces the operating efficiency of any power plant which may also result in requirement of larger conductor size and also causes voltage drop as power loss increases. So, it becomes necessity to improve power factor for the optimum performance of any electrical equipment. Power factor can be improved or controlled by using capacitance as reactive power. There are capacitor banks, for reactive power to improve power factor and for their correction which leads to plant management economically and technically. So, various aspects for improving power factor have been there in this very paper. APFC means active power factor correction—its basic and controlling methods have been discussed in this paper. Moreover, Proteus and Multisim have been used for the simulations which are given below. Keywords Power factor · Capacitor bank · Reactive power · Zero-cross detection · Triac driver circuit

1 Introduction In general, sinusoidal waves are there to represent the alternating current and over vast area to distribute, transmit, and generate electrical. The angle which is been there in between voltage and current, i.e., φ particularly known as power factor angle and K. Alam (B) · L. Sharma · N. Chopra Amritsar College of Engineering and Technology, Amritsar, India e-mail: [email protected] L. Sharma e-mail: [email protected] N. Chopra e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_8

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its cosine is known as power factor. Cosine φ represents that parameter of current is in phase with voltage particular. Meanwhile, in any electrical circuit, resistive loads bear unity power factor and inductive loads zero, respectively, also purely inductive ones but this can be taken as a theoretical proportion only. In practical basic, no purely inductive loads are there and no purely resistive loads too, some impurity is always there which tends to differentiate the loads with respect to induction or resistance accordingly. Lesser the value of power factor more the losses and if it is desired to be a coherent and productive operation to be performed in any plant in a well-significant manner then power factor must be close to unity and for this purpose capacitor as shunt ones is used. Requirement of capacitor value is as similar task to electrical loads in plant. Economically 0.8–0.9 value of power factor is taken during capacitors scheming. In today’s era of mechanical transformation, framework of power is getting perplexed day to day. So, significant monitoring is required for the proper transformation of energy so as to decrease losses and any kind of dissipation. Currently, most of us are working to overcome losses and to increase efficiency at heap, since with enlarge service of inductive loads. So, with enhancements in inductive loads, value of power factor in mass is getting low or less as inductive loads effect power factor in stack. High precision electronic device for automatic power factor rectification examines power factor from line voltage and current by measuring delay or deferral between them in the landing zero-cross of the waveforms by using internal clock of microcontroller, which mainly depicts stage point and slack φ between voltage and current flag and further describes the comparison in power factor (Cos φ) and at the same point the microcontroller figure at the pay essential and as needs be switches in several capacitors from banks particular capacitor to obtain unity power factor. This evolution is significant to improve and reshape single stage. Capacitor banks by means of small-scale processor support control shell; it will monitor the capacitor banks’ heavy different load current. Current transformer is used to measure the mass current for testing aspects and to use the small-scale processor for controlling purposes with capacitor procedure and to limit the exchange tasks and also to improve the control factor. Hence, by the use of series active filter which is economic approach to correct power factor, the proposed scheme as compared with traditional PFC has low requirements of power ratings, therefore there is reduction of cost, which leads to increase the efficiency and lower interference due to electromagnetic components. The major part to notice is that it will reduce the necessity of induction in vast [1].

2 Literature Review Power factor bears an essential role in electrical era due to its periodicity, reliability, efficiency, and economical operations also the proper usage of power as well. So as to make it more effective, there is necessity of its improvement and correction to improve efficiency of distribution system. Various research works has been performed in order

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to reduce the overall size, complexity, algorithms of device as well as requirement of compensative components to correct power factor. Choudhury introduced a device [2] named single-phase power factor improvement. That device using zero-cross detection was put into action in actual, and by switching the capacitors, there are bridge rectifier and chopper circuit for the correction of power factor and its properties were that it was cost effective and less complex with the usage of standard logic chips without using any microcontroller or ASIC. Afridi gave an automatic single-phase power factor improvement controller [3] and with the help of programmable logic controller (PIC), this controller was used in real time which depicts power factor with continuous monitoring system’s load and also the control action was there with suitable algorithm. By switching capacitor banks with different kinds of relay and where there the power factor lags, it improves it too. Rana et al. also came with similar design of an automatic power factor improvement with the help of microcontroller as in [6]. The proposed system did not use any potential transformer for the measurement of current; they have used known shunt resistor and a microcontroller. By the use of Ohm’s law and Atmega8, calculate the current following through the shunt resistor. Therefore, the system is cost effective for automatic power factor improvement. Tiwariet al. [4] too proposed the single-phase power factor correction by switching capacitor banks using microcontroller. Earlier the microcontrollers had ZCD to analyze signals of current and voltage whatever procedures were taken whether its auto-adjustable power factor correction or other things these all methods were there in order to improve power factor and make it unity. Raj et al. [5] similarly came with automatic power factor correction with the help of microcontroller like [6,3,4] but their prominence was domestic loads having major component as microcontroller which helped to improve power factor a lot. Ishaket al. [7] introduced analogous design similar [3–6] to maintain power factor likewise whenever there was any lag in power factor it usually managed by their Arduino UNO control circuit. In electrical engineering where power factor is the foundation to be studied and to be analyzed, various searches have been there to correct it, to improve, and to make it worth and cost effective as sometimes it is not economical to get unity power factor. There are various capacitor banks, synchronous condensers, and converters to improve and correct them particularly and to reduce all the losses and hindrances there in system or circuit. Switched reluctance motor controller drive is major application of this correction. Actually it is not suitable in power system to install capacitor system as they do not prove fruitful for the system rather bearing leading power factor even under load condition. To make it cost effective and more efficient, embedded system drive is utilized and proved a required solution for cost and energy saving [8]. A TRAIC can pass current in both directions, different from SCRs, and the chief intention for the work and research is to overcome limitation of earlier one and to make a perpetual device which is more efficient and cost effective as well. In electrical engineering, where power factor is the foundation to be studied and analyzed various research has been there to correct it, to improve and to make it worth

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and cost effective. There are various capacitor banks, synchronous condensers, and converters to improve and correct them particularly and to reduce all the losses, hindrances are there in system or circuit. Switched reluctance motor controller drive is major application of this correction. Actually it is not suitable in power system to install capacitor system as they do not prove fruitful for the system rather bearing leading power factor even under load condition. To make it cost effective and more efficient, embedded system drive is utilized and proved as a required solution for cost and energy saving [8]. These all parameters like cost, size reduction, and most important leading power factor and other qualities make a system worth goes hand in hand. If current waveform is focused and is selected to improve, i.e. removing harmonics and converting it to sin wave with less harmonics by tuning of circuits is implemented by analytical method which is referred as S2PFC (Single-Stage power factor correction) [9].

3 Hardware Description of Proposed System Proposed scheme consists of the following hardware components and software to automate continuous measurement of power factor, calculations of required capacitance, and firing of exact amount of capacitance in order to correct the power factor.

3.1 Hardware Required • • • • • • • • •

Current transformer Voltage transformer LM741 XOR 4070 Resistors Diodes Triac bt41-600 Driver circuit Capacitor bank

3.2 Software Required • . PROTEUS • ARDUINO IDE • NI Multisim 14.2

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Power factor is the ratio of real power and reactive power and is the cosine angle between the voltage waveform and current waveform. Power Factor = cos (); In case of AC appliances, apparent power can be calculated easily using multimeter and multiplying measured value of current and voltage. However, in order to get true power which is also known as real power, apparent power should be multiplied by the power factor and the resultant power is in Watt. This method is only applicable to the load having significant capacitive and inductive components but in case of purely resistive load where power factor is unity, apparent and real power is same. Oscilloscope can be used to see current waveforms and voltage waveforms and the difference between current waveform and current waveform. Power factor can be calculated by measuring the difference between current and voltage signals. The proposed circuit is designed in Multisim, and simulation is done in order to verify that the result obtained from measured value is true. In the simulation, it is assumed that current and voltage to the load are pure sinusoidal wave, so it is easy to calculate power factor. Proposed scheme uses op-amp UA 741 for zero-cross detection which is also known as sine wave to square wave converter. For the circuit diagrams, refer Fig. 1. The AC voltage source is taken, with reference of ground in the comparator. When the sinusoidal wave is in the negative half cycle, a negative DC pulse square is generated and when a sinusoidal wave is positive half, DC positive pulsed square wave is generated as shown in figure above. Next step is to compare the square waves; the waveform obtained is then compared with the help of XOR logic gate. The result is so obtained, when the waveforms of both quantities do not overlap with each other, which means both waveforms at zero cross should not overlap then the output is high. In order to verify the concept and working of exclusive OR circuit,

Fig. 1 Zero-cross detection

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refer figures below which explains the working of XOR and truth table obtained from it. When both inputs are high, i.e., A = 1 and B = 1, then the output at XOR gate is LOW PR1 = 0 or when both inputs are low, the resultant output at XOR gate is also LOW PR1 = 0, i.e., LOW as shown in Fig. 2. When one input is LOW and another is HIGH, then the resultant output at XOR gate is also LOW PR1 = 1, i.e., LOW as shown in Fig. 3 From above diagrams, it is concluded that the output of XOR gate is the time difference between two waveforms from zero-cross point. This difference in time can be utilized to calculate Power factor by the use of microcontroller. cos(φ) = Frequency × time difference between the waves × 360

(1)

where cos (φ) is the power factor 360 It is a constant which is used to convert in degrees The output obtained from XOR gate is shown above in Fig. 4, is the time difference (T 2 − T 1) = 5.006 ms. Using Eq. 1,

Fig. 2 Either both inputs are HIGH or LOW

Fig. 3 One input conjunctive of other

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Fig. 4 T 2 − T 1 output

(φ) = 50 × 0.0025 × 360 φ = 45◦ . Therefore, the angle difference between both waveforms is 90˚. Cos(45) = 0.7071 Power factor = 0.7071 Two square waves obtained from op-amp are then compared with the help of XOR gate (IC 4070BD_5v), and it will give high output when two square waves do not overlap each other and LOW when they overlap refer Fig. 5. Therefore, the output from XOR gate is time difference between two waves from the point of zero cross.

4 Working Algorithm and Proposed System The detailed working flowchart of proposed scheme is shown below in Fig. 6. For signal inputs, we have used CT and PT. The obtained signals from PT and CT are sent to zero-cross detection or sine wave to square wave converter circuit. The square wave generated from op-amp is then sent to the XOR gate which is HIGH when the difference between two square waves is detected. The output wave gives

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Fig. 5 XOR output

the time delay between the wave of current signal and voltage signal. The power factor is calculated using microcontroller, and required value of capacitance is also calculated. Real power is given as Power = Irms Vrms cos ϕ

(2)

Reactive power consumed by a load is given by power triangle VAR A = power × tan θ

(3)

VAR B = power × tan θ1

(4)

For reference

Reactive power of load is given by VAR = VAR A − VAR B

(5)

The current required I =

VAR Vrms

(6)

Value of capacitive reactance is given by XC =

Vrms I

(7)

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Fig. 6 Flowchart of proposed scheme

Final equation is for capacitor required to correct PF C=

1 2π f ×C

(8)

5 Operation Testing and Results The proposed system refers Fig. 7 which is experimentally tested and data are recorded against different types of loads, viz. resistive, inductive, and a combination

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Fig. 7 Proposed system

of resistive and inductive load. The proposed system consists of gate driving circuit, op-amp, XOR gate, current sensor, and voltage sensor to calculate power factor and fire Triac, the calculated amount of capacitance. The results obtained are discussed under several cases below.

5.1 Case 1: Resistive Load In order to testify resistive load, heating element of 247 W is connected to our system as shown in Fig. 8. All the electrical parameters can be seen below in figure. From the flowchart, it is clear if the power factor is more than 0.99, there is no need of correction because both current and voltage signals are in phase with each other. Therefore, the proposed system has not fired the Triac.

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Fig. 8 Resistive load

Fig. 9 Inductive load

5.2 Case 2: Inductive Load When inductive load is connected to the proposed system as shown in Fig. 9, there is phase delay between voltage signal and current signal. Microcontroller with the help of op-amp and XOR detects the delay. According to the delay, microcontroller calculates the value of capacitor required in order to correct the power factor. Firing of Triac is based on gradual increment and decrement of firing time in closed-loop operation.

5.3 Case 3: Mixed Load Combination of resistive and inductive load comprises of lamp and a transformer which is then connected to the system as shown in Fig. 10. The system detects the phase difference between the current and voltage signals, and the capacitance value is calculated with the help of delay and microcontroller. According to the calculated value of capacitance, the Triac is fired which is connected with a capacitor bank.

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Fig. 10 Mixed load

6 Conclusions This paper presents the low-cost method for the correction of power factor in a domestic single-phase load. The proposed system continuously monitors, calculates, and corrects the power factor. The proposed scheme uses Arduino-based programming environment for measuring the current, voltage, power, power factor, and required capacitance in order to correct. The proposed system is verified experimentally using different types of load. By the use of proposed system, the power factor can be improved up to the large extent as well as current consumption can be decreased. Acknowledgements The authors would like to express their sincere gratitude to Amritsar Group of Colleges, Amritsar, India for providing the support to proceed on the project during research work.

References 1. Pan Z, Peng F, Wang S (2005) Power factor correction using a series active filter. IEEE Trans Power Electron 20(1):148–153 2. Choudhury SM (2008) Design and implementation of a low cost power factor improvement device. In: TENCON 2008—2008 IEEE Region 10 conference, Hyderabad, pp 1–4 3. Afridi MN (2012) Design and implementation of microcontroller-based controlling of power factor using capacitor banks with load monitoring. Glob J Res Eng Electr Electron Eng 12(10):1– 8 4. Tiwari AK, Sharma D, Sharma VK (2014) Simulation analysis of automatic power factor correction technique using capacitive bank. Int J Sci Eng Technol Res 3(4):574–576 5. Raj VCEM, Lese S (2016) A novel approach for power factor correction using microcontroller in domestic loads. Middle-East J Sci Res 24(4):1042–1046 6. Rana MS, Miah MN, Rahman H (2013) Automatic power factor improvement by using microcontroller. Glob J Res Eng Electr Electron Eng 13(6):29–34

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7. Ishak NH et al (2015) A design of an automatic single phase power factor controller by using Arduino uno Rev-3. Appl Mech Mater 785:419–423 8. Mallika KS (2007) Topological issues in single phase power factor correction. NIT, Rourkela 9. Garcia O, Cobos JA, Prieto R, Alou P, Uceda J (2001) Power factor correction: a survey. In: 2001 IEEE 32nd annual power electronics specialists conference (IEEE Cat. No.01CH37230), vol 1. Vancouver, BC, pp 8–13

Techniques Behind Smart Home Automation System Using NLP and IoT Karisma Mohapatra, Mamata Nayak, and Ajit Kumar Nayak

Abstract Nowadays, home automation system goes through a huge development of technologies that are designed for less cost for operating the smart devices and strengthen greatly for the residents of the home. The aim is to develop a voicecontrolled home appliances with different types of language. It uses the Internet of things, artificial intelligence (AI), and NLP. Many types of automation appliances are available in the market using many technologies such as global system for mobile (GSM), Bluetooth, Wi-Fi, Li-Fi, and many more. These systems are focused on/off the appliances. We try to focus on the voice command to the mobile device, which helps us to interpret the command and sends the message to the appliances. In the article, we are planning to implement three basic home appliances ac light fan and CC-TV by using ZigBee technology and cloud-based IoT. The user gives the voice command and it will be interpreted by the mobile using NLP. The mobile acts as a central support, and it decides what type of operation should be fulfilled by appliances according to the request of the user. It may be a Web application or smartphone application that helps us transfer the data to the cloud. Keywords Home automation system · NLP · IoT · ZigBee technology · Voice control

K. Mohapatra (B) Department of Computer Science & Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] M. Nayak · A. K. Nayak Department of Computer Science & Information Technology, ITER, Siksha O Anusandhan (Deemed to be University), Bhubanwswar 751030, Odisha, India e-mail: [email protected] A. K. Nayak e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_9

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1 Introduction Smart home automation system is monitoring all gadgets through ZigBee Technology and IoT for analysis and processing of data [1]. There are many cloud services like IBM and Google cloud which are interactive application as a dialogue communication system. This system will change the communication platform of the home appliances by reducing the need for physical contact with them [2]. Here, the ZigBee technology is planned to design the purposed system by providing wireless voice commands [2, 3]. In this work, the possibilities of ZigBee technology with cloud-based services are described. To secure the data and provide built in functions, we use the IDIGI cloud services. TO link smart energy ZigBee devices to the cloud by connecting port X2e. By connects the device to the cloud service where data can be accessed by smartphone apps [3]. It introduced a smart system that is made up of a central controller with many sub-controllers designed for home appliances to work through ZigBee technology. It will develop wireless communication to operate the device [4]. A sensor node contains collection, computation, and communication units. We use various transceivers like RFM TR 1000, Hardware accelerators, ChipconCC 1000 to send and receive data for the communication unit. This unit also contains a microcontroller to perform the task [5].

2 Related Works There are so many attempts in creating a smart home automation system. Technical companies like Apple, Amazon, IBM, etc., have been making smart appliances for the user [6]. There are many smart appliances like Amazon Echo, Brilliant control, Logitech Harmony Elite; Arlo ultra is available for the user. Some home automation systems have depended upon the wireless sensors and Arduino boards. Smart mobile functionality is not included in the existing system which is indicated through it [7]. Some appliances using Arduino boards and other systems have replaced by Raspberry Pi using z-wave or Bluetooth technology. But it also does not support the user’s voice commands [8]. The existing home automation system offers various request for the home clients to access the linked devices. But it is highly complex and costly in architecture. The system allows users to control the appliances by WI-FI remotes. By this type of system, there is a lack of security and safety of the appliances [9]. The home automation system helps the physical disable people to make their life easier. These systems are made up of voice recognition to control the devices. These systems are made up of a voice recognition module and an Arduino microcontroller. This system also lacks the sounds of natural languages [10]. And other home automation systems utilize mobile Bluetooth services implementation. It has a small range and also it does not hold up a remote geographical area. Many other appliances do not utilize NLP and AI which are used for better performance of the user. Some appliances controlled by GSM technology [2, 11].

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Generally, the automation techniques are processed on the mode of enabling/disabling the operation of the appliances used by the user. They use numerous technologies like GSM, Bluetooth, and WI-FI. The home appliances are difficult to an interchange of commands through android applications in the existing systems [12].

3 ZigBee Network-Based Technology It provides a low range communication protocol standard for wirelessly connecting devices (sensors) [4]. It is low cost, powerless, and battery operated wireless sensors that is worked on IEEE 802.15.4 protocol and also operates with the 2.4 GHz ISM (Industrial, scientific, and medical) band [13]. The ZigBee technology data rate is dependent on the frequency. It uses physical and MAC layers of IEEE 802.15.4 [14]. a. MAC layer: It is present between the physical and network layer. This layer provides a PAN ID. b. Network layer: It is represented within the MAC layer as well as the application layer which is providing an environment for routing and mesh-tree networking. It also provides security to the ZIGBEE networks in the form of encryption. c. Application layer: Application support sub-layer and ZIGBEE device object define the application layer to take in charge of binding services over the local and space management network service. The ZigBee module is controlled by the protocol layers in the application framework [15]. d. Modes of operation: It transferred data in two ways. • Beacon mode. • Non-beacon mode. The active state of input data is continuously monitored by the router and coordinator in the state of the beacon. It requires more amount of power supply and also its power consumption is less because many devices are in an inactive state in the network [16]. When the data communication is over from end devices, sleep states are activated for routers and coordinators as shown in Fig. 1. In the non-beacon state, it is associated with a system architecture where components are “asleep” as in burglar alarms as well as a fire warning system. It shows believe in the CSMA and acknowledgment features for the communication [17] as depicted in Fig. 2.

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NETWORK DEVICE

CO-ORDINATOR

Beacon Data

Acknowledgement

Fig. 1 Beacon mode of communication

NETWORK DEVICE

CO-ORDINATOR

Beacon

Data

Fig. 2 Communication through non-beacon mode

3.1 ZigBee Topologies There are three types of ZigBee topologies which are most supported by the network devices [18] which are discussed below: • Star network-based topology: This network architecture is based on one primary coordinator with many devices used for communication. The coordinator is in charge of begin and monitoring the devices in the platform of a network. Any nodes communicate with the coordinator directly. It is generally represented in Fig. 3. • Cluster network-based topology: This network architecture is based on point-to-point network topology and by router, we can connect the end devices [30]. End devices need not be present in the coordinator’s range. But routers can communicate with other routers as shown in Fig. 4.

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Fig. 3 Star topology of ZigBee ZigBee coordinator ZigBee Devices

ZigBee coordinator ZigBee Routers ZigBee Devices

Fig. 4 Cluster-tree topology of ZigBee

ZigBee coordinator ZigBee Routers ZigBee Devices

Fig. 5 Mesh-tree topology of ZigBee

• Mesh-tree topology: This topology is the extended version of cluster-tree topology [31]. The end devices can communicate with routers and end devices. So it can be represented in Fig. 5.

3.2 Structure of ZigBee Protocol It designed with various types of protocol layers like Physical and MAC layers are built up a network of ZigBee and application layer while this protocol is completed. If we see the protocol layers which do not look like the open system interconnection networking model [19]. Whereas, the physical layer, data link layer, or medium access

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layers, as well as network layer, are integrated with the last part of the protocol architecture. Here, the transport, session, presentation, and the application layers belong to the application support sublayer and ZigBee devices object which is represented in the structure of ZigBee protocol [20]. Service access points (SAPs) are present in the middle of the ZigBee stack as given Fig. 6. In physical layer, signals are communicating, respectively. It provides data rate 250, 20, and 40 kbps. With help of the carrier sense multiple access collision avoidance, the signals can be transmitted in MAC layer [32]. Network layer is meant to be configuration of network, connection between the server and client and routing operations. In application support sublayer, provides necessary service for ZigBee device object and application objects according to the facility and requirement.

Fig. 6 Architecture of ZigBee protocol

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Application framework plays a role of mediator between the application objects and APS layer in the ZigBee-enabled devices which are connected through it [33]. To identify, begin, and initiate the other devices over the network.

4 IoT Platforms It is consisting of several layer technologies which give the opportunity to connect many entities with each other. It can be physical devices or sensors which give interesting data, actuators to control the states present everywhere [21]. This provides various kinds of automated properties that enhance the application for the gadgets which are connected to it [22]. It also takes care of the devices to be used in a range of its capabilities. It will act as a middleware when it connects to the remote devices to user applications. It is commonly defined as a bridge when remote devices to user applications are connected to each other. It handles all the communication between the hardware and application layers. It also entitled smart modules with cloud-based processing environment and its service [23]. Many platforms of IoT are categorized on the basis of scalability, customization, easy use, combination with other software, and security. The IoT plays a vital role which is called “an intelligent digital mesh,” for modern IoT platforms in respective areas [24]. • Digital twins—Resources can be monitored and controlled in a very powerful way. Digital twins give a digital representation of devices and systems in a better way. It will improve the response between internal and external events [25]. • Intelligent things—IoT ecosystem takes out perception from collected data and interaction in the most effective way by using an IoT ecosystem. It must be designed to support flexible interaction with AI systems [26]. • Cloud to the edge—Entities present in the IoT ecosystem are managed by data processing powers. The cloud-native model gives benefits when combined with cloud and edge computing architecture. DevOps are effectively handled by the development tool provided by the IoT platform [27].

5 System Architecture, Natural Language Processing Unit, and IoT In this paper, the user is controlling various home appliances by voice commands in the general format of language instead of complex computer commands [28]. NLP is a particular group in between the human and computer. There are so many challenges in language processing such as language perception, the meaning of the voice messages given by the human language format. The NLP-based algorithms are processed through deep learning (RNN) [29].

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The objective of this paper is to communicate or monitor the various home appliances by human voice commands. We decide to give voice commands to get rid of the monotonous process of clicking on different applications. We get the safety and personal connection with the system using robust features of natural language processing. First of all, the user login through Id and password in the android device. The user sends voice commands to the smartphone, which translates the commands with the technique of language processing (AI) and sends the acceptable command to the particular appliances. The smartphone plays a vital role in it. As per the user’s request, it decides what operation must be done by which appliance. Any application which should be designed for this model is accessed by desktop model, web model and smart phone. And the data used in this application is controlled by the cloud. In this model, we use a smartphone to give the voice command which is represented in Fig. 7. In this paper, we try to create a flexible home automation system through the ZigBee platform which reduces an expense for designing by the user. This system permits the user to operate the automated home appliances containing a remote control interface through ZigBee and Wi-Fi supported module for the smooth conduct of the appliances. To access the appliances, it provides a compatible interface to give the facility that a home gateway is needed. There is various kind of appliances interfaced to the mobile device using an Arduino microcontroller board that set ups an environment for the Internet of things and it responds to the voice commands with the help of the programmed Arduino Boards. This project will help us to reduce the

Fig. 7 Proposed system architecture

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Fig. 8 Voice app connection status

consumption of power. So the whole system is controlled by a voice-controlled app as depicted in Fig. 8. Arduino is free and proprietary software. It helps in sending and receiving instructions from certain electronic devices by the Internet. There are two parts in it. (i) Hardware, i.e., Arduino Uno circuit board (ii) Software, i.e., C++ to programme the board. Arduino Uno is just like microcontroller boards which build ATmega328P microcontroller (8-bit). It also supports the microcontroller by the help of voltage regulator and crystal oscillator. There are several components which include: (i) (ii) (iii) (iv) (v) (vi)

Fourteen input and output pins Six analog input pins USB connection Power barrel jack ICSP header Reset button.

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6 Conclusion In this paper, ZigBee technology, IoT, and NLP technique have been playing the main role for the smart home automation system. These features of the proposed technology provide an interface for smart communication through voice, which can be treated as a suitable technology for the home automation system. Most of the existing automation systems come after a specific set of instructions in order to interact with their home appliances. To get the better of all the existing issues, our project gives the facility for interacting with various home gadgets or appliances through voice commands which are embedded using Arduino with a smart mobile and ZigBee technology. But the natural language processing will help us for a better connection with the appliances by using voice instructions. By using ZigBee communications technology, a novel architecture for a home automation system is proposed.

References 1. Hamdan O, Shanableh H, Zaki I, Al-Ali AR, Shanableh T (2019) IoT-based interactive dual mode smart home automation. In: 2019 IEEE international conference on consumer electronics (ICCE), pp 1–2. IEEE 2. Rani PJ, Bakthakumar J, Praveen Kumaar B, Praveen Kumaar U, Santhosh Kumar (2017) Voice controlled home automation system using Natural Language Processing (NLP) and Internet of Things (IoT). In: 2017 third international conference on science technology engineering & management (ICONSTEM), pp 368–373. IEEE 3. Islam MdM, Farook MdN, Mostafa SMG, Arafat Y (2019) Design and implementation of an IoT based home automation. In: 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT), pp 1–5. IEEE 4. Biswal AK, Samantaray M. A novel approach for localization in sensor network 5. Tappler M, Aichernig BK, Bloem R (2017) Model-based testing IoT communication via active automata learning. In: 2017 IEEE International conference on software testing, verification and validation (ICST), pp 276–287. IEEE 6. Alexakis G, Panagiotakis S, Fragkakis A, Markakis E, Vassilakis K (2019) Control of smart home operations using natural language processing, voice recognition and IoT technologies in a multi-tier architecture. Designs 3(3):32 7. Baby CJ, Khan FA, Swathi JN (2017) Home automation using IoT and a chatbot using natural language processing. In: 2017 innovations in power and advanced computing technologies (i-PACT), pp 1–6. IEEE 8. Bajpai S, Radha D (2019) Smart phone as a controlling device for smart home using speech recognition. In 2019 International Conference on Communication and Signal Processing (ICCSP), pp 0701–0705. IEEE 9. Lin Y-W, Lin Y-B, Hsiao C-Y, Wang Y-Y (2017) IoTtalk RC: sensors as universal remote control for aftermarket home appliances. IEEE Internet Things J 4(4):1104–1112 10. Gunputh S, Murdan AP, Oree V (2017) Design and implementation of a low-cost Arduino-based smart home system. In: 2017 IEEE 9th international conference on communication software and networks (ICCSN), pp 1491–1495. IEEE 11. Adedokun G, Oladosu JA (2015) Development of a gsm-based remote control system for home electrical appliances. Acta Technica Corviniensis-Bull Eng 8(1):77 12. Ihedioha Ahmed C, Eneh Ifeanyichukwu I (2016) Home automation using global system for mobile communications (GSM). Int J Emer Technol Eng Res (IJETER) 4(1):54–58

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13. Obaid T, Rashed H, Abou-Elnour A, Rehan M, Saleh MM, Tarique M (2014) ZigBee technology and its application in wireless home automation systems: a survey. Int J Comput Netw Commun 6(4):115 14. Han D-M, Lim J-H (2010) Smart home energy management system using IEEE 802.15. 4 and zigbee. IEEE Trans Consum Electron 56(3):1403–1410 15. Ahmim A, Le T, Ososanya E, Haghani S (2016) Design and implementation of a home automation system for smart grid applications. In 2016 IEEE international conference on consumer electronics (ICCE), pp 538–539. IEEE 16. Santos D, Jessye CH, Lauradoux C (2015) Preserving privacy in secured ZigBee wireless sensor networks. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp 715–720. IEEE 17. Park HG, Seungwoo SHIN, Lee K, Jin YS (2019) Device implementing visible light communications and wireless network communications in dual mode and method of implementing thereof. U.S. Patent 10,523,320, issued December 31, 2019 18. Zhen T, Elgindy T, Shafiul Alam SM, Hodge B-M, Laird CD (2019) Optimal placement of data concentrators for expansion of the smart grid communications network. IET Smart Grid 2(4):537–548 19. Li C, Dong K, Jin F, Song J, Mo W (2019) Design of smart home monitoring and control system based on Zigbee and WIFI. In: 2019 Chinese Control Conference (CCC), pp 6345–6348. IEEE 20. Salman AD, Khalaf OI, Abdulsahib GM (2019) An adaptive intelligent alarm system for wireless sensor network. Indonesian J Electr Eng Comput Sci 15(1):142–147 21. Singh D, Pattanayak BK (2016) Analytical study of an improved cluster based routing protocol in wireless sensor network. Ind J Sci Technol 9(37):1–8 22. Singh D, Pati B, Panigrahi CR, Swagatika S (2020) Security issues in IoT and their countermeasures in smart city applications. In: Advanced Computing and Intelligent Engineering. Springer, Singapore, pp 301–313 23. Zhou L, Li X, Yeh K-H, Su C, Chiu W (2019) Lightweight IoT-based authentication scheme in cloud computing circumstance. Fut Gener Comp Syst 91:244–251 24. Gnoni MG, Bragatto PA, Milazzo MF, Setola R (2020) Integrating IoT technologies for an “intelligent” safety management in the process industry. Procedia Manuf 42:511–515 25. Curry E, Derguech W, Hasan S, Kouroupetroglou C, Hassan U, Fabritius W (2020) Building internet of things-enabled digital twins and intelligent applications using a real-time linked dataspace. In: Real-time Linked Dataspaces. Springer, Cham, pp 255–270 26. Zhang H, Li J, Wen B, Xun Y, Liu J (2018) Connecting intelligent things in smart hospitals using NB-IoT. IEEE Internet Things J 5(3):1550–1560 27. Wang T, Zhang G, Anfeng Liu Md, Bhuiyan ZA, Jin Q (2018) A secure IoT service architecture with an efficient balance dynamics based on cloud and edge computing. IEEE Internet Things J 6(3):4831–4843 28. Susan AKN, Ayare NS, Prema NS, Chandini SB (2020) Internet of things in home automation— a review In: Intelligent communication, control and devices. Springer, Singapore, pp. 773–779 29. Dwaraki A, Freedman R, Zilberstein S, Wolf T (2019) Using natural language constructs and concepts to aid network management. In: 2019 international conference on computing, networking and communications (ICNC), pp 802–808. IEEE 30. Arafat MY, Moh S (2019) Localization and clustering based on swarm intelligence in UAV networks for emergency communications. IEEE Internet Things J 6(5):8958–8976 31. Upadhyay M, Shah M, Bhanu PV, Soumya J, Cenkeramaddi LR (2019, January) Multiapplication based network-on-chip design for mesh-of-tree topology using global mapping and reconfigurable architecture. In 2019 32nd international conference on VLSI Design and 2019 18th international conference on embedded systems (VLSID), pp 527–528. IEEE 32. Wang Y, Chen C, Jiang Q (2019) Security algorithm of internet of things based on ZigBee protocol. Cluster Comput 22(6):14759–14766 33. Alsaif O, Saleh I, Ali D (2019) Evaluating the performance of nodes mobility for Zigbee wireless sensor network. In: 2019 international conference on computing and information science and technology and their applications (ICCISTA), pp 1–5. IEEE

Salp Swarm Optimized PID Controller for Frequency Control of Hybrid Power System with UC and UPFC Debidasi Mohanty and Sidhartha Panda

Abstract Our work utilizes a classical proportional-integral-derivative (PID) controller for load frequency control (LFC) of hybrid power system. The HPS consists of photovoltaic, wind, fuel cell, diesel energy generator and ultra-capacitor one unit of each. A recently proposed algorithm called salp swarm algorithm (SSA) is utilized to tune the parameters of PID controller. Initially, the system considered only ultracapacitor (UC) and the frequency deviation characteristics for different conditions have derived. Later, the system is simulated by considering both UC and unified power flow controller (UPFC) and the frequency deviation characteristics for the same are compared with the previous one. A combination of PID-UC-UPFC outperforms the PID-UC by stabilizing the frequency fluctuations under varied operating conditions. Keywords Load frequency control · Hybrid power system · PID controller · Unified power flow controller

1 Introduction Energy crisis is a major issue of the modern power sector in twenty-first century. Increased petroleum prices, huge load demand and limited fossil fuel are making the power system engineer to look for alternative energy resources to tackle the energy crisis problem [1]. This makes an opportunity to use natural resources which are abundantly available on the earth. So during last few decades, huge no. of research on implementation of renewable sources like wind, PV on conventional grid has been going on. This implies to the concept of hybrid power system [2]. Renewable sources like wind turbine generator (WTG) and photovoltaic generator (PVG) D. Mohanty (B) · S. Panda Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur 768018, Odisha, India e-mail: [email protected] S. Panda e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_10

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help in reducing greenhouse gas emissions thus reducing global warming [3]. Use of renewable sources offers the advantages of localized generations thus reducing the transmission line loss and its cost [3]. They also offer an easy of control and maintenance of the generations. For some remote areas where grid supply is difficult to reach, hybrid power system can still power up the area with its own generation [4]. Unlike conventional grid, HPS is a self-sufficient technology as it has the energy storage facility to provide reliable power. Renewable sources like WTG and PVG are primary components of the HPS. But these sources are stochastic in nature owing to environmental factors. To prevent any failure of power supply during the absence of renewable sources, distributed generations like fuel cell (FC), diesel energy generator (DEG) and energy storage system (ESS) are also implemented in the system [2]. The primary role of ESS is to store/supply the extra power during load and generation mismatch. When the generation exceeds the demand, the remaining power is stored as back-up. This backup power is utilized to meet the load during inadequate generation. Battery bank, flywheel, capacitors are the various ESSs used in HPS [5]. Our proposed work has considered ultra-capacitor (UC) as the ESS. In UC, capacitors are connected in series and parallel forming a bank like structure which provides the peak load. UC offers the advantages of zero maintenance and easy of charging-discharging [3]. FACTs controller are used in power system for reactive power compensation, phase angle regulation and stability enhancement [6]. Various FACTs controller have found their application on automatic generation control, load frequency control in recent years and shows remarkable result. In this paper, a unified power flow controller (UPFC) is considered for LFC. UPFC is an important FACTs controller having multiple applications like series compensation, shunt compensation and phase angle regulation [7]. HPS often faces various issues like frequency deviation and voltage instability because of fluctuation in renewable sources. Frequency deviation usually occurs during abrupt change in load and generation [2]. LFC works to diminish frequency fluctuation and stabilize the system. Recently, several intelligent controllers like fuzzy controller [5, 8], fractional-based controllers [2, 9] and adaptive controllers have been proposed to achieve the frequency control. However, these controllers face the disadvantages like complexity, difficulty in practical implementation and researcher’s enough knowledge on the controllers, etc. [10]. Proportional-integral-derivative (PID) controller is usually used controller by the researchers. PID controllers are mostly applied in industries because of its simplicity, low cost, minimum computation time and easy of implementation [10]. Moreover, finding the parameter values of the PID controller is quiet easy. Several papers on LFC of HPS are published so far. In [1, 3], frequency deviation characteristics of HPS by using various ESS like battery energy storage system (BESS), ultra-capacitor (UC) and flywheel storage system (FESS) are compared. A PI controller optimized by genetic algorithm for frequency control of microgrid is proposed in [11]. GA optimized PID controller for frequency stabilization of solar thermal-based HPS is proposed in [12]. Various controllers like I, PI, PD and PID controllers have compared for isolated HPS in [6,

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13, 14] papers used UPFC for automatic generation control (AGC) of power system containing hydrosources.

2 HPS Modelling The proposed HPS has WTG, PVG, FC and DEG each of one unit and is given in Fig. 1. It also includes one energy storage unit of ultra-capacitor (UC) and one unit of UPFC. Both are connected to the load side. The PID controller sends the command to DEG, FC, UC and UPFC. Parameters of each component are given in Table 1. • Photovoltaic Power Generation The transfer function model of PVG [2] is given G PV (s) =

K PV PPV = 1 + sTPV ϕ

Fig. 1 Transfer function model of the suggested HPS

(1)

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Table 1 HPS parameters [2, 14]

Elements

Gain

Time constant

PV

K PV = 1

TPV = 1.8

WTG

K WTG = 1

TWTG = 1

FC

K FC = 0.01

TFC = 4

DEG

K DEG = 0.003

TDEG = 2

UC

K UC = −0.7

TUC = 0.9

UPFC

–

TUPFC = 0.01

Power system

–

M = 0.4 D = 0.03

where K PV is gain and TPV is time constant. PPV , ϕ are PVG output power and solar radiation, respectively. • Wind Turbine Generator The WTG output [1] is specified by PW =

1 ρ Ab CP VW3 2

(2)

where Ab is the blade swept area,ρ the density of air, VW is the wind velocity, CP is the wind turbine power coefficient and is derived from [2]. The WTG is given by a linear first-order transfer function G WTG (s) =

K WTG PWTG = 1 + sTWTG PW

(3)

where K WTG is gain and TWTG is time constant. • Diesel Energy generator The controller controls the mass flow rate of DEG by giving appropriate control signal and is given by G DEG (s) =

K DEG 1 + sTDEG

(4)

where K DEG is gain and TDEG is time constant. • Fuel Cell Controller sends the control signal to the actuator of FC based on the frequency deviation which then again controls the hydrogen flow rate of the FC. Thus, the power output of the FC is controlled. FC can be given by

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Fig. 2 Representation of ultra-capacitor

G FC =

K FC 1 + sTFC

(5)

where K FC is gain and TFC is time constant. • Ultra-Capacitor UC is considered as ESS for our system and given by its rate constraint nonlinearity [5]. G UC (s) =

K UC 1 + sTUC

(6)

where K UC and TUC are the gain and time constant of ultra-capacitor. The equivalent representation of UC is shown in Fig. 2. • Unified Power Flow Controller Flexible AC transmission system has been making itself a wise choice for various power system applications such as voltage stability, real and reactive power support, power oscillation damping and frequency stabilization. Out of all the FACTS-based controllers, UPFC is the most versatile one in terms of improving transient stability, controlling power flow and voltage support [15]. It also helps in improving dynamic as well as steady-state system performance. For LFC approach, UPFC can be denoted through transfer function and given by G UPFC (s) =

1 1 + sTUPFC

(7)

where TUPFC is the UPFC time constant. • Equivalent Power system The power system model is given by G sys (s) =

f 1 = Pe Ms + D

(8)

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where D(=0.03) and M(=0.4) are equivalent damping and inertia constant of the rotating part of the proposed HPS.  f and pe is the frequency variation and power variation, respectively. pe is the difference between the total demand and total generation.

3 Objective Function The sole objective of the proposed work is to diminish the frequency fluctuations. Various performance indices are used to represent the objective functions so that the desired transient response as well as steady-state response is achieved. Integral of absolute error (IAE) is a well-known performance index which is mostly employed in various similar kinds of system designs. IAE minimizes the substantial errors which happens during transient period and reduces small errors during steady-state period. Thus, IAE is chosen as performance index in our paper over other norms. The optimized objective function is denoted by Tsim | f |dt J=

(9)

0

where  f is frequency deviation.

4 PID Controller Design The proportional controller reduces the rise time thus making the system faster but unable to make zero steady-state response. The shortcoming of the proportional controller is reduced by integral controller, i.e. making zero steady-state error but it worsens the dynamic response. On the other hand, a derivative controller improves the dynamic response as well as stability increasing the sensitivity towards noise [6, 13]. Thus, using any of the above-mentioned controller never fulfils the overall system performance under disturbances. A combination of the three controllers (proportional-integral-derivative) with a wisely chosen controller values improves the system dynamic as well as steady-state behaviour. PID controller has found its practical applications due to its robust nature and simplicity. Though several other intelligent controllers have proposed so far, but the conventional PID controller is widely chosen as the first priority in industrial applications. The PID controller is given in Fig. 3. The PID controller is given by

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Fig. 3 Structure of PID controller

G(s) = K P + K D s +

KI s

(10)

where K P , K D & K I are proportional, derivative and integral gains, respectively. The range of the parameters [2] is taken as [0, 60].

5 Salp Swarm Algorithm SSA is formulated on the swarming movement of the biggest swarms on the planet, i.e. salp [16]. It is believed that the navigating and foraging behaviour of the salps help them to achieve the best promising location by a coordinated action. The complete population of salp is categorized into two groups: leader and followers. The first position of the salp sequence is always occupied by the leader. The leader directs the other swarms. Follower salps pursue the leader directly or indirectly. The position of the salp is characterized by a n-dimensional search space. The food source T is target for the salp in the search boundary. The location of the leader always represented by,  x 1j

=

T j + c1 ((ub j − lb j )c2 + lb j ) c3 ≥ 0.5 T j − c1 ((ub j − lb j )c2 + lb j ) c3 ≤ 0.5

(11)

where x 1j and T j denote the positions the leader and food source in the jth dimension. The leader updates its location with respect to the food source only. The lower and upper bound of jth dimension are given by lb j , ub j . c1 , c2 and c3 are random numbers. The coefficient c1 is represented as 

4i c/ IM c1 = 2e−

2

(12)

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where i c and I M denote the present iteration and the maximum no. of iteration, respectively. c2 and c3 parameters are uniformly created any numbers in [0,1]. The position of the other salps (i.e. followers) are updated by the equation given by x ij =

1 2 at + v0 t 2

(13)

6 Result and Analysis The described HPS is simulated in MATLAB/Simulink R2016a environment. The system is initially simulated by taking the ultra-capacitor (UC) only and then both the ultra-capacitor and unified power flow controller (UPFC) are considered for comparison purpose. Effectiveness of the combination of UC-UPFC in suppressing the frequency deviation over only UC is validated for three different conditions. These are: 1. Step change in wind, PV and load demand. 2. Random variation of load at constant wind and PV power. 3. Robustness of the system under severe system uncertainties. PID controller parameters are tuned by the SSA algorithm for first condition and the tuned parameter values remains same for second condition. The values are given in Table 2. Least value of the objective function ( jmin = 0.0985) is derived with the combination of UC-UPFC as compared to the use of only UC ( jmin = 0.1144).

6.1 Case 1 Power output of the WTG (PWTG ), PV (PPV ) and the total renewable power (PT ) are given in Fig. 4. The load variation (PL ) for the total simulation time is also given in Fig. 4. Initially, the wind power output is 0.21 pu and PV power output is 0.5 pu up to 20 s. So the total generated power is 0.7 pu (as shown in PT ) and load demand is 0.3 pu. The extra generated power is reserved in UC. The frequency variation Table 2 Parameters of PID-UC and PID-UC-UPFC conditions

Parameters

PID-UC

PID-UC-UPFC

KP

20.2353

55.7304

KI

25.6443

37.9406

KD

3.3711

7.6700

jmin

0.1144

0.0985

Salp Swarm Optimized PID Controller …

125

Fig. 4 Renewable sources power output and load demand

characteristic is given in Fig. 5. With UC-UPFC, the error in frequency deviation is reduced as compared to UC. At t = 15 s, all the energy sources and the demand changes to different level as shown in Fig. 4. From 15 to 40 s, the total generation from renewable sources is 0.37 pu. But the peak demand increases to 0.5 pu for t = 15 s to t = 30 s. During this period, the demand is higher than the generation. The surplus power is mitigated by the DEG, FC, UC and UPFC. It has seen from Fig. 5 that with UC and UPFC, the oscillations in frequency deviation is almost nil. At t = 30 s, generation remains the same, whereas load reduces to 0.4 pu. The proposed system is able to meet the demand with the coordination of renewable sources (wind

Fig. 5 Frequency deviation characteristic (Case 1)

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and PV), distributed sources (DEG and FC), energy storage system(UC) and FACTS controller (UPFC). In fact, the use of FACTS controller (UPFC) reduces the peak overshoot as given in Fig. 5.

6.2 Case 2 The proposed system is simulated with constant wind and PV power output of 0.1 pu and random load fluctuation as shown in Fig. 6. The load varies randomly from 0.14 pu to 0.16 pu throughout the simulation time of 20 s. The frequency deviation characteristic is shown in Fig. 7. The figure clearly demonstrates that PID controller along with UC and UPFC is able to make frequency deviation fluctuations as minimum as possible as compared to PID with UC. This makes a conclusion that UPFC plays a major role in controlling the frequency disturbances along with UC for a power system. Fig. 6 Random load variation (case 2)

Fig.7 Frequency deviation characteristic (case 2)

Salp Swarm Optimized PID Controller …

127

Fig. 8 Frequency deviation characteristic (case 3)

6.3 Case 3 The effect of the UC-UPFC on the robustness of the system under severe abnormal condition has verified by making severe disturbances on the system and compared with that of only UC. Under this condition, WTG is completely turned off, PVG power output is reduced by 25%, load demand increased by double and the inertia constant (M) increased by 500%. The frequency deviation characteristic under this condition is depicted in Fig. 8. Under this condition, severe oscillations occur in frequency deviation when the system contains only UC. With the incorporation of UPFC along with UC, minimum frequency fluctuations occur and the system stabilizes within 3s.

7 Conclusion In this paper, load frequency control of HPS by SSA optimized PID controller is suggested. At first, the HPS is simulated by considering only ultra-capacitor under different system operating conditions. Then, the FACTS controller UPFC is incorporated with the UC for the HPS. Combination of UC and UPFC shows superior performance than with only UC under varied system operating conditions. With UPFC, the robustness of the system is also improved for severe transient conditions. So, UPFC is able to improve the stability of the HPS when used with energy storage system like ultra-capacitor.

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References 1. Lee DJ, Wang L (2008) Small-signal stability analysis of an autonomous hybrid renewable energy power generation/energy storage system part I: time-domain simulations. IEEE Trans Energy Convers 23(1):311–320 2. Pan I, Das S (2015) Fractional order AGC for distributed energy resources using robust optimization. IEEE Trans Smart Grid 7(5):2175–2186 3. Ray P, Mohanty S, Kishor N (2010) Small-signal analysis of autonomous hybrid distributed generation systems in presence of ultracapacitor and tie-line operation. J Electr Eng 61(4):205– 214 4. Bhatt A, Sharma MP, Saini RP (2016) Feasibility and sensitivity analysis of an off-grid micro hydro–photovoltaic–biomass and biogas–diesel–battery hybrid energy system for a remote area in Uttarakhand state India. Renew Sustain Energy Rev 61:53–69 5. Pan I, Das S (2016) Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO. ISA Trans 62:19–29 6. Nandi M, Shiva CK, Mukherjee V (2019) A moth–flame optimization for UPFC–RFB-based load frequency stabilization of a realistic power system with various nonlinearities. Iranian J Sci Technol Trans Electr Eng 43(1):581–606 7. Lal DK, Barisal AK (2017) Comparative performances evaluation of FACTS devices on AGC with diverse sources of energy generation and SMES. Cogent Eng 4(1):1318466 8. Bevrani H, Habibi F, Babahajyani P, Watanabe M, Mitani Y (2012) Intelligent frequency control in an AC microgrid: online PSO-based fuzzy tuning approach. IEEE Trans Smart Grid 3(4):1935–1944 9. Pan I, Das S (2014) Kriging based surrogate modeling for fractional order control of microgrids. IEEE Trans Smart Grid 6(1):36–44 10. Hasanien HM, El-Fergany AA (2019) Salp swarm algorithm-based optimal load frequency control of hybrid renewable power systems with communication delay and excitation crosscoupling effect. Electr Power Syst Res 176:105938 11. Nandar CS (2013) Robust PI control of smart controllable load for frequency stabilization of microgrid power system. Renew Energy 56:16–23 12. Das DC, Roy AK, Sinha N (2012) GA based frequency controller for solar thermal–diesel–wind hybrid energy generation/energy storage system. Int J Electr Power Energy Syst 43(1):262–279 13. Mahto T, Mukherjee V (2016) Evolutionary optimization technique for comparative analysis of different classical controllers for an isolated wind–diesel hybrid power system. Swarm Evolut Comput 26:120–136 14. Pradhan PC, Sahu RK, Panda S (2016) Firefly algorithm optimized fuzzy PID controller for AGC of multi-area multi-source power systems with UPFC and SMES. Eng Sci Technol Int J 19(1):338–354 15. Panwar A, Sharma G, Nasiruddin I, Bansal RC (2016) Frequency stabilization of hydro–hydro power system using hybrid bacteria foraging PSO with UPFC and HAE. Electr Power Syst Res 161:74–85 16. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

Implementation of Fuzzy Hysteresis Controller for a Three-Phase Photovoltaic Multilevel Inverter Gayatri Mohapatra and Manoj Kumar Debnath

Abstract In this article, a novel type-2 fuzzy-based hysteresis controller is suggested to control the load current of the three-phase induction motor. The spider monkey optimization technique (SMO) provides optimized gains of the fuzzy logic controller with an objective of attaining minimum distortion of the load current. The dynamic response of the system is observed by implementing PID, type-1 and type-2 fuzzy controllers independently with hysteresis controller to minimize the error. The supremacy of the proposed fuzzy-based hysteresis controller is justified in terms of harmonic contents. Keywords Fuzzy logic controller · Hysteresis controller · Multilevel inverter · Spider monkey optimization

1 Introduction The pulse width modulated voltage control for AC motor drive indirectly defines the stator/rotor current control because of the current dependency of the torque. Among different current control methods, hysteresis based one is the most adoptable because of its simplicity, fast current response loop, and in build peak current limit capability [1, 2]. Current controls of VSI-empowered inverters are playing vital role in the power electronics based drive industries due to their robust controller strategies. Hysteresis (h-band) current controller is one of the widely used and easily tuned regulators. The conventional h-band controller gives accurate and fast response with the limitations of the phase’s interference and the vast changes of the switching frequency, resulting erratic inverter operation, and asymmetrical output current responses. This can get ran over by a novel method of the modulations of the bands [3–6] The variable band hysteresis control has an advantage of reducing the effect of generation of additional harmonic which is developed in fixed band owing to the control of maximum current ripple at the point of fundamental oscillation. Reduced G. Mohapatra (B) · M. K. Debnath ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_11

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commutation loss and increased voltage handling capability can be observed among the inverters by this novel method [7–11]. Many researchers have published articles to develop new concepts in these areas. Some of them are given below; A hysteresis-based three-phase, current-controlled, voltage source PWM inverters for constant switching frequency is presented in [3, 4] for fixed and variable band. A review of different current regulation methods like hysteresis, linear, predictive, neural, and fuzzy dependent controllers is debated in [5, 6]. A new adaptive hysteresis current controller is presented in [7] with adaptive hysteresis band where switching patterns are modified by fuzzy logic rules. A multiband hysteresis current controller is described in [8] for a new inverters (Jillbridge) for constant frequency of switching among the IGBTs. A new hysteresis technique with reduced common mode switching for three-phase MLI is proposed in [9, 10]. A dual mode-based controller and different degree of freedom is discussed in [11–13] for the automatic generation control in a multi-area system with SMO and fuzzy controller. A simple unipolar maximum switching frequency limited hysteresis current control strategy for the grid-connected inverter is given in [14, 15]. A realtime-based data measurement with high sampling frequency, for both the current error and the negative half switching period is proposed in the same. Different controllers for the speed control of the induction motor as well as current regulation are proposed in [16–18]. The optimized switching angle is also estimated using curve fitting techniques. The fixed and sine-band hysteresis controllers are proposed in [19]. A study on the VSI-based distribution static compensator for compensation of neutral, source, and PCC harmonic distortion, voltage regulation using different types of current control methods like PID and PSO is done in [20]. A close examination of the literatures explains that the optimization method, switching frequency, selection of the hysteresis band, and the choice of controller gain parameter plays an important role in the current control of the system under study. A detail investigation on the controller implies that the PID controller is the simplest controller which is used in the closed-loop operation but finds its limitations in the field of adaptive control for frequent variation of the input applied to it. The salient features of the research work in this article are to: • Construct a novel hysteresis band controller for the speed control of a three-phase induction motor. • Optimize the scaling factors of the fuzzy and PID controller employing spider monkey optimization method. • Observe the system pursuance in different disturbances. • Assess the advantage of the proposed fuzzy-hysteresis controller during different disturbance to substantiate the supremacy over PID-hysteresis controller.

Implementation of Fuzzy Hysteresis Controller …

131

Fig. 1 Gives the generalized schematic of the cascaded H-bridge inverter

2 System and Controller Modelling 2.1 The Inverter Output Voltage Modelling The inverter with multiple levels with calculated set of IGBTs and capacitors (acting like a DC sources) for a predefined output potential is explained in Fig. 1. The pattern of switching adds the individual bridge voltages which get amplified at the load point. The arrangement of switches in the H Bridge helps in resisting the reduced voltages across each switch. The CHB converter is a set of exclusive H_bridge kept in a chain with the advantage of accruing less number of components as compared to other multilevel topology and hence being cost effective [1, 2]. The per phase bridge voltage in the Fourier’s series of representation [16–18] VAn can be as per Eq. 1. The pre-defined rms VAn can be obtained by optimizing the delay angles given in Eq. 2 [5, 6]. ∞  4E dc [cos ine(hβ1 ) + · · · + cos ine(hβs )] sin(hwt) xπ x=1,3,5

(1)

cos(β1 ) + · · · + cos(β S ) = S × M 1 ; . . . ; cos(xβ1 ) + · · · + cos(xβ S ) = 0

(2)

VAn (wt) =

2.2 Small Signal Modelling of the Load The controller using the system can be tuned if the load and other system parameters are modeled. The three-phase IM can be modeled as in Eq. 3.

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⎡

⎤ ⎡ ⎤ L s ωs ρ Lm L m ωs vqs Rs + ρ L s ⎢ vds ⎥ ⎢ ⎥ −L s ωs Rs + ρ L s −L m ωs ρ Lm ⎢ ⎥=⎢ ⎥ ⎣ vqr ⎦ ⎣ ρ Lm L m (ωr − ωs ) Rr + ρ L r L r (ωr − ωs ) ⎦ vdr ρ Lm L r (ωr − ωs ) Rr + ρ L r −L m (ωr − ωs ) ⎡ ⎤ i qs ⎢ i ds ⎥ dωm ⎥ ×⎢ ⎣ i qr ⎦, Te = TL + J dt

(3)

i dr V dqs is the d q component of the voltage, Rsr and L sr are the stator and rotor passive element, ws is the slip speed, and wr is the rotor speed. T e is the electrical torque, T L is the load, and T m is the mechanical Torque.

3 System Investigated In this proposed work, a three-phase seven-level cascaded H-bridge inverter is designed with MATLAB Simulink environment as given in Fig. 2. The photovoltaic Cell of 200 V output each is connected to each H Bridge. Each of the bridge is equipped with four IGBTs, one PV array and one capacitor. Different variation of the torque and h-band is made and the dynamic response of the system in the form of current and FFT analysis is scrutinized while controlling the current within the reference value. The optimization of the gains of the PID and the fuzzy controller has been executed by implementing the total harmonic distortion as the fitness function

Fig. 2 Gives the detail system simulated with the control diagram

Implementation of Fuzzy Hysteresis Controller …

133

Fig. 3 Gives the block diagram of the PID controller

as given in Eq. 4.

h

 vAnz THD =  v z=2 funda

(4)

V az is the output voltage of the individual H-bridge and V fund is the fundamental component of load voltage [1, 2, 16].

4 Controller Structure 4.1 PID Controller The PID controller is able to find a balance among the three control terms like proportional, integral, and derivative to maintain the output within the set ranges within minimum time frame [11, 12, 19, 20] The diagram in Fig. 3 shows the arrangement of different gain factors where the controller continuously calculates an error (the difference between a desired set point and a measured process variable) and applies a correction factor to maintain a stability.

4.2 Fuzzy Logic Controller This controller has an upgraded disturbance handling efficacy due to the modulated membership function, as compared to type-1 fuzzy. For this controller, the primary membership grade is same as that of the type-1 fuzzy set in a predefined boundary as given in Fig. 4a, which takes three functions of membership defined as negative big (nf), zero (zf), and positive big (pf). In conformity to each fundamental membership, there exists a secondary function (which can also be within same or different boundary w.r.t. the type 1), as in Fig. 4b; this defines the probability of the prime member. The system output is a fuzzy value and required to get converted to a real number using a suitable defuzzyfication approach. The fuzzy approach is based on the error and the current variables as given in Eqs. 6 and 7 [7, 8, 12]. A three-dimensional type-2

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Fig. 4 a, b Give the fuzzy membership functions of the primary and type-2 controllers, respectively

fuzzy set can be defined as in Eq. 8 ∗

∗

e(t)k = ω −ωk , e(t) = e(t)k − e(t)k−1 k

i dqrk (t) =

∗

i

dqr(k−1)

∗

+∂ i

(5) (6)

dqrk

F(A) = (THD, β), μ F(A) (∀THD, β)|∀THD, β JTHD | ⊆ [a, b]

(7)

Here, F(A) is the type-2 Fuzzy set, a, b are the set boundary preferably [0, 1], μF(A) is the type-1 Fuzzy set, THD, and β are the primary and secondary membership functions, respectively. The input to the controller is r(t), error of the system is e(t), disturbance inserted to the system is d(t), output of the controller is y(t), e(t)k kth • • time error of the system, ωk rotor speed at kth instant, e(t) change of error, ω(t) change of speed, and L 1 , L 2 gain factor for the fuzzy. The compiled block diagram of the proposed controller is given in Fig. 5 and the rule table is given in Table 1.

Fig. 5 Gives the block diagram of the fuzzy controller

Implementation of Fuzzy Hysteresis Controller … Table 1 Defines the rule set for the fuzzy controller

Error

135 nf

zf

pf

 Error nf

nf

nf

zf

pf

nf

zf

pf

zf

zf

pf

pf

4.3 Hysteresis Controller This is the most robust controller which basically performs the current control of the system by regulating the allowable band of the signals [6, 8–10, 14, 15, 19]. The algorithim applied for the hysteresis controller is given below where switches of the bridge of arm_a are mentioned as assigned in Fig. 1. The other two arms as arm_b and arm_c take the error_b and error_c, respectively. The error is defined as the difference between the I ref and actual per phase load current. I ref is given in the appendix which is the reference load current. The hysteresis band can be changed if another H-bridge is added to the system. If (error_a> =hb) { Switch_4 is on Else if Switch_1 is on Else no change } End If (error_a< = (-hb)) { Switch_1 is on Else if Switch_4 is on Else no change } End

If (error_a>=hb) { Iref_sl= Ir_inst1-Ir_inst2 Il_sl=Il_in1- Il_in2 If (Il_sl>=Iref_sl) { Switch_3=Gate_3; Switch_2=Gate_2; Else Switch_3=Gate_2; Switch_2=Gate_3; } End Else if (error_a= PL

NO

(BaƩery Discharging)

PPV + PWT = PL + PBAT (Charging)

Is PPV + PWT + PBAT+ PDG = PL

YES PPV + PWT + PBAT = PL +

Dummy load

Fig. 2 Simulation and optimization procedure flowchart

Fig. 3 Weibull probability distribution function

where ρ a , C PW , ηW are density of air, coefficient of wind turbine performance, and the combined efficiency of the wind turbine generator, respectively, whereas AW, υ a , and f (t) are the wind turbine swept area (m2 ), the wind velocity (m/s), and the wind probability density function, respectively. The wind speed is of probabilistic nature and, therefore, it is normally described by the help of a probability density function. The wind speed is measured near the ground surface which can be modified for a particular hub height as given by the following equation [16].

Reliability Assessment and Cost Estimation …

173

 v = vi

H Hi

α (2)

where v = wind speed at height H, vi = wind speed at reference height H i α = −1/7 for open land. The wind speed is considered to be a weibull distribution and is represented by the probability density function [9] f (v) =

    v k k  v k−1 . . exp − c c c

(3)

where c = scale factor having unit of speed, k = shape factor that is dimensionless, and v = wind speed (Fig. 3). The average power output of the wind turbine generator can be determined ∞ Pw,avg =

Pw . f (v) . dv

(4)

0

PW is the power output of the wind turbine generator and is a function of wind velocity given by the expression; ⎧ v k −v k ⎪ ⎨ PR . vRk −vcck for vc ≤ v ≤ vR Pw = PR for vR < v < vF ⎪ ⎩ 0 otherwise

(5)

where PR ,vc , vR , and vF are the rated power, cut-in wind speed, rated wind speed, and cut-off wind speed. k is the Weibull shape parameter.

2.2 Modeling of Photovoltaic System Solar panels receive light emission from the sun and convert it to electrical energy through the photovoltaic effect. The output of solar panel is highly affected by the climate, especially the solar irradiance, ambient temperature, season of the year, the tidal surfaces. In the research, a simplified simulation model is used to determine the solar panel module performance and to evaluate of output power generator; the manufacturer’s data for the solar module, the solar radiation that reaches the surface panels, and the ambient temperature are used as model inputs for the solar radiation

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every hour at the site under study. The computation method and the power generation by the solar panel are PPV and is given by the equation given as [3]. PPV = NPV × ηPV × Am × G t

(6)

The efficiency of solar panel power generator is given by the equations ηPV = ηref × ηpc [1 − β(Tc − Tc ref )]

(7)

Tc can be calculated from equation  Tc = Ta +

NOCT − 20 800

 × Gt

(8)

where Ta,NOCT = 20 ◦ C and G t,NOCT = 800 W/m2 for wind speed of 1 m/s. The rated power ηC = ηTref [1 − βref (Tc − Tref )]

(9)

Solar radiation measured in addition to temperature measurements is necessary to calculate the output power of the PV system. The power supplied by the panels is calculated by the equation  PPV−out = PPV ×

G G ref

 × [1 + K T (Tc − Tref )]

(10)

PPV−out = form the PV cell power output, G = solar radiation (W/m3 ), G ref = solar radiation at reference condition (G ref = 1000 W/m2 ), Tref = cell temperature at reference conditions (Tref = 25 ◦ C), K T is the temperature coefficient of the maximum power (K T = −3.7 × 10−3 (1/°C)) for mono and poly-crystalline Si. The cell temperature Tc = Tamb + (0.0256 × G), where Tamb is the ambient temperature.

2.3 Modeling of Battery Storage System The battery used here is with an intention to store the excess renewable energy and deliver to the load when ever required. During charging, it behaves as a load to the system and is expressed as [17]. E Bat(t) = E Bat.(t−1) × (1 − σ ) +



 E i(t) −

E AC.load(t) + E DC.load(t) ηlnv

 × ηBat.Ch (11)

Reliability Assessment and Cost Estimation …

175

When load demand is greater than the renewable energy, battery discharge to the load is given in the equation E Bat(t) = E Bat.(t−1) × (1 − σ ) −



 E i(t) −

E AC.load(t) + E DC.load(t) ηlnv

 × ηBat.Disch (12)

where E Bat(t) and E Bat(t−1) are energy status at time t and (t – 1). ηBat.Ch , ηBat.Disch are efficiency during charging and discharging which is 87% and 85% in the present study. ηlnv and σ are inverter efficiency and self discharge rate of the battery (0.2% per day), respectively.

2.4 Modeling of Diesel Generator A diesel generator (DG) is small power back up during peak load demand. When the renewable energy power along with the battery storage power is not sufficient to meet the load demand, diesel generator is used to supply power to the micro-grid system. The operation of diesel generator has identical principle as that of a conventional generator. The fuel cost can be formulated by the expression [4]. F = cf

T

2 a PDG(t) + b PDG(t) + c

(13)

t=1

where a, b, c are the cost coefficients of the diesel generator and cf is price of one liter of diesel fuel.

3 Proposed Management Strategy The objective of this paper to introduce an algorithm to find the cost generation in hybrid micro-grid system and satisfying the defined reliability factor under different strategy. The reliability assessment model and renewable use factor have been used for proposed hybrid system.

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3.1 Operational Strategy Strategy 1: The hourly load demand is supplied from the diesel generator only without renewable energy sources. This condition may happen during extreme climatic conditions where the power generation from photovoltaic and wind turbine generators is not possible or during maintenance of the units. Reliability and cost of hybrid generation system are determined. Strategy 2: The photovoltaic (PV) and wind turbine generator (WTG) supply power to the load without storage battery and diesel generator. The reliability and cost of generation of generation are determined. A dummy load can be used to absorb the surplus energy. Strategy 3: The load supplied only from photovoltaic cell and wind turbine generator with the backing of the storage battery and reliability assessment and cost of power generation is calculated. Strategy 4: The hourly varying load is primarily supplied from photovoltaic and wind turbine generator. The excess power generated from these sources is stored in the battery to its maximum capacity and can be used when renewable power is not meeting the load demand. The diesel power is integrated whenever there is deficient of power from the above sources. The reliability assessment and cost of system are estimated.

3.2 Reliability Assessment In a standalone micro-grid system, reliability of power supply is an important aspect of its operational management. When the renewable sources are insufficient to meet the load demand, the system loses its reliability. Reliability assessment is mostly affected by the renewable energy generated and to some extent the load pattern. Loss of load probability (LOLP) is a specialized model for reliability assessment and is characterized as [18]. T LOLP =

0

Deficit load time × 100 T

(14)

Lower value of LOLP indicates higher reliability of hybrid renewable energy system.

Reliability Assessment and Cost Estimation …

177

3.3 Renewable Factor (RF) The performance of the hybrid renewable micro-grid system is highly impacted by environmental condition. The main objective is to minimize the use of diesel generator for lowering the carbon emission and reducing the cost of generation. Therefore, the maximum use of renewable energy helps in reducing the cost as well as the carbon emission. The renewable energy factor can be modeled as  Renewable Factor = 1 − 



 PDG × 100 (PPV + PW + PDG )

(15)

where PPV , PW , PDG are power generated by photovoltaic, wind turbine generator, and diesel generator, respectively. This can be stated that the renewable factor is 100% when load is supplied from renewable sources which is the ideal operating condition. This also lowers the cost of operation and reduces the carbon emission.

4 Result and Discussion Resources data presented for the present study is obtained from the energy survey carried out in rural communities in South Africa [10]. The hourly load data, wind speed, and solar irradiance are given in Table 1. The generator parameters are presented in Table 2. The power generated by wind turbine generator and PV are determined using Eqs. (5), and (10), respectively. The surplus generated power is stored in the battery bank. The battery behaves as a load during charging and source while discharging to the load within its minimum and maximum limits as in Eqs. (11) and (12). The main use of diesel generator is to meet the load demand and to improve the reliability of the system when renewable sources are not able to generate adequately. In the present work, an algorithm has been developed as shown in Fig. 2 for operation of hybrid renewable energy system in standalone mode. Mathematical model has been developed for reliability assessment and cost estimation. In this study, the fuel cost of diesel generator has been considered for analysis. The operational cost of renewable energy sources has been neglected in this study as being small. The power generated by PV and wind turbine generator is shown in Figs. 4 and 5. The status of the battery during each hour of the day is presented in Fig. 6. The output of the diesel generator is shown in Fig. 7. Table 3 shows cost of operation of DG, reliability assessment, and renewable factor for different strategies. It can be observed that in strategy 1, when the DG is operating alone without renewable sources, the cost of operation is maximum and reliability of power supply is 100%. But the renewable energy factor is zero. In strategy 2, when renewable sources supply power, though the cost reduces to almost zero, the reliability also decreases to 45.43%.

178 Table 1 Hourly load and renewable data

Table 2 Generator parameters

N. C. Sahu et al. Time (hours)

Load (kW)

Solar irradiance

Wind speed (m/s)

1

0.2

0.000

0.821

2

0.3

0.000

1.665

3

0.1

0.000

0.998

4

0.0

0.000

0.956

5

0.3

0.000

2.549

6

0.0

0.000

2.558

7

2.4

0.000

2.775

8

0.6

0.002

3.754

9

4.3

0.141

2.948

10

5.6

0.417

2.828

11

3.2

0.687

2.870

12

1.6

0.904

2.525

13

0.3

1.062

1.766

14

2.0

1.061

2.576

15

0.4

0.987

2.017

16

0.8

0.846

2.282

17

3.9

0.679

3.116

18

1.8

0.464

2.626

19

1.7

0.208

3.427

20

1.9

0.043

2.972

21

2.9

0.000

2.543

22

0.9

0.000

2.336

23

0.7

0.000

1.863

24

0.3

0.001

1.232

Parameters

Ratings

Sampling time (t)

1h

PV-rated power

4 kW

Wind-rated power

1 kW

Battery

5 kW

DG rated power

8 kW

Diesel fuel price

1.42$/l

a

0.2411

b

0.0814

c

0.4332

Reliability Assessment and Cost Estimation …

Fig. 4 Power generated from PV

Fig. 5 Power generated from wind turbine

179

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N. C. Sahu et al.

Fig. 6 Power status of battery

Fig. 7 Fuel cost of diesel generator Table 3 Result for different strategies

Strategy

Operation cost of DG ($)

Renewable factor (%)

Reliability assessment (%)

Strategy 1

53.8931

00

100

Strategy 2

0.00

100

45.43

Strategy 3

0.00

100

83.33

Strategy 4

19.7945

49.52

100

Reliability Assessment and Cost Estimation …

181

Fig. 8 Reliability assessment of the proposed hybrid system

Fig. 9 Renewable factor for proposed hybrid system

In strategy 3, when battery bank is used to store the excess renewable energy, the reliability factor increases to 83.33%. In strategy 4, the integration of diesel generator with renewable sources with battery storage increases the reliability to 100% and the cost also reduces (Figs. 8 and 9).

5 Conclusion This paper proposes a novel solution for optimum use of renewable sources for an independent hybrid energy system. In this work, the solar irradiance, ambient temperature, and wind velocity have been used for theoretical analysis with probabilistic approach. Due to the nature dependence of renewable sources, the maximum use of these sources is not advisable because of reliability of power supply. The ideal condition for reliability and cost is to operate renewable hybrid system that has been presented in this paper. Different operating strategies have been discussed in this paper to determine reliability, cost, and renewable use factor. This approach can be implemented for other renewable sources for future work.

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References 1. Borowy BS, Salameh ZM (1996) Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system. IEEE Trans Energy Convers 11(2):367–375 2. Maleki A, Rosen MA, Pourfayaz F (2017) Optimal operation of a grid-connected hybrid renewable energy system for residential applications. Sustainability 9(8):1314 3. Borowy BS, Salameh ZM (1994) Optimum photovoltaic array size for a hybrid wind/PV system. IEEE Trans Energy Convers 9(3):482–488 4. Kusakana K, Vermaak HJ (2014) Hybrid diesel generator/renewable energy system performance modeling. Renew Energy 67:97–102 5. Hassan MA, Abido MA. Optimal design of microgrids in autonomous and grid connected modes using particle swarm optimization. IEEE Trans Power 6. Koutroulis E, Kolokotsa D, Potirakis A, Kalaitzakis K (2006) Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Sol Energy 80:1072e88. 7. Maleki A, Rosen MA, Pourfayaz F (2017) Optimal operation of a grid-connected hybrid renewable energy system for residential applications. Sustainability 9(8):1314 8. Ould Bilal B, Sambou V, Ndiaye PA, Kebe CMF, Ndongo M (2010) Optimal design of a hybrid solarewinde battery system using the minimization of the annualized cost system and the minimization of the loss of power supply probability (LPSP). Renew Energy 35:2388e90 9. Rathore A, Patidar NP (2019) Reliability assessment using probabilistic modelling of pumped storage hydro plant with PV-Wind based standalone micro grid. Int J Electr Power Energy Syst 106:17–32 10. Kusakana K (2016) Optimal scheduling for distributed hybrid system with pumped hydro storage. Energy Convers Manage 111:253–260 11. Kusakana K, Vermaak HJ (2014) Hybrid diesel generator/renewable energy system performance modeling. Renew Energy 67:97–102 12. Vermaak HJ, Kusakana K (2014) Design of a photovoltaic–wind charging station for small electric Tuk–tuk in DR Congo. Renew Energy 67:40–45 13. Kusakana K (2015) Minimum cost solution of isolated battery-integrated diesel generator hybrid systems. South African University Power and Energy conference (SAUPEC 2015) 14. Razykov TM et al (2011) Solar photovoltaic electricity: current status and future prospects. Solar Energy 85(8):1580–1608 15. Mohamed MA, Eltamaly AM, Alolah AI (2016) PSO-based smart grid application for sizing and optimization of hybrid renewable energy system. PLoS ONE 11(8):e0159702 16. Yeh TH, Wang L (2008) A study on generator capacity for wind turbines under various tower heights and rated wind speeds using Weibull distribution. IEEE Trans Energy Convers 23(2):592–660 17. Borowy BS, Salameh ZM (1996) Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system. IEEE Trans Energy Convers 11(2):367–375 18. Garcia RS, Weisser D (2006) A wind-diesel system with hydrogen storage: joint optimisation of design and dispatch. Renew Energy 31(14):2296–2320

An Ontology Based Matchmaking Technique for Cloud Service Discovery and Selection Using Aneka PaaS Manoranjan Parhi, Bhupendra Kumar Gupta, and Binod Kumar Pattanayak

Abstract As we are moving ahead with cloud computing, preference to cloud services is getting increased day by day. These services mostly seem to be significantly identical in their functionality except their key attributes like storage, computational power, price etc. As of now there is no uniform specification for defining a service in cloud domain. In order for specification of the identical operations and publication of the services on the websites, different cloud service providers tend to use completely different vocabulary. The process of requesting for a cloud service becomes merely challenging task as a result of increasing number of selection parameters and QoS constraints. Hence, a reasoning mechanism is very much required for service discovery that could resolve the resemblance appearing across different services by inferring with the respective cloud ontology. In this paper, an ontology based matchmaking technique has been proposed for cloud service discovery and selection using a distributed cloud platform called Aneka Platform as a Service (PaaS). Keywords Cloud computing · Cloud service discovery and selection · Quality of service (QoS) · Cloud ontology · Aneka · Platform as a service (PaaS)

M. Parhi (B) · B. K. Gupta · B. K. Pattanayak Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India e-mail: [email protected] B. K. Gupta e-mail: [email protected] B. K. Pattanayak e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_15

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1 Introduction As an innovative modern technology, Cloud Computing tends to leave a significant impact on the Information Technology (IT) industry as a whole in the recent past, where large and popular enterprises like IBM, Google, Amazon Web Service and Microsoft continue to strive in order to provide a relatively more robust cloud computing services that are cost effective. However, the properties such as no up-front investment lowered operating cost, enhanced scalability, reduced risks in business and less maintenance expenses make cloud computing extensively attractive for its clients and also the business owners [1, 2]. Task scheduling is another issue in cloud computing [3, 4]. It is worth mentioning that the cloud services published on the web by different service providers can be smoothly accessed by intended customers by the help of web-portals. As mentioned earlier that several cloud services possess similar functionality and hence, it becomes important to identify the appropriate service which can necessarily comply with the desired service requirements as requested by the customers. Therefore customers most of the time find it difficult in making choice of the most suitable cloud service provider to fulfill their objectives due to the following reasons [5, 6] 1. There is no standardized naming conventions implemented by different cloud service providers. For example, Amazon Web Service named it’s computing services as “EC2 Compute Unit”, where the same services provided by GoGrid are commonly known as “Cloud Servers”. 2. Description of the services that are not formatted, pricing strategies along with the rules pertaining to SLA that are used by different cloud service providers as displayed in their respective website. 3. The modification incorporated in the above mentioned information are not reported to the users that makes it difficult job in order to achieve manually and compare the configuration of the services as reported in the websites of different cloud service providers along with the documentation that represents the only source of information that are available. 4. Traditional search engine in the internet such as Google, MSN, Yahoo and others are not necessarily capable of performing reasoning as well as comparison of various relations that persist among several categories of cloud services along with their configuration. There are many service discovery techniques proposed in the literature [7] in the context of cloud computing based on QoS. But It has been found lack of uniformity of concepts in these researches. Similarly logical solutions are provided by different studies without considering the current status of cloud service providers. Thus, there arises a necessity of an intelligent strategy for cloud service discovery in order for searching an accurate service that possesses higher accuracy and more importantly, searching swiftly with respect to the criteria as imposed by the user request. In this chapter, an efficient ontology based service matchmaking technique is proposed that necessarily takes into consideration the preferences of the customer in

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order for calculation of similarity level in the relevance of descriptions of services provided by two service providers. The application is deployed in Aneka cloud, a Cloud Application Platform, which gives run time support to implement it using different models. In this context, It supports thread programming model for development of the distributed application. Here, the matching technique focuses on selecting an appropriate cloud service provider with higher success rate, thereby consuming lesser response time. The remaining part of this chapter is organized as follows. In Sect. 2, Aneka PaaS: An Application Platform for Cloud Computing is explained briefly. The work flow of the proposed model for service discovery and selection followed by description of corresponding cloud ontology is elaborated in Sect. 3. In Sect. 4, the proposed ontology based matchmaking technique is explained along with algorithms followed by a case study. The implementation of proposed model using Aneka PaaS is explained in Sect. 5. At the end, Sect. 6 concludes the chapter with the contributions made in this work and possible extension in future.

2 Aneka PaaS: An Application Platform for Cloud Computing In order to facilitate the development, deployment, controlling and monitoring the cloud distributed applications Aneka Platform as a Service (PaaS) is used. It is associated with different software components as follows [8]: • Client Libraries of Aneka: These are the libraries also known as Application Programming Interfaces (APIs) which are used to build the cloud application based on Task, Thread and Map-Reduce Programming models. Once an application is developed successfully using such libraries, the execution and deployment becomes quite easier in Aneka Clouds. • Aneka Cloud: It is a network of cloud which is created virtually by the help of Aneka Master and Aneka Worker containers. These containers combinely work in order to execute the cloud applications. The Aneka cloud use the resources which are provisioned from different sources such as private, public, hybrid cloud, networks of computers. Servers containing multiple cores etc. • Aneka Containers: These are the different software components which are deployed on different machines of Aneka cloud. The services which are hosted and offered by different containers vary according to the two roles such as Aneka Master and Aneka Worker. The Aneka Containers are developed using Service Oriented Architecture (SOA) which contains the core and other services that are easily pluggable according to the requirements of client application [9] and [10]. Further the new services can be created or existing services can be replaced by making Aneka more customizable and extensible system. • Aneka Master: This is a type of container whose primary objective is to orchestrate, control and monitor the execution of users application. The services provided by

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this container are resource scheduling, resource provisioning, reporting, accounting and data storage. • Aneka Worker: This is a type of container whose primary objective is to execute every individual task that is part of users application. The services provided by this container are resource provisioning and execution. • Aneka Management Studio: It is a GUI that permits for construction, monitoring and configuration of both static and dynamic Aneka Clouds. The required resources are added manually in static as well as in dynamic cloud, the Aneka Master is set up with the resource provisioning services in order to request and release the machines dynamically from different cloud service providers. The Aneka Management Studio acts as a central coordinator that interacts with different containers and their services to generate the reports to observe the status of the containers, resource utilization details and associated cost.

3 Proposed Model The proposed model is based on Service Oriented Architecture (SOA) as shown in Fig. 1, comprises of the following phases as described below.

3.1 Client Request First of all, the client generates a request for discovering the desired cloud service making use of a graphical interface. The client needs to make a choice of values corresponding to the functional as well as non-functional attributes like Central Processing Unit, EC2 Compute Unit, Random Access Memory, Hard Disk space, service availability, network bandwidth, cost, service rating and so on.

3.2 Cloud Service Discovery In the next step, the request from the client is executed by a software component named as Semantic Query Processor that principally relies on Simple Protocol and RDF Query Language (SPARQL) [11]. Then a matching process is carried out between the values of user specified technical parameters with those values stored in the proposed cloud ontology triple store by applying the proposed semantic matchmaking technique.

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3.3 Cloud Service Selection Then the values matched functionality in the previous step are further processed by another Semantic Query Processor making use of SPARQL and semantic rules in order for matching of predefined values of QoS with those values that are available in the proposed cloud ontology triple store. Once the matching process is accomplished, the requested ranked cloud service is presented to client via GUI and with this, the entire process is terminated.

3.4 Cloud Ontology Triple Store In this model, the Cloud ontology Triple Store is developed on the basis of cloud IaaS domain model. This ontology describes cloud infrastructure services and their

Fig. 1 Proposed model for cloud service discovery and selection

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Fig. 2 The proposed cloud ontology comprises of classes and subclasses

functional and QoS attributes. It is represented as semantic registry under Service Oriented Architecture for service registration, discovery and selection. The cloud service domain knowledge are obtained from various resources, cloud ontology [12], cloud taxonomy [13], and the industry based standards [14]. This ontology is represented in terms of RDF (Resource Description Framework) which is based on SubjectPredicate-Object expression known as Triple Store. All the classes along with the subclasses of the proposed cloud ontology Triple Store are depicted in Fig. 2 which is created using Protege Ontology editor [15].

4 Proposed Matchmaking Technique The proposed semantic matchmaking technique is explained as follows. In the proposed framework, the Semantic Query Processor acts as a major component which uses the thread model of Aneka PaaS. It creates multiple threads to take multiple parallel client requests (functional parameters) from a graphical interface during the service discovery phase and generates a query in terms of SPARQL to find semantically equivalent cloud services. The process of matchmaking is illustrated in Algorithm 1, which begins with a set of iterations which are applied over all services associated with the resources from client request. The getServicesBySemanticAnnotation() method is used for retrieving the services and their semantic score is calculated by the help of semanticSimilarity() method. The semanticSimilarity() method is used to compute the semantic relevance which lies among the resources of the service published (from C R_ser vice) and request of the resources (from C R_quer y). If the semanticSimilarity() method returns a positive value, then a logarithm function is applied to score which is finally obtained in order to reduce the score value. By referring to Tversky similarity model [16], the semantic relevance between two different resources is calculated by semanticSimilarity() method (Algorithm 2). While comparing two resources, the method getConcepts() returns their cloud concepts, and the method getProperties() returns the list of their object and data properties

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from the proposed cloud ontology Triple store. Then the method TverskySimilarityMeasure() (Algorithm 3) is invoked for calculating the degree of relevance on the basis of common and different features with respect to the compared sets. The method semanticSimilarity() returns the final score value by finding the average among the matching score of cloud concepts, object and data properties. In this work, the Tversky’s model is used for matching the cloud services based on semantics. This model is treated as one of the best feature based similarity model which takes into account the features those are shared by two concepts and also distinguishing the features specific to each. Further it can be explained with a case study as follows. Let us consider cloud resource r s represents (Infrastructure as a Service (IaaS)) which is part of cloud request made by client and cloud resource r s  represents Network as a Service (NaaS) which is part of cloud service advertisement published by provider. Both resources are concepts and annotated within same ontology. In this case, both IaaS and NaaS are similar with respect to their common features, such as Network, Security and Firewall and dissimilar with respect to the features like servers, OS, virtualization, Memory, Data Center etc. Let F1 be the set of features of Infrastructure as a Service (IaaS) = {Servers, Memory, OS, Virtualization, Network, Data Center, Security, Firewall} Let F2 be the set of features of Network as a Service (NaaS) = {VPN, Mobile Network Virtualization, Network, Firewall, Security} Let CF be the Features which are shared or common in between them i.e. {Network, Firewall, Security} U F1 = {OS, Virtualization, Servers, Memory, Data Center} U F2 = {VPN, Mobile Network Virtualization}. Now the cloud similarity measure between IaaS and NaaS can be computed by applying Eq. 1 as follows: cloud_sim =

|C F| |C F| + |U F1 | + |U F2 |

(1)

3 cloud_sim = 3+5+2 = 0.3. The result as shown in Table 1 represents the comparison between client request and the responses generated after discovery. After completion of discovery phase the next phase is initiated by another Semantic Query Processor, called selection phase. During this phase, a list of most suitable cloud services are obtained based on non-functional QoS requirements of a user. However, some complex QoS requirements may not be satisfied by some of these services. Hence, a ranking system is proposed for the services which are matched in the previous phase. This ranking system comprises some semantic rules which are written using SWRL (Semantic Web Rule Language) [17]. These rules describe the alternative choice of services on the basis of QoS constraints. These rules uses an open source OWL-DL reasoner named as pallet [18] which provides the features to check the consistency and integrity of ontology, determine and calculate the classification hierarchy, describe the inference rules and respond the SPARQL queries.

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Algorithm 15.1 Semantic Cloud service Matchmaker Input: Receives a collection of cloud resources C R_quer y explaining the intended service functionalities Output: Generates a list of cloud services C S  ⊆ C S which are semantically matched function cloudServiceMatchmaker(C R_quer y) CS=getServicesBySemanticAnnotation(C R_quer y) for each cs ∈ C S do scor e − value = 0 C R_ser vice = getAnnotationCloudResources(cs) for each r s  ∈ C R_ser vice do for each r s ∈ C R_quer y do scor e − value = scor e − value + semanticSimilarity(r s, r s  ) end for end for if scor e − value > 0 then cs.semantic − scor e − value=log(1+scor e − value) C S  = C S  U {cs} end if end for return C S  end function

Algorithm 15.2 semanticSimilarity() Input: Receives r s and r s  which represents two cloud resources Output: Generates a similarity score which refers semantic relevance between two resources r s and r s  function semanticSimilarity(r s, r s  ) C_r s=getConcepts(r s) C_r s  =getConcepts(r s  ) P_r s=getPropeties(r s) P_r s  =getPropeties(r s  ) S_cloud_concepts=TverskySimilarityMeasure(C_r s, C_r s  ) S_cloud_ pr oper ties=TverskySimilarityMeasure(P_r s, P_r s  ) sim − scor e = AVERAGE (S_cloud_concepts, S_cloud_ pr oper ties) return sim − scor e end function

Algorithm 15.3 TverskySimilarityMeasure() Input:Receives F1 and F2 as two list of cloud features Output: Compute a similarity cloud_sim between 0 and 1 function TverskySimilarityMeasure(r s, r s  ) C=CloudCommonFeatures(F1 and F2 ) U F1 =CloudUniqueFeatures(F1 and F2 ) U F2 =CloudUniqueFeatures(F2 and F1 ) cloud_sim = |C|+|U |C| F1 |+|U F2 | return cloud_sim end function

An Ontology Based Matchmaking Technique … Table 1 Client request versus result obtained after discovery Parameters Client request Cloud service category Operating system Cost ($) RAM (GB) Network bandwidth (GB/s) EC2 compute unit Processor type Number of cores CPU clock (GHz) Hard disk drive (GB)

Infrastructure as a service Windows 10 0.02 8 16 2 Core i5 (6th generation) 8 2.4 200

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Result obtained after discovery Network as a service Windows Vista 0.03 6 14 4 Core i3 (6th generation) 4 3.6 500

Table 2 Semantic rules Semantic_Rule 1: CloudProviders(?s), hasServiceAvailability(?s, ?availability), hasServiceCost(?s, ?cost), hasServiceRating(?s, ?score), higherThan(?cost, 0.2), lowerThan(?availability, 90.0), lessThan(?score, 6.0) → hasServiceRemoved(?s, true) Semantic_Rule 2: CloudProviders(?s), double[≥0.1, ≤0.2](?cost), double[≥6.0, ≤8.0](?score), double[≥90.0, ≤95.0](?availability), hasServiceAvailability(?s, ?availability), hasServiceRemoved(?s, false), hasServiceCost(?s, ?cost), hasServiceRating(?s, ?score)→CloudProviderMatchedList(?s), hasServiceRank(?s, “2”∧∧ int) Semantic_Rule 3: CloudProviders(?s), hasServiceAvailability(?s, ?availability), hasServiceRemoved(?s, false), hasServiceCost(?s, ?cost), hasServiceRating(?s, ?score), higherThan(?availability, 95.0), greaterThan(?score, 8.0), lowerThan(?cost, 0.1)→CloudProviderMatchedList(?s), hasServiceRank(?s, “1”∧∧ int)

In this work, some semantic rules as shown in Table 2 are written using SWRL that describe the ranking of desired service on the basis of QoS attributes in cloud IaaS domain. Let us consider the three rules (Semantic_Rule1, Semantic_Rule2, Semantic_Rule3) have been defined in such a way that Semanic_Rule1 > Semantic_Rule2, Semantic_Rule1 > Semantic_Rule3. It indicates that the first semantic rule has high precedence over second and third semantic rules. In other words it can be said that Semantic_Rule1 must be executed before Semantic_Rule2 and Semantic_Rule3. Semantic_Rule1 removes any such service having cost higher than 0.2$/h, availability lower than 90% and service rating lower than 6.0 in a ten-point rating scale. Hence, Semantic_Rule2 and Semantic_Rule3 are to be applied only on cloud services which satisfy Semantic_Rule1. Semantic_Rule2 assigns a rank of two points to any cloud service whose cost lies between 0.1 and 0.2$/h and availability between 90 and 95% and service rating between 6.0 and 8.0, where Semantic_Rule3 generates only one point rank to the cloud services those have a cost lower than 0.1$/h and availability higher than 95% and service rating higher than 8.0.

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Fig. 3 GUI for cloud service discovery

5 Implementation Using Aneka PaaS The proposed framework has been implemented and simulated using Aneka distributed PaaS as it supports multiple programming models along with integration of various virtual machines to obtain the result in more secure and scalable environment. The implementation of this work has been carried out with the following hardware and software specifications: Intel Core i3 2 GHz processor, 4 GB Random Access Memory (RAM) and Windows 10 Professional Operating System. The model has been implemented using Aneka Cloud Management Studio 5.0 and Microsoft Visual Studio 2017 with .NET Framework 4.5. The proposed ontology was designed using Protege OWL API. The source code has been written using C# programming language and it is executed successfully to obtain the desired output as per the users requirement. In a graphical user interface (GUI), the customers have to choose the appropriate values of functional parameters according to his/her requirements. In this work, the functional parameters like number of cores, Bandwidth, Random Acess Memory capacity, Hard disk space etc. are considered. Generally, the cloud providers contain multiple functional parameters which may not be available with all at a time. Therefore, some common parameters are considered as a result the proposed ontology acts like unified ontology. The Semantic Query Processor uses the thread model of Aneka PaaS and process the query generated by the consumer using the SPARQL query language [11]. Figure 3 shows the GUI of Cloud Service Discovery. Figures 4 and 5 shows the simulated results for 10 threads under Aneka distributed platform.

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Fig. 4 Aneka analytics-I

Fig. 5 Aneka analytics-II

6 Conclusion and Future Work In this paper, a model has been proposed that integrates a semantic matchmaking technique with the proposed cloud ontology Triple Store. This model primarily contributes towards the description of the cloud service along with their attributes in a standardized and consistent way using ontology. This ontology assist the users to discover the desired suitable service as per the requirements as specified in users request. By the help of a graphical user interface, a user can select the request for discovery of desired cloud service. Consequently the request can be handled on the basis of proposed cloud ontology and the corresponding reasoning rules and finally the most relevant service is obtained based on QoS parameters. The framework has been implemented using Aneka distributed PaaS which is one of scalable and reliable technology in cloud terminology. In future, it is planned to implement the entire

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system with considering the security issues along with semantic matchmaking for PaaS and SaaS types of cloud services.

References 1. Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1(1):7–18 2. Dillon T, Chen W, Chang E (2010) Cloud computing: issues and challenges. In: 24th IEEE international conference on advanced information networking and applications. IEEE Press, New York, pp 27–33 3. Mohapatra S, Panigrahi CR, Pati B, Mishra M (2020) A comparative study of task scheduling algorithm in cloud computing. In: Advanced computing and intelligent engineering, vol 1089. Springer, Singapore, pp 325–338 4. Mohapatra S, Panigrahi CR, Pati B, Mishra M (2019) MSA: a task scheduling algorithm for cloud computing. Int J Cloud Comput 8(3):283–297 5. Parhi M, Pattanayak BK, Patra MR (2018) A multi-agent-based framework for cloud service discovery and selection using ontology. J Service Oriented Comput Appl 12(2):137–154. https://doi.org/10.1007/s11761-017-0224-y 6. Parhi M, Pattanayak BK, Patra MR (2018) An ontology-based cloud infrastructure service discovery and selection system. Int J Grid Util Comput 9(2):108–119 7. Hayyolalam V, Kazem AA (2018) A systematic literature review on QoS-aware service composition and selection in cloud environment. J Netw Comput Appl 110:52–74 8. Vecchiola C, Chu X, Buyya R (2009) Aneka: a software platform for .NET based cloud computing. In: Advances in parallel computing, vol 18, pp 267–295 9. Huhns MN, Singh MP (2005) Service-oriented computing: key concepts and principles. IEEE Internet Comput 9(1):75–81 10. Petrenko AI (2014) Service-oriented computing in a cloud computing environment. Comput Sci Appl 1(6):349–358 11. SPARQL 1.1 Query Language. https://www.w3.org/TR/sparql11-query/ 12. Al-Sayed MM, Hassan HA, Omara FA (2019) Towards evaluation of cloud ontologies. J Parallel Distrib Comput 126:82–106 13. Hoefer CN, Karagiannis G (2010) Taxonomy of cloud computing services. In: IEEE GLOBECOM workshop on enabling the future service oriented internet, pp 1345–1350 14. NIST Cloud Computing Standards Roadmap. https://www.nist.gov/sites/default/ 15. Protege Ontology Editor. http://protege.stanford.edu/ 16. Tversky A (1977) Features of similarity. CPsych Rev, 327–352 17. SWRL: a semantic web rule language combining OWL and RuleML.www.w3.org/Submission/ SWRL/ 18. Sirin E, Parsia B, Grau BC, Kalyanpur A, Katz Y (2007) Pellet: a practical OWL-Lreasoner. J Web Semant 5(2):51–53

Proper Harmonic Analysis of Load Current of a Single Phase 5-Level Voltage Source Inverter Using HCC Sushovit Das, Uma Shankar Das, Asish Kumar Sahoo, Shuvangi Mishra, and Tapas Kumar Mohapatra

Abstract Multilevel inverters (MLI) are as of now being examined and employed in various mechanical and industrial applications. The Five-level inverter is one which is suitably employed in the medium to high-power applications; its point of interest mainly includes the ability to lessen the harmonics and reduce the current or voltage ratings of the semiconductor devices used. Owing to this feature, high quality output voltages and input currents may be obtained. Furthermore, due to its intrinsic component redundancy, it is easily available. Due to these highlights and the functionalities available, the multi-level inverter was perceived as a significant option in the medium voltage inverter sector. This article provides the information about actual harmonic content of the load current instead of the average value of the harmonic content over a period. A 5-level MLI with different DC supply (230 and 115 V) is presented. Two control techniques such as SPWM (Sinusoidal Pulse Width) and HCC (Hysteresis Current Control) techniques are adopted. FFT (Fast Fourier Transform) is used in MATLAB to determine the harmonic components of the load current. This FFT analysis is done for load current over a period and also using windowing technique and the results of both are also compared. The HCC technique provides less THD (Total Harmonic Distortion) in comparison to SPWM. S. Das · U. S. Das · A. K. Sahoo · S. Mishra · T. K. Mohapatra (B) ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] S. Das e-mail: [email protected] U. S. Das e-mail: [email protected] A. K. Sahoo e-mail: [email protected] S. Mishra e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_16

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By windowing technique we are able to determine the actual value of the harmonic components. Keywords Multilevel inverter (MLI) · Hysteresis current control (HCC) · Fast Fourier transform (FFT) · Total harmonic distortion (THD)

1 Introduction The power electronics devices generally switch power semiconductors at progressively high frequencies so as to lessen harmonics and reduce the sizes of the passive components used. However, at high power levels, with an increase in switching losses, the switching frequency also increases. There are several techniques that have been proposed, and talked about for reducing the switching losses [1], which includes the construction and operation of multilevel inverters. As the nonlinear loads are continuously proliferating, harmonic distortion is now being considered as one of the serious issues that corrupts the quality of power. A multilevel inverter (MLI) can be defined as a power electronic converter which is used to provide an output of a desired AC voltage level using multiple lower level input DC voltages. The MLI differs from a two-level inverter with respect to increased number of levels of voltage in the voltage waveform. The concept of multilevel inverters begins with the three-level inverter. MLIs are suitably used for high voltage applications on account of their capacity to obtain output voltage waveform with a superior harmonic spectrum and accomplish higher voltages with a constrained maximum rating of the device used. Nowadays, the MLIs have become popular in power applications, as they can fulfil the increasing need of power rating and quality of power related with diminished harmonic distortions and lower electromagnetic interference. In recent years, MLIs have gained much attention owing to their various advantages such as [2] • Simple augmentation in view of modular architecture. • Better harmonic specification, which essentially reduces the size of the channel due to the generation of a waveform almost like a sinusoidal waveform. ) which may reduce problems identified with elec• Reduction of voltage stress ( dv dt tromagnetic interference. • Reduced switching losses because of lower switching frequency and lower voltage stress on the devices. • Combination of MLIs with sustainable power sources, (for example, photovoltaic, wind, and power devices), which can improve vitality collecting and sharing of load. • Comparing two-level inverter topology with equal power ratings, MLIs often have the preference that the harmonic components of the line-to-line voltages supplied to the load are diminished preferably by their switching frequencies.

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Table 1 Components of one phase of a 5-level multilevel inverter Types of MLI No. of switches used No. of diodes used Diode clamped MLI Flying capacitor MLIS Cascaded H bridge MLI Cascaded hybrid MLI

No. of capacitors used

8 8 8

12 Nil Nil

4 10 Nil

6

Nil

2

MLIs have recently been used for induction machines and engines, dynamic rectifiers, filters, sustainable power interfaces, flexible AC transmission systems (FACTS) and static compensator. The output voltage waveform with low THD is desirable, however with an increase in the number of levels, the equipment necessity and complexity of the circuitry additionally builds, making the control increasingly convoluted and complicated. The exchange off is between value, weight, multifaceted nature and a smooth output voltage with lower THD.

2 Multilevel Inverter Topology The commonly used MLI topology classified into three types are: a. Diode Clamped MLI (DC-MLI) b. Flying Capacitor MLI (FC-MLI) c. Cascaded H-Bridge MLI (CHB-MLI). The topology of the hybrid and asymmetric hybrid inverters have been created by the blend of the existing MLI topology or by applying separate DC bus levels. The basic topology of various MLIs are shown in Fig. 1. The component count of the different topology of MLIs has been shown in Table 1. The diode clamped MLIs are widely used in motor drive applications. However, the number of clamping diodes, capacitors and switches increases for the DC-MLIs, the complexity of the circuit increases beyond three level MLI. The FC-MLIs produce multilevel output voltage waveform clamped by capacitors, which are heavy and progressively costly when contrasted with the clamping diodes. For these, complex control is required to keep up the capacitors’ voltage balance which is a major drawback. Among the different MLI topology, the Cascade H-Bridge MLI (CHBMLI) has the least circuit complexity (as can be seen in Table 1). The Cascade Hybrid MLI topology is a modification of the CHB-MLI with lesser component count and is presented in this chapter.

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Fig. 1 The basic topology of MLIs

2.1 Operation Principle of the Cascade Hybrid Multilevel Inverter [3] Figure 2 shows the single-phase topology of the Cascade Hybrid MLI. The bottom part consists of one leg with two switches S1 and S2 with a DC power source (Vdc ), similar to a half bridge inverter. The output of this part is considered to be v1 . The top part consists of two legs with two switches in each leg, namely Sa1 , Sa2 , Sa3 and Sa4 with a DC power source V2dc , similar to a full bridge voltage source inverter. The output of this part is considered to be v2 .

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The output voltage is obtained by the relation v = v1 + v2 For example, for output voltage v to be zero, we can either set v1 = +V(dc/2) and v2 = −V(dc/2) or v1 = −V2 dc and v2 = +V2 dc such that v = v1 + v2 = 0. The voltage v1 is either +V2 dc when S1 is closed or −V2 dc when S2 is closed. The voltage v2 is either when Sa1 , Sa4 is closed; 0 when Sa1 , Sa3 or Sa2 , Sa4 is closed and −V2 dc when Sa2 , Sa3 is closed Table 2 shows the switching scheme developed for the Cascade Hybrid MLI.

3 Hysteresis Current Control (HCC) Technique To represent the output waveform in the desired form, using the Hysteresis Current Control (HCC) technique, limit bands are set on either side of the output waveform [4, 5]. The inverter switches are worked inside cut-off limits. The reference sine wave of required frequency and magnitude, created by the control circuit is compared with the actual signal. The upper switch in the half-bridge is switched OFF when the signal reaches a specified hysteresis band, and the lower switch is switched ON. The lower switch is turned off as the signal exceeds the lower limit, and the upper switch is turned ON. The actual signal wave inside the hysteresis band limits is compelled to follow the sine reference wave. To produce this repaying or compensating current, a controlled current inverter is required. Hysteresis current regulation is a technique

Fig. 2 Single phase topology of the 5-level cascade hybrid MLI

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Table 2 Switching scheme for the cascade hybrid multilevel inverter Sa1 Sa2 Sa3 Sa4 S1 S2 V1 0 1 1 1 0 1 0 0 0 1

1 0 0 0 1 0 1 1 1 0

1 1 0 1 1 0 0 1 0 0

0 0 1 0 0 1 1 0 1 1

1 1 1 1 1 0 0 0 0 0

0 0 0 0 0 1 1 1 1 1

+Vdc 2 +Vdc 2 +Vdc 2 +Vdc 2 +Vdc 2 −Vdc 2 −Vdc 2 −Vdc 2 −Vdc 2 −Vdc 2

V2 −Vdc 2

0 +Vdc 2

0 −Vdc 2 +Vdc 2

0 −Vdc 2

0 +Vdc 2

V = V1 + V2 0 +Vdc 2

+Vdc +Vdc 2

0 0 −Vdc 2

−Vdc −Vdc 2

0

for regulating a voltage source inverter with the goal of producing an output current that matches a reference current waveform This technique regulates the switches asynchronously in an inverter to scale the current up and down via an inductor, with the intention of tracking a reference current signal. Hysteresis Current Control (HCC) is a method used to monitor a voltage source inverter to shift the load current/actual current to match the reference current. Load current and reference current are used to power the inverter switches [6]. A hysteresis current controller is actualized with a closed loop control framework. The switches in an inverter are controlled using an error signal, e(t). This error is the difference between the ideal desired current, Iref (t), and the actual current being infused by the inverter, Iactual (t). Around the stage where the error occurs at the upper limit, the transistors are switched to drive the current down. Around a point where the error approaches a lower limit, the current is required to increase. The minimum and maximum estimations of the mistake signal are emin and emax respectively. Lower and upper hysteresis band limits are explicitly associated with the minimum and maximum error. When the reference current is changed, the actual current needs to remain inside the cut-off limit points. The amount of ripples in the output current of the inverter is directly controlled by the range of the error signal (emin − emax ) which is known as the hysteresis band. This is illustrated in Fig. 3. The Hysteresis Current Control technique is the most widely used approach because of its good stability, robust behaviour and dynamic performance. It is easy to implement and independent of load parameter changes.

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Fig. 3 Basic principle of hysteresis control

4 Harmonic Analysis The term Harmonics alluding to power quality would suggest the purity of the output waveform is in its sinusoidal structure. Commercial, industrial and modern force framework plans require good quality of power. For the electrical supply to be ideal, it should be a perfect sinusoid without any distortion. Should the waveform be skewed from its natural and desirable configuration, it is referred to as harmonic distortion. Harmonic distortion is probably the most significant problem relevant to power quality and it creates a few disturbing power system effects. Frequencies that are an integral multiple of the fundamental frequency created by non-linear electrical and electronic hardware are termed as Harmonics in the power circuits. The fundamental frequency on combining with the harmonic sine wave forms repetitive, non-sinusoidal mutilated wave shapes. It is therefore important to analyse the harmonics and reduce it. The harmonic analysis in this chapter is done using the Fast Fourier Transform and by using the concept of Windowing. FFT analysis by conventional method provides the average value of the harmonic data over a period. By this process we are unable to find the actual amplitude of the harmonic component. To find out the actual amplitude of harmonic components windowing technique is implemented in the article. Windowing is done by taking intervals and scanning through the entire time period to know the highest amplitudes and the frequency at which they exist. This method gives more accuracy in finding

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the magnitude of harmonics, furthermore, this also reduces the chances of error in harmonic analysis.

5 Simulation Results and Analysis The inverter model was designed using MATLAB Simulink and was verified using an R-L-E load for the values of R as 10 , L as 4.5 mH and E as 20 V. The simulation model is shown in Fig. 4. The simulation was performed for both leading and lagging values of E for a DC supply of 200 V. The load voltage and current result is also compared with SPWM technique [3]. The load voltage and current wave forms of SPWM and HCC technique are shown in Figs. 5 and 6. The Total Harmonic Distortion (THD) of the load current is 16.35% in SPWM and 5.07% in HCC technique respectively. After completing the Harmonic analysis by both Fast Fourier Transform using MATLAB Simulink and by Windowing method using MATLAB Script file it can be seen that the FFT command computes the average value of the order of harmonics over a complete cycle. But the average does not signify that the value of the order of harmonics is the same at each point of a full cycle. At some instance the value of the particular order of harmonic could be more than the average value. But by using the Windowing method, the wave is divided into number of samples and then scanned each of the samples for the particular order of harmonics to get the maximum value of the order of harmonics. This can further reduce the chances of Electromagnetic Interference (EMI) in electrical circuits. It is the disturbance generated by any external source due to the presence of harmonics that affects the electrical circuit by electromagnetic induction, electrostatic coupling or conduction. This usually occurs in the order beyond 10 kHz. In this chapter the harmonic analysis is done up to 40 kHz. And no such presence of EMIs was found. Table 3 shows the comparative study of the results obtained in Fast Fourier Transform with the results of the Windowing technique.

6 Conclusion This paper deals with the proper harmonic analysis of the load current of a 5-level inverter using both the Fast Fourier transform and the windowing technique. The total harmonic distortion (THD) is reduced by using MLI (5-level) and by the Sine Pulse Width Modulation (SPWM) and Hysteresis Current Control (HCC) techniques. The inverter model was designed using MATLAB Simulink and was verified experimentally using an R-L-E load (R is 10 , L is 4.5 mH and E is 20 V). The THD of the load current in the case of SPWM technique is found out to be 16.35% whereas the THD of load current in case of HCC method is found to be 5.07% with

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Fig. 4 Voltage source inverter (5-level) with R-L-E load Table 3 A comparative study between fast Fourier transform and windowing Harmonic order Frequency (Hz) Normal FFT Windowing technique h5 h7 h9 h11 h13 h15 h17 h19

250 350 450 550 650 750 850 950

0.18 0.28 0.07 0.02 0.39 0.16 0.47 0.25

12.62 7.28 2.33 1.76 3.84 4.06 2.82 1.12

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Fig. 5 Simulation output current and voltage for sinusoidal pulse width modulation (SPWM)

Fig. 6 Simulation output current and voltage for hysteresis current control (HCC)

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the same value of other parameters. From this, we can understand the robust and dynamic nature of the Hysteresis Current Control technique. And from the harmonic analysis, a large difference in the magnitude of harmonics observed from normal FFT method and the windowing method can be seen. This gives a drastic contrast of the percentage. And to eliminate these harmonics, appropriate filters can be designed. There are a couple of fascinating highlights of utilizing the Cascade Hybrid arrangement of the MLI which incorporate less components count, less losses due to switching and better output voltage/current waveform. The most critical criteria in this chapter is the minimization of harmonics in the inverter output current. In this manner, the nearness of appropriate control methods, in the force electronic converters, for robust dependability, abrupt reaction, ideal following capacity and eradication of error are inevitable.

References 1. Corzine KA (2000) A hysteresis current-regulated control for multi-level drives. IEEE Trans Energy Convers 15(2):169–175 2. Vijeh M, Rezanejad M, Samadaei E, Bertilsson K (2019) A general review of multilevel inverters based on main submodules: structural point of view. IEEE Trans Power Electron 34(10):9479– 9502 3. Thongprasri P (2011) A 5-level three-phase cascaded hybrid multilevel inverter. Int J Comput Electric Eng 3(6):789 4. Colak I, Kabalci E, Bayindir Ramazan (2011) Review of multilevel voltage source inverter topologies and control schemes. Energy Conv Manage 52(2):1114–1128 5. Dey AK, Mohapatra TK, Mohapatra KK A novel hcc scheme to reduce switching loss and harmonics in 1-phase h-bridge inverter. In: 2020 IEEE 9th power India international conference (PIICON). IEEE, pp 1–6 6. Sujitha N, Ramani K (2012) A new hybrid cascaded h-bridge multilevel inverter-performance analysis. In: IEEE-international conference on advances in engineering, science and management (ICAESM-2012). IEEE, pp 46–50

A New Sparse Array Configuration for Direction of Arrival Estimation P. Raiguru, A. K. Srivastava, A. Dandpat, and R. K. Mishra

Abstract This paper proposes a new sparse array (SA) which consists of two uniform linear arrays (ULAs) with different element separation and an additional element. It is a modified version of Nested array which follows the twodimensional representation like enhanced nested array (ENA). This proposed sparse array provides almost equal aperture length and degree of freedom (DOF). It has reduced mutual coupling effects as compared to other existing nested like an array. Based on these good properties of the proposed sparse array, it is used for the direction of arrival (DOA) estimation. The weight function of the proposed array is evaluated analytically. Numerical simulations using spatial-smoothing multiple signal classification (SS-MUSIC) indicate that the proposed SA performed better as compared to other nested like array in the presence of mutual coupling. Keywords Direction of arrival · Mutual coupling · Sparse array

1 Introduction The direction of arrival (DOA) estimation is an important topic in various applications such as RADAR, SONAR, wireless communication, astronomy, and tomography [1– 5]. Antenna array is used to collect a spatial sampling of sources impinging on it. In P. Raiguru (B) · A. K. Srivastava · A. Dandpat Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] A. K. Srivastava e-mail: [email protected] A. Dandpat e-mail: [email protected] R. K. Mishra Department of Electronics Science and Technology, Berhampur University, Berhampur, Odisha 760007, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_17

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general, a K element uniform linear array (ULA) can estimate maximum up to K-1 number of sources with the use of traditional DOA estimation methods such as modelbased approach [6, 7] and subspace-based approach [8, 9]. Least mean square (LMS) algorithm, normalized LMS, and its variations are some model-based approaches, and multiple signal classification (MUSIC) and its variations are some sub-spacedbased approach. However, ULA has the limitation of less degree of freedom (DOF) and aperture length as the inter-element spacing is half wavelength. As ULA elements are closely spaced, the DOA estimation performance is severely affected in non-ideal conditions such as mutual coupling. This limitation may be overcome by increasing DOF, i.e., resolving more sources with few antenna elements, increasing aperture length, and increasing the inter-element spacing between the adjacent elements. To achieve this, sparse array has been received considerable interest in recent years. There are numbers of sparse array, non-uniform linear array (SNLA) is designed in the literature such as minimum redundancy array (MRA) [10], nested array (NA) [11], and co-prime array (CA) [12]. The number of array elements is minimized by the redundancy of the element spacing in MRA. A K element MRA has a holefree virtual array and largest aperture length. However, MRA has no closed-form expression for element position. Nested array (NA), a constructed sparse array, has been proposed in the literature. Two uniform linear arrays (ULAs) with different inter-element spacing are combined in a systematic way to construct the NA. It is possible to achieve a maximum of O(K 2 ). DOFs from K physical elements. Many modified nested like arrays are also generated in literature. Enhanced nested array (ENA) [13] is a modified nested array generated by a two-dimensional representation of NA. It has a larger aperture length, DOF, and hence improved DOA estimation performances compared to NA. However, the first sub-array of NA or ENA is closely spaced which leads to a mutual coupling effect. So, the performance is affected in the presence of mutual coupling [14, 15]. Co-prime array (CA) is designed to overcome this mutual coupling effect but has more missing virtual elements which affect the degree of freedoms (DOFs). Keeping these challenges in mind, this paper contributes a partially augmented Sparse array. We compare the aperture length, DOFs, and spectrum estimation performance in both the absence and presence of mutual coupling. The DOA estimation performance is ULA, and equivalent sparse array geometry (NA, ENA, and proposed array) is analyzed. The proposed approach performs better compared to other sparse arrays in the presence of mutual coupling. The outlines of the paper are organized as follows. Section 2 reviews the signal model with mutual coupling. Section 3 summarizes the sparse array configuration. In Sect. 4, we analyzed the DOA estimation performances through different numerical simulations. The paper is concluded in Sect. 5.

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2 Signal Model with Mutual Coupling L Assume ‘L’ narrow-band uncorrelated sources from different directions {θ˜ }l=1 within the range of ±90° impinge normal to the N element of the array. Figure (1) shows the schematic representation of the array. The element position of the element is denoted as S, and it is the integer multiple of inter-element spacing d. Here, d = λ/2, and λ is the carrier wavelength. The received signal of the array is.

˜ x(t) = As(t) + n(t), A = CA

(1)

Here, s(t) = [s1 (t), s2 (t), s3 (t) … sL (t)]T denotes the signal vector, n(t) denotes the Additive White Gaussian Noise (AWGN) and uncorrelated with the sources. ˜ = [a(θ˜1 ), a(θ˜2 ), . . . , a(θ˜L )] in the N × L array manifold matrix and a(θ˜l ) = A [e jπn 1 sin θl , e jπn 2 sin θl , . . . , e jπn K sin θl ]T is the steering vector of the array. C is the N × N mutual coupling matrix. For simplicity, the B-banded Toeplitz matrix is used for the mutual coupling model [14]. The mutual coupling co-efficient of matrix C can be obtained as follows,  c|n 1 −n 2 | , if|n 1 − n 2 | ≤ B, Cn 1 ,n 2 = (2) 0, otherwise, where c0 is the self-coupling coefficient and its value is 1 for each existing antenna element. Here, c1 is the nearest and strongest mutual coupling coefficient, and it can be expressed as c1 = ce jπ/3 where c is the value varies in between 0.1 to 1 [14]. Other coupling coefficients can be found from it with the equation cm = c1 e− j (m−1)/8 /m 2 ≤ m ≤ B and m = |n 1 − n 2 |. It is noted here that if the MC effect is not considered, than C is an identity matrix. The covariance matrix of the received signals X(t) is Rxx = E[x(t)x H (t)] = ARssA H + σn2 I N

(3)

Fig. 1 Graphical representation of antenna array

θ

n1 n2 …………………………………………… ………..……………….nK

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where RSS = E{s(t) sH (t)} = diag[σ1 2 , σ2 2 ,…., σL 2 ]. In practice, theoretical RXX is unavailable, and it is replaced by the sample covariance matrix as T 1  ˆ Rxx = x(t)xT (t) T t=1

(4)

ˆ is where T denotes the number of snapshots. Vectorization of Rxx ˆ z = vec(Rxx) = (A∗  A)p + σn2 vec(I N )

(5)

Here,  denotes the Khatri-Rao product, p = [σ1 2 , σ2 2 ,…, σL 2 ] denotes the source power, and σn 2 vec(IN ) denotes the deterministic noise vector and (A*  A) behaves like an array manifold of the virtual array. The element position of this virtual array is given by difference co-array V. V = {n 1 − n 2 |n 1 , n 2 ∈ S}

(6)

The number of DOF and the aperture length of a sparse array is determined from this DCA. Now, z in (5) can be seen as received signals, and DOA can be estimated using the different adaptive algorithms like SS-MUSIC [11].

3 Sparse Array Configuration The schematic diagram of different arrays is shown in Fig. (2). The red color antenna elements belong to the first sub-array which are closely spaced. The mutual coupling effect is more due to these elements, and it affects the DOA estimation performances. Figure (3) shows the two-dimensional representation of different arrays except for ULAs. It is noted here that though it is the only two-dimensional representation which indicates the linear array, not the planar array [13]. The 2D representation of linear array is a topology that is established by two steps. The whole array structure is divided into multiple layers, and the same size is maintained. All the layers are stacked from bottom to top. Nested array consists of two ULAs, N 1 element dense array, and N 2 element sparse array with two different inter-element spacing d and D = (N 1 + 1)d, respectively. The schematic diagram (in Fig. 2b) of eight-element NA (where N1 = N2 = 4) can also be represented as prototype nested array is shown in Fig. 3a. The 2D representation of NA consists of two levels {1, 2, 3, 4} and {5, 10, 15, 20}. The element locations of it is {1, 2, 3, 4, 5, 10, 15, 20}. Enhanced nested array (ENA) is a modified version of NA, which is generated from the 2D representation of NA. Figs. 2c and 3b show the schematic diagram and 2D representation of eight-element ENA which is multilevel, and its element position is {1 2, 3, 4, 9, 13, 17, 21}. The level 1 of NA and ENA is ULAs which consists of

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1 2

3

4

5

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(a)

6 7

d

(b) 1

2 3

4

2 3

4

5

D

d

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(c) 1

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21

d

(d) 1

3 4

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Fig. 2 Schematic diagram of eight elements arrays a ULA, b NA, c ENA and d Proposed array

S={1,2,3,4,5,10,15,20} (a)

S={1,2,3,4,9,13,17,21} (b)

S= {1,3,4,9,12,17,19,21} (c)

Fig. 3 Two-dimensional representation of eight elements arrays a NA, b ENA, and c proposed array

N 1 numbers of consecutive elements. We modified the 2D representation of nested array with different levels to avoid consecutive elements present in the first level of spares array and generate a new array from the 2D representation of NA. But, it is partially augmented array, which is not a hole-free array. The element position of proposed sparse array is {1, 3, 4, 9, 12, 17, 19, 21} (in Fig. 3c). The array performance

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is investigated by comparing weight function, aperture length, DOF, and spectrum estimation in the following section.

4 Result Analysis This section verifies the effectiveness of the proposed sparse array in both ideal and non-ideal conditions. All signals are equi-power, and the SNR is 0 dB. DOA estimation performance is evaluated using the SS-MUSIC algorithm.

4.1 Weight Function of Different Arrays The weight function is a repeated number of elements in a virtual array. It depends on the size of the difference co-array. The weight function of different arrays, i.e., ULA, NA, ENA, and proposed array, is generated using (5) as shown in Fig. (4). It shows that ULA, NA, and ENA have filled element positions. The ULA has DOF (aperture length) of 15 as its aperture length is −7 to 7. Similarly, the NA and ENA have 39 8

8

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(b)

(a)

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(d)

Fig. 4 Comparison weight functions of eight elements arrays a ULA, b NA, c ENA, and d proposed array.

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(−19 to 19) and 41 (−20 to 20) DCA (aperture length). The aperture length of the proposed array is −20 to 20; with two missing elements, i.e., two hole positions at ± 19. So, the degree of freedom of proposed SA is 39. It indicates that the proposed array is not a hole-free array. So, it is a partially augmented sparse array. We found that the prosed array has some aperture length as NA. But it has fewer numbers of repetitions as compared to NA and ULA.

4.2 Estimation Analysis Without Mutual Coupling Effect The numerical simulation of spectrum estimation of different arrays in the absence of mutual coupling is shown in Fig. (5). Ten uniformly distributed sources from different directions within the range of ± 25° are impinging on the different arrays having eight elements each. These DOAs are estimated using the SS-MUSIC algorithm, and the peak of the spectrum shows the estimated angles in each case. The root means square error (RMSE) of the angle is determined to compare the performances of arrays. Eight ULAs estimate only seven DOAs, and all other sparse arrays can estimate ten DOAs (Fig. 4). It indicates that the sparse array can estimate more sources with only a few physical elements. Again, the DOA estimation performance is compared between the sparse arrays. The RMSE of DOA of each array is approximately 0.04, 0.0007, 0.0007, and 0.0007 for ULA, NA, ENA, and proposed array, respectively. It indicates that the performance of the proposed array is approximately equal to the other nested like a filled array in ideal conditions.

4.3 Mutual Coupling Effect on the Estimation The analysis is further extended by considering the mutual coupling effect of the different arrays. For simplicity, we consider eight sources impinging on eight element Arrays (ULA, NA, ENA, and proposed array) from different directions. Eight sources uniformly distributed in the range of ± 20° are impinging on different arrays which is mentioned as vertical grid lines in Fig. (6). Here, magnitude of c1 is chosen as 0.4, and other coefficients are determined using (2). The narrow peaks show the estimated DOAs of different sources. It shows the DOA estimation of eight ULA which can detect only six spectrums, and its RMSE is approximately 0.07. All other sparse arrays, i.e., NA, ENA, and proposed sparse array, estimate eight sources approximately 0.05, 0.06, and 0.004 RMSE, respectively. It indicates that the proposed sparse array performs better as compared to other nested like arrays in the presence of mutual coupling.

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Spectrum (dB)

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(d) Fig. 5 Comparison of MUSIC Spectrum of eight elements arrays a ULA, b NA, c ENA, and d proposed array without mc effect.

5 Conclusion A new partially augmented sparse array is proposed in this paper. The proposed array performance for DOA estimation is compared with ULA and nested like arrays (NA and ENA) at low SNR. The numerical simulation result demonstrates the performance of the proposed array in terms of weight function, consecutive DOF, aperture length, and spectrum estimation in both ideal and non-ideal conditions. The proposed array

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(a)

(b)

(c)

(d) Fig. 6 Comparison of MUSIC Spectrum of eight elements arrays a ULA, b NA, c ENA, and d proposed array with mc effect

performs almost equal in the absence of the mutual coupling compared to the other array and performs better in the presence of mutual coupling.

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References 1. Orton M, Fitzgerald W (1999) A Bayesian approach to tracking multiple targets using sensor arrays and particle filters. IEEE Trans Signal Process 47(10):2644–2654 2. Godara LC (1997) Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations. Proc. IEEE 85(8):1195–1245 3. Nielsen U, Yan JB, Gogineni S, Dall J (2017) Direction-of-arrival analysis of airborne ice depth sounder data. IEEE Trans Geosci Remote Sens 55(4):2239–2249 4. Dey N, Ashour AS, Shi F, Sherratt RS (2017) Wireless capsule gastrointestinal endoscopy: Direction-of-arrival estimation based localization survey. IEEE Rev Biomed Eng 10:2–11 5. Ogawa H, Mizuno A, Hoko H, Ishikawa H, Fukui Y, Ogawa H et al (1990) A 110 GHz SIS receiver for radio astronomy. Int. J. Infrared Millimeter Waves 11(6):717–726 6. Shengkui Z, Zhihong M, Suiyang K (2006) Modified LMS and NLMS algorithm with a new variable step size. IEEE Int Con 1–6 7. Li J, Li J (2010) A novel based variable step-size LMS algorithm based on Decorrelation. IEEE Int. Con (CISP) 7:3291–3294 8. Schmidt R (1986) Multiple emitter location and signal parameter estimation. IEEE Trans Ant Propag 34(3):276–280 9. Roy R, Kailath T (1989) ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Trans Acoust Speech Signal Process 37(7):984–995 10. Moffet A (1968) Minimum-redundancy linear arrays. IEEE Trans Antennas Propag 16(2):172– 175 11. Pal P, Vaidyanathan PP (2010) Nested arrays: A novel approach to array processing with enhanced degrees of freedom. IEEE Trans Signal Process 58(8):4167–4181 12. Tan Z, Eldar YC, Nehorai A (2014) Direction of arrival estimation using co-prime arrays: a super-resolution viewpoint. IEEE Trans Signal Process 62(21):5565–5576 13. Zhao P, Hu G, Qu Z, Wang L (2019) Enhanced nested array configuration with hole-free co-array and increasing degrees of freedom for DOA estimation. IEEE Commun Lett 23(12):2224–2228 14. Basikolo T, Ichige K, Arai H (2018) A novel mutual coupling compensation method for underdetermined direction of arrival estimation in nested sparse circular arrays. IEEE Trans. Antenna Propag 69909–69917 15. Fang Y, Zhu S, Wang H, Zeng C (2019) DOA Estimation via ULA with mutual coupling in the presence of non-uniform noise. Digital Signal Processing, 97

Forecasting of Net Asset Value of Indian Mutual Funds Using Firefly Algorithm-Based Neural Network Model Sarbeswara Hota, Sarada Prasanna Pati, and Pranati Satapathy

Abstract Mutual funds are considered as the simpler and hassle-free investment mechanism in India. The performance of various mutual funds is analysed by the investors before investing their money. Out of the different performance indicators of mutual funds, net asset value (NAV) happens to be one of them. The NAV data are nonlinear in nature. In this paper, the FLANN model is used for the 1-day and 5-day ahead NAV forecasting of two Indian mutual funds. The weights of the FLANN model are optimized using one of the bio-inspired algorithm, i.e. firefly algorithm. The prediction performance of the FLANN-FA model and basic FLANN are compared using RMSE and MAPE as evaluation measures. The simulation results indicate that the proposed model outperformed the basic FLANN model. Keywords Mutual fund · Firefly · Prediction performance · Functional expansion · Mean square error

1 Introduction Mutual fund is a financial intermediary that is pooled by the money of the investors for combined investment in various financial instruments. The profits earned by the fund are shared between the investors excluding its expenses. The resources of mutual fund increase by selling the units to the public [1]. Every mutual fund is associated with a professional fund manager. Its primary responsibility is to manage the fund. S. Hota (B) Department of Computer Application, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] S. P. Pati Department of Computer Science and Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] P. Satapathy Department of Computer Science and Applications, Utkal University, Bhubaneswar, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_18

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The fund manager utilizes his management skills for the investment of the fund in securities including various other financial instruments. Mutual funds in India are open to every common people. The Indian mutual fund industry is perceiving a rapid growth due to the increase in the personal financial assets, infrastructure development and increase in public awareness. Mutual funds now become the preferred investment option of the common people [2]. NAV is an important component associated with mutual fund. The investors and the financial advisors study the NAV for a period of time as it is considered as one of the performance measures of the mutual fund and also a financial time series data [3]. Various statistical models have been developed to forecast the financial time series data [4, 5]. Most of these statistical models assume that the data set is generated from a linear process. However, the current financial time series data are dynamic, chaotic and highly nonlinear. The most important aspect of forecasting models is to handle the incomplete and irregular data. The neural-network-based models are suitable for financial time series forecasting task [6]. The literature study indicates that various neural-network-based models have been used in exchange rate, stock price indexing ad NAV forecasting. But in [7], the authors used backpropagation algorithm for training the artificial neural network model. The authors in [8] utilized artificial neural network model to forecast the performance of Morningstar mutual funds. They proposed a multilayer perceptron model that is optimized with GRG2 nonlinear optimizer. The authors in [9] used the FLANN model to forecast the NAV of different Indian mutual funds. The NAV data were combined with the statistical features to form the input pattern. The input pattern was trigonometrically expanded. For training the FLANN model, 80% of the data set were taken and for testing purpose, rest 20% of the NAV data set were considered. The authors compared the prediction performance of FLANN model with MLP. The traditional training method used in FLANN model suffers from slower convergence rate and local minima. So different bio-inspired algorithms are proposed for the optimization of FLANN model. The goal of this paper is to use the firefly algorithm with the FLANN, i.e. FLANN-FA model for the 1-day and 5-day ahead NAV forecasting of UTI Equity mutual fund and TATA Dividend Yield Fund-direct growth mutual fund. The distribution of different sections in this paper is as follows. Section 2 describes the FLANN model and the firefly algorithm. Section 3 deals with the simulation study, and conclusion of this work is provided in Sect. 4.

2 Methodologies This section describes the FLANN model and the firefly algorithm.

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Fig. 1 Block diagram of FLANN

2.1 FLANN Model The structure of the FLANN is different from the multilayer perceptron (MLP). The MLP contains one or more hidden layer along with one input layer and one output layer. But the FLANN model contains only the input layer and one output unit. So it is a single-layer model without any hidden layer [10–12]. Each input unit goes through functional expansion using sine and cosine trigonometric functions and thus increases the dimension. After expanding the inputs, they are multiplied with weights and then summed up to produce the output using the activation function. The output is calculated using Eq. (1).   g = f c + W.X T

(1)

Here the output and bias are denoted using g and c, respectively. The activation function is denoted as f , the input vector as X and the weight values as W. The least mean square (LMS) algorithm has been used to update the weights. Figure 1 shows the block diagram of FLANN model.

2.2 Firefly Algorithm (FA) As developed by Yang et al., firefly algorithm is a newly proposed bio-inspired optimization algorithm based on the light-emitting characteristics of the fireflies [13].

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Various researchers have used FA in their application domains [14–16]. This flashing nature of fireflies helps in communication for mating purpose and searching for prey. The fireflies normally get attracted towards the brighter fireflies and the brightness at a particular position refers to the value of the fitness function of the particular problem. The brightness decreases with increasing distance. The implementation of FA is based on the brightness and attractiveness. Brightness determines the location superiority of a firefly. Attractiveness decides the movement of the fireflies. Attractiveness of a firefly at position r is given by Eq. (2) β(r ) = β0 e−γ ri j 2

(2)

where β0 represents the intensity of the firefly at r = 0 and γ is the fixed light absorption coefficient at source. The Euclidean distance between two fireflies xi and x j is calculated using Eq. (3).   d     2   xi,k − x j,k ri j = x i − x j = 

(3)

k=1

The attractiveness of firefly i to another more attractive firefly j is determined by Eq. (4)  2 xi = xi + β0 e−γ ri j x j − xi + α(rand − 0.5)

(4)

where ri j is the distance between xi and x j , β0 represents the attractiveness at ri j , γ is the fixed light absorption coefficient such that γ ∈ [0, ∞), α is the randomization parameter. rand is a random number generator between 0 and 1. Algorithm FLANN-FA-NAV 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

Initialize population size as N and maximum number of generations as G. Generate randomly N initial solutions that represent weights of the FLANN. Calculate the intensity I for each firefly x N as the Mean Square Error. Calculate the attractiveness of each firefly x N Set T=1 Repeat steps 7 to 13 While ( T < G ) Repeat steps 8 to 12 For i= 1 to N Repeat steps 9 to 12 for j= 1 to i then if Move firefly to using Equation ( 4) Update using Equation (2) Evaluate the fitness using MSE and update intensity. T= T+1 Return the best solution.

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Table 1 Data sets description Name of the mutual fund Period of data

Total no. of available NAV data

Total no. of generated patterns

UTI Equity mutual fund 01.03.2007 to31.03.2017

2866

2855

TATA Dividend Yield Fund-direct growth

1214

1203

02.01.2013 to 01.12.2017

3 Simulation Study The simulation work for the 1-day and 5-day ahead NAV forecasting includes NAV data set preparation, training and testing of the proposed FLANN-FA model.

3.1 Description of Data set The NAV data of the two Indian Mutual funds, i.e., UTI Equity mutual funds and TATA Dividend Yield Fund-direct growth are collected for this simulation study. The NAV data of UTI Equity mutual fund are collected from 1 March 2007 to 1 March 2017. Similarly, the NAV data of TATA Dividend Yield Fund-direct growth are collected from 2 January 2013 to 1 December 2017. The descriptions of these two data sets are given in Table 1. A window size of 12 is taken for considering the input pattern, and four statistical features, i.e., mean, standard deviation, kurtosis and skewness are added with the actual NAV data.

3.2 Training and Testing the Model The optimization of the FLANN model is performed using firefly algorithm. In the firefly algorithm, the population size is 40, and 100 number of iterations are considered during training of the model. The brightness is set as 0.4, and the absorption coefficient is set as 0.5 in this work. The objective function is to minimize the mean square error (MSE). After the MSE value converges, the weight values are taken for the testing purpose. During testing, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) as shown in Eqs. (5) and (6) are evaluated and considered as the performance evaluation measures. Also, the actual NAV and predicted NAV are plotted in Fig. 2 for both the data sets using FLANN-FA model. RMSE =

1 (X i − Yi )2 s i=1 S

(5)

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Fig. 2 Actual versus predicted NAV for 1-day ahead for a UTI Equity mutual fund and b TATA Dividend Yield Fund-direct growth

MAPE =

S  X i −Yi  i=1  X i  S

× 100

(6)

Tables 2 and 3 describe the RMSE and MAPE values, respectively, for the 1-day and 5-day ahead NAV prediction of both the data sets using FLANN and FLANN-FA models. Figure 2 demonstrates the plotting of the actual and predicted NAV using FLANNFA model, and it reflects very close overlapping.

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Table 2 RMSE values of both data sets using FLANN and FLANN-FA model NAV data set

Number of days ahead

FLANN

FLANN-FA

UTI Equity mutual fund

1-day

3.4876

2.8965

5-day

4.2280

3.7567

TATA Dividend Yield Fund-direct growth

1-day

4.5740

3.7734

5-day

3.6628

3.2570

Table 3 MAPE values of both data sets using FLANN and FLANN-FA model NAV data set

Number of days ahead

FLANN

FLANN-FA

UTI Equity mutual fund

1-day

2.8429

2.0934

5-day

3.765

3.1256

1-day

3.125

2.7693

5-day

2.463

1.87

TATA Dividend Yield Fund-direct growth

From the above simulation results, it is concluded that the proposed FLANN-FA model outperformed the FLANN model in 1-day ad 5-day ahead NAV forecasting of both the Indian mutual funds.

4 Conclusion This paper focuses the experimental performance analysis of FLANN-FA model with FLANN model for the 1-day and 5-day ahead NAV forecasting of UTI Equity mutual fund and TATA Dividend Yield Fund-direct growth. The firefly algorithm is used to determine the optimal weights of the FLANN model during training process. During testing, the RMSE and MAPE values are taken as the performance measures. The simulation results indicate that FLAN-FA model produced better results as compared to FLANN model in NAV forecasting of two of these Indian mutual funds.

References 1. Cuthbertson K, Nitzsche D, Sullivan NO’ (2016) A review of behavioral and management effects in mutual fund performances. Int Rev Finan Anal 44:162–176 2. Kale J, Panchapagesan V (2012) Indian mutual fund industry, opportunities and challenges. IIMB Manage Rev 24(4):245–258 3. Cavalcante R, Brasileiro R, Souza V, Nobrega J, Oliveira A (2016) Computational intelligence and financial markets: a survey and future directions. Expert Syst Appl 55:194–221 4. Gardner E (1985) Exponential smoothing: the state of the art. J Forecast 4(1):1–28 5. Domingos SD, de Oliveira JF, de Mattos Neto PE (2019) An intelligent hybridization of ARIMA with machine learning models for time series forecasting. Knowl-Based Syst 175:72–86

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6. Faraway J, Chatfield C (1998) Time series forecasting with neural networks: a comparative study using the airline data. Appl Stat 47:231–250 7. Chiang WC, Urban TL, Baldridge GW (1996) A neural network approach to mutual fund net asset value forecasting. Omega Int J Manage Sci 24(2):205–215 8. Indro DC, Jiang CX, Patuwo BE, Zhang GP (1999) Predicting mutual fund performance using artificial neural Networks. Omega Int J Manage Sci 27(3):373–380 9. Anish CM, Majhi B (2015) Net asset value prediction using FLANN model. Int J Sci Res 4(2):2222–2227 10. Misra BB, Dehuri S (2007) Functional link artificial neural network for classification task in data mining. J Comput Sci 3:948–955 11. Dehuri S, Roy R, Cho SB, Ghosh A (2012) An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. J Syst Softw 85(6):1333–1345 12. Rout A, Dash P, Dash R, Bisoi R (2017) Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach. J King Saud Univ-Comput Inf Sci 29(4):536–552 13. Yang XS (2008) Firefly algorithm. Nat-Ins Metaheur Algorith 20:79–90 14. Senthilnath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm Evolution Comput 1(3):164–171 15. Fister I, Fister Jr I, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evolution Comput 13:34–46 16. Bebarta DK, Venkatesh G (2016) A low complexity FLANN architecture for forecasting stock time series data training with meta-heuristic firefly algorithm. Computat Intell Data Mining 1:377–385

Toward Ultimate Scaling: From FinFETs to Nanosheet Transistors T. P. Dash , E. Mohapatra , Sanghamitra Das, S. Choudhury , and C. K. Maiti

Abstract In this work, a TCAD-based simulation approach has been proposed to analyze 3-nm long p-type nanosheet (NS) field-effect transistors (FETs). The effects of the number of conducting channels on device performance have been studied in detail. As a proof-of-concept, the advanced nonplanar FinFET, nanowire FET, and nanosheet FET are compared. Nanosheet transistors show the best performance, and it seems to be the most suitable contender for future technology nodes. Keywords Nanosheet FETs · FinFETs · Nanowire · 3 nm · Linearized Boltzmann transport (LBT) · TCAD

1 Introduction The microelectronics industry is facing increasing physical dimensional limits; the gate length in particular cannot be reduced due to its fundamental limit and also the precision of the manufacturing processes (mainly the lithography). Transistors today are in the nanometer range. The TSMC has even opted for this solution for T. P. Dash (B) · E. Mohapatra · C. K. Maiti Department of Electronics and Communication Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] E. Mohapatra e-mail: [email protected] C. K. Maiti e-mail: [email protected] S. Das Department of Electronics and Communication Engineering, Silicon Institute of Technology, Bhubaneswar 751024, India e-mail: [email protected] S. Choudhury Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_19

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its 16 nm technology node, whereas Samsung and GlobalFoundries 14 nm node are now in production. Intel has implemented circuits with transistors at 10 nm technology node, and GlobalFoundries is actively working on making transistors at 7 nm technology node. It was a fin-shaped trigate nonplanar MOSFET (also called trigate FinFET), which shows superior subthreshold performance due to better gate control. In this architecture, the current flows in long vertical flanks that is why it is better to make narrow FinFETs (for better control of the electrostatics). The 3-D geometry of such nonplanar FinFET structures imposes new challenges, especially at the computational level. Thus, it becomes essential to choose the appropriate transport model for nanoscale nonplanar devices in advance technology nodes. The physical behavior of carriers inside nonplanar FinFETs is not as simple as in planner devices. A minimal number of research reports are available till date explaining carrier transport phenomena at nanoscale. Accurate simulation of these nonplanar devices and their modeling aspects have several challenges to overcome. At nanoscale, the incorporation of quantum effects in the transport model is essential. Several research groups have reported ballistic effects as its ultimate limits [1, 2]. Other research groups have reported lateral devices, GAA FETs such as nanowire FETs (NWFETs) and nanosheet FETs (NSFETs), to overcome such limitation [3–8]. This ultimate scaling involves going beyond many manufacturing challenges in order to make operational chips consisting of billions of transistors. The vertically stacked NSFETs are considered to be possible descendent to trigate FinFETs to continue with scaling challenges [9]. As predicted, 5 nm technology is not going to be ready for manufacturing until 2025, and it will be some sort of FinFET (possibly gateall-around silicon nanosheet or similar type of devices). So it is time for exploring new device structures through predictive simulation toward ultimate scaling. Finding solutions for the technical challenges are of interest in the context of this work. Here, a TCAD-based approach is proposed to simulate advanced devices in the extreme scaling limit, even at 3 nm gate length. This approach has been applied to design advanced devices, including horizontally stacked nanosheet (NSFETs) and nanowire (NWFETs) gate-all-around FET and FinFET structures. We show that a wide design space for the 5/3 nm nodes can be explored and optimized with rigorous physics-based modeling. There are five sections in this work, including the introduction in Sect. 1. In Sect. 2, the simulation environment for nonplanar 3D devices has been presented. The physics-based approach has been described in this section. The detailed electrical performance analysis of a 3-nm long NSFETs has been performed in Sect. 3. Section 4 presents a comparison of the performances of FinFET, NWFET, and NSFET. The conclusive remarks have been presented in Sect. 5.

2 Simulation Environment The NSFET with 3 nm gate length is considered for simulation. The NSFET is designed on a 350-nm thick substrate. The sheet height and width are 6 nm and

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12 nm, respectively. A 1.5-nm-thick HfO2 layer is used as a high-k gate dielectric surrounding the SiO2 of 0.5 nm thickness across the channel (sheet). The feasible design aspects, along with detailed electrical analysis, are performed for p-channel NSFET. The n-type doping profile of channel and p-type doping of source/drain region are maintained at 1016 cm−3 and of 1020 cm−3 , respectively. The metal gate work function is set to be 4.17ev. The ‘device structure’ (see Fig. 1a) and ‘flow chart of simulation’(see Fig. 1b have been shown in Fig. 1. To evaluate the electrical performance, we have to select the appropriate transportation model. The choice of the method in the simulation of the transport properties depends on the structure of the device. The device under investigation is partitioned in segments, and for each segment, a material is assigned. The transport equations which have been solved are assembled for each segment and the boundaries in between them. Depending on the material, the active transport equations, and the activated physical models have been implemented [10]. A discretization in space is required to solve these kind of systems, which is accomplished by defining a geometry mesh.

Fig. 1 a Structure of 3-nm-long p-type NSFETs (top), b Flowchart of simulation (bottom)

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The equations are assembled for each grid point. Such an equation system cannot be solved directly. Therefore, Newton’s iteration method has been applied. Densitygradient (DG) and drift–diffusion (DD) models arise to be the fast and powerful tool to support predictive technology development for the microelectronics industry. The density gradient model, along with quantum corrections, has been used in this simulation work. The complete simulation approach has been presented in the flow chart shown in Fig. 1b [11]. The simulation approach involves the following steps, • • • •

Computation of band structure of nanosheet using tight binding model [12], Calculation of the charge density using a semiclassical model. Solution of a 2D Poisson’s equation in the cross section of the nanosheet. Solution to linearized Boltzmann transport (LBT) in nanosheet channel [13]. Following LBT, the current density can be expressed in terms of the wave vector, Jn = −

q0 (2π)

 V p (k) f p1 (k)dk

(1)

where V p (k) denotes the group velocity and f p1 is the distribution function of holes. The distribution of holes can further be expressed as a function of the electric field as [12] f p1 = q0 V p (k)τ p (k) · E

d f p0 dE

(2)

where τ p is the microscopic relaxation time of holes and f 0 is the Fermi–Dirac distribution.

3 Electrical Performance Analysis Usually, in any p-type FET, the holes are confined to channel either by specified geometry or by applied potential. The concept of hole confinement in such as small geometry nanosheet (3 nm) can be calculated by solving the Schrodinger Eq. (3) considering quantum mechanical effect. For the calculation of potential, the parabolic energy band configuration has been taken into consideration, as stated in the last section.  2  ∇ · m −1 · ∇ + V ψ = Eψ 2

(3)

The potential profiles of NSFETs have been shown in Fig. 2. Three NSFETs structures are considered for analysis based on their number of conducting channels or sheets with a dimension of ( W ∗ H = 6 ∗ 12 nm) and length of 3 nm.

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Fig. 2 Potential profile in NSFETs having a three sheets, b two sheets, and c single sheet at V G = VD = −0.7 V. (Top: sheets 3D view from source to drain, Bottom: a cross-sectional view of sheet)

The drain current (I D ) can be worked out following Eq. (1). The transfer characteristics and output characteristics for NSFET having a single channel (1 NS), two channels (2 NS), and three channels (3 NS) have been plotted in Fig. 3. The I D value becomes almost double and thrice in 2NS and 3NS compared to 1NS for both linear and saturated drain voltages (−0.05 V and −0.7 V) in the transfer characteristics (Figs. 3a and b. The saturated drain current almost follows similar enhancement for 2NS and 3NS compared to 1NS in the I D -V D curve shown in Fig. 3c. These enhancements can be justified with the help of the density of charges. As in multiple sheets, the density of holes gets doubled and thrice as compared to a single sheet. Besides the density of charges and more number of conductive channels, low S/D parasitic resistance and higher mobility also contribute to increment in the current. The subthreshold performances are also extracted and presented in Fig. 4, to have a complete study of 3-nm-long channel p-type NSFET. The device parameters such as threshold voltage (VTH ), subthreshold slope (SS), on current (ION ), and off state current (IOFF ) has been found out and plotted with variation in sheet numbers. It can be observed that VTH of 1NS is −259.5 mV and is decreasing with more number of sheets. It makes the multisheet devices more suitable for low-power application. The SS values also reduce from 170 mV/decade to 61 mV/decade from 1 to 3NS. The ION /IOFF ratio maintains at an order of 102 irrespective of the number of sheets. The overall subthreshold performances of NSFETs indicate that 3NS NSFET is a feasible and very promising device for future technology node to come.

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20

2 NS

Drain Current (Amp) (Log Scale)

15

L G=3nm

1E-6 10 1E-7

(a)

5 W NS= 12nm H NS= 6 nm

1E-8 0.0

-0.2

-0.4

-0.6

-0.8

Drain Current (  Amp ) (Linear Scale)

1 NS

Drain Current (Amp) in Log Scale

1E-5

@ Vg=-0.05V

0

@Vd=-0.7V

3NS

60

2NS 1NS

50

1E-5 LG=3nm

40 30 1E-6

20 (b)

WNS = 12nm 10 HNS = 6 nm

1E-7

-1.0

0.0

-0.2

Gate Voltage (volts)

-0.6

-0.8

0

-1.0

Gate Voltage(Volts)

50

@Vg=-0.7V

3NS 2NS

40 Drain Current (  Amp)

-0.4

Drain Current (  Amp ) in Linear Scale

70

1E-4 3 NS

1NS

L =3nm 30 G

20 10

W NS= 12nm H NS= 6 nm

(c)

0 0.0

-0.2

-0.4

-0.6

-0.8

-1.0

Drain Voltage (Volts)

Fig. 3 Id-V g characteristics a at V d = −0.05 V. b V d = −0.7 V c Id-V d characteristics at V g = −0.7 V 1E-4

V TH (Volts)

L G=3nm

120

-100

100

-50

80

0

(a) 1

Vd=-0.05V

2 3 Number of Sheet(s)

60

SS (mV/Dec)

Vd=-0.7V

-150

ION (Amp) (log Scale)

-200

140

1E-5

LG =3nm

1E-6

1E-5

1E-7

Vd=-0.05V

Vd=-0.7V

1E-6 W NS= 12nm H NS= 6 nm

(b)

1E-9

1E-7 1

1E-8

I OFF (Amp) (log Scale)

180 WNS = 12nm H NS= 6 nm 160

-250

2

3

Number of Sheet(s)

Fig. 4 Subthreshold performances vs. the number of nanosheets a Vth (black line), and SS (blue line) and b Ion (black line) and Ioff (blue line), respectively

Toward Ultimate Scaling: From FinFETs to Nanosheet Transistors Table 1 Performance comparison sheet of FinFET, NWFET, and NSFET

Electrical performance

231

FinFET

NWFET

NSFET

Id Sat (µA)

13.13

16.36

18.61

Ion_lin (µA)

6.24

8.70

9.91

Ion_sat (µA)

23.05

30.98

35.89

Ioff_lin (pA)

42.8

22.27

10.28

Ioff_sat (pA)

77.50

50.76

18.68

Vt_lin(-) (mV)

188.30

213.41

224.71

Vt_sat(-) (mV)

171.99

188.44

210.63

SS_lin (mV/Dec)

74.38

75.82

72.12

SS_sat (mV/Dec)

70.99

74.85

72.15

4 Comparison of FinFET, NWFET, and NSFET As NSFETs are potential contenders among all other nonplanar devices such as nanowire FETs (NWFETs) or FinFETs, for future technology nodes to come, it will be interesting to compare their performances. There are three 3D devices (FinFET, NWFET, and NSFET) with an equal volume of the channel are taken into consideration for this comparison. The structural details of FinFET, NWFET, and NSFET have been reported earlier by our Group [6, 14, and 15], respectively, with few modifications in dimensions of the channel (length, height, width, and radius). The ‘length’ and ‘area of cross-section’ of the channel are taken 14 nm and 150nm2 , respectively, for all devices for comparison. The area of cross section of the channel in FinFET (W ∗ H = 5 × 30 nm) is made equal with NFET (n ∗ πr 2 = 2 ∗ π ∗ 4.9 nm2 ) and with the NSFETs (n ∗ W ∗ H = 2 ∗ 6 ∗ 12.5 nm), where n is the number of conducting channels. The performance matrix has been presented in Table 1. The highest drive current and lowest off state current can be observed in the case of NSFET compared to the other two. The ID_sat shows 25 and 42% improvement in NWFETs, NSFETs compared to FinFET, respectively. The other critical device parameters for all the devices are within a suitable range. This indicates the NSFETs can be the best possible candidate for the future node to come.

5 Conclusion This work presents a brief simulation study of 3-nm-long silicon NSFETs having multi-sheets (horizontal channels). A feasible simulation environment based on band structure has been implemented to solve the Schrodinger equation and to include quantum effects. The detailed performance analysis starting from potential profile to subthreshold performance has been predicted for such extremely scaled devices. As a proof-of-concept equi-volume (equal volume of the channel), FinFET, NWFET, and NSFET are compared, and their performance matrix is listed in Table 1. NSFETs

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provide better performance from all aspects compared to FinFET and NWFETs. This study is expected to provide a platform for device engineers to optimize the electrical performances at advanced technology nodes.

References 1. Erlebach A, Lee KH, Bufler FM (2016) Empirical ballistic mobility model for drift-diffusion simulation. In: ESSDERC 2016. IEEE, Lausanne, pp 420–423 2. Zhang S et al (2019) Quantum transport study of Si ultrathin-body double-gate pMOSFETs: I-V, C-V, energy delay, and parasitic effects. IEEE Trans. Electron Devices 66(1):655–663 3. Loubet N et al (2017) Stacked nanosheet gate-all-around transistor to enable scaling beyond FinFET. In: 2017 symposium on VLSI technology. IEEE, Kyoto pp T230–T231 4. Skotnicki T et al (2008) Innovative materials, devices, and CMOS technologies for low-power mobile multimedia. IEEE Trans. Electron Devices 55(1):96–130 5. Dash TP et al (2019) Vertically-stacked silicon nanosheet field effect transistors at 3 nm technology nodes. In: IEEE international conference on devices integrated circuits (DevIC-2019). IEEE, Kolkata, pp 99–103 6. Dey S et al (2019) Design and simulation of vertically-stacked nanowire transistors at 3nm technology nodes. Phys. Sci. 95(1):014001 7. Barraud S et al (2015) Opportunities and challenges of nanowire-based CMOS technologies. In: Proceedings of S3S. IEEE, CA, pp 1–3 8. Barraud S et al (2017) Performance and design considerations for gate-all-around stackedNanowires FETs. In: Proceedings of IEDM. IEEE, CA, pp 29.2.1–29.2.4 9. IRDS: Executive Summary, International Roadmap for Devices and Systems (2017) 10. Minimos-NT User Manual (2017) 11. The electronic structure calculations and part of the self-consisted calculations can also be performed online on nanoHUB.org using the code “Band structure Lab”, Home page, https:// nanohub.org/tools/bandstrlab/. Last Accessed 20 Jun 2020 12. Neophytou N, Baumgartner O, Stanojevic Z, Kosina H (2013) Band structure and mobility variations in p-type silicon nanowires under electrostatic gate field. Solid. State. Electron. 90:44–50 13. Neophytou N, Kosina H (2011) Atomistic simulations of low-field mobility in Si nanowires: influence of confinement and orientation. Phys Rev B Condens Matter Mater Phys 84(8):085313 14. Dash TP et al (2019) Stress-induced variability studies in tri-gate FinFETs with source/drain stressor at 7 nm technology nodes. J. Electron. Mater 48(8):5348–5362 15. Dash TP et al (2020) Strain-engineering in nanowire field-effect transistors at 3 nm technology node. Phys. E Low-Dimensional Syst. Nanostructures 118:113964

Performance of a Free Space Optical Link with ACO-OFDM-Based Signal Transmission Under Beam-Wander-Dominated Atmospheric Turbulence Sabita Mali and Jayashree Ratnam Abstract Free space optical (FSO) communication technology has been identified as a promising, complementary solution to the existing radio frequency (RF) solution because of its support for high speed data transmission. Though FSO has tremendous potential to compete with existing communication technologies, turbulence in atmosphere affects the propagation path of the optical beam and limits its system performance. This paper investigates the impact of turbulent atmosphere on the received optical irradiance which carries an asymmetrically clipped optical OFDM (ACO-OFDM) data signal. The FSO communication system employs intensity modulation/direct detection (IM/DD) scheme. An analytical model is developed to capture the turbulence effect with the assumption that the beam-wander plays a dominant role in determining the intercepted signal irradiance at the receiver aperture. The bit-error rate (BER) of the photo-detected signal is evaluated for weak-, moderate- and strong-turbulent channels assuming that the optical beam has Gaussian distributed profile whose beam width is affected by beam-wander. Numerical simulation results provide an insight into the role of various system parameters on received signal for a desired performance. Keywords Free space optical (FSO) · ACO-OFDM · Turbulence · Beam-wander · Bit-error rate(BER)

1 Introduction As the demand for high speed data in wireless communications increases day by day with the significant increase in the number of users, radio frequency (RF) spectrum becomes one of the scantiest resources in the world. RF technologies have limitations of regulated spectrum, spectrum congestion, expensive licensing and low S. Mali (B) · J. Ratnam ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] J. Ratnam e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_20

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speed broadband connections. Fiber optic-based communication such as passive optical network (PON) and fiber to the home (FTTH) are being used but these require permits for digging, time consuming installation, and a high installation cost. Free space optical (FSO) communication technology has been identified as a powerful and promising complementary and/or alternative wireless optical solution to the existing RF solutions [1, 2]. In FSO, the optical-beam-modulated data is transmitted through unguided channel instead of guided fiber optic channel. FSO systems offer several advantages such as license-free large spectrum (0.5–1.5 THz) with a potential to support terabit per second (Tb/s) throughput, low power requirements, immunity from interception, and electromagnetic interference from other RF band services, inherent security, and operation in unlicensed bands. FSO communication has immense scope to set up transmission links where it is difficult or impossible to lay optical fiber cables. This feature gives FSO technology immense scope in grooming smart cities for a well-connected society with both military and civilian applications. Though FSO has tremendous potential to compete with existing communication technologies, the growth of FSO technology is hampered by various parameters like building motion, atmospheric losses due to weather (i.e., rain, fog, haze, and aerosol particles), and atmospheric turbulence. These parameters reduce the link deployment distance and hence interrupt the communication. Atmospheric losses due to bad weather condition can be partly compensated by operating FSO at a longer wavelength. However, performance degradation due to atmospheric turbulence is still a challenging problem and directly affects the quality of received signal due to fading. It can occur even in clear weather condition for any duration of day and night. So the fading due to atmospheric turbulence is the major performance limiting factor in FSO-based systems. The important effects of atmospheric turbulence on the laser beam are: beam spreading, beam-wander, and phase-front distortion which cause variations within in the propagating optical beam [3]. These are due to variations in the temperature and pressure of the air along the optical path through the channel which causes refractive index fluctuations [4]. These physical changes in the free space channel are classified into weak, moderate, and strong regimes. Depending on the size of eddies (turbulence-induced air-masses), the resultant effect on the light irradiance could be a combination of beam spread, beam-wander, and beam scintillation. When the size of turbulence eddies exceeds the beam size, then the beam is deflected from its propagating path in a random fashion and is called a beam-wander. As a result, the center of the beam is displaced on the plane of receiver for which the “short-term” spot size turns into a wider beam called “long-term” spot size which could lead to link failure. The short-term spot size is characterized by free space beam parameter and results in weak turbulence. But the long-term spot size is mostly due to beam-wander effect under large scale turbulence [5]. In recent times, the most widely used modulation technique in optical communications is orthogonal frequency division multiplexing (OFDM) because of its resilience against channel dispersion and its high spectral efficiency. But OFDM can not be directly used in optical communication as OFDM signals are bipolar in nature.

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Optical systems use intensity modulation techniques where only unipolar signals can be transmitted. The direct-current-biased optical OFDM and asymmetrically clipped OFDM (ACO-OFDM) [6] are two such widely adopted multi-carrier technologies in the optical domain. Several theoretical and analytical studies investigated the effect of turbulence on the performance of an FSO link [3, 7, 8]. In this paper, an analytical model is developed to capture the turbulence effect with the assumption that the beam-wander plays a dominant role in determining the intercepted signal irradiance at the receiver aperture. We examine the BER performance of an ACO-OFDM-based free space optical link for weak-, moderate-, and strong-turbulent channels assuming that the optical beam has Gaussian distributed profile whose beam width is mainly affected by turbulence-induced beam-wander.

2 System Description Figure 1 illustrates the block schematic of an ACO OFDM-based FSO system. System components include a baseband OFDM transmitter which consists of a data modulator, a subcarrier-mapping subsystem, a zeroclipping subsystem, an optical transmitter, and an optical receiver. Input data sequence is transformed to parallel data channels and data on each channel is QAM modulated to obtain complex symbol vectors. The modulator employs Hermitian symmetry property to generate a real signal subsequently used to drive the optical source. OFDM modulator applies an Npoint inverse fast Fourier transform (IFFT) on the resulting symbol vector, where it is mapped onto only half (odd frequencies) the number frequency subcarriers. In this

Fig. 1 Block schematic of an ACO OFDM-based FSO system

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case, even-numbered subcarriers need not be demodulated and only odd-numbered subcarriers carry information from which data needs to be extracted. The resulting discrete OFDM signal is digital-to-analog-converted to produce a real-valued, timevarying baseband OFDM signal.Since an intensity modulated system does not allow negative signals to carry data, the baseband OFDM signal undergoes zero-clipping to produce unipolar signal which serves as the drive current for an laser diode. This unipolar optical signal is referred to as ACO OFDM signal which is transmitted through free space channel.

2.1 Asymmetrically Clipped Optical OFDM Signal Power In our proposed system, we consider a laser source with input–output characteristic (or load line) bounded by a lower cut-off point,Cl = 10 mA and an upper cut-off point, Cu = 200 mA which is equivalent to 13 dB linear dynamic range. The OFDM signal amplitude varies between Imax and Imin , whereas the signal amplitude driving the laser is limited by Cl and Cu . We assume that the lower cut-off point Cl is close enough to the lowest amplitude of the unipolar baseband OFDM signal Imin .The currents and powers involved in the conversion of electrical signal to optical signal are given by Eqs. (1), (2), (3) and (4) Imin = Cl

(1)

Imax = Iavg . PAPRamp

(2)

where PAPRamp is the ratio of the peak signal amplitude normalized with respect to its average value. The OFDM baseband signal power and the OFDM signal power captured by the load line are given by [6] Imax

Psigpow−baseband = ∫ z 2 p(z)dz

(3)

Imin cu

Psigpow−captured = ∫ z 2 p(z)dz

(4)

cl

where, z represents the instantaneous unipolar drive current Iaco which is modeled as a clipped Gaussian distributed random variable, with a mean  Pavg−baseband & p(z) = Pavg−baseband /(2 × π ) and variance of value of 2   2 z−z ) √ 1 [9]. Whenever the signal amplitude level falls beyond the exp − ( 2σavg 2 2 2σ

bounds of the source load line, clipping noise is generated. The total clipping noise 2 power σcli p is given by

Performance of a Free Space Optical Link with ACO-OFDM-Based … 2 2 2 σcli p = σuc + σlc

237

(5)

˙In the case of an electrical ACO-OFDM, since we assumed that Imin = Cl (as C − < i s >2 ) + 2eB < i s > +σclip σnoise_turb

(16)

Error performance of the FSO link is evaluated by considering the signal and noise powers in electrical domain. The signal-to-noise ratio(SNR) at the receiver in the absence of turbulence using Eqs.(13) and (15) is given by  SNR0 =

Averaged signal power = Total noise power

R × Pr0xopt

2

2 σnoise

(17)

Recent literature [5, 11] reveals that an optical beam which comes under the effect of turbulence-induced beam-wander has an effective SNR SNRbeam−wander =

2 R × Pr xopt 2 σnoise_turb

(18)

Substituting Eqs. (14), (16) and (17) in Eq. (18), we obtain a comprehensive expression for SNR under beam-wander dominated turbulence given by SNR0    SNRbeam−wander =     5 5 2 2 × 1 + 1.33σ R2  6 1 + 1.33σ R2  6 + A.x SNR0 + σclip /4e2 B 2 × SNR0

(19)

  5 The factor 1 + 1.33σ R2  6 is known as turbulence factor due to beam-wander and we denote it by X turb . A is the aperture averaging factor which is the ratio of normalized irradiance variance at the receiver aperture of diameter D to that of a point receiver (D = 0) [5]. For a photo-detector with relatively large surface area, A is very small and for L = 500 m, it is 0.005 which is very small.

in our model Hence, the term A × SNR0 can be neglected. Thus, the turbulence factor acts as a scaling factor to estimate the effective noise variance and the effective SNR under beam-wander effect reduces to SNRbeam−wander =

SNR0    2 X turb 1 + σclip × X turb /4e2 B 2 × SNR0

(20)

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Now, the BER of the photo-detected signal can be evaluated using the standard complementary error function in terms of the QAM size M and SNR under beamwander as given by ⎛ √

 ⎞  M −1 3 × SNRbeam_wander ⎠ BER = ⎝ √ √ er f c 2(M − 1) Mlog2 M

(21)

4 Numerical Results and Discussion We now study the ACO-OFDM-based FSO link performance by varying the transmitted optical signal power in the range −15 dBm to 15 dBm (i.e., 0.03–32 mW) in terms of bit-error rate. Various system parameters used for calculation is given in Table. 1. Figures 3 and 4 plot the irradiance across the receiver aperture with respect to radial distance in the absence and presence of turbulence, respectively, for an ACOOFDM-based signal transmit power of 3 mW. From Fig. 3, we observe that when the atmosphere is free from turbulence, the peak received irradiance at the receiver aperture varies as 0.175 W/m2 , 0.158 W/m2 and 0.14 W/m2 for W0 = 5 mm, 10 mm, and 15 mm over a free space optical link distance L = 100 m. The full-width-halfmaximum FWHM values of the corresponding optical beams are approximately equal to 110 mm. In Fig. 4, we compare the received OFDM signal irradiance for different levels of turbulence by considering an optical beam width W0 = 10 mm while all other parameters remaining the same as that for non-turbulent FSO channel. We observe that the peak received irradiance is 0.16 W/m2 at weak turbulence with Cn2 = 2×10−15 m−2/3 , 0.145 W/m2 at moderate turbulence level with Cn2 = 2×10−11 m−2/3 and decreases to 0.09 W/m2 at strong turbulence level with Cn2 = 2 × 10−10 m−2/3 . The corresponding FWHM beam widths are found to be 125 mm, 140 mm, and Table 1 Parameters used in ACO-OFDM-based FSO system

Parameter

Value

Diameter of receiver lens (D)

200 mm

Transmitter beam spot radius (W0 )

10 mm

Beam divergence angle (θ)

1 m rad

Peak wavelength (λ)

1550 nm

Responsivity of photo diode (R )

0.9 A/W

Modulation bandwidth (B)

20 MHz

Sub-carrier count (N)

128

QAM constellation size (M)

256

Fig. 4 Irradiance across the receiver aperture versus radial distance in the presence of turbulence for different Cn2

0.18 W =5mm 0

0.16

W 0 =10mm W 0 =15mm

0.14

D=200m L=100m =1mrad Paco=3mW

0.12 0.1 0.08 0.06 0.04 0.02 -100

Irradiance across receiver aperture, Irl(W/m 2 )

Fig. 3 Irradiance across the receiver aperture as a function of radial distance in the absence of turbulence for different transmitter beam size

S. Mali and J. Ratnam Irradiance across receiver aperture w/o turbulence , Irl(W/m 2 )

242

-80

-60

-40

-20

0

20

40

60

80

100

Receiver aperture radial distance,r in mm

0.18 Cn 2 =2x10 -15 m-2/3

0.16

Cn 2 =2x10 -11 m-2/3 2

Cn =2x10

0.14

-10

m

-2/3

0.12 0.1 0.08 L=100m D=200m =1mrad W 0 =10mm

0.06 0.04

=1550nm Paco=3mW

0.02 0 -100

-80

-60

-40

-20

0

20

40

60

Receiver aperture radial distance,r in mm

80

100

180 mm, respectively, due to beam-wander-dominated atmospheric turbulence. Hence, a receiver aperture of 200 mm effectively captures a major portion of the irradiance of the information-carrying optical beam. Figure 5 shows BER versus ACO-OFDM signal transmit power for different Cn2 value for two propagation distances of L = 500 m and L = 700 m. Power penalty due to turbulence is the increase in transmit signal power needed to produce the same BER (say 10−12 ) as that of a system without turbulence. It gives an estimate of the power needed to compensate for system degradation due to turbulence. We can observe that a signal with PAPR value of 18 dB, incurs a power penalty of 3 dB (6–3 = 3 dB) for weak turbulence channels as the propagation distance increases by 200 m from L = 500 m without compromising signal quality. The FSO link gives satisfactory error performance at L = 500 m with a power penalty of 2.5 dB at moderate levels

Performance of a Free Space Optical Link with ACO-OFDM-Based … Fig. 5 System BER versus optical transmitted power for different L

10

0

2

-15

m

-2/3

2

-11

m

-2/3

2

-10

m

-2/3

Cn =2x10 Cn =2x10

10

243

Cn =2x10

-5

ACO OFDM N=64,M=256

BER

PAPR=18dB D=200mm 10

-10

W =10mm 0

=1mrad =1550nm B=20Mhz Solid line

10

-15

L=500m L=700m

Dotted line

-15

-10

-5

0

5

10

Optical transmitted power,dBm

of turbulence. However, signal BER deteriorates to unacceptable level (>10–4 ) when the link distance is increased by 200 m. Further, the link performance under strong turbulence is however seriously compromised (BER > 10–2 ) even at L = 500 m. Figure 6 illustrates BER versus optical transmitted power plots for an OFDM signal with QAM size M = 256 and M = 1024. It may be noted that this amounts to a four-fold increase in data transmission rate which compensates for the underutilized sub-carriers (even-numbered ones) in ACO-OFDM systems. It is evident from the figure that the power penalty for a four-fold increase in data rate is 5 dB 100

Fig. 6 System BER versus optical transmiited power for diffrent QAM constellation size M

2

-15

m

-2/3

2

-11

m

-2/3

2

-10

m

-2/3

Cn =2x10 Cn =2x10

BER

10-5

10-10

10-15

-15

Cn =2x10

ACO OFDM N=64 PAPR=18dB L=500m D=200mm W =10mm 0

=1mrad =1550nm B=20Mhz Solid line M=256 Dotted line M=1024

-10

-5

0

5

Optical transmitted power,dBm

10

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Fig. 7 System BER versus optical transmitted power for different PAPR

10

0

2

-15

m

-2/3

2

-11

m

-2/3

2

-10

m

-2/3

Cn =2x10 Cn =2x10

10

Cn =2x10

-5

Solid line

PAPR=18dB

BER

Dotted line

PAPR=20dB

ACO OFDM 10

-10

M=256,N=64 L=500m D=200mm W =10mm 0

=1mrad =1550nm 10

-15

-15

B=20Mhz

-10

-5

0

5

10

Optical transmitted power,dBm

under weak turbulence to achieve the desired bit-error rate. In moderately turbulent channels, improvement in data speed is achieved for a power penalty figure but at the cost of degraded BER value 10–5 . Figure 7 presents BER versus optical transmit power plots for different PAPR values. It is observed that for a 3 dB increase in PAPR from 17 to 20 dB, the power penalty is less than 0.5 dB with slight deterioration in BER value, for moderately turbulent channels. However, weakly turbulent channels are more resilient toward signal PAPR levels and provide good signal quality BER < 10−12 at the cost of a power penalty of 4 dB. Finally, as illustrated by Figs. 5, 6, and 7, the transmit power range with desired BER performance lies between 2 to 7dBm, thereby providing a useful dynamic range of 5 dB

5 Conclusion In this paper, we examine the link performance of an ACO-OFDM-based FSO system under three different atmospheric turbulence. The system performance is evaluated in terms of bit-error rate by developing ananalytical model which takes into account a turbulent atmospheric channel with a dominant beam-wander effect on a zeroclipped, unipolar OFDM signal carried on an optical carrier with a gaussian beam profile. The fluctuating beam irradiance captured over a receiver aperture is used to evaluate the effective signal-to-noise ratio and hence the received signal BER. From the results, it is observed that BER performance deteriorates with the increase of channel turbulence in terms of refractive index structure parameter Cn2 . We examined the power penalties involved in increasing the link distance, QAM constellation size,

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and PAPR value of OFDM signal in order to achieve a specified BER performance for the FSO link. The useful dynamic range for the chosen set of parameters is also identified. Numerical simulation results provide an insight into the role of various system parameters on received signal with desired performance.

References 1. Ghassemlooy Z, Poopola W, Rajbhandari S (2013) Optical wireless communications system and channel modelling. CRC Press,Taylor and Francis Group with MATLAB. First Edition, 2013, ebook. ISBN: 978-1-4398-5235-4. 2. Kaushal H, Kaddoum G (2017) Optical communication in space: challenges and mitigation techniques. In: IEEE Communications Surveys & Tutorials 3. Motlagh AC, Ahmadi V, Ghassemlooy Z, Abedi K (2008) The effect of atmospheric turbulence on the performance of the free space optical communications. IEEE Proceedings 4. Anbarasi K, Hemanth C, Sangeetha RG (2017) A review on channel models in free space optical communication systems. Optics Laser Technol 97:161–171 5. Larry CA, Ronald L.P, Hopen CY (2001) Laser beam scintillation with applications. SPIE Optical Engineering Press, Bellingham, Washington 6. Ratnam J, Mali S (2020) Power-efficiency in asymmetrically-clipped optical OFDM system with truncated-PAPR. In: ICICCT 2019-system reliability, quality control, safety, maintenance and management. Springer Nature Singapore Pte. Ltd. https://doi.org/10.1007/978-981-138461-5_79 7. Prokes A (2009) Modelling of atmospheric turbulence effect on terrestrial FSO link. Radio Eng 8(1) 8. Kaushal H, Kaddoum G, Jain VK, Kar S (2017) Experimental investigation of optimum beam size for FSO uplink. Optics Commun 400:106–114 9. Dissanayake SD, Armstrong J (2013) Comparison of ACO-OFDM, DCO-OFDM and ADOOFDM in IM/DD systems. J Lightwave Technol 31(7) 10. Jiang D, Yuan Y, Lin Y, Lyu T, Zhu B, Yan Y (2018) Mean irradiance profile of a Gaussian beam under random jitter. Optics Express 26(21):27472 11. Andrews LC, Phillips RL (1998) Laser Beam Propagation through Random Media. SPIE Press, Bellingham 12. Reza SA, Arshad MA (2019) Novel method of wireless data transfer through a variable focus lens-induced irradiance modulation of a Gaussian beam. 10.1109/ACCESS.2017.2651804

A Comparative Analysis of Demand Response on Different Operational Strategies of Battery Energy Storage System for Distribution System Sachin Sharma, Khaleequr Rehman Niazi, Kusum Verma, and Tanuj Rawat

Abstract This paper investigated the impact of different battery energy storage (BESS) operational strategies with demand response (DR) and wind-based renewable generation to enhance the performance of the distribution network. The optimal coordination of multiple BESSs, DR, and wind generation is used to minimize network energy loss. This coordinated operational problem is subjected to the distribution system constraints such as feeder current limit, voltage range, and power balance. The suggested methodology is tested on the IEEE 33-bus radial distribution network. A genetic algorithm (GA) is used to investigate the different operational strategies. The results show that the optimal coordination problem can reduce the system losses and also maintain the voltage within the specified limits. The implementation of BESS with DR and wind reduces 62.42% of network losses and it helps the distribution system operator for the selection of BESS operation strategy when the demand of the consumers is controllable. Keywords Wind generation · Demand response · Distribution network

1 Introduction For the increasing issues related to global warming along with a more efficient grid, power utilities have seen renewable energy development exponentially in recent years. The renewable-resource volatility and instability have raised the complexity S. Sharma (B) · K. R. Niazi · K. Verma · T. Rawat Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan 302017, India e-mail: [email protected] K. R. Niazi e-mail: [email protected] K. Verma e-mail: [email protected] T. Rawat e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_21

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of demand with generation supply matching and created new difficulties in sustaining network stability. As a consequence, the more flexible devices such as demand response (DR) and battery energy storage (BESS) are required to satisfy abnormalities of the distribution network. The latest developments have centered on energy storage technology, motivated by the desire to incorporate higher rates of green energy deployment and to compensate for the peak demands. There is a significant interest in batteries as a potential method to handle volatile renewable energy across all current storage technologies. Recent literature analyzes the scheduling issue of energy storage and the uses of battery in structures of expanded renewable resources, leading to increasing interest in energy storage [1–3]. The BESS is being applied at both the distribution and transmission system stages. The advantages of BESS in renewable-resourced transmission networks using models of security-constrained is investigated in [4, 5]. The impact of BESS for the distribution network is discussed in [6–8]. In [6], coordination of BESS with reactive DGs is used to minimize the grid consumption cost and network loss. In [7], the advantages of utilizing renewable resource with BESS in microgrids are analyzed using a short-term scheduling method. In [8], a BESS interaction model is developed to analyze the question of optimum allocation of batteries in a microgrid. However, BESS is charge during peak hours and discharge during low demand periods kind of strategy is implemented. In [9], different BESS operational strategies are compared with the effect of renewable-based DGs. But, its impact on DR is not investigated. Many approaches are investigated for the implementation of DR in the distribution network [10]. In the majority of instances, aggregators behave passively in distributed DR management ideologies, where they only communicate with endusers through group energy profile details or data on grid prices. On the opposite, end-users are involved in reacting to the data obtained from the aggregator and manage their demand. Aggregators may also play an important role and promote consumer engagement by integrating DR programs with grid organizations. As a consequence, end-users are not more involved in the decision-making phase, where aggregators take decisions with the behalf of participated consumers by taking care of the economy and its comfort. Moreover, to integrate the operational benefits in the distribution network, there are many authors reported the implementation of DR with BESS. The complete 24 h of operation cost and carbon emission cost minimized with the time of use-based DR is analyzed in [11]. In [12], the impact of DR with BESS and PV to minimize the annual energy loss, reverse power flow is analyzed. Smoothing the wind generation impact with energy storage was studied in [13]. BESS with DR to smooth the control of the tie line power and minimize the carbon emissions was discussed in [14]. In [15], the multi-agent method was investigated to minimize the peak load with the coordination DR and BESS at the distribution level. In this paper, the three different strategies with adding the benefits of DR and wind generation together are analyzed. The comparative analysis of framed cases helps the distribution system operator to select the BESS strategy in order to maximize operational benefits.

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2 Problem Formulation The mathematical formulation of optimal coordination of multiple wind generation and different operational strategies of BESSs with DR for distribution network is described here. In this study, the objective of network loss minimization with the improvement of node voltage deviation is considered. Therefore, the network loss minimization of 24 h is mathematically expressed as: min( f ) =

24 

Td PLOSS

(1)

Td =1 Td where PLOSS represent the network loss for T d time period. The network loss is calculating with the help of Eq. (2) and is taken from [16].

Td = PLOSS

Ng Ng  

    αiTj d PiTd PJTd + Q iTd Q TJd + βiTjd Q iTd P jTd − PiTd Q Tj d ∀ Td (2)

i=1 j=1

  αiTj d = ri j cos δiTd − δ Tj d /ViTd V jTd

(3)

βiTjd = ri j sin(δiTd − δ Tj d )/ViTd V JTd

(4)

where ViTd , V jTd , PiTd , P jTd , Q iTd , Q Tj d , ri j and δiTd represents the voltage of respective nodes, real and reactive power, resistance between two nodes and voltage angle of ith and jth node for the T m time period, respectively. Constraints: Network constraints mth d I Tf w,i j ≤ Ia,i j

PiTd = ViTd

Ng 

∀ Td , i, j

  V jTd Yi j cos θi j + δ Tj d − δiTd

(5)

∀ Td , i

(6)

  V jTd Yi j sin θi j + δ Tj d − δiTd ∀ Td , i

(7)

j=1

Q iTd

= −Vi

Td

Ng  j=1



V P¯d,i gi )

(20)

where, Nd  24 

P¯d,i =

i=1 Td =1

Td

Td Pd,i

(21)

Strategy 2: In this strategy, charging and discharging of BESS are taking care of the availability of wind generation. Therefore, BESS is charged during the periods of high wind generation (i.e., from 1 to 15 h) and discharged during the peak demand periods.

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 Td PB(Cg i ,Dgi )

=

Td PB(C i f 1 ≤ T ≤ 15 gi )

(22)

Td PB(D else gi )

Strategy 3: In this strategy, the objective of the problem is the deciding factor for the BESS operation. The optimization algorithm is taken care of the charging and discharging time of BESS as per the considered objective function. The mathematical formulation of the considered problem is mixed-integer, nonlinear, and non-convex. Therefore, a well-established genetic algorithm (GA)-based optimization algorithm is used to solve the simultaneous coordination of DR with different BESS strategies in presence of wind generation. Following steps are performed to initialize the algorithm for determining the network loss of the considered problem: I. II. III.

IV. V. VI.

Read the network data and run the forward/backward sweep load flow analysis of the base case for calculating the network loss. Integrate the optimal size of wind generation and multiple BESSs. After that determined the optimal BESS dispatch power depending on the battery operational strategies and scheduling of controllable demand to absorb that wind generation. Determine the network loss after optimal coordination of BESS and DR. Apply the crossover and mutation operator to find the optimal solution. Repeat the above steps until the minimum network loss is achieved.

4 Result Discussion In order to validate the problem of optimal coordination of multiple wind generation and DR with different BESSs operation strategies, it is implemented on IEEE 33bus radial distribution system. The network load and line data are derived from [18]. The network active and reactive power demands are 3715 kW and 2300 kVar, respectively. The node voltage is assumed to be in the limit of 0.90–1.05 p.u. Three wind generators reside at 14, 24, and 30 nodes of distribution network as shown in Table 1. It is assumed that two BESSs at node 14 and 24 of the distribution network are allocated. Different DR levels reflect customer load involvement for similar demand response programs. However, it is assumed that 20% of DR level is used in this study. Consequently, it is necessary to monitor the responsive load the function of DR aggregator is important and it is advised the participants to better schedule the demand. This research illustrates the technological problems of the Table 1 Location and Sizes of considered distributed energy resources

Particulars

Wind generators

Location

142,430

BESSs 1424

Sizes

1,048,100,51128 (in kW)

500,05000 (in kWh)

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253

distribution network in consideration of DR with different operational strategies of BESS. The various cases are framed and analyzed to show the feasibility of the suggested methodology: Case 1: Base case with standard 33-bus distribution system without any integration of modern technologies. Case 2: Integration of wind generation into the considered distribution system. Case 3: Implementation of DR alone into the distribution network. Case 4: In this case, wind generation and DR are implemented with the firstoperational strategy of BESS. Case 5: In this case, wind generation and DR are implemented with the secondoperational strategy of BESS. Case 6: In this case, wind generation and DR are implemented with the thirdoperational strategy of BESS. The case 1 is used for highlighting the other modification in the standard distribution system. In this case, the network losses are 3.7097 MWh and minimum node voltage is 0.9319 p.u that is poorer than all the other framed cases. The peak demand for this case is 6519 kW and that is again maximum among all the cases. Case 2 is implemented with the integration of wind generation and the network losses are reduced to 1.9253 MWh from 3.7097 MWh (i.e., of the base case). The peak demand is reduced to 24.96% from case 1 as shown in Table 2 and Fig. 1. In case 3, the DR alone is implemented and the network losses are reduced to 3.0969 MWh. It is only with the shift of demand from peak periods to low demand periods and it is shown in Table 2 that the peak demand is reduced to 21.74%. However, the combined effect of wind generation with DR and strategy 1 of BESS is analyzed in case 4. The BESS is charging during low demand periods (i.e., from 1 to 8 h) and discharge during peak demand periods nearly onwards 20 h, respectively, as shown in Fig. 2. Case 5 is implemented with strategy 2 of BESS, and its charging is shifted up to the first fifteenth hours, and discharging is during peak hours as presented in Fig. 3. The case 6 is the best as maximum network losses are reduced. The peak is maximum reduced in strategy 2 but offpeak and peak jointly combination is maximum reduced in case 6. Therefore, it is reflected in the maximum network loss reduction. The charging of BESSs at the location 14 is started from 3 to 14 h and discharging is from 17 to 22 h, Table 2 Impact of all the farmed cases on the considered objective function Cases

Energy losses (MWh)

Minimum mean of node voltage (p.u.)

Peak demand (kW)

Percentage peak reduction (%)

Case 1

3.7097

0.9319

6519

–

Case 2

1.9253

0.9660

4892

24.96

Case 3

3.0969

0.9327

5102

21.74

Case 4

1.4095

0.9662

3052

53.18

Case 5

1.4841

0.9661

2559

60.75

Case 6

1.3940

0.9659

3034

53.46

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Fig. 1 Grid demand for all the framed cases

Fig. 2 Battery energy status for case 4

respectively. The BESS at location 24 is a charge for 8 to 12 h and discharge from 14 to 21 h respectively as shown in Fig. 4.

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Fig. 3 Battery energy status for case 5

Fig. 4 Battery energy status for case 6

5 Conclusion This paper aims to improve the network losses for complete one day of distribution network by optimal coordination of multiple wind generation and DR with different operational strategies of BESSs. Genetic algorithm is used to address the issue of system energy loss minimization for the active distribution system. To show the importance of coordination of multiple distributed energy resources with different operational strategies of BESSs, six multiple cases are framed and analyzed. The Strategy 3 approach of BESS with a shiftable demand pattern provides greater versatility in charging and discharging patterns. From the analysis, it is inferred that Strategy 3 provides the highest reduction in network losses relative to two other operating approaches. The findings of the simulation demonstrate that the optimal

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timing of BESS with a strong wind capacity has a significant effect on the network losses. The findings also demonstrate that the suggested BESS technique maintains the magnitude of node voltages within the acceptable limits and removes the fluctuations in intermittent nature of wind.

References 1. Tuohy A, O’Malley M (2011) Pumped storage in systems with very high wind penetration. Energy Policy 39(4):1965–1974 2. Pudjianto D, Aunedi M, Djapic P, Strbac G (2013) Whole-systems assessment of the value of energy storage in low-carbon electricity systems. IEEE Trans Smart Grid 5(2):1098–1109 3. Li N, Hedman KW (2014) Economic assessment of energy storage in systems with high levels of renewable resources. IEEE Trans Sustain Energy 6(3):1103–1111 4. Daneshi H, Srivastava AK (2012) Impact of battery energy storage on power system with high wind penetration. In: PES T&D. IEEE, pp 1–8 5. Lu B, Shahidehpour M (2005) Short-term scheduling of battery in a grid-connected PV/battery system. IEEE Trans Power Syst 20(2):1053–1061 6. Sharma S, Niazi KR, Verma K, Rawat T (2020) Coordination of different DGs, BESS, and demand response for multi-objective optimization of distribution network with special reference to Indian power sector. Int J Electri Power Energy Sys 121 7. Logenthiran T, Srinivasan D (2009) Short term generation scheduling of a microgrid. In” TENCON 2009–2009 IEEE Region 10 Conference. IEEE, pp 1–6 8. Chen SX, Gooi HB, Wang M (2011) Sizing of energy storage for microgrids. IEEE Trans Smart Grid 3(1):142–151 9. Sharma S, Niazi KR, Verma K, Rawat T (2019) Impact of multiple battery energy storage system strategies on energy loss of active distribution network. Int J Renew Energy Res (IJRER) 9(4):1705–1711 10. Hussain M, Gao Y (2018) A review of demand response in an efficient smart grid environment. Electric J 31(5):55–63 11. Setlhaolo D, Xia X (2016) Combined residential demand side management strategies with coordination and economic analysis. Int J Electr Power Energy Syst 79:150–160 12. Sharma S, Niazi KR, Verma K, Thokar RA (2019) Bi-level optimization framework for impact analysis of DR on optimal accommodation of PV and BESS in distribution system. Int Trans Electri Energy Syst 29(9):e12062 13. Teleke S, Baran ME, Bhattacharya S, Huang AQ (2010) Rule-based control of battery energy storage for dispatching intermittent renewable sources. IEEE Trans Sustain Energy 1(3):117– 124 14. Wang D, Ge S, Jia H, Wang C, Zhou Y, Lu N, Kong X (2014) A demand response and battery storage coordination algorithm for providing microgrid tie-line smoothing services. IEEE Trans Sustain Energy 5(2):476–486 15. Nunna HK, Doolla S (2013) Energy management in microgrids using demand response and distributed storage—A multiagent approach. IEEE Trans Power Deliv 28(2):939–947 16. Sharma S, Niazi KR, Verma K, Meena NK (2019) Multiple DRPs to maximise the technoeconomic benefits of the distribution network. J Eng 2019(18):5240–5244 17. Safdarian A, Degefa MZ, Lehtonen M, Fotuhi-Firuzabad M (2014) Distribution network reliability improvements in presence of demand response. IET Gener Trans Distrib 8(12):2027– 2035 18. Baran ME, Wu FF (1989) Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Power Eng Rev 9(4):101–102 19. Sharma S, Niazi KR, Verma K, Rawat T (2020) A bi-level optimization framework for investment planning of distributed generation resources in coordination with demand response. Energy Sourc Part A Recovery, Utilizat Environm Effects 1(18)

Design and Analysis of Hands-Free Wireless Charging System Pradyumna K. Sahoo, Priyansh P. Jena, Ashutosh Patro, Abhijit Roy, Biswaranjan, Swain, Renu Sharma, Durga Prasanna Kar, and Satyanarayan Bhuyan

Abstract In order to elucidate a hands-free wireless charging system for electronic as well as electrical consumer devices, comprehensive analysis of wireless power transfer system has been carried out in this paper. The equivalent circuit model, electromagnetic simulations and coil modeling reveal the influence of parameters on WPT system and it provides the design guidelines for a low power wireless battery charging system. It has been observed both theoretically and experimentally that the wireless power transfer characteristics depend on the frequency, resonant capacitor, coil-geometry as well as vertical and horizontal offsets. The comprehensive analysis and practical measurements provide the guidelines to build up a suitable resonant inductive coupling-based wireless charging system for consumer appliances. Keywords RF system · Wireless charging · Electromagnetic resonance

P. K. Sahoo (B) · P. P. Jena · A. Patro · A. Roy · Biswaranjan · Swain · R. Sharma Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] P. P. Jena e-mail: [email protected] A. Patro e-mail: [email protected] A. Roy e-mail: [email protected] Biswaranjan e-mail: [email protected] R. Sharma e-mail: [email protected] D. P. Kar · S. Bhuyan Department of Electronics and Communication Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_22

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1 Introduction In recent eras, wireless power transfer technology is receiving large attention as an alternative to the conventional plug-in power transfer method [1–6]. Although the inductive-based wireless power transfer (IC-WPT) system has been used for different types of applications [7–12]; unfortunately, the IC-WPT system’s efficiency considerably decreases for a very small physical separation gap with increased transmitter and receiver coil sizes. On the contrary to overcome this problem, the researchers [13] have proposed a resonant inductive coupling WPT (RIC-WPT) system which is based on the well-known principle of magnetic resonance coupling (MRC). This system has been proven to be a non-radiative type and also providing better efficiency at a larger distance. Although MRC-based WPT system has been adopted for wireless charging of electrical and electronic devices, to make the charging process more suitable and viable under non-deal charging scenarios, it is extremely essential to identify the effect of operating design parameters on the performance of WPT system. Therefore, in this work, the effects of design parameters on the arrangement of the WPT system have been examined. After analyzing the parameters, a hands-free practical wireless charging system has been elucidated.

2 Design Setup of RIC-WPT System and Equivalent Circuit The schematic diagram of RIC-WPT system is illustrated in Fig. 1. It entails typically a resonant inductively coupled transmitter and receiver coil with source-side power electronics and receiver side electric load. A high-frequency power source converter

Receiver coil

Receiver Compensation Circuit

Electric Load High Frequency Power Source

Transmitter Compensation Circuit

Transmitter coil

Fig. 1 Schematic diagram of RIC-WPT system

Design and Analysis of Hands-Free Wireless Charging System

IT R S

CT

M

RT

VS

LT

IR

LR

RR

259

CR

RL

AC

Fig. 2 Equivalent circuit diagram of RIC-WPT system

is used which converts the main electric supply 230VAC 50 Hz to a high-frequency power source. In Fig. 2, the electrical equivalent circuit of RIC-WPT system is depicted. The power transfer is enabled due to the magnetic field coupling among the coils.

3 Coil Structure Simulation The simulation and coil modeling are done using I3D EM simulation tool. Simulation of the two-coil system was first carried out to investigate the optimum coil parameters, such as no. of turns (n), the diameter of the wire (w) and spacing between wires (s). The coil outermost diameter is fixed to 45 cm. The other coil parameters are varied as shown below: Simulation-1: Wire diameter (w) is varied [shown in Fig. 3]. The number of turns (n) is same so that the total wire length of the spiral is same for all structures. Simulation-2: Spacing between wires (s) is varied [shown in Fig. 4]. The wire diameter is same for all structures, but the number of turns is adjusted to have the same total length of the spiral. Simulation-3: No. of turns (n) is varied [shown in Fig. 5]. The wire diameter and

Fig. 3 Coil structures for simulation-1

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Fig. 4 Coil structures for simulation-2

Length=7.5m

Length=11.4m

Length=14.3m

Length=17m

Length=17.7m

Fig. 5 Coil structures for simulation-3

spacing are same for all structures. So, the total length of the spiral is varied as shown in the figure. The two-coil system is shown in Fig. 6. The simulation conditions are: Spacing between TX-coil and RX-coil is 5 cm and 10 cm, and

Fig. 6 Two-coil system

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The offset of the TX-coil and RX-coil is 10 cm and 20 cm. The frequency characteristics (S21) for different coil structures of simulation-1, 2, 3 are shown in Figs. 7, 8 and 9. It is seen that the system resonant frequency is varied with respect to the spacing between coils and the number of turns, whereas S21 loss depends on the wire diameter and the number of turns. The influence of vertical gap of coils on wireless system’s efficiency is investigated. The measured efficiency with respect to vertical gap of coils has been given in Fig. 10. This result reveals the influence of the coil parameters on the loss and the frequency of operation which has to be essentially considered during suitable design of WPT system.

Fig. 7 Efficiency characteristics of simulation-1

Fig. 8 Efficiency characteristics of simulation-2

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Fig. 9 Efficiency characteristics of simulation-3 100

d = 12 cm d = 15 cm

Energy transfer efficiency (%)

90

80

70

60

50

40 24

28

32

36

40

44

Operating frequency (kHz) Fig. 10 Measured efficiency vs. vertical gap of coils

In order to know the effect of horizontal offset, the experiment is done varying the receiver coil separation gaps horizontally keeping fixed the transmitting coil. The obtained results are illustrated in Fig. 11. It also indicates that the resonant frequency is changed with the variation of receiver gap horizontally.

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263 50

80 60 40 40

Efficiency at 32.20 KHz Efficiency at resonance Tuned frequency

20 0

0

4

8

12

16

20

24

Operating frequency (KHz)

Power transfer efficiency (%)

100

30

Horizontal offset between coils (cm) Fig. 11 Measured system’s efficiency with horizontal offset

4 Conclusion In summary, the coil structure simulation is done to delineate the influence of shape, number of turns and radius of the coil wireless power transfer system’s performance. In addition to this, the frequency dependency on efficiency, the influence of vertical as well horizontal gap of coils on the system’s performance has been elucidated. This analysis reveals that it is essential to think about the resonant frequency of operation, vertical distance and horizontal gap for designing a hands-free wireless charging system.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Pedder DAG, Brown AD, Skinner JA (1999) IEEE Trans Industr Electron 46:23–30 Kim CG, Seo DH, You JS, Park JH, Cho BH (2001) IEEE Trans Industr Electron 48:1238–1247 Kar DP, Nayak PP, Bhuyan S (2015) Appl Phys Lett 107:133901 Sahany S, Biswal SS, Kar DP, Sahoo PK, Bhuyan S (2019) Progress Electromag. Res. M 79:187–197 Deng B, Jia B, Zhang Z (2016) Progress Electromag. Res. C 69:1–10 Samal SK, Kar DP, Sahoo PK, Bhuyan S, Das SN (2017) Innovations in power and advanced computing technologies (i-PACT), 1–4 (2017) Covic GA, Boys JT (2013) IEEE J. Emerging Selected Topics Power Electron. 1:28–41 Shinohara N (2012) Wiley Interdisciplinary Reviews. Energy Environ 1:337–346 Kim J, Choi W, Jeong J (2013) Progress Electromag. Res. 138:197–209 Elliot G, Covic G, Kacprzak D, Boys JT (2006) IEEE Trans Magn 42:3389–3391 Sallan J, Villa JL, Llombart A, Sanz JF (2009) IEEE Trans. Ind. Electron. 56:2140–2149 Swain B, Nayak PP, Kar DP, Bhuyan S, Mishra LP (2016) Review of scientific instruments. 87 Kurs A, Karalis A, Moffatt R, Joannopoulos JD, Fisher P, Soljacic M (2007) Sci. Express 317:83–86

Fault Tolerance Investigation of Solar Photovoltaic Strings Operating Under NOCT Priya Ranjan Satpathy , Sobhit Panda, Bibekananda Jena, and Renu Sharma

Abstract Solar photovoltaic strings are installed for reliable power generation to feed the connected loads. However, the strings encounter enormous faults during operation resulting in severe power loss. In this paper, the sensitivity and reliability of a string are studied for eight different faults that are categorized under four major faults, i.e., module faults, connection faults, mismatch faults, and bypass diode faults. The entire investigation is carried out in MATLAB environment for a string of six series modules connected to a resistive load and rheostat operating under nominal operating cell temperature condition. The effect of these faults in the string is studied using the power output and loss, power–voltage characteristics curves, and parameters behavior analysis. It is found that the strings are highly susceptible to these faults resulting in severe power loss and hence, degrade the overall system performance. Keywords Photovoltaic · Faults · Mismatch · Bypass diode · Power loss

1 Introduction The growing environmental pollution and concern for oil have increased the demand for renewable power generation worldwide [1]. Among all renewables, the solar photovoltaic (PV) power generation has attracted the energy market worldwide due P. R. Satpathy (B) · B. Jena · R. Sharma Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] B. Jena e-mail: [email protected] R. Sharma e-mail: [email protected] S. Panda Department of Instrumentation and Electronics Engineering, College of Engineering and Technology, Bhubaneswar, Bhubaneswar 751003, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_23

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to its inexhaustible, pollution-free, noiseless, abundance characteristics [2]. The main demerit that PV systems face during operation is the power output fluctuation due to the intermittent behavior of solar irradiance and weather condition [3]. This scenario mainly affects the operation and functionality of the PV system at central control units and utility grid resulting in severe frequency deviation, system shutout, etc. [4]. Also, a PV system faces certain other demerits such as high initial installation cost, multiple generating source integration issues, and need of storage that needs to be overcome to increase the reliability [5]. The PV strings are the major component of a PV system that is formed by connecting a number of modules in a series configuration [6]. The dependence of PV string power output to the solar irradiance and temperature results in nonlinear output characteristics [7]. The characteristics of the PV string get more complicated when the modules of the string operate under nonuniform irradiance conditions that mainly occur due to partial shading [8]. This type of scenario is mainly considered as mismatch fault where the characteristics of the PV string exhibits multiple peaks in which the maximum power point tracker (MPPT) fails to discriminate between the actual and local peaks resulting false tracking and power loss [9]. Also, the performance of the PV string degrades due to the presence of various defects and faults resulting in severe power loss [10]. The other most common faults encountered by the PV strings are module faults, connection faults, and bypass diode faults that reduce the power generation and efficiency of the strings [11]. It is very important to understand and analyze the characteristics of the PV string under faults for efficient utilization and design of fault-detecting algorithms. In this paper, the effect of various faults on the performance of string has been studied in the MATLAB/Simulink environment using a PV string with six modules connected to a load and rheostat operating at nominal operating cell temperature (NOCT) condition. The fault scenarios for the PV string considered in the study include module faults, connection faults, mismatch faults, and bypass diode faults. The performance has been studied in term of power generation, power loss, parameter behavior, efficiency, and power–voltage (P–V) characteristics curves analysis

2 System Description In this section, the module mathematical modeling, specification of the system, and fault scenarios considered in the study have been described.

2.1 Mathematical Modeling of the PV Modules Solar PV modules are the combination of series-connected cells represented by an electrical circuit shown in Fig. 1. The mathematical modeling of the PV module in MATLB/Simulink can be carried out using Eq. (1) [12].

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Fig. 1 Electrical circuit representing of a PV module

RS

+

IO IPh

Table 1 Specification of PV module at STC and NOCT

Load

Parameters

Rating at STC Rating at NOCT

Maximum power (PM )

50 W

34.55 W

Maximum voltage (VM )

17.47 W

15.09 W

Maximum current (IM )

2.866A

2.28A

Open-circuit voltage (VOC ) 21.56 V

IPV = I ph

IPV RSh

19.35 V

Short-circuit current (ISC )

3.104A

2.509A

No. of series cells

36

36

      VPV + IPV Rs VPV + Rs IPV −1 − − Io exp A Rsh

(1)

where I PV and I ph are the current output and photo-generated current of module, A is the ideality factor, V PV represent the voltage of the cell, RS represents the series resistance, and RSh represent the shunt resistance.

2.2 NOCT Specification of the PV Module Nominal-operating cell temperature (NOCT) is a condition where the PV modules are tested at 800 W/m2 irradiance and 45 °C operating temperature irrespective of tilt angle to study the performance under real-time scenario more closely. The specification of the PV module at standard testing condition (STC), i.e., 1000 W/m2 and 25 °C, and NOCT condition has been given in Table 1 [13].

2.3 Fault Scenarios and Test Benches The faults scenarios considered in the study are mainly categorized into four major categories, i.e., module faults, connection faults, mismatch faults, and bypass diodes. The pictorial representation of these fault scenarios has been given in Fig. 2. The descriptions of all the fault scenarios considered in the study have been given in Table 2.

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Fig. 2 Fault scenarios for a PV string

Table 2 Fault scenarios considered in the study Fault type

Description

Symbol

Module fault

Short-circuit of any modules

F1

Inverted PV module

F2

Open circuit of any module

F3

Connection fault

PV modules connected by a resistance

F4

Mismatch fault

Partial shading in modules

F5

Bypass diode fault

Short-circuit of any bypass diode of module

F6

Inverted bypass diode in any module

F7

Shunted bypass diode in any module

F8

3 Results and Discussions The performance of the PV string has been studied under eight different faults scenarios that are categorized under four major faults, i.e., module faults, connection faults, mismatch faults, and bypass diode faults. The performance of the string under faults has been compared to that of healthy (no-fault) scenario. The string has been directly connected to a resistive load of 30. The models have been simulated for 6 secs where the faults are applied exactly at 3 secs to study the behavior of the power output of the strings. Also, the power–voltage (P–V) characteristics curves of the string during healthy and fault scenarios have been extracted by using a rheostat. The maximum power generation of the PV string during healthy (no-fault) scenario has been found as 184.4 W (under load condition) and 236.35 W (P–V

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curves analysis). The behavior of the parameters such as maximum voltage (V M ), maximum current (IM), open-circuit voltage (V OC ), and short-circuit current (ISC ) has been studied by comparing with respect to the values of V M (102.14 V), IM (2.31 A), V OC (129.39 V) and I SC (2.48 A) extracted from the characteristics curves under healthy scenario.

3.1 Module Faults These types of faults mainly occur in PV strings due to short-circuiting or opencircuiting or inverting of any connected module. In this study, the above-mentioned faults have been applied to the fifth module of the PV string. Short-circuit fault (F1). The power output under load and P–V curve of the string during short-circuit fault has been depicted in Fig. 3a and b, respectively. It has been found that the string power decreased to 181 W under load condition encountering a loss of 3.4 W. Also, from the characteristics curves, it can be noted that the string has generated 196.69 W encountering a power loss of 39.66 W. Inverted fault (F2). The power output under load and P–V curve of the string during module inverted fault has been shown in Fig. 4. The power graph shown in 186

No Fault

Module Short-Circuit Fault

Power (W)

185

183

Sudden reduction of power

Fault F1 Applied 181

179

1

2

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4

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Time (s)

(a) 250 No Fault

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Power (W)

200 150 100 50

00

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Voltage (V)

(b) Fig. 3 Performance of PV string under short-circuit fault (F1). a Power output under load. b P– V curve

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Power (W)

190

Inverted Module Fault

180 170 160 150 140 1

2

3

4

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6

Time (s)

(a) 250 No Fault

Inverted Fault

Power (W)

200 150 100 50

0

20

40

60

80

100

120

Voltage (V)

(b) Fig. 4 Performance of PV string under inverted module fault (F2). a Power output under load. b P–V curve

Fig. 4a clearly indicates that the load connected string has generated power equal to 143.8 W encountering a power loss of 40.6 W. The P–V curve (Fig. 4b) represents that the string has generated lower power (144.19 W) as compared to healthy scenario. Open-circuit fault (F3). An open-circuit fault mainly occur due breaking of connection wires in the PV modules. During this fault, the string has generated zero power output resulting in 100% power loss. This type fault can cause blackout in the entire PV system with complete system failure.

3.2 Connection Fault (F4) This type of fault has been created by adding a resistance between the fourth and fifth module of the PV sting that mainly occurs due to poor connection between the modules or aging of the cable junction. The power output under load condition and P–V curve of the string during connection fault have been depicted in Fig. 5a and b, respectively. It has been found that the string connected to load has generated lower power (182.1 W) as compared to the healthy scenario (184.4 W). The maximum power generated from the P–V curve has been found as 210.14 W encountering a power loss of 26.21 W.

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Time (s)

(a) 250 No Fault

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Power (W)

200 150 100 50 0

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Voltage (V)

(b) Fig. 5 Performance of PV string under connection fault (F4). a Power output under load. b P–V curve

3.3 Mismatch Fault (F5) Mismatch loss mainly occur when the modules of the PV string receive more than one irradiance levels resulting power loss. The power output and P–V curve of the string during mismatch loss have been depicted in Fig. 6a and b, respectively. It has been found that under load condition, the string has generated a maximum power of 153.5 W encountering a total power loss of 30.9 W.

3.4 Bypass Diode Fault The faults that occur due to bypass diodes are categorized into three categories: bypass diode short-circuit fault, inverter bypass diode fault, and shunted bypass diode fault. Bypass diode short-circuit fault (F6). This type of fault mainly occurs due to short-circuiting of the bypass diode of the connected modules. The power output and P–V curves of the string have been depicted in Fig. 7. It has been found that the

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Power (W)

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Voltage (V)

(b)

Power (W)

Fig. 6 Performance of PV string under mismatch fault (F5). a Power output under load. b P–V curve 185 184.5 184 183.5 183 182.5 182 181.5 181 180.5 180

No Fault

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250

No Fault

Bypass Diodes Short-Circuit fault

200 150 100 50 0 0

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100

120

Voltage (V)

(b) Fig. 7 Performance of PV string under bypass diode short-circuit fault (F6). a Power output under load. b P–V curve

Fault Tolerance Investigation of Solar Photovoltaic Strings … No Fault

Power (W)

190

273

Inverted Module Fault

180 170 160 150 140 1

2

3

4

5

6

Time (s)

(a) 250 No Fault

Inverted Fault

Power (W)

200 150 100 50

0

20

40

60

80

100

120

Voltage (V)

(b) Fig. 8 Performance of PV string under inverted bypass diode fault (F7). a Power output under load. b P–V curve

string has generated maximum power equal to 181 W (load condition) and 196.69 W (P–V curve). Inverted bypass diode Fault (F7). The power generation and P–V curve of the string during F7 have been shown in Fig. 8a and b, respectively. It has been found that the string has generated 143.8 W (under load condition) and 196.69 W (P–V curve). Shunted bypass diode fault (F8). During this fault scenario, the maximum power generated by the string under load condition and variable load condition has been found as 181.3 W and 207.09 W, respectively. The efficiency of the string during this fault has been calculated as 14.52%. The maximum power generation (PG ), power loss, and efficiency (η) of the PV string during various fault scenarios has been represented in Table 3. The behavior of the PV string parameters during various fault scenarios has been given in Table 4. The table can be used to design simple fault detection algorithm to detect or locate the fault in PV strings. The parameters have been compared with the string parameters under no- fault condition where ‘↓’ indicates decrease in value, ‘↑’ indicates increase in value, and ‘-’ indicates no change in value. The above results clearly indicate that the faults have a huge impact on the performance of the PV string that mainly results in the reduction of power output and

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Table 3 Performance of the PV string under fault Fault name

Symbol

PG (W)

Power loss (W)

Efficiency (%)

No fault

–

236.35

0

16.57

Module short-circuit fault

F1

196.69

39.66

13.7

Inverted module fault

F2

144.19

92.16

10.11

Open-circuit fault

F3

0

236.35

0

Connection fault

F4

210.14

26.21

14.74

Mismatch fault

F5

153.71

82.64

10.78

Bypass diode short-circuit fault

F6

196.69

39.66

13.79

Inverted bypass diode fault

F7

198.72

37.63

13.93

Shunted bypass diode fault

F8

207.09

29.26

14.52

Table 4 Parameters behavior of the PV string during fault Faults

VM

IM

VOC

ISC

F1

↓

↓

–

–

F2

↓

↓

–

↓

F3

↓

↓

↓

–

F4

↓

↓

–

↓

F5

↓

↓

–

–

F6

↓

↓

–

–

F7

↓

↓

–

–

F8

↓

↓

–

–

efficiency of the PV string. Also, it has been seen that the faults have a huge impact on the parameters of the PV string mainly V M and I M .

4 Conclusion In this paper, the sensitivity of a PV string operating at NOCT to various faults, i.e., module faults, connection faults, mismatch faults, and bypass diodes faults, have been studied. It has been found that these faults have a serious impact on the performance of the PV string resulting in severe power loss and efficiency reduction. Also, the behavior of the parameters of the PV string during faults has been studied and found that the maximum voltage and current of the string get reduced during all the faults scenarios. So, the change in these parameters can be used to detect or locate faults in the PV strings.

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References 1. Zhang Y, Hua QS, Sun L, Liu Q (2020) Life cycle optimization of renewable energy systems configuration with hybrid battery/hydrogen storage: a comparative study. J Energy Storage 30:101470 2. Satpathy PR, Sharma R, Jena S (2017) A shade dispersion interconnection scheme for partially shaded modules in a solar PV array network. Energy 139:350–365 3. Eltamaly AM, Farh HM (2020) PV characteristics, performance and modelling. in modern maximum power point tracking techniques for photovoltaic energy systems. Springer, Cham, pp 31–63 4. Jena S, Kar SK (2020) Defining indispensability of storage for raised renewable penetration in conventional and thermoelectric coupled microgrid: Modeling, analysis and validation. Int J Energy Res 44(7):5947–5967 5. Sumathi S, Kumar LA, Surekha P (2015) Application of MATLAB/SIMULINK in solar PV systems. In Solar PV and wind energy conversion systems. Springer, Cham, pp 59–143 6. Mäki A, Valkealahti S (2011) Power losses in long string and parallel-connected short strings of series-connected silicon-based photovoltaic modules due to partial shading conditions. IEEE Trans Energy Convers 27(1):173–183 7. Satpathy PR, Sharma R (2019) Power and mismatch losses mitigation by a fixed electrical reconfiguration technique for partially shaded photovoltaic arrays. Energy Convers Manag 192:52–70 8. Satpathy PR, Jena S, Sharma R (2018) Power enhancement from partially shaded modules of solar PV arrays through various interconnections among modules. Energy 144:839–850 9. Mohapatra A, Nayak B, Das P, Mohanty KB (2017) A review on MPPT techniques of PV system under partial shading condition. Renew Sustain Energy Rev 80:854–867 10. Satpathy PR, Jena S, Panda S, Sharma R (2020) Electrical faults in photovoltaic modules: analysis, characterization and detection. In: Innovation in electrical power engineering, communication, and computing technology. Springer, Singapore, pp 265–275 11. Chine W, Mellit A, Lughi V, Malek A, Sulligoi G, Pavan AM (2016) A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renewa Energy 90:501– 512 12. Satpathy PR, Sharma R (2020) Parametric indicators for partial shading and fault prediction in photovoltaic arrays with various interconnection topologies. Energy Convers Manag 219:113018 13. Satpathy PR, Sharma R (2018) Power loss reduction in partially shaded PV arrays by a static SDP technique. Energy 156:569–585

Frequency Control of an AC Microgrid with Fractional Controller Narendra Kumar Jena, Subhadra Sahoo, Amiya Kumar Naik, Binod Kumar Sahu, and Kanungo B. Mohanty

Abstract The objective of this paper puts forth the frequency regulation in the wake of load perturbation in a two-area microgrid in the presence of a classical fractional order controller. In this proposed model, various conventional and non-conventional sources are integrated. Sporadic nature of natural sources such as inconsistent wind speed, variant solar irradiance and variable active power injection creates problem in the frequency regulation of the ac microgrid. Other than this, low inertia of distributed generators (microturbine generator and diesel generator) or no inertia of energy storing elements invokes the stability problem against the perturbation of power injection or power demand. To overcome all these difficulties, proper and vigorous secondary controller is obligatory. Here, a fractional-order proportional-integralderivative (FOPID) controller designed by symbiotic organisms search (SOS) algorithm is used. To approve the credibility of FOPID controller, its performance is contrasted with PID and type-2 fuzzy PID controller. Further, frequency regulation is introspected by injecting variable solar power, wind power and load which deliberate the robustness of the proposed controller. Keywords Frequency regulation · Microgrid · Fractional order classical controller · SOS algorithm N. K. Jena · S. Sahoo · A. K. Naik · B. K. Sahu (B) Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] N. K. Jena e-mail: [email protected] S. Sahoo e-mail: [email protected] A. K. Naik e-mail: [email protected] K. B. Mohanty Department of Electrical Engineering, National Institute of Technology, Rourkela, Rourkela, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_24

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1 Introduction In an isolated territory, microgrid (MG) is an ultimate solution for electrification. In addition to this, continuous ascent in load demand, inadequacy of conventional sources and adverse effects of conventional sources to the environment brings about to develop MGs integrating different non-conventional, as well as replenishable energy sources. MGs in off-network condition unlike grid-connected condition face acute regulation of the voltage and frequency. The cause of this problem arises due to: (a) low inertia and no inertia of microturbine-based distributed generators, and battery energy storage systems, respectively, and (b) irregular and non-uniform renewable energy resources. To get rid of this control issue, suitable secondary controller is necessary, which brings the frequency and voltage deviation into a set value as explored in [1]. The LFC study of MGs is explored by different control strategies, e.g. classical controllers [2, 3], fractional order (FO) classical controllers [4–7], fuzzy-PI (FPI) controller [8], FPI with type-2 concept [9, 10], robust controllers like H∞ and µsynthesis-based controller [11], H∞ controller [12], predictive controller [13], etc. In classical control technique, it is amply difficult to make the steady-state error to zero with a prolonged oscillation due to intermittent load change or sharp load perturbation. To overcome these conditions, intelligent control or some advanced nonlinear control techniques are applied. For fuzzy controller, a researcher must have matured and well-versed knowledge to define the membership functions. Any improper formulation takes the system into unstable region or the response may prevail large steady-state error. Similarly, modern control techniques are quite difficult to implement and incur high cost, and all the states are measurable. Hence, researchers are giving stress on PID controller which is simple and can be implemented easily in the industries. The classical PID controllers are upgraded to milk out a fast response lessening the steady-state error. In this kind of PID controllers, the order of the differentiator and integrator is fractional, hence named as fractional/noninteger PID controller (FOPID). In this FOPID controller, two extra tuning knobs associated with its as usual gains, able to diminish the overshoot/undershoot, steady time and steady-state error adequately. In addition to control structure, its proper design is imperative to reach out a stable and faster response. In this perspective, optimization techniques are endorsed to get the objective. Some optimization techniques such as grasshopper optimization [2], artificial sheep algorithm [3], dragonfly and pattern search algorithm [6], i-SCA algorithm [7], SOS [14, 15] are applied by different researchers to enumerate the gain parameters of their proposed controllers. In this paper, a two-area microgrid network [9] is investigated to bring an ameliorated performance by using a FO controller with the help of SOS optimization technique.

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The sole objectives of this paper are ascribed as follows: a. A two-area microgrid is modelled and its frequency regulation is carried out by a FO controller whose gains are calculated endorsing SOS technique. b. Performance observed by the FOPID is compared with PID controller as well as with the result evaluated by type-2 FPI controller [9]. c. The robustness of the FOPID controller is inspected by injecting different combinations of variation of power generation and power consumption.

2 Proposed Microgrid, Controller and SOS Algorithm 2.1 Microgrid Model In Fig. 1, the MG comprises of different distributed generators (DGs) such as wind turbine generator (WTG), photovoltaic (PV) unit, microturbine generator (MTG), diesel generator, fuel cell (FC), fly wheel (FH) and battery energy storage unit. Here, two such microgrids are interconnected by a tie-line as shown in Fig. 2 [9]. The linearized model with parameters of each unit is referred from [5, 9, 11]. In the MG,

Fig. 1 Diagram of a microgrid (MG)

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Fig. 2 Two-area model of MG

the power generated by all DGs is balanced by load demand, which is expressed by Eq. (1). And with the incremental load change, power balance equation changes to Eq. (2). PL = PP V + Pwtg + PM T + PDi + PFC ± PF H ± PB E S

(1)

PL + PP V + Pwtg + PM T + PDi + PFC + PF H + PB E S = 0 (2) From Fig. 2, the frequency deviation is written as in Eq. (3). f =

p D+s M

(3)

where p = PP V + Pwtg + PM T + PDi + PFC + PF H + PB E S − PL , D is coefficient of damping, and M is inertia constant.

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Fig. 3 a Operational region of PID and FOPID controller, b FOPID controller structure

The dynamic response of the presented model is evaluated by using PID and FOPID controller with the help of SOS algorithm which are elaborated in the subsequent sections.

2.2 Fractional Order PID Controller In contrast to the PID controller, fractional order PID controller [16] carries the noninteger integral and derivative operators. In this controller, it is flexible to truncate the minimum and maximum peaks of the response, because two extra tuning parameters in addition to three gains as usual are available. In fractional order controller (P I ϕ D λ /FOPID), ϕ and λ are non-integer quantities subjected to the integral and derivative operators, respectively, whose values are chosen from the shaded area as shown in Fig. 3a. The structure of FOPID is portrayed in Fig. 3b, and its control signal is expressed in Eq. (4). ϕ

u FOPID (t) = k p e(t) + ki Dt e(t) + kd Dtλ e(t)

(4)

where e(t) is the error signal passed through the controller. FOPID’s transfer function is given in Eq. (5). G FOPID (s) = k p +

ki + kd s λ sϕ

(5)

2.3 Optimization Technique The SOS algorithm [15] impersonates the harmonious cooperation methodology generally got by various creatures to suffer and dissipate in the biological framework. Living beings never seclude themselves to lead their life as a result of reliance on

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various species for food and even continuance. Symbiosis exemplifies the holding between in any two sorts of living beings for their taking care of affiliation. Most of the time, living creatures get advantage of each other or one gets advantage from the other without harming the other living being. Consequently, as indicated by their reliance with one another, the SOS methodology consists of three different phases like mutualism, commensalism and parasitism which are pointed out in Fig. 4 expressively.

3 Result and Discussion The system response of the proposed model in Fig. 1 is carried out in the presence of PID and FOPID controller subjecting five different conditions. These conditions are (a) injection of 1% load change with constant generation of wind and solar power, (b) variable solar power with constant wind and load perturbation, (c) variable wind power with constant solar power and load perturbation, (d) variable load with constant wind and solar power and (e) variable solar, wind and load perturbation. The controller parameters evaluated by SOS algorithm are given in Table 1. Further, the performance in terms of transient indices (undershoot, overshoot, settling time) of the FOPID controller is contrasted with type-2 fuzzy PID controller [9] as given in Table 2.

3.1 Case-1 Dynamic Behaviour Under Injection of 1% Load Change with Constant Generation of Wind and Solar Power Here, a step load of 0.01 pu is injected, keeping constant solar power generation of 0.14 pu and wind power generation of 0.05 pu. The time diagram of the frequency deviation in each area and the tie-line power deviation are blended in Fig. 5. The overshoot of frequency deviations in area-1 ( f 1 ), area-2 ( f 2 ) and tie-line power deviation (Ptie ) due to FOPID controller is 1.857 MHz, 2.368 MHz and 0.146 mpu, respectively, whereas the deviations of frequency and tie-line power due to type-2 fuzzy PID controller [9] are 60 MHz, 62 MHz and 0 pu, respectively. The data analysis in Table 2 shows that the undershoot of frequencies and tie-line power of the working system with FOPID has offered an improvement of 100%, 0% and 99.02%, respectively, with that of type-2 FPID [9] that was produced. For overshoot, the frequencies in two areas have been improved to 96.9% and 96.18%, respectively, in comparison with type-2 FPID [9] that was produced. Overshoot in tie-line power deviation of paper [9] produces 0, whereas the FOPID has produced 0.146. Similarly, the average time to reach the steady-state/settling time by FOPID controller is 3.73 s, but for type-2 FPID is 6.47 s. So, the FOPID controller provides a prompt response with an

Frequency Control of an AC Microgrid with Fractional Controller

Fig. 4 Flow chart of SOS algorithm

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Table 1 Gain parameters of controllers Controllers

k pi

kii

kdi

ϕi

λi

PID1

20.1177

5.1835

46.6398

–

–

PID2

26.3009

20.6098

50.0000

–

–

FOPID1

81.4802

52.0027

58.2629

0.9800

0.9800

FOPID2

85.1352

52.1409

9.3728

0.9800

0.9800

Table 2 Performance index Freq/Tie-line Type-2 fuzzy controller [9]

PID controller

u shoot oshoot tsett (sec) u shoot

FOPID controller

oshoot tsett (sec) u shoot

oshoot tsett (sec)

 f1

-1

60

6.8

-6.352 10

8.52

0

1.857 3.25

 f2

0

62

6.2

-1.632 3.865 8.24

0

2.368 4.38

Ptie

-20

0

6.42

-0.586 1.125 9.15

-0.196 0.146 3.86

improvement of 42.35%. The whole analysis concedes that the FOPID controller has offered an enhanced performance substantially. In this juncture, the FOPID controller ameliorates the system performance adequately in comparison with type-2 fuzzy PID controller [9]. In addition to this, the SOS-based FOPID controller performs better than the SOS-based PID controller as depicted in Table 2.

3.2 Case-2 Dynamic Behaviour Under Variable Solar Power with Constant Wind Power and Load Perturbation Finding a staunch response by FOPID controller in contrast to type-2 fuzzy PID controller [9], the test system is subjected to a wider solar power variation, keeping the load disturbance constant and wind power generation constant. The excursion of frequency deviation in area-1 and tie-line power is shown in Fig. 6. The inset curve of Fig. 6 points out that the overshoot, undershoot and settling times are changing meagrely which confirms the robustness of the FOPID controller.

3.3 Case-3 Dynamic Behaviour Under Variable Wind Power with Constant Solar Power and Load Perturbation On the other way, with the variation of wind power generation, keeping constant solar power generation and constant load perturbation, the system response is observed in Fig. 7. The frequency deviation in area-1 against highly perturbed wind power

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(a)

(b)

(c) Fig. 5 a Excursion of frequency deviation in area-1, b excursion of frequency deviation in area-2 and c excursion of tie-line power deviation

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Fig. 6 Excursion of frequency deviation and tie-line power deviation with the variation of solar power

Fig. 7 Excursion of frequency deviation and tie-line power deviation with the variation of wind power

deviates scantily with a fast response as obvious in the inset figure of Fig. 7. The FOPID controller brings the steady-state error to zero with a very small settling time which ensures the robustness of the controller.

3.4 Case-4 Dynamic Behaviour Under Variable Load with Constant Wind Power and Solar Power Under this scenario, a variable load is injected keeping rest sources generation constant. The injected load curve as well as the frequency deviation in area-1 and power deviation in the tie-line are portrayed in Fig. 8. The system dynamics has improved amply as reflected by the inset curve shown in Fig. 8. The system dynamics shows very small deviation in frequency with prompt response.

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Fig. 8 Excursion of frequency deviation and tie-line power deviation with the variation of load

Fig. 9 Excursion of frequency deviation and tie-line power deviation with the variation of wind power, solar power and load

3.5 Case-5 Dynamic Behaviour Under Variable Solar Power, Wind Power and Load Perturbation In this case, both the generation and load are varied in a chaotic manner. The system responses ( f 1 ,Ptie ) are shown in Fig. 9. The frequency deviation in the inset diagram of Fig. 9 undergoes a very small undershoot (1.543 MHz) against a load disturbance of −0.1 pu, whereas the undershoot of the frequency deviation is zero against a load perturbation of 0.01 pu, because in the former case, load was negative and power injections are negative by which frequency decreases instantly. The prompt dynamic response with less undershoot and overshoot confers the credibility of robustness of the FOPID controller.

4 Conclusion The frequency regulation of the two-area microgrid is carried out by employing an SOS-based designed PID and FOPID controller. The dynamic response produced by

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FOPID controller is superior to that of PID controller. Further, the dynamic response produced by the type-2 fuzzy PID controller [9] is ameliorated by the proposed FOPID controller. In addition to this, the robustness of this controller is validated by injecting different combinations of variable energy source and load. The dynamic stability of this model can be improved by using intelligent and advanced nonlinear controllers in future work.

References 1. Yang J, Zeng Z, Tang Y, Jun Y, He W, Yunliang H (2015) Load frequency control in isolated micro-grids with electrical vehicles based on multivariable generalized predictive theory. Energies 8(3):2145–2164 2. Barik AK, Das D (2018) Expeditious frequency control of solar photovoltaic/biogas/biodiesel generator based isolated renewable microgrid using grasshopper optimisation algorithm. IET Renew Power Generat 12(14):1659–1667 3. Xu Y et al (2018) Load frequency control of a novel renewable energy integrated micro-grid containing pumped hydropower energy storage. IEEE Access 6:29067–29077 4. Babaei M, Abazari A, Muyeen SM (2020). Coordination between demand response programming and learning-based FOPID controller for alleviation of frequency excursion of hybrid microgrid. Energies 13(2): 442 5. Jena, NK et al (2020) Frequency regulation in an islanded microgrid with optimal fractional order PID controller. In: Advances in intelligent computing and communication. Springer, Singapore, 447–457 6. Khadanga RK et al (2018) Design and analysis of tilt integral derivative controller for frequency control in an islanded microgrid: a novel hybrid dragonfly and pattern search algorithm approach. Arabian J Sci Eng 43(6):3103–3114 7. Sahu PC, Prusty RC, Panda S (2020) Optimal design of a robust FO-Multistage controller for frequency awareness of an islanded AC Microgrid under i-SCA algorithm. Int J Ambient Energy Just-Accepted 1 8. Yu D et al (2019) Dynamic multi agent-based management and load frequency control of PV/Fuel cell/wind turbine/CHP in autonomous microgrid system. Energy 173:554–568 9. Sahu PC et al (2018) Improved-salp swarm optimized type-II fuzzy controller in load frequency control of multi area islanded AC microgrid. Sustain Energy Grids Netw 16:380–392 10. Khooban MH, Gheisarnejad M (2020) A novel deep reinforcement learning controller based type-II fuzzy system: frequency regulation in microgrids. IEEE Trans Emerging Topics Comput Intell 11. Bevrani H, Feizi MR, Ataee S (2015) Robust frequency control in an islanded microgrid: H∞ , and µ-synthesis approaches. IEEE Trans Smart Grid 7(2):706–717 12. Singh VP, Mohanty SR, Kishor N, Ray PK (2013) Robust H-infinity load frequency control in hybrid distributed generation system. Int. J. Elect. Power Energy Syst. 46:294–305 13. Banis F et al (2019) Load–frequency control in microgrids using target-adjusted MPC. IET Renew Power Gener 14(1):118–124 14. Nayak JR, Shaw B, Sahu BK (2020) Novel application of optimal fuzzy-adaptive symbiotic organism search-based two-degree-of-freedom fuzzy proportional integral derivative controller for automatic generation control study. Int Trans Electri Energy Syst 30(5), e12349 15. Min-Yuan C, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112 16. Podlubny I (1999) Fractional-order systems and PI/sup/spl/lambda//D/sup/spl/mu//controllers. IEEE Trans Autom Control 44(1):208–214

Effectiveness of Backpropagation Algorithm in Healthcare Data Classification Ch Chandra Sekhar, Nibedan Panda, B. V. Ramana, B. Maneesha, and S. Vandana

Abstract Nowadays, researchers are trying to reveal better consequences by acting on machine learning (ML) algorithms. The notion behind this study is to represent the fundamental machine learning algorithms and its applicability in current scenario. Backpropagation is considered as one of the classic supervised algorithms for training and classifying the feedforward neural networks. The concept of backpropagation used as a means in neural networks for transmitting entire error back to lessen the loss is termed as backpropagation network (BPN). We have considered BPN for classification as it is flexible, less complex, and performs better with noise-free data. The experimental analysis has been carried out by gathering dataset from UCI storehouse. Popular datasets like cancer, diabetes, heart, and liver are chosen for study. The classifier efficiency has been shown by observing its lower RMSE value and better accuracy with other factors also. By developing a BPN-based classifier system, it may ascertain physicians to deal with health-related problems. Keywords Machine learning · Neural network · Backpropagation neural network · Classification · Optimization

C. Chandra Sekhar · N. Panda (B) · B. V. Ramana · B. Maneesha · S. Vandana Department of Information Technology, Aditya Institute of Technology and Management (AITAM), Tekkali, K Kotturu 532201, Andhra Pradesh, India e-mail: [email protected] C. Chandra Sekhar e-mail: [email protected] B. V. Ramana e-mail: [email protected] B. Maneesha e-mail: [email protected] S. Vandana e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_25

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1 Introduction Machine learning (ML) is an application of artificial intelligence which has flexibility to automatically acquire and progress from competence while not being specifically programmed [1]. The main concentration is on schedule of computer programs which access available information to learn for itself, and the same concept is associated with computational measurements and numerical optimization [2]. Multiple methods of ML are predominantly used across disciplines, such as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. These are used to automate various tasks which are considered to be complex jobs for humans such as recognition of image and generating texts and different Web-based games [3]. ML is going to have a huge impact on the economy and on life in general. Some of the use cases are manufacturing, retail, energy demand, and supply chain optimization. It also uses the information to train an appliance to generate patterns, and depending on nature and type of pattern, the machines can able to take further precise resolutions while dealing with new-fangled data [4]. Classification is a supervised learning proposition in ML, and it classifies the given data or information into classes. Classes are alluded as target, label, and category which will be anticipated depending on data [5–7]. Classification performs on both structured and unstructured data. The assignment is approximating the mapping capacity from input factors to discrete output factors. The fundamental objective is to recognize which class the new data will fall into. Logistic regression, support vector machines (SVM), K-nearest neighbors (K-NN), kernel support vector machines (K-SVM), Naive Bayes, decision trees, and random forest classifications are all come under linear and nonlinear models of classification. The optimization in “Backpropagation neural network” (BPN) is utilized for finding or calculating errors in the feedforward stage. Optimization is the most widely used in ML, and this technique has some of the loss function or cost function which is going to minimize the errors with better accuracy. Optimization provides a toolkit of modeling of algorithmic techniques. ML accomplishes responsibilities by simplifying instances which is inexpensive and realistic as compared with manual programming. BPN is one of the evolutions in neural networks (NN), which is used as a classifier and is equal to the Bayesian discriminant. The technique of BPN primarily focuses on fine-tuning of parameters allied with a neural network and support to reduce the error rate acquired in previous iteration. It consists of three layers of neurons such as input, hidden, and output. Every node in the layer is linked to all other nodes in remaining layer to get the required outcome. The links among nodes are directed means movement of data occurs in only one way. Backpropagation could be a short kind for “backward propagation of errors,” and it is a typical technique of training artificial neural network (ANN) which helps to calculate the gradient of loss and performs according to all parameters present within the network. Advantages of backpropagation algorithm are quick, easy, and simple to code, and it has no constraints to tune except the numbers of input. It is a versatile technique because it never depends on previous data regarding the network and does

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not require any special decisions of the features to be learned. Backpropagation networks are classified into two types: static backpropagation and recurrent backpropagation [8]. BPN simplifies the network structure by its components weighted links that have the smallest amount of result on the trained network. BPN helps to assess the impact that a given input variable has on a network output. The information gained from this analysis should be depicted in rules. Backpropagation is particularly helpful for deep neural networks engaged on weak projects [9]. Backpropagation has a particular drawback because it depends on the input data that demands learning rate for constant learning. Hence, backpropagation may be quite sensitive to clangorous information and useful for resolving various complications. The rest of the paper is organized as follows: Literature review is presented in Sect. 2, details about BPN is presented in Sect. 3, Sect. 4 presents the experimental setup, dataset details, and result analysis, and Sect. 5 summarizes the study.

2 Review of Literature This section represents a close analysis of backpropagation network (BPN) that is associated to neural networks. BPN is considered as one of the most popular supervised learning algorithms used to train feedforward neural networks. The first algorithm of BPN was projected by Rumelhart et al. to resolve the multilayer perceptron’s issues [10]. Lin et al. given a prognostic model on survival of patients with diabetic foot. BPN is utilized for genetic rule optimization by cluster analysis. Some of the prediction models (PM) like proportional hazard regression analysis, etc., were estimated and compared. BPN has better AUC in contrast with other considered models. As a result, suggested model got better prediction effect used for diabetic foot patients [11]. Alkronz et al. built up a multilayer ANN model for testing the mushroom datasets and furthermore proposed to check the mushrooms are either edible or noxious [12]. Jiang et al. projected that, the three comparative common ML models as well as BPN, gradient boosting machine (GBM), and random forest (RF) were assumed to plot the likelihood of Zika rampant occurrence spread over entire world. 50 demonstrating methods were accompanied centered on a training dataset. The BPN model gained the maximum analytical accurateness with 50 modeling training datasets [13]. Dimililer et al. proposed a precise framework for maize plant recognition where farmers used some special equipment for dominant crops and BPN is utilized as a classifier for maize plant. BPN integrates one board computer platform and tested completely with different pictures as a result BPN was found to be encouraging to differentiate the maize plant from the various unsafe herbs [14]. Leema et al. proposed a classified clinical dataset by using particle swarm optimization (PSO) and gradient descent-based backpropagation. In this model, optimal set of neural network weights is adjusted by the weights of NN and particularly used three benchmark clinical datasets. The enforcement of the trained neural network classifier has compared with the present gradient descent backpropagation rule as a result, the model got better accuracy for standard disease datasets [15]. Saba et al. projected a

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recognition system for the fatty liver disease (FLD). Compared to previous important enhancements, BPN used to categorize liver disease into usual and unusual classes, as a result confirms better classification accuracy to detect liver diseases [16]. Balaji et al. proposed an automatic detection model of dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). Support vector machine (SVM), combined K-nearest neighbors (K-NN), and BPN used to classify the normal hearts, which are affected with diagnose dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). Experiments over sixty echocardiogram videos expose that the planned system may be effectively utilized to notice and diagnose DCM and HCM [17]. Liu et al. proposed a novel model to anticipate the water temperature in aquaculture by utilizing the empirical mode decomposition (EMD) and BPN. EMD-BPN has the most noteworthy prediction, precision, and better speculation execution, so these two models are acted as an effective modeling tool for predicting the water temperature [18]. Suresh et al. developed an optimization technique by exploiting BPN for a fruitful forecast system in healthcare systems. This algorithm was appraised centered on convergence, sensitivity, specificity, positive precision value, and accuracy [19]. Wang et al. proposed a BPN model to forecast weekly range of contagious diarrhea by means of meteoric factors as input variable. In the dataset, they contain the prediction models; they are support vector regression (SVR), random forest regression (RFR), multivariate linear regression (MLR), and BPN. To sidestep the matter of overfitting in systems training period, they used fivefold cross-validation technique and BPNN is employed to predict the infectious diarrhea exploitation meteorological factors [20]. Li et al. developed a virtual sample generation (VSG) named genetic algorithmbased virtual sample generation (GABVSG), where BPN is considered as operative learning scheme to create nonlinear systems, and such type of systems may show unbalanced results on smaller datasets. This model gives the superior original training data without the involvement of artificial instances [21]. Nawi et al. suggested that BPN is generally utilized in ANN, and it is one of the well-known reformation methods for discovering the ideal weight sets during the preparation procedure for obtaining the convergence. So, a Levenberg Marquardt primarily based BPN trained with cuckoo search algorithmic rule is used for quick convergence speed of the hybrid neural networks learning algorithmic rule. Compared with the previous algorithmic rule, i.e., artificial bee colony (ABC), this model ends the quick convergence speed and rate [22]. Aruna et al. proposed a neural system model as DIAGNET for identifying gastrointestinal syndromes. DIAGNET is a blend of BPN and radial basis functions neural network (RBFNN). Fuzzy values are taken care of as contributions to the DIAGNET. For testing the exactness, specificity and receiver operating characteristics (ROC) are utilized as the markers. Accordingly, DIAGNET is a superior answer for complicated, nonlinear clinical choice emotionally supportive networks [23]. Furthermore, a huge number of metaheuristic-based classifiers are available in the literature, which follows some natural evolution and by observing the behavioral pattern of various creatures [5–7]. All the classifiers have potential to classify the tremendous amount data evolving everyday due to rapid growth in digitalization.

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Hence, observing all the classifiers and usefulness of BPN, we have castoff BPN as a classifier due to its flexibility, simplicity, and underlying advantages.

3 Overview of Back Propagation Algorithm BPN is one of the evolutions in neural networks. Numerous quantitative phenomena are intended by neural networks, and this rule classifies the input patterns. This rule offers a procedure for changing the weights, and it is principally applied to multilayer feedforward for interconnection of perceptron consisting with nodes. BPN is used as a classifier and it is equal to the Bayesian discriminant function for large set of individual training sets. BPN holds three phases as: (i) feedforward of the input training pattern, (ii) calculation and backpropagation of error, and (iii) weight updating. BPN holds three layers as: input layer, hidden layer, and output layer, represented in Fig. 1 [24]. Feedforward neural network which tests, offers outcome rapidly, input–output parameters won’t be unchanged and permutations of weight even if it has error function. Weight update algorithmic program is especially used for BPN technique, and it is higher cognitive process for prediction. If there is any error within the method, then this rule may be sent and propagated back to hidden unit to minimize the error. The main theme of this rule is to balance the potential of respond and response. So, BPN helps to find out the local minima and also trains the input patterns with some of the learning factors: preliminary weights, learning rate, updating strategy, magnitude, and nature of the training set. The architecture of BPN training algorithm states each and every steps associated with the entire process. It has some of the phases like initialize the weights, to calculate the parameter value at hidden layer which is presented as flow representation in Fig. 2. Resultant at hidden layer can be computed by using Eq. (1) Bias

1

1

I1

Y1

Y1

Y2

Y2

H1 I2 H2 I3 Input layer

Hidden layer

Fig. 1 Basic architecture of BPN

Output layer

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Training data samples

Evaluate outcome by using activation function in feed forward stage

No Calculate error in back propagation manner

Is Outcome equals target?

No Yes

Is Error minimum?

Update the parameters

Yes

Model is ready for prediction

Fig. 2 Flowchart for proposed BPN model

H = B + (I 1 × W 1) + (I 2 × W 2) + (I 3 × W 3)

(1)

To acquire the parameter value associated with hidden layer using activation functions, here, the active function is computed by Eq. (2) H A1 = 1.0/(1.0 + exp(H ))

(2)

The required output from hidden layer to output layer is computed by using Eq. (3) O = B + (H 1 × W 4) + (H 2 × W 5) + (H 3 × W 6)

(3)

If the required output and resultant output are same, then the model is ready for data classification. Otherwise, the model will follow Eq. (4) to achieve better

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classification outcome. The total correlation factor which is also termed as the error is computed by Eq. (4) T E = (T E − O) × O × (1 − O)

(4)

Finally, after the completion of each iteration based on the error, we have to update the weights and bias of the output unit. As a result, we may expect perfect accuracy of a given problem. For the updation of the weights, we are using the basic formula which is denoted in Eq. (5) delta =



(delta × W )

(5)

where H → calculated parameter value from input layer to hidden layer B → bias value I1 → first parameter of input layer where I2, I3 follows same procedure W 1 → weight of first node where W 2, W 3, W 4, W 5, W 6 follows same procedure O → calculated parameter value from hidden layer to output layer of Y 1 and Y 2 H1 → first parameter of hidden layer where H2, H3 follows same procedure OA1 → activation function on computed outcome

4 Experimental Setup and Result Analysis We have used MATLAB 2015a environment for experimentation purpose which includes CPU of i5 @4.1 GHz, memory 4 GB, and 64-bit OS. For the experimentation purpose, four datasets have been considered as cancer, diabetes, heart, and liver. The chosen datasets are separated into training and testing category which are assessed over a maximum of hundred number of iterations with twenty distinct executions. Training-to-testing data ratio is maintained to 80:20. The outcomes are accounted in terms of minimum RMSE, average, standard deviation, sensitivity, specificity, prevalence, accuracy, and error rate. The details of the datasets are presented in Table 1. The proposed BPN model is assessed utilizing four standard datasets accessible from UCI store, named as: cancer, diabetes, heart, and liver [25]. Results are procured by Table 1 Description of benchmark datasets Datasets for classification

Number of features

Training samples

Test samples

Class numbers

Cancer

9

683

120

2

Liver

6

345

70

2

8

768

150

2

13

270

60

2

Diabetes Heart

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Table 2 Experimental results from datasets Dataset/algorithm Cancer

Diabetes

Heart

Liver

BPN

Error rate (%)

Accuracy (%)

Min RMSE

0.1027

0.833

99.16

Avg.

0.1045

22.85

77.14

1.85

98.14

20.58

79.41

STD

0.0013

Specificity

1

Sensitivity

0.98

Prevalence

49.16

Min RMSE

0.3017

Avg.

0.3218

STD

0.0256

Specificity

0.95

Sensitivity

0.58

Prevalence

29.28

Min RMSE

0.1997

Avg.

0.1660

STD

0.0328

Specificity

1

Sensitivity

0.96

Prevalence

48.14

Min RMSE

0.3712

Avg.

0.3730

STD

0.0018

Specificity

0.97

Sensitivity

0.61

Prevalence

30.88

executing this BPN model on said datasets concerning to RMSE, standard deviation, higher order precision, error rate, accuracy, sensitivity, specificity, and prevalence. From the outcomes reported in Table 2, improved accuracy, lower error rate, standard deviation, higher average, and improved specificity and sensitivity value confirm its supremacy as a better classifier. Finally, it is clear that the proposed BPN model gives better execution performance and exactness in terms of accuracy.

5 Conclusion In this paper, BPN model has assessed over four standard datasets picked from UCI repository. Prime concern behind the utilization of algorithm is to escalate the better outcome. The underlying arrangement of qualities which are weights and biases

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picked messily, then progressive cycle underlies parameters arrive at its optimality after number of iterations. This BPN model tried to be dependent on minimum RMSE, average, SD, accuracy, specificity, sensitivity, and prevalence. Reported result shows that the BPN model displays better exactness for classification and furthermore gives ideal arrangement of weights and biases. The BPN effectively maintains a strategic distance from the nearby optima issue as it adjusts the usage and examination. In future, the BPN can be utilized for taking care of genuine designing issues and furthermore the same will be compared with modern recent metaheuristic algorithms.

References 1. Panda N, Majhi SK (2019) Improved salp swarm algorithm with space transformation search for training neural network. Arabian J Sci Eng 1–19 2. Panda N, Majhi SK (2020) Improved spotted hyena optimizer with space transformational search for training pi-sigma higher order neural network. Comput Intell 36(1):320–350 3. Duarte EP Jr, Pozo AT, Beltrani P (2020) Smart Reckoning: Reducing the traffic of online multiplayer games using machine learning for movement prediction. Entertain Comput 33:100336 4. Panda N, Majhi SK, Singh S, Khanna A Oppositional spotted hyena optimizer with mutation operator for global optimization and application in training wavelet neural network. J Intell Fuzzy Syst (Preprint) 1–14 5. Panda N, Majhi SK (2020) How effective is the salp swarm algorithm in data classification. In: Computational intelligence in pattern recognition. Springer, Singapore, pp 579–588 6. Mirjalili S (2015) How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Appl Intell 43(1):150–161 7. Panda N, Majhi SK (2019) How effective is spotted hyena optimizer for training multilayer perceptrons. Int J Recent Technol Eng 4915–4927 8. Hecht-Nielsen R (1992) Theory of the backpropagation neural network. In: Neural networks for perception. Academic Press, pp 65–93 9. Su H, Li G, Yu D, Seide F (2013) Error back propagation for sequence training of contextdependent deep networks for conversational speech transcription. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, pp 6664–6668 10. Rumelhart DE, Durbin R, Golden R, Chauvin Y (1995) Backpropagation: The basic theory. Theory, Architectures and applications Backpropagation. Psychology Press, London, pp 1–34 11. Lin C, Yuan Y, Ji L, Yang X, Yin G, Lin S (2019) The amputation and survival of patients with diabetic foot based on establishment of prediction model. Saudi J Biolog Sci 12. Alkronz ES, Moghayer KA, Meimeh M, Gazzaz M, Abu-Nasser BS, Abu-Naser SS (2019) Prediction of whether mushroom is edible or poisonous using back-propagation neural network 13. Jiang D, Hao M, Ding F, Fu J, Li M (2018) Mapping the transmission risk of Zika virus using machine learning models. Acta Trop 185:391–399 14. Dimililer K, Kiani E (2017) Application of back propagation neural networks on maize plant detection. Procedia Comput Sci 120:376–381 15. Leema N, Nehemiah HK, Kannan A (2016) Neural network classifier optimization using differential evolution with global information and back propagation algorithm for clinical datasets. Appl Soft Comput 49:834–844 16. Saba L, Dey N, Ashour AS, Samanta S, Nath SS, Chakraborty S, Sanches J, Kumar D, Marinho R, Suri JS (2016) Automated stratification of liver disease in ultrasound: An online accurate feature classification paradigm. Comput Methods Programs Biomed 130:118–134

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17. Balaji GN, Subashini TS, Chidambaram N (2016) Detection and diagnosis of dilated cardiomyopathy and hypertrophic cardiomyopathy using image processing techniques. Eng Sci Technol Int J 19(4):1871–1880 18. Liu S, Xu L, Li D (2016) Multi-scale prediction of water temperature using empirical mode decomposition with back-propagation neural networks. Comput Electr Eng 49:1–8 19. Suresh A, Harish KV, Radhika N (2015) Particle swarm optimization over back propagation neural network for length of stay prediction. Procedia Comput Sci 46:268–275 20. Wang Y, Li J, Gu J, Zhou Z, Wang Z (2015) Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China). Appl Soft Comput 35:280–290 21. Li DC, Wen IH (2014) A genetic algorithm-based virtual sample generation technique to improve small data set learning. Neurocomputing 143:222–230 22. Nawi NM, Khan A, Rehman MZ (2013) A new Levenberg Marquardt based back propagation algorithm trained with cuckoo search. Procedia Technol 11:4 23. Aruna P, Puviarasan N, Palaniappan B (2007) Diagnosis of gastrointestinal disorders using DIAGNET. Expert Syst Appl 32(2):329–335 24. Leung H, Haykin S (1991) The complex backpropagation algorithm. IEEE Trans Signal Process 39(9):2101–2104 25. Bache K, Lichman M (2013) UCI Machine Learning Repository [http://archive.ics.uci.edu/ ml]. University of California, School of Information and Computer Science, Irvine, CA, 28

A Review on High-Impedance Ground Fault Detection Techniques in Distribution Networks Debadatta Amaresh Gadanayak

Abstract Despite extensive research, the detection of arcing high-impedance faults (HIFs) in the low and medium voltage transmission and distribution networks remains a formidable challenge. Conventional overcurrent relays fail to detect the highimpedance faults due to their low fault current. However, even the modern digital relays utilizing the advanced signal processing techniques and expert classifiers are not able to solve the issue with conviction because of the random, nonlinear, and asymmetric nature of the arcing current. A lot of research work is carried out to improve the detection accuracy of the HIFs with a higher level of security against non-HIF transient events. This paper proposes a comprehensive review of the traditional as well as modern HIF detection schemes along with their advantages and drawbacks. Keywords High-impedance ground faults · Distribution networks · Arcing · Classifiers · Overcurrent relays

1 Introduction When an energized overhead distribution line conductor comes in contact with a highimpedance object like a tree trunk or concrete structure, or a broken live conductor falls on a quasi-insulating surface such as sandy or asphalt ground, the fault current drawn is often not enough to be detected by the conventional overcurrent relays. Such faults are categorized as high-impedance faults. Although HIFs normally do not cause instant damage to the power system equipment due to lower current magnitude, the downed conductors can cause loss of life and property due to shock-hazard and arcignited fire [1]. One of the significant characteristics of HIF is the existence of the arcing phenomenon. Arcing brings nonlinearity and randomness into the HIF current which further complicates the detection task. The magnitude and the characteristics of the HIF current and voltage depend upon a wide range of factors such as types of D. A. Gadanayak (B) ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_26

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material and ground in contact, load characteristics, and even the weather conditions [2–4]. So even when the analysis is in different domains such as synchronous frame, frequency domain, or time–frequency domain, it is difficult to find a fixed unique characteristic for all kinds of HIFs. Hence, it is an arduous task to design an accurate protection scheme for HIFs [5].

2 Problem Definition Typical waveforms at the point of relaying in a modified IEC microgrid [6] during HIF simulation using the HIF model proposed in [7] is given in Fig. 1a and b. Figure 2a and b show the actual current drawn by the fault and its V-I characteristics. It can be observed that the deviations in the current and voltage waveforms during HIF is negligible and hence cannot be detected by either overcurrent or undervoltage conventional relays. The current drawn by the fault also shows random behavior. The origin of the randomness is contributed by the arcing phenomenon which is an integral part of the HIF. Other irregularities include asymmetric current waveform and intermittent arcing periods. Hence, lower fault current magnitude is not the only challenge with the HIF detection, the randomness of current also plays an important role in making the task difficult even when the analysis domain is chosen as the frequency domain, time–frequency domain, etc.

Fig. 1 Current and voltage waveforms at relaying point during HIF

Fig. 2 Current drawn at HIF terminal and V-I characteristics

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The current magnitude-based protection schemes cannot reliably detect highimpedance arcing ground faults. Hence, HIF protection schemes developed in literature are mainly based on the analysis of the current and voltage waveforms in a transformed domain. The analysis domain of feature extraction can be time-domain analysis such as mathematical morphology and sequence components or can be frequency domain like Fourier transform or can be time–frequency domain like wavelet analysis, Stockwell transform, etc. The increasing popularity of the datadriven classifiers has also contributed to the HIF detection techniques. Different data-driven classifiers such as decision trees, neural networks, fuzzy systems, and neuro-fuzzy systems have been used for the HIF detection by taking either feature from the above-mentioned transformed domains or from the raw time-domain data of the HIF current or voltage waveforms. Reviews of the protection schemes in this area from different perspectives can be found in the literature [8–10]. For simplicity, these HIF detection techniques can be divided into two broad categories depending upon the classifiers used for fault detection, the ones that use simple threshold-based classifiers and the ones that use complex data-driven classifiers.

3 Simple Threshold-Based Classifiers for High-Impedance Faults A review of some standard high-impedance fault detection algorithms that are currently used in the industry is given in [11]. The algorithms are energy algorithm, randomness algorithm, arcing phase signature algorithm, and spectral analysis algorithm. The energy-based algorithm supervises energy content in the frequency bands between low-order harmonics and higher-frequency algorithms, and any significant change in the components is taken as a HIF signature. The randomness algorithm monitors randomness associated with the fault current in terms of the energy contents of the non-fundamental frequency components for HIF detection. Arcing current surge occurs at a specific range of phase angles of the voltage waveform, which is distinguishable in the high-frequency components. This feature is used by the arcing phase signature algorithm. The spectral analysis algorithm uses changes in the average Fourier coefficients of the current signal to detect and locate the HIF in a transmission line. Although the detailed implementation of any of the above algorithms is not presented in this work, however, it provides a foundation for HIF detection schemes based on the current and voltage characteristics. Maximum works present in literature are directly or indirectly influenced by one or more algorithms presented in this paper. An HIF protection scheme exclusively for sandy soil in 15 kV distribution feeders is presented in [12]. It uses two methods based on second harmonic and third harmonic density function, respectively. However, these methods cannot be generalized for all kind of HIFs. Paper [13] presents a scheme based on DWT. The number of bursts and the time difference between consecutive bursts in the high-frequency

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components of the current signal is used for fault detection. Paper [14] presents an HIF detection scheme based on the presence of high-frequency components above 2 kHz in the arcing current waveforms. It uses the Fourier transform and the Welch spectral estimation principle for averaging spectral estimation outputs. [15] presents an intricate HIF detection scheme. It composes three stages of analysis, such as shortterm, medium-term, and long-term analysis. In the short-term analysis, second, third, fourth, fifth, seventh, and ninth harmonic components of the current signal are estimated and compared with the predefined thresholds for each harmonic component. Each time these harmonics are higher than the threshold, a counter is increased. Similarly, in the medium-term analysis, standard deviations of means of harmonics in the short-term analysis are calculated and compared with the predefined thresholds. In the long-term analysis, the standard deviations obtained in the medium-term analysis are used to find the accumulated probability. The percentage for each harmonic component at the end of long-term analysis is used to indicate the existence of the arc. [16] presents a HIF detection scheme based on negative sequence current, third, and fifth harmonic components and the angle between the third harmonic current and the phase voltage. Paper [17] uses a method for HIF detection by monitoring phase current for sudden, persistent, and random change in high-frequency energy levels of the current signals. A HIF detection technique using flicker and asymmetric nature of the random HIF current signal is proposed in [18]. The flicker is measured by comparing the positive peaks and the negative peaks of the consecutive cycles in the current waveform, and the asymmetry is measured by comparing the positive and negative peaks of each cycle. Then, a simple threshold is used for HIF detection. In [19], HIF detection scheme involves the analysis of a revised crest factor (RCF) and the average of voltage and current signals. The revised crest factor is the ratio of the peak value to the mean value of the signal. It demonstrates that a combination of the sign of the change in RCF and the sign of the change in mean value is unique for HIF as compared to other disturbances such as capacitor switching, tie switching, and arc processing. In paper [20], abnormality in the current signal is detected based on the even harmonics content, and HIF is detected based on the ratio between the seventh and the third harmonic components, as well as the ratio between the fifth and the third harmonic components. [21] proposes an HIF detection and location scheme in a distribution network based on DWT multiresolution analysis of the voltage signals at the main substation. The scheme is based on matching the features of the first-, second-, and third-level coefficients with a predefined database. A fast and computationally efficient HIF detection scheme based on the wavelet transform is proposed in [22]. Although the scheme is equally effective with the discrete wavelet transform, it uses the maximum overlap discrete wavelet transform (MODWT) because MODWPT does not involve downsampling and hence is faster in real-time applications. The wavelet coefficient energy with border distortions is used to detect HIFs. Also, a detailed scheme using the wavelet coefficient energy to discriminate HIFs from other dynamic events such as other kinds of faults in the transmission and distribution lines, capacitor connection/disconnection, and transformer switching has been sketched out. [23] proposes HIF detection scheme using current from the current transformer (CT) installed in the distribution network for

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different purposes like metering, etc. The current is processed through the discrete wavelet transform, and either the second-level or the third-level detail coefficients are taken as the signature of the HIF. The standard deviation of these coefficients normalized by the magnitude of the current is compared with a detection criterion to distinguish HIFs. Paper [24] presents an HIF detection scheme based on only the first-level decomposition of the voltage and current signals using a hard-thresholdingfiltering-based discrete wavelet transform. The fast-rising transient energy of the detail coefficient is used to detect the HIF, while the energy of the approximate coefficient is used to discriminate it from other transient events such as solid faults, switching of capacitor banks or branches. [25] proposes an HIF section identification technique by DWT analysis of the voltage waveforms. The average of the detail coefficients of the voltage waveform between two consecutive nodes is calculated and compared with the previously stored database by the average absolute deviation method. However, the technique concentrates only on faulty section identification and useful only when the HIF is identified by an efficient algorithm. Paper [26] presents a HIF detection scheme based on mutual differences between absolute sums of the fourth-level high-frequency wavelet packet transform coefficients of the three-phase voltage waveforms by comparing them with a threshold value. However, it uses a sampling frequency of 200 kHz, therefore difficult to implement in real-time. Paper [27] presents a method for HIF detection based on continuous monitoring of the third harmonic current phase angle obtained using the Stockwell transform. Windowed standard deviation of the third harmonic phase angle is used as the feature, and a self-adaptive threshold is used for fault classification. A mathematical morphology-based approach is designed in [7, 28]. It uses a closing–opening difference operator (CODO) to monitor the voltage waveform distortions caused by the inception of the HIF. The occurrence of HIF is determined when the CODO operator output becomes greater than a predefined value repeatedly. Paper [29] presents a relational logic between the sum of even harmonic current components up to eighth harmonic, the sum of odd harmonic current components up to ninth harmonic, and the third harmonic current component magnitude to detect HIF. HIF phase identification is performed by comparing average total harmonic distortion (THD) of three-phase currents with THD of individual phase currents. Paper [30] provides a HIF detection scheme based on the absolute sum of d3 discrete wavelet transform coefficients of the voltage waveform over one cycle. It also performs the fault direction identification based on the sum over two cycles of the d3 power waveform which is obtained by multiplying d3 coefficient of voltage and current. A HIF detection and location method using fault distance and fault parameter estimation approach formulating it as a minimization problem are given in [31]. This approach only uses the node voltage and node current signals and operates in two steps. In the first step, the line capacitance is neglected and, the parameter estimation for each sample is performed using the least square estimation method upon a set of linear equations. The changes in the estimated parameters can be used to detect HIF. In the second step, the line capacitance is considered, and the residual function minimization-based iterative search technique is applied for parameter estimation on a nonlinear set of equations. For the second step, the parameters estimated in the first step are taken as the initial

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values for the iterative search algorithm. This method can perform fault location and parameter estimation with a remarkably high degree of accuracy within one cycle of the inception of the HIF. However, the accuracy of such parameter estimationbased methods heavily depends upon the high-impedance fault models from which the state equations are derived. An interharmonic-based scheme is proposed in [4]. Finite impulse response (FIR) filters are used to extract the interharmonic parts in the current signal, and an interharmonic index is calculated as the percentage of energy content in the interharmonic parts with respect to the fundamental current. This index is compared to the average of all the past indexes within 1 s to detect HIF. The results seem promising and logical because interharmonics are an integral part of HIF. However, this method uses a window length of 60 cycles because of the design constraints of the FIR filters which increases both calculation complexity and detection time.

4 Data-Driven Complex Classifier for High-Impedance Faults In [3], means and root mean squares of the first-level and the second-level DWT coefficients of the current signal are taken as the feature set, and a simple Bayes classifier is used for HIF detection. [32] and [33] propose a pattern classification approach to HIF detection using DWT. The voltage and current waveforms are processed through DWT, and the root mean square values of the lower-order DWT coefficients are taken as the features to be classified by the nearest neighbor rule-based classifier into the fault and no-fault categories. A Stockwell transform and support vector machine-based HIF detection scheme are proposed in [34]. Similarly, wavelet transform and support vector machine-based HIF detection scheme are proposed in [35] by the same group of authors as in [34]. The current signals are processed through S-transform and wavelet transform in [34] and [35], respectively. Various features of the S-transformation output matrix and wavelet coefficients are obtained to be classified by SVM into the fault and no-fault categories [34, 35]. In [36], the HIF detection scheme is based on a decision tree-based classifier. The feature set consists of the root mean square value of the current signal, and the amplitudes of the second, third, and fifth harmonic components divided by the root mean square value of the current and the third harmonic component phase of a single cycle window. Authors in [37] proposed a HIF detection scheme based on the wavelet packet transform of the current waveform and the extreme learning machine classifier. This pilot protection scheme finds the sums of absolute values of the 26th node coefficients of the fourth-level wavelet packet transform decomposition of the three-phase current waveforms at the sending end and the receiving end of the transmission line and calculates different interphase differential features. These features are compared with a threshold and then presented to the ELM. The output of the comparator and the ELM decides the occurrence of HIF in that line. An HIF detection scheme using wavelet transform and

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simple evolving connectionist systems (SECoS)-based classifier is proposed in [38]. The samples of the detailed coefficients are directly given as input to the classifier. Several wavelets and a number of different classifiers such as multilayer perceptron, probabilistic neural network, support vector machine, and simple connectionist systems are evaluated. The Bir2.8 wavelet with the SECoS classifier combination is found to give the best classification accuracy. The HIF detection scheme in [39] uses S-transform and TT-transform with a probabilistic neural network classifier. In the S-transformation case, two vectors are constructed from the S-matrix obtained from the S-transformation of the half-cycle current signal by row-wise maximizing and column-wise maximizing, respectively. The energy contents and the standard deviations of these two vectors are used as the features. Similarly, in the TT-transform case, the features are the energy contents and the standard deviations of the TT-contour and the time index. In [40], the multiresolution morphological gradient is applied to the current waveform at levels 1 and 2. The average and the standard deviation of the first-level gradient signal and the maximum and minimum of the second-level gradient signal are taken as the features for disturbance detection using precalculated thresholds. If a disturbance is detected, these morphological features are used by three multilayer perception neural networks to classify HIF for three different phases. In a similar approach, a close–open average mathematical morphology filter is used in [41] to eliminate noise from the current signal, and six features such as mean, standard deviation, energy, entropy, kurtosis, and skewness of the processed signal are obtained to train a data mining model (decision tree). In [42], the current signal is processed through a morphology gradient product filter (MGPF), which is the product of closing–opening gradient filter (COGF) and closing–opening–opening– closing gradient filter (COOCGF). Eight features such as energy, entropy, standard deviation, skewness, mean, sum, kurtosis, and variance are extracted from the MGPF output, and a random forest-based classifier is used for HIF classification. The random forest classifier is then transformed into a fuzzy rule-based classifier for better classification accuracy. In the HIF detection scheme presented in [43], wavelet transform was performed on current signals for both denoising and feature extraction purposes. For feature extraction, the denoised current signal is decomposed up to the third level. The detail signals in all three levels are divided into several segments. The root mean square of the first-level detail coefficients, the mean square of the second-level detail coefficients, and the absolute maximum value of third-level detail coefficients of each segment are chosen as the features. Two methods are proposed for HIF detection. The first one involves the genetic algorithm for optimum feature identification and the Bayes classifier for HIF detection. The second method involves principal component analysis for optimum feature identification and the artificial neural network for HIF detection. In [44], three different data-driven technologies such as the principal component analysis with Hotteling’s T2 statistics, the Fisher discriminate analysis, and the multiclass support vector machine (SVM) are applied on a data structure containing phase voltages of an IEEE 13 node test system to detect and classify HIFs. Better results are obtained by the multiclass SVM compared to the other two techniques both in terms of fault detection and HIF discrimination with no-fault transient events. Paper [45] uses an adaptive extended Kalman filter to estimate fundamental,

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third, fifth, seventh, eleventh, and thirteenth harmonic components of the current signal and two classifiers, the probabilistic neural network, and the feedforward neural network are used to discriminate HIF events from non-HIF events. A similar approach is proposed in [46] where extended Kalman filter is used to estimate fundamental, third, fifth, seventh, eleventh, and thirteenth harmonic components of the current signal and a random forest-based ensemble decision tree classifier is used to detect HIFs. In [47], a new time–frequency distribution analysis methodology using Cohen’s generalization of the quadratic time–frequency distributions is proposed for HIF current analysis. The optimized feature set, which includes the energy content in the current signal and the joint time–frequency moments are classified by a support vector machine-based pattern classifier to detect HIFs. Paper [48] proposes a new technique for radial systems in which a pulse with 1 ms of front time and 250 ms of the tail is injected to the feeder inlet, and the return signal is analyzed for HIF detection. The first 20 frequencies of the Fourier spectra of the returned signal are used to train and test a neo-fuzzy neuron network for fault detection. Although these data-driven classifiers can map any kind of complex relationship, they are prone to overfitting and suffer from greater computational burdens. The performance of such algorithms in real time depends on the diversity of the dataset and the type of features used for training. For example, they may give terrible decisions if trained with a biased dataset. Moreover, entirely data-driven classifiers such as neural networks are “black boxes” having limited ability to explicitly identify possible causal relationships [49]. So, in some cases, such applications without a backup plan for the second level of checking can be potentially dangerous.

5 Conclusion The aim of this work is to discuss existing high-impedance ground fault techniques and their drawbacks. High-impedance ground fault detection techniques have been divided into two categories, those who use simple threshold-type classifiers and those who use complex data-driven classifiers. In the simple classifier-based schemes, the fault features are obtained from the current and voltage waveforms in different transformed domains. The analysis domain of feature extraction can be time-domain analysis such as mathematical morphology and sequence components or can be frequency domain like Fourier transform or can be time–frequency domain like wavelet analysis. Frequency-domain feature such as second, third, fourth, fifth, seventh, ninth, or higher harmonic components-based protection schemes is based on purely empirical observations of the FFT spectrum of the current and voltage signals during HIF and their effectiveness for all kinds of HIFs in all scenarios of distribution system operation is doubtful. Detailed wavelet coefficient-based protection schemes perform poorly in distinguishing no-fault transient events like capacitor switching from HIFs. Sequence current-based protection schemes are ineffective for unbalanced systems and unbalanced load change cases. Morphological feature-based protection schemes like those using mathematical morphology are effective only when current

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and voltage waveforms are distortion-free. Interharmonic-based protection schemes are effective because the electric arc is an integral part of high-impedance faults. However, accurate measurement of interharmonics using Fourier-based methods or bandpass FIR filters for a current or voltage signal with a lower sampling rate requires a large analysis window, which increases both the calculation complexity and the detection time. The second category of HIF detection algorithms that use complex data-driven classifiers are prone to overfitting and suffer from greater computational burdens. The performance of such algorithms in real time depends upon the diversity of the dataset and the type of features used for training. They may give terrible decisions if trained with a biased dataset.

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Voltage Stability Index and Butterfly Optimization Algorithm-Based DG Placement and Sizing in Electrical Power Distribution System Pritish Kumar Mohanty and Deepak Kumar Lal

Abstract Distributed generation (DG) includes both conventional and renewable energy resources, which are an environment-friendly alternative to traditional energy systems. Due to growing load demand and environmentally friendly behavior, its installation is picking up intrigue around the world. The most favorable location and capacity of DG in distribution system can decrease system power losses, make better system node voltage profile and delay financing in supply systems lines up-gradation. The paper focuses on a method for DG allocation established on voltage stability index (VSI). The sizes of the different types of DGs are evaluated using Butterfly Optimization Algorithm (BOA). The method reduces the search space, simple and needs less computational time for the solution. To assess the potency of the preferred algorithm, the method is tested on hypothetical 33 buses and 69 buses distribution systems. The effects on voltage profile and line loading with the allocation of various types of DGs are studied. Keywords Radial distribution system · Voltage stability index · Distributed generation · Continuation power flow

1 Introduction Due to growing population and changing lifestyle of human beings, the demand for energy rises. The conventional power generation sources unable to fulfill the ever increasing load demand due to social, environmental and economic issues. Managing with the emerging technologies and increasing energy demand, the nonconventional or alternative power generation sources receiving much attention. The

P. K. Mohanty · D. K. Lal (B) Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur 768018, Odisha, India e-mail: [email protected] P. K. Mohanty e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_27

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concept of distributed generation (DG) is an emerging approach and its installation is gaining interest worldwide. DG is an electric power generation unit installed inside the distribution networks [1–4]. DG includes both conventional and renewable energy resources, which are an environment-friendly alternative to traditional energy systems [4]. The influences of integration of DG in the power system may depend on various technical attributes, such as technology, size of the units, operation and control approaches and location in the power network. Location at most favorable site and size of DG can subtract system power losses, outdo voltage profile and delay investment on supply systems lines extension and up-gradation. But, if DGs are not properly coordinated, located and designed to work with existing network components, it raises various power quality issues. Therefore, connection and increasing penetration of DGs should be carefully evaluated and planned. Various load flow methodologies have been developed for distribution system. The load flow study is essential to collect the information about bus voltages, line currents and line power flow. The study is useful for distribution system planning. The load flow program for distribution system derived from Newton–Raphson and Gauss–Seidel methods do not execute effectively. Therefore, different load flow methodologies have been developed for the distribution system [5–9]. Since the benefits of DG integration are site-specific, many analytical [10–13] and heuristic [14–24] methods are proposed. A procedure for allocation of DG using continuation load flow by determining the most appropriate bus in the distribution system has been proposed in [25]. DG is installed on the ascertained critical bus and its size is increased until achieving the objective function. A voltage stability index (VSI) is formulated from the voltage equation and identifies the critical buses [26]. A novel fast VSI is presented for conducting voltage stability analysis of the distribution systems [27]. The index indicates the maximum capacity limit before voltage collapse. Few works have been proposed for voltage stability betterment by placing DG at the most sensitive buses [28–34]. The paper investigated the outcome of DG location and capacity on voltage stability study of the radial distribution system. The analysis is performed using a steady-state VSI [31–34]. The paper presents a procedure for DG allocation and sizing based on the VSI. The DG is placed at the identified sensitive bus. Then a heuristic algorithm called Butterfly Optimization Algorithm (BOA) is used to estimate the most suitable size of DG for lower network power losses. It is a newly developed metaheuristic algorithm [35]. The method reduces the search space, simple and needs less computational time for the solution. Followed by introduction in Sect. 1, the load flow solution, derivation of VSI and problem formulation are presented in Sect. 2. Overview of BOA and the procedure for solving the optimal placement problem are presented in Sect. 3. Results and discussions on the test systems are demonstrated in Sect. 4. Eventually, an inference is outlined in Sect. 5.

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2 Methodology This section presents brief overlook of load flow solution used for distribution system, derivation of VSI from Distflow equations and problem formulation.

2.1 Load Flow Solution A direct method proposed by J. H. Tengis exercised for the load flow solution of the distribution system [5]. The technique is robust and time-efficient.

2.2 Derivation of VSI from Distflow Equations The derivation of the VSI is obtained from the voltage equation applied in the Distflow load flow technique [34]. The derivation of VSI is as follows. Consider lineconductor impedance z = r + j x is associated between nodes 1 and 2 as appeared in Fig. 1. |I2 |2 =

P22 + Q 22 V22

(1)

|I1 |2 =

P12 + Q 21 V12

(2)

If loads are not connected at the buses 1 and 2, then P2 = P1 − Ploss

(3)

Q 2 = Q 1 − Q loss

(4)

 Ploss =

Fig. 1 Two bus power distribution system

P22 + Q 22 V22

 r

(5)

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 Q loss =

P22 + Q 22 V22

 x

(6)

From Eqs. (3), (4), (5) and (6), Eq. (7) becomes,  |I1 |2 =

P2 +



P22 +Q 22 V22

 2   2 2  2 P +Q r + Q2 + 2V 2 2 x 2

V12

(7)

Substituting the left-hand side of Eq. (7) with Eq. (1) results, 



 V24 + V22 2(P2 r + Q 2 x) − V12 + P22 + Q 22 r 2 + x 2 = 0

(8)

Also, Eq. (8) can be written as follows, 

V24 + 2V22 (P2 r + Q 2 x) − V12 V22 + P22 + Q 22 |z|2 = 0

(9)

In general Eq. (9) can be written as an Eq. (10), 2

 

2 4 2 2 Pk+1rk,k+1 + Q k+1 x,k+1 − Vk2 Vk+1 + 2Vk+1 + Pk+1 + Q 2k+1 z ,k+1 = 0 Vk+1 (10) From Eq. (10), line receiving end powers can be derived as (11) and (12), ⎤  

   cos2 θz,k+1 V 4 − V 4 − z k,k+1 2 Q 2 ⎥  2

⎢ k+1 k+1 k+1 z k,k+1 Pk+1 = ⎣− cos θz,k+1 Vk+1 ±  ⎦ 2 Q 2 2 −2Vk+1 k+1 x k,k+1 + Vk Vk+1 ⎤ ⎡     2

 sin θz,k+1 V 4 − V 4 − z k,k+1 2 P 2 ⎥  2

⎢ k+1 k+1 k+1  z k,k+1 Q k=1 = ⎣− sin θz,k+1 Vk+1 ± ⎦ 2 P 2V 2 −2Vk+1 r + V k+1 k,k+1 k k+1 ⎡

(11) (12)

From Eqs. (11) to (12), the real value of active and reactive power at the receiving end will occur on the state below, 2 4 4 2 2 cos2 (θzk ) Vk+1 − Vk+1 − z k,k+1 Q 2k+1 − 2Vk+1 Q k+1 xk + Vk2 Vk+1 ≥0

(13)

2 2 4 4 2 2 sin2 (θzk ) Vk+1 − Vk+1 − z k,k+1 Pk+1 − 2Vk+1 Pk+1 rk,k+1 + Vk2 Vk+1 ≥0

(14)

Adding each side of Eq. (13) and (14), we get 2

 2  2 4 2 Pk+1rk,k+1 + Q k+1 xk,k+1 − Pk+1 − Vk+1 − 2Vk+1 + Q 2k+1 z k,k+1 ≥ 0 2Vk2 Vk+1 (15) Equation (15) can be acted toward a bus voltage stability index (VSI) for a distribution system as follows,

Voltage Stability Index and Butterfly Optimization Algorithm …

 2 4 2 Pk+1rk,k+1 + Q k+1 xk,k+1 V S Ik+1 = 2Vk2 Vk+1 − Vk+1 − 2Vk+1 2

2  − Pk+1 + Q 2k+1 z k,k+1

315

(16)

Knowing the bus voltages and branch currents from load flow study, VSI of each node can be calculated. The method is simple and easily applicable to the radial distribution networks. The bus resulting minimum VSI is accepted as the most appropriate bus.

2.3 Problem Formulation The total real power loss (PT Loss ) and reactive power (Q T Loss ) loss in the section between buses k and k + 1 of the distribution system are framed as (17) and (18), PT Loss =

n−1 

PLoss (k, k + 1)

(17)

Q Loss (k, k + 1)

(18)

k=1

Q T Loss =

n−1  k=1

With inclusion of DG, (17) and (18) becomes as follows, PDG, T Loss =

n−1 

PLoss (k, k + 1)

(19)

Q Loss (k, k + 1)

(20)

k=1

Q DG, T Loss =

n−1  k=1

where n is the total number nodes or buses. The objective function of the DG placement problem is constructed to minimize the PT Loss and is given by, Minimize F = minPT Loss

(21)

Subject to the constraints, n  k=2

PDG,k ≤

n  k=2

PL(k) +

n−1 

PLoss (k, k + 1)

(22)

k=0

|Vk |min ≤ |Vk | ≤ |Vk |max

(23)

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max and, Ik,k+1 ≤ Ik,k+1 (24) Where PL(k) is the connected load at respective node k.PDG,k is the DG capacity at bus k.

3 Butterfly Optimization Algorithm The BOA is a nature-motivated stochastic algorithm governed by the rule based on food searching strategy and mating behavior [35]. The butterfly discharges aroma and the aroma will proliferate over a separation. This is also related to the fitness of butterflies. Different butterflies can identify it. Through it, they can grant their information with others and construct a consolidated social learning framework. In a circumstance, when they are incapable to distinguish smell from the air, at that point, it will continuously fly stochastically. The aroma/fragrance is formulated as an encouragement force as stated in Eq. (25). f = c Ia

(25)

where f , c, I and a are perceived intensity of the fragrance, sensory modality, enhancement force and power exponent, respectively. The estimation of c and a ranges between 0 and 1. The algorithm is put into effect in three distinct stages, e.g., beginning, operation and finishing stages. At first in the beginning phase, the program contemplates the structure capacity and its answer space. The approximations of the algorithm variables are similarly designated. At that point, the calculation makes an underlying populace and places of butterflies stochastically in the quest territory for improvement, with their aroma and wellbeing regard determined and put away. During the computation, a required dimension of memory is spared to store their data. The operation stage has been guided with two very important sub-periods, (i) global explore period and (ii) local explore period. In global period, the butterflies movement in the direction of the fittest butterfly/solution Gbest ∗ is as (26),

x it+1 = x it + rand

2

 × Gbest ∗ − x it × f i

(26)

where xit is the result vector. The aroma of i th butterfly is f i and the random number rand varies in the between 0 and 1. Local explore period is demonstrated as (27),

x it+1 = x it + rand

2

 × x tj − x tk × f i

(27)

where x tj and xkt are selected from the solution region employed and chosen randomly from the existing population.

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The number of iteration is agreed as termination rule of the program in final phase. Here, the best arrangement is found. Quest for nourishment and mating accomplice by butterfly can happen at both local and global scales. Taking into account physical nearness and different elements like rain, wind and so on, look for nourishment can have a huge portion in a general mating accomplice or nourishment looking through exercises of butterflies. The typical parameters of the BOA considered for the given problem are as follows, the number of search agent (N) = 40, the maximum number of iteration Itmax = 100, switch probability (p) = 0.8,a = 0.1 and c = 0.1. The flowchart of the algorithm is presented in Fig. 2. In the current work, the optimal site for installation of DG is found by computing VSI and most sensitive bus. The size of the DG is determined using the optimization technique. The DG size which outcomes least PT Loss in the system is accepted as optimal. The BOA-based technique for determining the optimal capacity of DG to minimize PT Loss in the system is as follows: Step 1: Feed system data and constraints. Step 2: Run the load flow program at the base case of the distribution test system. Compute the VSI using (16) of each bus and form the priority list. Allocate the DG unit at top-ranked bus having minimum VSI. For multiple DGs placement at multiple locations, the candidate locations are minimum VSI buses in different branches. Step 3: The initial population and positions of butterflies/search agents are generated randomly on dimensions in the solution tract. The iteration counter is set to be iter = 0. Step 4: DG can be operated at different power factors. But, at a time in the entire optimization process, keep the power factor of DG constant. The locations of search agents are DG capacities. For each search agent, if the bus voltages are inside the ranges/limits, calculate the summation of lines real power loss PT Loss . If not, search agents are infeasible. Step 5: For each search agent, look at its objective value (minimize PT Loss ) with the individual best. If the outcome is lower than previously saved data, set this value as the contemporary best, and register the relating agent position/DG size. Step 6: Select the pursuit operator related with the minimum independent best of all search agents, and fix the estimation of the position and goal as the present general best. Step 7: Revise the locations of butterflies/agents using Eqs. (26) and (27) separately. Step 8: Whenever the iter arrives at the most extreme limit, proceed to Step 9. Something else, update iteration sign iter = iter + 1, and return to Step 4. Step 9: Save the most favorable result to the objective issue. The leading position of the agents incorporates the ideal size of DG or multi-DGs, and the relating fitness or objective value.

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Fig. 2 Flowchart of BOA

4 Results and Discussions The analysis is accomplished with DG operating with different power factors. Initially, a load flow work is performed for the distribution test systems to collect information about line losses, line loading and bus voltages. The outcomes of the load flow solutions are used to find the VSI at each nodes and the most critical bus. The most suitable location of DG is that most tender bus. Four types of DGs are observed.

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Type-I: DG is working at unity power factor injecting active power only to the system. Type-II: DG is working at 0.866 power factor introducing both active and reactive powers to the system. Type-III: DG is working at 0.866 power factor introducing active power and drawing proportionately reactive powers. Type-IV: DG is managing only reactive power into the system. The distribution test system-1 is a 33 bus radial distribution system as exhibited in Fig. 3. The test system has 32 sections with a load of 3.715 MW and 2.3 MVAr [31]. The PT Loss and Q T Loss for the base case found from load flow are 210.9884 kW and 143.1376 kVAr, respectively. After the load flow solution is performed for the distribution test system, VSI is calculated as presented in Fig. 4. The most sensitive bus is selected as bus 18 which has minimum VSI of 0.6684. The optimal size is found using the BOA algorithm which results in minimum PT Loss and satisfying

Fig. 3 Single line diagram of the 33 bus test system

Fig. 4 VSI of the 33 bus radial distribution test system

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voltage limits and line constraints. The process can be repeated for the placement of different types and multiple DGs. Figure 5 reports the variation of system PT Loss with different capacities of DGs at bus number 18 in the test system. It is clearly seen from Fig. 5 that the PT Loss lowers with an increase of DG size up to a certain amount, and after that, the system power loss increases. Most suitable locations and size of DGs are listed in Tables 1 and 2 for Type-I and Type-II DGs, respectively. The placement of the optimal size of DGs result system performance betterment in terms of lesser PT Loss and Q T Loss and improvement in the system voltage profile

Fig. 5 Variation of PT Loss with different capacity of DG at bus number 18 in the 33 bus radial distribution test system

Table 1 Simulation outcomes of Type-I DG placement in the 33 bus test system System

Bus number

Base case

Size of DG (MVA)

Summation of line losses PT Loss (kW)

Q T Loss (kVAr)

–

210.9884

143.1376

1 DG

18

0.8526

145.7219

100.8564

2 DG

18

0.6674

100.0545

72.1633

33

1.0029

Table 2 Simulation outcomes of Type-II DG placement in the 33 bus test system System

Bus number

Base case

Size of DG (MVA)

Summation of line losses PT Loss (kW)

Q T Loss (kVAr)

–

210.9884

143.1376

1 DG

18

1.0279

123.5722

87.6975

2 DG

18

0.7536

50.31

41.3433

33

1.3137

Voltage Stability Index and Butterfly Optimization Algorithm …

321

as shown in Figs. 6 and 7. The presence of the DGs results nearly to a flat system voltage profile. The comparisons of the usefulness of placement of different types of DGs on system voltage profile are presented in Fig. 8. Figure 8 shows that Type-II DG provides superior performance as compared to others. The superiority of the proposed algorithm is compared with particle swarm optimization (PSO), gray wolf optimization (GWO) and grasshopper optimization algorithm (GOA) implemented on allocation and capacity finding of two numbers of Type-I DGs in the distribution test system-1. The initial population of all the algorithms is selected same, i.e., 30 numbers. In terms of convergence time, the convergence curve found using BOA is far superior to PSO, GWO and GOA techniques as shown in Fig. 9 (Table 3). To confirm the applicability of the implemented approach, it is essential to test on some other distribution test system. Thus, the intended methodology is extended to

Fig. 6 Voltage profile of distribution test system-1 for different placement scenarios of Type-I DG

Fig. 7 Voltage profile of distribution test system-1 for different placement scenarios of Type-II DG

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Fig. 8 Voltage profile of distribution test system-1 with different types of DGs at bus number 18

Fig. 9 Variation of objective value with each iteration for 2DG sizing in the test system-1

Table 3 Simulation outcomes of Type-III and Type-IV DG placement in the 33 bus test system System

Bus number

Size of DG (MVA)

Summation of line losses PT Loss (kW)

Base case

Q T Loss (kVAr)

–

210.9884

143.1376

Type-III DG

18

0.4679

189.0268

128.3915

Type-IV DG

18

0.4798

189.0921

129.7642

a hypothetical 69 bus distribution system [32, 33]. The test system-2 has 68 sections with a total connected load of 3.80 MW and 2.69 MVAr, put in an appearance in Fig. 10. The total real and reactive power losses of the system in base case are 224.7006 kW and 102.0382 kVar, respectively. Figure 11 shows the VSI at different

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Fig. 10 Single line diagram of the 69 bus test system

Fig. 11 VSI of the 69 bus radial distribution test system

buses and bus number 65 is found to be the most sensitive bus which has minimum VSI. Thus, DG is placed and the optimal size of DG is determined using the BOA algorithm. For two numbers of DGs placement, bus numbers 65 and 27 are selected as candidate locations. The system power loss decreases with the placement of DGs as presented in Table 4. The system voltage profile with one DG and two DGs is appeared in Fig. 12. The system performance indices improved. The system is achieving toward flat bus voltage profile which approaches unity.

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Table 4 Simulation outcomes of the 69 bus test system System

Bus number

Base case 1 DG

65

2 DG

27, 65

Size of DG (MVA)

Summation of line losses Active power (kW)

Reactive power (kVAr)

–

224.7006

102.0382

1.4384

112.0561

55.1194

0.4474, 1.3811

100.359

49.866

Fig. 12 Voltage profile of distribution test system-2 for different placement scenarios of Type-I DG

5 Conclusions Keeping in mind the growing power demand, environmental issues and need to supply good quality power to consumers, the research is conducted in the field of distribution system planning, operation and control in the presence of DGs. In this paper, a VSI concept is implemented for obtaining the most suitable location of DG. For only one DG sizing, the continuous power flow method is sufficient to explore the appropriate DG capacity. But for multiple DGs sizing at different locations, it is difficult to find optimal capacities. In this situation, intelligent techniques give excellent results. Nowadays, the intelligent techniques are implemented in different fields of engineering and technology for solving problems and reducing human efforts. The heuristic optimization algorithm BOA is used for optimal sizing of only one DG and multi-DGs which are reducing the PT Loss in the considered in the 33 buses and 69 buses test systems satisfying all system constraints. The method is simple, speedy, accurate and easily applicable for solving the network problems in deciding the sizes and locations of DGs. Thus, numerous benefits such as minimizing line losses, improving line loading of distribution system and its techno-economic profits encourage distribution system planning incorporating DGs.

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The network reconfiguration in addition of DGs in the system for system performance improvement in terms of techno-economic profits of the networks will be considered in future research work. The DG placement benefits are site-specific. Due to unequal load growth at different buses, the most appropriate bus for DG allocation may change. Once DG/DGs is/are installed, it is not beneficial and possible to change its locations frequently. At this situation, network reconfiguration gives excellent results. The placement of new alternative energy sources such as electric vehicle charging/discharging points and fuel cells/energy storage devices in coordination with shunt capacitors are another emerging area of research. Acknowledgements The present work was in some measure supported by AICTE, New Delhi, India MODROB Project (Ref. no. 9-44/RIFD/MODROB/Policy-1/2016-17) and TEQIP-III Cell, VSSUT, Burla, Odisha, India.

References 1. Ackermann T, Andersson G, Söder L (2001) Distributed generation: a definition. Electric Power Syst Res 57(3):195–204 2. Patnaik B, Mishra M, Bansal RC, Jena RK (2020) AC microgrid protection–a review: current and future prospective. Appl Energy 271:115210 3. Biswal PK, Lal DK, Rout B (2015) Distributed Generation: Benefits, Issues and Challenges. Grenze Int J Eng Technol 1(1):30–36 4. El-Khattam W, Salama MMA (2004) Distributed generation technologies, definitions and benefits. Electric Power Syst Res 71(2):119–128 5. Teng JH (2003) A direct approach for distribution system load flow solutions. IEEE Trans Power Delivery 18(3):882–887 6. Suresh M, Sirish TS, Subhashini TV, Daniel Prasanth T (2017) Load flow analysis of distribution system using artificial neural networks. In: Proceedings of the 5th international conference on frontiers in intelligent computing: theory and applications. Springer, Singapore, pp 515–524 7. Ghatak U, Mukherjee V (2017) A fast and efficient load flow technique for unbalanced distribution system. Int J Electr Power Energy Syst 84:99–110 8. Ghatak U, Mukherjee V (2017) An improved load flow technique based on load current injection for modern distribution system. Int J Electr Power Energy Syst 84:168–181 9. Murari K, Padhy NP (2018) A network-topology-based approach for the load-flow solution of AC–DC distribution system with distributed generations. IEEE Trans Industr Inf 15(3):1508– 1520 10. Wang C, Nehrir MH (2004) Analytical approaches for optimal placement of distributed generation sources in power systems. IEEE Trans Power Syst 19(4):2068–2076 11. Acharya N, Mahat P, Mithulananthan N (2006) An analytical approach for DG allocation in primary distribution network. Int J Electr Power Energy Syst 28(10):669–678 12. Lee SH, Park JW (2009) Selection of optimal location and size of multiple distributed generations by using kalman filter algorithm. IEEE Trans Power Syst 24(3):1393–1400 13. Gözel T, Hocaoglu MH (2009) An analytical method for the sizing and sitting of distributed generators in radial systems. Electr Power Syst Res 79(6):912–918 14. Hung DQ, Mithulananthan N (2013) Multiple distributed generators placement in primary distribution networks for loss reduction. IEEE Trans Industr Electron 60(4):1700–1708 15. Mithulananthan N, Than O, Phu LV (2004) Distributed generator placement in power distribution system using genetic algorithm to reduce losses. Thammasat Int J Sci Technol 9(3):55–62

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16. Singh D, Singh D, Verma KS (2007) GA based optimal sizing & placement of distributed generation for loss minimization. Int J Electric Comput Eng 2(8):556–562 17. Krueasuk W, Ongsakul W (2006) Optimal placement of distributed generation using particle swarm optimization. In: Proceedings of power engineering conference in australasian universities. Australia 18. Lalitha MP, Reddy NS, Reddy VV (2010) Optimal DG placement for maximum loss reduction in radial distribution system using ABC Algorithm. Int J Rev Comput 3:44–52 19. Moradi MH, Abedini M (2012) A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int J Electr Power Energy Syst 34(1):66–74 20. Arya LD, Koshti A, Choube SC (2012) Distributed generation planning using differential evolution accounting voltage stability consideration. Int J Electr Power Energy Syst 42(1):196– 207 21. Kumar S, Mandal KK, Chakraborty N (2019) Optimal DG placement by multi-objective opposition based chaotic differential evolution for techno-economic analysis. Appl Soft Comput 78:70–83 22. Das B, Mukherjee V, Das D (2016) DG placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization. Appl Soft Comput 49:920–936 23. Suresh MCV, Belwin EJ (2018) Optimal DG placement for benefit maximization in distribution networks by using Dragonfly algorithm. Renew Wind, Water Solar 5(1):1–8 24. Truong KH, Nallagownden P, Elamvazuthi I, Vo DN (2019) A quasi-oppositional-chaotic symbiotic organisms search algorithm for optimal allocation of DG in radial distribution networks. Appl Soft Comput 77:567–583 25. Hedayati H, Nabaviniaki SA, Akbarimajd A (2008) A method for placement of DG units in distribution networks. IEEE Trans Power Delivery 23(3):1620–1628 26. Rahman TKA, Jasmon GB (1995) A new technique for voltage stability analysis in a power system and improved load flow algorithm for distribution network. In: IEEE international conference on energy management and power delivery EMPD’95, vol. 2, pp 714–719 27. Musirin I, Rahman, TA (2002) Novel fast voltage stability index (FVSI) for voltage stability analysis in power transmission system. IEEE student conference on research and development, 2002. Proceedings of SCOReD, vol., no., pp 265–2168 28. Jamian JJ, Aman MM, Mustafa MW, Jasmon GB, Mokhlis H, Bakar AHA (2013) Comparative study on optimum DG placement for distribution network. University Teknologi Malaysia, pp 199–205 29. Aman MM, Mustafa MW, Jasmon GB, Mokhlis H, Bakar AHA (2012) Optimal placement and sizing of a DG based on a new power stability index and line losses. Int J Electr Power Energy Syst 43(1):1296–1304 30. Ettehadi M, Ghasemi H, Vaez-Zadeh S (2012) Voltage stability-based DG placement in distribution networks. IEEE Trans Power Delivery 28(1):171–178 31. Kashem MA, Ganapathy V, Jasmon GB, Buhari MI (2000) A novel method for loss minimization in distribution networks. In: IEEE international conference on electric utility deregulation and restructuring and power technologies proceedings DRPT, pp 251–256 32. Harrison GP, Piccolo A, Siano P, Wallace AR (2008) Hybrid GA and OPF evaluation of network capacity for distributed generation connections. Electr Power Syst Res 78(3):392–398 33. Baran ME, Wu FF (1989) Optimal capacitor placement on radial distribution systems. IEEE Trans Power Delivery 4(1):725–34 34. Eminoglu U, Hocaoglu MH (2007) A voltage stability index for radial distribution networks. In: IEEE 42nd international universities power engineering conference UPEC, pp. 408–413 35. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734

An Economic Evaluation of the Coordination Between Electric Vehicle Storage and Photovoltaic in Residential Home Under Real-Time Pricing Kumari Kasturi , Sushil Kumar Bhoi , and Manas Ranjan Nayak Abstract Presently, the development of grid-interactive homes (GIH) is becoming an additional wing for the extension of power system. The optimal coordination of residential grid-interactive solar photovoltaic (PV) and electric vehicle (EV) are presented in this paper. Optimal operation of local generation is framed as mixedinteger linear programming (MILP). Owners of GIH can utilize their local generation smartly, to get more benefits economically by reducing the operational cost of GIH in order to adequate the energy demand. A pioneer work of this paper is to utilize demand-side management (DSM) with real-time pricing (RTP) so that GIH has suitable decisions for energy planning and can find optimal capacity for PV with EV. The integrated PV output is formulated by considering different real parameters, so that accurate results can be achieved. Moreover, the optimal charge/discharge of EV can give additional economic support to owners of GIH. Reduction in the total cost of GIH can be noticed from the obtained results due to optimal operations of PV, EV and grid. Keywords Grid-interactive homes (GIH) · Energy consumption · Mixed-integer linear programming (MILP) · Photovoltaic (PV) · Electric vehicle (EV)

1 Introduction The scarcity of fossil fuel leads to the innovative use of different renewable sources like solar photovoltaic (PV), wind turbine, etc. [1, 2]. But the sporadic nature of these K. Kasturi (B) Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] S. K. Bhoi Department of Electrical Engineering, Government College of Engineering, Kalahandi, Bhawanipatna, Odisha, India M. R. Nayak Department of Electrical Engineering, CAPGS, Biju Patnaik University of Technology, Rourkela 769004, India © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_28

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sources poses different challenges to the users, which can be solved by integrating electric vehicles (EVs) with other local generations [3, 4]. Some studies related to the operation of residential microgrid power systems combined with distributed generation (DG) units are presented in this section. Authors analyzed the performance of a microgrid by solving unit commitment problems using a genetic algorithm in [5]. To get economical benefit, a solar-based residential microgrid operated with a feed-in tariff was considered in [6]. Day-ahead scheduling for renewable sources integrated with microgrid was presented in [7]. Two different demand response programs (DRPs) were considered to ensure more benefit to the system operators. EVs are incorporated into microgrid as a novel technology to enhance its performance. In studies, EVs are connected to microgrid as standalone or combined with other renewable sources. The operational cost of microgrid was minimized by a robust optimization technique in [8] to evaluate the optimal operation of the existing grid incorporated with plug-in hybrid electric vehicles (PHEVs). A non-linear optimization algorithm was used to get optimal scheduling for microgrid integrated with EVs operated as a vehicle to grid (V2G) mode [9]. An unbalanced microgrid was incorporated with V2G operation to get improves its performance in [10]. Authors considered different risk factors of microgrid when it was operated with EVs to get market opportunities [11]. The contributions of this proposed work are: (a) Considered real data of a city, to do optimal operation of grid-interactive homes (GIH). (b) Operate EVs of GIH to handle sporadic nature of PV and gain economic benefit. (c) Reduction in total operating cost of GIH by utilizing demand-side management (DSM) with real-time pricing (RTP) structure. (d) Proposed an inclusive optimization technique based on mixed-integer linear programming (MILP) to evaluate an optimal size of PV for GIH. Modelling of the system is elaborated in Sect. 2 and the problem is framed in Sect. 3. MILP is defined in Sect. 4, whereas Sect. 5 evaluates obtained results. Then Sect. 6 concludes the paper.

2 System Model Figure 1 shows GIH architecture with the incorporation of PV and EV. GIH is also connected to the utility grid to exchange powers based on their available energy. Several input data are required from EV owners such as initial and final state of charge (SOC) of EV, limits of charging/discharging power and parking time interval of EVs at GIH. Additionally, critical and non-critical load profile of GIH and global horizontal radiation of PV are also given as input parameters to the system. A central controller named as an energy center is connected as an interface between utility grid and different local generations to utilize demand-side management (DSM) so that

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329

Critical load

Grid

Energy center

PV system

Electric vehicle

Non-critical load

Fig. 1 Proposed PV/EV based GIH

Table 1 The scenario’s probability

Scenario

Probability

Scenario

Probability

1

0.04

11

0.04

2

0.06

12

0.05

3

0.02

13

0.07

4

0.09

14

0.03

5

0.05

15

0.04

6

0.06

16

0.05

7

0.07

17

0.06

8

0.09

18

0.01

9

0.08

19

0.03

10

0.05

20

0.01

GIH can achieve more benefit. In this proposed study different probability scenarios are considered as given in Table 1.

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2.1 Photovoltaic (PV) System Model The PV output can be formulated using Eq. (1) [12]. s rated PPV (t) = d × ηPV × ηinv × PPV ×

E ir (t) × [1 + β(T (t) − Tstd )] E irstd

(1)

Fig. 2 Global horizontal irradiance of PV

Global horizontal irradiance (kW/m2)

where d(= 89%) is the power de-rating coefficient that summarizes the effects of shadowing, cable and switching losses and dust accumulation on the array, ηPV (= 15%) is the output to input power ratio of a PV cell, ηinv (= 97%) is the inverter rated (= 5 kW) is the power rating of the PV array installed in the system, efficiency, PPV std E ir and Tstd are the GHI and the temperature at standard test conditions, respectively, and β is the temperature coefficient of the PV cell. The average daily GHI (E ir (t)) in kW/m2 and the ambient temperature (T (t)) in °C for different scenarios are given in [13] and shown in Figs. 2 and 3, respectively.

Ambient temperature (0C)

Fig. 3 Ambient temperature

Hours

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Table 2 RTP structure

Electricity tariff (INR/kWh)

Peak periods (7:00 AM-1:00 PM and 4:00 PM-10:00 PM)

Off-peak periods (1:00 PM-4:00 PM and 10:00 PM-7 AM)

s Export (αEXP )

s ) Import (αIMP

s Export (αEXP )

s ) Import (αIMP

6.80

3.80

4.20

2.20

2.2 Electric Vehicle User Behavior In this study, owners of EVs usually go to work in the beginning hours of the day and return home at evening. The charging/discharging of EV is done, when the electric cars are parked at GIH, i.e. from 1.00 a.m. to 10:00 a.m. and 5:00 p.m. to 12:00 a.m. When they are parked in the workplace, i.e. from 11:00 a.m. to 4:00 p.m., there is no exchange of power during that period. They have same charging/discharging rate, when parked at home. During travelling, the average energy consumed by EV is 3 kWh. The charging/discharging EVs depends upon RTP. As per RTP a specified tariff is imposed as per energy consumption. According to RTP, during peak power consumption the electricity tariff more so the cost of energy consumption increases which is considered as peak hours and rest hours of the day are off-peak hours. So charging of EVs can handle by DRPs to avoid additional load in such periods. The users of GIH can reduce their payments by managing their energy consumption efficiently with the help of DRPs. The considered RTP is shown in Table 2. If at i th parking slot, k th EV is connected, the charging/discharging rate of EVs can be derived as [14]:  E V,s (t) = Pi,k

s e ≤ t ≤ ti,k PPs L ,i (t) ti,k 0 otherwise

(2)

s e where ti,k and ti,k are the initial and finale instant of charging/discharging. The state of charge (SOC) of EV battery can be calculated as:

SOC (t + 1) = SOCinitial +

T 

SOC (t)

(3)

t=1

where SOCinitial (= 50%) is the initial SOC of the EV battery. SOCmin and SOCmax of EV battery are considered as 10% and 90%, respectively.

2.3 Load Model The studied GIH is having two types of load such as critical loads (CL) and noncritical loads (NCL). The critical loads are television, refrigerator, computers and

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Critical load (kW)

Fig. 4 Critical load profile of GIH

Fig. 5 Non-critical load profile of GIH

Non-critical load (kW)

Hours

Hours

lightings whereas geyser, stove and washing machine are considered as non-critical load. All load profiles for all scenarios are given in [13] and shown in Figs. 4 and 5, respectively.

3 Problem Formulation The main aim is to find an optimal capacity for local generations of GIH by satisfying all system constraints.

3.1 Objective Function 3.1.1

Minimization of Total Cost of GIH

The objective function is framed as:

An Economic Evaluation of the Coordination Between Electric …

⎛ f obj = min⎝

sc  s=1

Ψs ×

TP  

333

⎞

 s (t) × α s (t) − P s (t) × α s ⎠ PIMP IMP EXP EXP (t) + C PV + BDC

t=1

(4) where s is the scenario index, sc is the number of scenarios, TP is the time period s s (t) and PEXP (t) are the imported and exported power from utility grid (= 24 h), PIMP s s (t) and αEXP (t) are the price of power at time t for each scenario, respectively, αIMP imported and exported from utility grid time t for each scenario, respectively,Ψs is the probability of different scenario, CPV is the capital cost of PV array for one day. The details of the calculation of C PV are described in the reference paper [12]. Here the capital cost of PV array is taken as 150 INR/Wp. The cost associated with the degradation of the battery caused by continuous charging and discharging of the batteries is known as battery degradation cost (BDC), the details of calculation are given in [14].

3.2 System Operational Constraints E V,s s s s s PIMP (t)/PEXP (t) = PCL+NCL (t) − PPV (t) − Pi,k (t)

(5)

EV EV E ch/dch ≤ E Parking

(6)

EV E demand =

NE V 

[(S OCmax − SOCinitial ) × E bat ]

(7)

n=1

SOCmin ≤ SOC(t) ≤ SOCmax

(8)

min ≤ P (t) ≤ P max PPV PV PV

(9)

s s (t) is the total load of the GIH for each scenario at time t, PPV (t) is where PCL+NCL E V,s the PV output power for each scenario time t, Pi,k (t) is the charging/discharging power of EVs for each scenario time t, (−ve & +ve signs are taken for charging & EV is the power exchange by the EV discharging power of EVs, respectively), E ch/dch EV batteries, E Parking is the maximum energy that can be exchanged by the EVs during the accessible parking hours, N E V is the total no. of EVs of GIH, E bat is the energy max are the minimum and storage capacity of the individual EV battery, PPmin V and PP V maximum PV output power.

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4 Mixed-Integer Linear Programming (MILP) The objective of the proposed method is to minimize total cost of GIH to get optimal capacities for local generation within the specified system constraints using MILP.

4.1 MILP Algorithm The detailed algorithm is given below: Step-1: Initialization of variables, i.e. possible size of PV. Step-2: Specify upper and lower limits of variable as per rating of the system components, initial status and system constraints. Step-3: Evaluate the fitness value for each feasible solution. Step-4: Display the optimal value.

5 Result and Analysis The proposed analysis is formulated to enhance the performance of GIH. The owner of GIH is using two electric vehicles, and they can exchange available power to utility grid according to their mode of operation. The battery capacity of EV is 40 kW. The numerical optimization results with and without RTP are shown in Table 3. Obtained result shows that the owner of GIH can have more profit with RTP as compared to without RTP. By applying RTP of DSM, the total cost of GIH is reduced and getting 9.56% profit. The imported cost is 4321.85 INR without RTP, whereas it is reduced to 3264.87 INR with RTP. On the other hand, GIH are exporting more power to utility grid with RTP, and gaining more profit by utilizing its existing local generations optimally. Import and export cost of GIH for a day is shown in Figs. 6 and 7 respectively. Import cost reduces with RTP as shown in Fig. 6, so the total cost of GIH reduces. During early morning and after evening hours when PV is not available, EVs are Table 3 Numerical optimization results Parameters

Value Without RTP

Number of PV array

5

Total cost of GIH (INR) ( f obj ) 309.28 (+ve means owner of GIH has to pay to the utility grid) Import cost (INR) Export revenue (INR)

With RTP 5 −338.85 (-ve means utility grid has to pay to the owner of GIH)

4321.85

3264.87

14944.43

27072.02

335

Import cost (INR)

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Hours

Export cost (INR)

Fig. 6 Import cost in day

Hours

Fig. 7 Export cost in day

allowed to charge their batteries from utility grid so that they can be used during peak hours when the load is more. Whereas export cost increases with RTP and owner of GIH is more economically benefited. Figure 8 shows the variation in SOC of EV battery in a day. During the initial hours of the day till 7.00 a.m., EV batteries start charging until reached their SOCmax , i.e. 90% as they are off-peak hours of the day. As peak hours start at 7.00 a.m., they are allowed to discharge their power but reserve required SOC for their rest travelling hours of the day. At 5.00 p.m. as they are reached at homes, they start discharging up to minimum SOC, i.e. 10%. Rest of the hours they are ideal till the next off-peak hour, i.e. 11.00 p.m. and after that they again start charging.

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Fig. 8 Variation of SOC in day

6 Conclusion In this paper, MILP is employed for optimal operation of GIH which is integrated with PV and EV. RTP mechanism of DSM is implemented to enhance the system’s economic performance so that owners of GIH can be benefited. As per the simulation results, optimal economic operation of GIH is obtained by optimal sizing of PV, which is proficiently used to supply the required energy demand and also charge EV batteries. From this work, it is concluded that the owner of GIH can get more benefit using RTP.

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7. Nikmehr N, Najafi-Ravadanegh S, Khodaei A (2017) Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty. Appl Energy 198:267–279 8. Bahramara S, Golpîra H (2018) Robust optimization of micro-grids operation problem in the presence of electric vehicles. Sustain Cities Soc 37:388–395 9. Aluisio B, Conserva A, Dicorato M, Forte G, Trovato M (2017) Optimal operation planning of V2G-equipped Microgrid in the presence of EV aggregator. Electr Power Syst Res 152:295–305 10. Rodrigues YR, de Souza AZ, Ribeiro PF (2018) An inclusive methodology for Plug-in electrical vehicle operation with G2V and V2G in smart microgrid environments. Int J Electr Power Energy Syst 102:312–323 11. Shamshirband M, Salehi J, Gazijahani FS (2018) Decentralized trading of plug-in electric vehicle aggregation agents for optimal energy management of smart renewable penetrated microgrids with the aim of CO2 emission reduction. J Clean Prod 200:622–640 12. Nayak CK, Nayak MR (2018) Technoeconomic analysis of a grid-connected PV and battery energy storage system considering time of use pricing. Turkish J Electr Eng Comput Sci 26(1):318–329 13. Barhagh SS, Abapour M, Mohammadi-Ivatloo B (2020) Optimal scheduling of electric vehicles and photovoltaic systems in residential complexes under real-time pricing mechanism. J Clean Prod 246:119041 14. Kasturi K, Nayak CK, Nayak MR (2019) Electric vehicles management enabling G2V and V2G in smart distribution system for maximizing profits using MOMVO. Int Trans Electr Energy Syst 29(6):e12013

Particle Filter and Entropy-Based Measure for Tracking of Video Objects Jyotiranjan Panda and Pradipta Kumar Nanda

Abstract In this paper, a particle filter-based novel approach is proposed for tracking the video objects in both outdoor and indoor scenes. This scheme uses the time motion history of the scene for improved tracking. Initially, the target is modeled by the particle, and the entropy of the target frame is considered. Object detection in the time motion frame is achieved by searching for the particle having the entropy which incurs minimum error with that of the target model. Object detection is followed by object tracking which is achieved by the motion of the particles in the particle filter algorithm. The proposed algorithm is tested with three data sets, namely I_MC_02 (LASIESTA), kite-surf (DAVID-2016), and O_SU_01 (LASIESTA). The performance of the algorithm is compared with those of other two existing algorithms, and the proposed algorithm is found to exhibit improved performance. Keywords Particle filtering · Color entropy · Timed motion history image · Tracking

1 Introduction Object tracking is one of the important areas of research in computer vision. The presence of camouflage, occlusion, dynamic background, clutter, camera motion, intensity variation, deformation of the target in the scene makes the problem challenging. As object state distribution is non-Gaussian and the object itself is moving of its own interest, the problem becomes non-Gaussian and nonlinear type which makes it more challenging. In such uncertainty and complexity, particle filter (PF) [1] is found to be more effective than other existing methods. PF-based algorithm is

J. Panda (B) · P. K. Nanda Image and Video Analysis Lab, Department of Electronics and Communication Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected]; [email protected] P. K. Nanda e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_29

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based on the Monte Carlo model [2] and optimal Bayesian estimation. For the statespace modeling of state variables distribution, PF utilizes a set of weighted random samples. These samples are known as particles [3]. PF iteratively uses the Bayesian estimation to obtain newly updated samples, which can posteriorly reconstruct the state variables distribution that represents the system in the next time step. Based on the feature similarity between the observed image and the target object model [4], the weights of the particles are determined. Therefore, it is crucial to determine a robust and reliable feature for developing an effective tracking algorithm using a particle filter. When the object appearance and it’s surrounding background change during tracking, the use of fixed features tends to cause tracking failure. Hence, judicious selection of the potential discriminative feature to improve the robustness and tracking accuracy of the tracker is another important issue to develop a high-performance tracking system. In this regard, Collins et al. [5] have proposed an online feature selection mechanism that can adaptively select the top-ranked discriminative features by computing two-class variance ratios. Further, Liang et al. [6] have proposed a method by embedding the adaptive selection into a mean-shift tracking framework in which ranking of the features is achieved based on Bayes error rate. A color histogram is used for the color feature-based object tracking method [7, 8] in particle filtering framework. Even if the color distribution of the target is mostly stable, this feature fails to accurately describe the object in case of intensity variation, occlusion, and camouflage in the scene. Like color, edge, and motion features are also preferred to handle such a problem. Due to the presence of many textural attributes in the outdoor scene, texture [9, 10] of the object can be an appealing feature for object tracking in an outdoor scene. Ding et al. [11] have proposed a color and texture feature fusion method in the particle filtering framework and established a reference target model for it. Their proposed method performs well in case of illumination change. Panda et al. [12] have proposed a block-based fusion method with texture and color features to enhance the tracking accuracy in an outdoor scene. The proposed method more effectively overcomes the effect of camouflage and illumination change. In this paper, a particle filter-based new object tracker is proposed, and the same is validated in both indoor and outdoor scenes. The proposed tracker uses the entropy of the weighted mean color feature in the RGB framework. The detection of the object is carried out in the time motion history frame [14, 15] to enhance the detection accuracy and in turn the tracking ability. Initially, the target is modeled by the particle, and the weighted mean entropy of the target frame in RGB color space is considered. Object detection in the time motion frame is achieved by searching for the particle which incurs minimum error with that of the target model. Object detection is followed by object tracking which is achieved by the motion of the particles in the particle filter algorithm. This paper is organized as follows. The proposed scheme is presented in Sect. 2, and time motion history of the object is presented in Sect. 3. The object detection principle is presented in Sect. 4 while estimation and tracking by PF based modeling are explained in Sect. 5. Analysis of the results is described in Sect. 6 whereas conclusions and future research directions are presented in Sect. 7.

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Initialization of tMHI frame and target model entropy of the target.

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Observed frames

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Determination of similarity index of the particles compared to the target model and assignment of weights to the particle.

Resampling

Determination of mean state or tracking

Fig. 1 Block diagrammatic representation of the proposed approach

2 Proposed Scheme It is known that the image entropy does not directly take into account texture information and frequency information of an image but it has a statistical significance. To boost the object detection attribute with the help of entropy, instead of taking entropy of gray image, we have taken weighted mean entropy of RGB planes as our feature for object detection and tracking. Instead of taking the normally observed frame for our search space, we have taken the time motion history image (tMHI) frame as our search space to have confidence in detection and tracking accuracy. The process details are shown in Fig. 1. A rectangular template is chosen to represent the target object, which is characterized by weighted mean entropy. Two other reference frames are used to have the tMHI image frame. By searching for the particle with the entropy which incurs minimum error with that of the target model on the tMHI image frame, we have achieved object detection with high accuracy. After two such observations, the velocity vectors in x and y directions are computed to complete the state vector that represents the object state. The weight of each particle  w  in the particle filter tracking algorithm is calculated by using the similarity measure of targets and tracking rectangular regions. The resampling procedure is applied to have the best possible weights and corresponding particles to estimate the mean state of the object of interest more accurately. Followed by estimation, tracking of the object is accomplished.

3 Time Motion History of the Object The motion history image (MHI) approach [14] is a view-based temporal template method that is simple but robust in representing movements and is widely employed

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for action recognition, motion analysis, and other related applications. In the MHI, the silhouette sequence is condensed into grayscale images, while dominant motion information is preserved. Therefore, it can compactly represent a motion sequence. This MHI template is also not so sensitive to silhouette noises, like holes, shadows, and missing parts. These advantages make these templates suitable candidates for motion and gait analysis. Primarily, it keeps a history of temporal changes at each pixel location, which decays over time. The MHI expresses the motion flow or sequence by using the intensity of every pixel in a temporal manner. An MHI image is computed as follows.  M H It (x, y) =

t if|Dt (x, y)| >σ M H It−1 (x, y) otherwise.

(1)

where Dt (x, y) contains the difference images and σ is the difference threshold. Timed motion history image (tMHI) is a novel method of motion segmentation [14, 15] that is an extension of MHI. A history of temporal changes is stored at each pixel location which decays over time. The tMHI is updated together with the timestamp of the current system. The tMHI image can be computed as,  t M H Iσ (x, y) =

τ if current silhouette at(x, y) 0 else if t M H Iσ (x, y) 0) signifies the kernel parameter denoting the width of the Gaussian kernel. The output of the KELM model can be represented as:

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⎤T K (x p , x1 ) ⎥ ⎢ . ⎥ αN f (x p ) = ⎢ ⎦ ⎣ . K (x p , x N )

−1 IN αN = TN + ΩN C

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⎡

(17)

(18)

where, α N , c, and I N represents the output weight of the KELM network, penalty parameter, and identity matrix, respectively. A multi-scale Gaussian kernel at different scales is used to avoid constantly valued kernel as an improvement to the conventional kernel-based ELM. For better performance, it needs to found out the optimal values of the regularization parameter C, weights, and the kernel function parameter γ , due to the fact the KELM performance as a classifier generally affected by these factors.

5.2 Optimal KELM Parameter A set of NP vectors denoting the initial population vectors are assumed to represent the initial solution points for optimal searching [22]. Each vector comprises the penalty parameter C, the weights, and the width of each kernel function as indicated in Eq. (19). θ = {C, w1 , w2 , . . . , wn , γ1 , γ2 , . . . , γn }

(19)

where, the weights are initialized within the limit (0, 1). The n symbolizes the number of Gaussian kernels. The major aim is to diminish the number of misclassified patterns. The fitness function helps to find out the best solution among possible solutions in solution space as expressed in Eq. (20). fitnessi =

1 1 + MPi

(20)

where MPi set for the total number of misclassified patterns found for an ith solution. The point to note is PSO optimize the kernel function parameter only in the training process. After finding the optimal parameter at the end of the training, no iteration will be carried out in the application.

6 Result Analysis The performance of the proposed approach is analyzed as follows.

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6.1 Case 1: Grid-Connected and Looped Configuration In this case, the IEC test system is operated in grid-connected mode with looped configuration. For an extensive study, the system is subjected to symmetrical as well as asymmetrical faults where the distance of inception varies in the range of 5–25 km between Bus_1 and Bus_2. Here, the current signals are processed through HHT to obtain the IMFs, and the most significant are considered to calculate the spectral energy and differential energy from both buses. Then, the features associated with the differential energy are given below in Table 1. As shown in Table 1, LL fault (Phase A–C) and LLG fault (Phase B–C) have the highest and lowest mean value of differential energy, respectively, that describe the severity of fault in the system. However, the median also follows the same trend as the mean in case of maximum and minimum value. From Table 1, it is clear that the maximum value lies with LL fault (Phase A–C) for standard deviation also whereas the minimum value lies with LG fault (Phase B). To utilize the data further for advanced algorithms and probability distribution functions, it should be verified. The randomness quotient is decided from entropy that is maximum for LL fault (Phase A–B) and the minimum is with LLG fault (Phase B–C) with a value of around 22%. From this tabular data and comparative analysis, it is evident that in looped configuration LL fault is more dangerous as compared to other faults.

6.2 Case 2: Grid-Connected and Radial Configuration In this case, the system is analyzed with grid-connected with a radial configuration. To maintain the homogeneity in research the same system with all common fault types and parameters like earlier are considered are shown in Table 2. Here, the highest value is associated with LLG fault (Phase A–B), whereas in looped configuration LLG fault (Phase A–B) has the lowest values. The average value determined by the mean is maximum for LL fault (Phase A–B) and the minimum value lies for LG fault (Phase C), which is also the same for the median. The comparative study from Tables 1 and 2 shows that for standard deviation, the LL fault (Phase C–A) has the highest value in looped configuration and lowest in case of radial configuration. However, the degree of randomness, i.e. entropy is maximum for LLG fault (Phase A-B) and the magnitude is almost the same with the looped one whereas minimum for LG fault (Phase C) with around 30%. Unlike looped one radial configuration shows a heterogeneous pattern. Irrespective of fault types Phase A–B and LG fault (Phase C) are severely affecting the system.

0.1436

0.0002

46.304

0.0409

0.0557

0.0540

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Median

0.0097

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35.897

3.47e−07

0.1021

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66.107

0.1533

0.3546

0.3892

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37.758

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Ibg

LG

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Max

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0.0001

0.0452

0.0957

35.621

5.11e−08

0.5349

Ibcg

Table 2 Differential energy features for grid-connected and radial configuration

0.0312

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0.8083

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71.381

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LLL

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0.0627

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0.2403

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74.047

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PERFORMANCE (%)

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100

99.0264

100

97.0588

98 96

397

100 97.0588

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94 92 90 Accuracy (%) Radial topology

Security (%)

Dependability (%)

Looped topology

Fig. 3 Performance analysis of grid-connected mode of operation in both topology

7 Discussion In this work, a tri-level architecture is used for fault detection of a microgrid by integrating a time-frequency transform and data-based algorithms. The DG integrated test system is simulated by considering the grid-connected mode with the looped and radial configuration for both symmetrical and asymmetrical faults. Each configuration consists of 1100 data points that are processed in KELM with a ratio of 70:30 for training and validation. The comparative features are discussed in the aforementioned cases and reliability factors are illustrated in Fig. 3. For radial configuration, accuracy is 100% whereas 95% in the case of looped configuration. However, dependability is also the same for radial topology whereas 97% in the case of looped structure. Moreover, security represents the “false alarm,” which indicates the number of cases predicted as faults but originally they are not fault events. In terms of security looped configuration has a slight advantage with 99% whereas radial has 97%. From Fig. 3, it is evident that KELM based methods are more accurate for radial topology although looped configuration also shows significant results. All parameters in gridconnected mode are ranging from 95 to 100% which determines the robustness of KELM based method on DG integrated microgrid protection. The dependability that is vital for the design of concrete protection algorithms shows accurate results of 97–100% in both topologies for grid-connected mode using the KELM algorithm. This shows that the KELM-HHT based algorithm is robust, reliable, and secure in case of fault detection.

8 Conclusion The work represents a novel KELM-HHT integrated fault classification algorithm for the IEC microgrid system integrated with different DGs. For detailed analysis, the

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system is operated in grid-connected mode subjected to all symmetrical and asymmetrical faults by varying the distance, fault inception angle, and fault resistance. Then, the statistical features are analyzed for both radial and looped topologies. It is observed that for both topologies LL fault is more critical than other kinds of faults. Then the differential energy features are simulated through an optimized KELM algorithm for a more accurate result. It depicts that optimized KELM based detection techniques are fast, robust, and reliable. The quotients like security and dependability show excellent results that further prove the efficacy of this novel technique architecture for fault detection in a DG integrated microgrid system.

Appendix 1. Utility: rated voltage = 120 kV, f = 60 Hz, three-phase rated short-circuit = 1000 MVA, base voltage = 120 kV, X/R ratio = 10. 2. Transformers: TR_1: rated MVA = 15 MVA, f = 60 Hz, rated kV = 120 kV/25 kV, R1 = R2 = 0.00375 pu, L1 = L2 = 0.1 pu, Rm = 500 pu, X m = 500 pu. TR_2 and TR_5: rated MVA = 12 MVA, f = 60 Hz, rated kV = 2.4 kV/25 kV, R1 = R2 = 0.00375 pu, L1 = L2 = 0.1 pu, Rm = 500 pu, X m = 500 pu. TR_3: rated MVA = 12MVA, f = 60 Hz, rated kV = 575 V/25 kV, R1 = R2 = 0.00375 pu, L1 = L2 = 0.00375 pu, Rm = 500 pu, X m = 500 pu. TR_4: rated MVA = 10MVA, f = 60 Hz, rated kV = 575 V/25kV, R1 = R2 = 0.00375 pu, L1 = L2 = 0.00375 pu, Rm = 500 pu, X m = 500 pu. 3. Distribution Lines: DL_1, DL_2, DL_3, DL_4, DL_5: f = 60 Hz, r 1 = 0.125 Ω/km, r 0 = 0.447 Ω/km, l1 = 1.1e − 3H/km, l0 = 3.47e − 3H/km, c1 = 10.1766e − 9 F/km, c0 = 4.5e − 9F/km, Line length = 30 km each. 4. Loads: L_1, L_2, L_3, L_4, L_5, L_6: rated voltage = 25 kV, f = 60 Hz, Total active power = 24 MW, Total inductive reactive power = 12 MVAR. 5. Distributed Generation: • DG_1, DG_4: (Synchronous generator) rated MVA = 9 MVA, rated voltage = 2.4 kV, f = 60 Hz, X d = 1.56 pu, X d = 0.296 pu, X d = 0.177 pu, X q =  = 0.05 s, 1.06 pu, X q = 0.177 pu, X l = 0.052 pu, Td = 3.7 s, Td = 0.05 s, Tqo Rs = 0.0036 pu, H = 1.07 s, F = 0.1 pu, p = 2. • DG_2: (Synchronous generator and full-scale converter (Type 4) detailed model wind farm) rated MVA = 6 MVA, rated voltage = 575 kV, f = 60 Hz, X d = 1.305 pu, X d = 0.296 pu, X d = 0.252 pu, X q = 0.474 pu, X q = 0.243 pu,   = 4.49 s, Tdo = 0.0681 s, Tq = 0.0513 s, Rs = 0.006 pu, H X l = 0.18 pu, Tdo = 0.62 s, F = 0.1, p = 1 • DG_3: (DFIG based wind farm) rated MVA = 9 MVA, rated voltage = 575 kV, f = 60 Hz, Rs = 0.023 pu, Lls = 0.18 pu, Rr  = 0.0016 pu, Llr  = 0.16 pu, L m = 2.9 pu, H = 0.685 s, F = 0.01, p = 3

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References 1. Sarangi S, Sahu BK, Rout PK (2020) Distributed generation hybrid AC/DC microgrid protection: a critical review on issues, strategies, and future directions. Int J Energy Res 44(5):3347–3364 2. Tan C, Ramachandaramurthy VK (2010) Novel wide area fault type classification technique 3. Karimi H, Nikkhajoei H, Iravani R (2007) A linear quadratic Gaussian controller for a standalone distributed resource unit-simulation case studies. In: 2007 IEEE power engineering society general meeting, pp 1–6 4. Patnaik B, Mishra M, Bansal RC, Jena RK (2020) AC microgrid protection–a review: Current and future prospective. Appl Energy 271:115210 5. Dehghani M, Khooban MH, Niknam T (2016) Fast fault detection and classification based on a combination of wavelet singular entropy theory and fuzzy logic in distribution lines in the presence of distributed generations. Int J Electr Power Energy Syst 78:455–462 6. Kanakasabapathy P, Mohan M (2015) Digital protection scheme for microgrids using wavelet transform. In: 2015 IEEE international conference on electron devices and solid-state circuits (EDSSC), pp 664–667 7. Parikh UB, Bhalja BR, Maheshwari RP, Das B (2007) Decision tree based fault classification scheme for protection of series compensated transmission lines. Int J Emerg Electr Power Syst 8(6) 8. Samantaray SR, GezaJoos, Kamwa I (2012) Differential energy based microgrid protection against fault conditions. In: 2012 IEEE PES innovative smart grid technologies (ISGT), pp 1–7. IEEE 9. Kar S, Samantaray SR (2014) Time-frequency transform-based differential scheme for microgrid protection. IET Gener Transm Distrib 8(2):310–320 10. Gururani A, Mohanty SR, Mohanta JC (2016) Microgrid protection using Hilbert–Huang transform based-differential scheme. IET Gener Transm Distrib 10(15):3707–3716 11. Mishra M, Rout PK (2017) Detection and classification of micro-grid faults based on HHT and machine learning techniques. IET Gener Transm Distrib 12(2):388–397 12. Huang NE (2014) Hilbert-Huang transform and its applications, vol 16. World Scientific 13. Huang NE, Wu Z (2008) A review on Hilbert-Huang transform: method and its applications to geophysical studies. Rev Geophys 46(2) 14. Yi-bing L, Wu Q, Ma Zy, Yan K-g (2006) An improved Hilbert-Huang transform and its application in faults signal analysis. In: 2006 international conference on mechatronics and automation, pp 2426–2431. IEEE 15. Osman S, Wang W (2019) A new Hilbert-Huang transform technique for fault detection in rolling element bearings. In: Predictive maintenance in dynamic systems. Springer, Cham, pp 207–230 16. Navot A, Shpigelman L, Tishby N, Vaadia E (2006) Nearest neighbor based feature selection for regression and its application to neural activity. In: Advances in neural information processing systems, pp 996–1002 17. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501 18. Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122 19. Li G, Niu P (2013) An enhanced extreme learning machine based on ridge regression for regression. Neural Comput Appl 22(3–4): 803–810 20. Mishra M, Panigrahi RR, Rout PK (2019) A combined mathematical morphology and extreme learning machine techniques based approach to micro-grid protection. Ain Shams Eng J 10(2):307–318 21. Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybern) 42(2):513–529 22. Wong CM, Vong CM, Wong PK, Cao J (2016) Kernel-based multilayer extreme learning machines for representation learning. IEEE Trans Neural Netw Learn Syst 29(3):757–762

Improving the Performance of AVR System Using Grasshopper Evolutionary Technique Sunita S. Biswal, D. R. Swain, and Pravat Kumar Rout

Abstract The design of the optimal controller with an appropriate setting of its parameters is very much essential for an automatic voltage regulator (AVR) system. Even though a lot of research has been undertaken in the past few decades, as no unanimously accepted methodology does not result yet, it is an open and very important field of research for designing an optimal controller for AVR. In this paper, an advanced version of the classical proportional-integral–differential (PID) control technique based on fractional calculus is designed titled as fractional order-PID control (FO-PID) controller for the AVR operation. The new strategy has been employed for computing the gain parameters of the controller unlike the conventional controller used generally in the real-time applications of an AVR. To enhance further the performance of FO-PID, a grasshopper evolutionary technique (GET) has been adapted for optimally setting the parameters for the enhanced performance of the controller. A comparative analysis of the proposed GET-FO-PID controller to justify its performance with conventional tuned PID controller using Ziegler– Nichols, Nelder Mead, and GET is presented and analyzed with. It has been demonstrated that the proposed approach produces a substantial improvement in the AVR system response with better controllability and stability. To justify the performance of the proposed GET-FO-PID controller including the AVR, time-domain analysis is also presented through MATLAB simulation. Keywords Automatic voltage regulator (AVR) · PID controller · Fractional order-PID controller (FO-PID) · Grasshopper evolutionary technique (GET) S. S. Biswal (B) · P. K. Rout Department of Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] P. K. Rout e-mail: [email protected] D. R. Swain Department of Electrical Engineering, College of Engineering and Technology, Bhubaneswar, Bhubaneswar 751003, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_34

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1 Introduction In an automatic control technique point of view, an AVR system is characterized by its dynamic behavior which evaluates the quality required to conclude a control task/problem [1]. Although lots of robust and adaptive controllers are researched for various control design applications, PID controllers are still used extensively in the industrial automation process and various power control applications due to its simplicity in design and cost-effectiveness [2]. Recently, the advanced digital controllers with different control strategies are gaining interest in these applications [3–5]. It is since the conventional constant gain parameter in the PID controller needs to be tuned frequently with the change in the scenario of the systems. Apart from that, the performance is restricted within a narrow range of operation. In order to improve the performance and stability of AVR operation, it is necessary to develop an adequate method to tune the gain parameters of the PID controller. With this issue, this study motivates to develop an efficient method to compute the gain parameters of the PID controller. The conventional PID controller could not manage real-time errors and need frequent adjustments for better performance. Therefore, to eliminate the effect of these difficulties and to ensure an optimum output, tuning of parameters for the conventional controllers becomes an outstanding issue in the present fast-changing power system scenario. Many systematic techniques have been implemented to solve this problem. One of those techniques is Ziegler–Nichols (ZN), a classical and first PID tuning rule [6] proposed in 1942 to fine-tune the PID controller parameters. The ZN is a very influential rule for tuning the PID controller. At present, this rule is acknowledged as a standard in control systems practice. Even though the ZN-PID controllers are successfully applied in many cases, till like conventional PID, it is not independent completely to its variation of parameters and difficult to compute the gain parameters in complex system design. Very often it results in approximate calculation along with large computational time that makes it difficult to achieve better and robust performance. Later, to circumvent these issues and challenges related to ZN techniques, Nelder Mead (NM) optimization method has been developed with the tuning rule employed for tuning the PID controller gain parameters [7]. The NM optimization algorithm will search for the best possible values K p , K i , and K d from a specified step response requirement. Despite a better model in comparison to ZN, it is not easy for the ZN method to find out near-optimal parameters for PID controllers for larger and complex systems and conditions. Many times, all the design needs are not satisfied by the optimization techniques. For this purpose, this has been advisable to enhance the search area or to enhance the potentialities of conventional controllers by the addition of novel characteristics. As discussed above, it has been found that the PID controllers failed substantially to perform for higher-order, nonlinear, and complex systems. Based on these issues, to improve the controller performance, an innovative control approach based on the fractional integral and fractional derivative is suggested in this study. This new type of PID controller based on fractional calculus was developed by I. Podlubny [8] and

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called as fractional order-PID (FO-PID) controller. Based on past research, it has been shown that the FO-PID controllers have the potential to increase the closedloop performance of the AVR system over the conventional PID controller [9]. All the discussed conventional PID controllers are the specific cases of the fractional orderPID controller. FO-PID controller uses the general concept of the conventional types of PID controller and develops it from point to plane [10–12]. This development sums up additional flexibility to design the controller and so that real-world processes can be controlled more accurately. Recently, evolutionary optimization techniques are used in many engineering problems to find optimally many system variables. Looking at the simplicity in methodology, faster convergence, reliability, and robustness in optimum result output, a novel GET has been utilized to adjust the parameters for PID and FOPID controller [13–15]. This work is based on the design of the FO-PID controller applying the GET tuning process to the AVR system [16]. Also, GET is considered as an optimizer to find out the optimal parameter values of proposed FO-PID titled combined as GET-FO-PID controller. Comparative result analysis is provided with the GET-FOPID controller and GET-PID, ZN-FO-PID [17], and NM-FO-PID [18] controllers. It has been observed from the results that GET-FO-PID provides better time-domain and frequency-domain performance over other PID controllers in the AVR system models [19, 20]. The major contributions to this work done are as follows: 1. To justify the stability study of this proposed work along with the time-domain analysis, an extensive frequency-domain analysis has been presented ranging from bode, root locus, Nyquist, and pole-zero plots. 2. FO-PID application to AVR operation has been applied and investigated against the conventional controller to result in an improved controller in terms of better performance. 3. An evolutionary and stochastic-based GET is suggested and applied for the optimal computation of the gain parameters of the controller. 4. Different cases according to the events occurred in real-time conditions are undertaken during testing to justify its practical application. This article is organized in the following sections. Section 2 explains the basic structure of the PID and FO-PID controller. The transfer function of the AVR model with descriptions is discussed in Sect. 3. Section 4 introduces the grasshopper evolutionary technique (GET). In Sect. 5, the application of the GET-FO-PID controller in the AVR model has been explained and the result of mathematical simulations through comparisons has been discussed [21, 22]. In conclusion, Sect. 6, the final considerations of the paper are presented.

2 Structure of Controllers In this section, the control design of both PID and FOPID is described in detail.

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Fig. 1 Conventional PID controller in a closed-loop system block diagram demonstration

2.1 Conventional PID Controller Structure The title “PID” is a composition of “proportional, integral, and derivative” [2]. This controller is popular because of its robust performance and its functional simplicity. The functionality of this versatile controller can be thought of as a great form of phase lead-lag compensation with one pole at origin and the other at infinity. A PID controller has also acknowledged as the “three-term” controller for the reason that its output summation is of three conditions, proportional, integral, and derivative and each of these conditions is dependent on the error rate among the input and the output. Its transfer function is generally represented as in the form of parallel structure as follows: C(s) = K p + K i ×

1 + Kd × s s

(1)

where K p denotes proportional gain, K i denotes integral gain and, K d denotes derivative gain. In Fig. 1, R(s) is the input, E(s) is an error, V (s) denotes the controller output, C(s) denotes the controller transfer function, and Y (s) is the total system output. PID controller robustness, sensitive to error variations, and performance, particularly for complex and nonlinear systems, are needed to be further extended as it fails to arrive at satisfactory results due to its linear characteristics. Furthermore, the constant gain parameters use of the PID controller particularly in a changing scenario needs to compute to find the gain parameters optimally. This motivates us to go further for a different approach to dynamically vary or to compute optimally the PID controller.

2.2 FO-PID Structure It is necessary to the extend the performance of PID by introducing modern structure and implementing it with different types of tuning methods. These lead to an innovative creation of PID and this extension of PID is known as the fractional order-PID (FO-PID) controller. Professor Podlubny proved that the superiority of this controller over the conventional PID controller when it is used for the fractional-order control systems. Due to recent advancements in technologies, it is very difficult to design a conventional PID controller for highly complex higher-order control systems. With

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fractional PID controller, this problem not occurred and also more robustness of controller can be achieved with extra gain margin and phase margin. This type of controller can enhance system stability than the classical type PID. The block diagram demonstration of the FO-PID controller has been shown in Fig. 2 As shown in the fig, including PID parameters, there are two extra parameters to be adjusted (K p , K i , λ, K d , and μ). The two extra parameters λ, and μ are real numbers and they representing the order of an integrator and order of a differentiator. The transfer function of fractional PID has the form: C(s) = K p + K i ×

1 + Kd × sμ sλ

(2)

where C(s) the denotes controller transfer function, s1λ denotes the integrator term and s μ denotes the differentiator term. These two terms are the additional parametric quantities which provide good dynamical properties to a fractional-order system. So, one of the important benefits of FO-PID is the improved dynamical control system, which is well explained by fractional calculus. The mathematical expression based on the differential equation on FO-PID controller is as follows [11]: c(t) = K p × e(t) + K i × D −λ + K d × D μ e(t)

(3)

where c(t) denotes the controller transfer function in the time domain, D is the integrodifferentiator operator. If λ and μ are 1, then the controller behaves as a conventional PID controller, if λ is 1 and μ is 0, then it is a PI controller, if λ is 0 and μ is 1, the controller behaves as a PD controller, if both λ and μ are 0, then it is a P controller or else it is a FO-PID controller. In this study λ and μ are varied between 0 and 2. So, it can be concluded that all the conventional PID controllers are the exceptional cases of the FO-PID controller. Although the performance of the AVR system has been upgraded by this controller but it’s difficult to tune and implement in real-time applications. In modern days, researchers are focused on initiate novel procedures for the tuning of FO-PID controllers. Therefore, many tuning rules apply a set of design statements for tuning FO-PID in both the time and frequency domain. But it is very difficult to achieve better results in both the domains for FO-PID. Therefore, the control performance of FO-PID controllers will be extremely reliant on the selected tuning technique that has its intrinsic merits and demerits. Fig. 2 FO-PID controller block diagram representation

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3 Structure of the AVR The AVR model is considered among the major control loops in a power generation system and its system stability also contributes significantly to a key role in the power system. The configuration of the AVR model made up of an amplifier, an exciter, a synchronous generator, a feedback sensor, and a controller. The AVR control model provides a terminal voltage V t (s) of a generator. The objective of AVR is to keep up the magnitude of this voltage at a predetermined value. This terminal voltage can be improved by the excitation control of the generator. The terminal voltage is then fed to a feedback sensor. A reference terminal voltage is set as V r (s). Error is measured by taking the difference between V r (s) and V t (s). This error voltage V e (s) signal is then amplified and applied to control the excitation of the generator. The structure of the AVR model and its dynamic response without a controller is shown in Fig. 3. In Fig. 3, Vr (s) is the input, Ve (s) is the error, Vf (s) is a feedback sensor output, and Vt (s) is the generator’s output. Table 1 represents the transfer functions of each model used in the AVR system with gain limits which are represented by parameter ranges and time constant values. The complete transfer function for the AVR model is as follows:

Fig. 3 a AVR closed-loop system block diagram representation and b its dynamic response without the controller

Table 1 Transfer function with parameter values of the AVR model Model types

Transfer function

Parameter ranges

Amplifier

K1 1+sT1

10 ≤ K 1 ≤ 400

K 1 = 10

0.02 ≤ T1 ≤ 0.1

T1 = 0.1

Exciter

Generator

Feedback Sensor

K2 1+sT2

K3 1+sT3

K4 1+sT4

Values of parameters

1 ≤ K 2 ≤ 200

K2 = 1

0.4 ≤ T2 ≤ 1

T2 = 0.4

0.7 ≤ K 3 ≤ 1

K3 = 1

1 ≤ T3 ≤ 2

T3 = 1

K4 = 1

K4 = 1

0.01 ≤ T4 ≤ 0.06

T4 = 0.1

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Fig. 4 a Root locus, b bode, c pole-zero, d Nyquist representation of AVR system without a controller

0.1 × s + 10 Vt (s) = 4 Vr (s) 0.0004 × s + 0.045 × s 3 + 0.555 × s 2 + 1.51 × s + 11

(4)

Applying the transfer function of the AVR model given in Eq. 4, the root locus plot, bode plot, pole-zero plot, and Nyquist plot are represented in Fig. 4a–d and it has been concluded that the closed-loop response is unstable. In many research papers, the AVR system gain and time constant had been reviewed. For better accessibility, here a linearised AVR system is analyzed in terms of the large time constant and avoiding saturation or other nonlinearities. In this work, to understand the AVR system through improved dynamic response, some different control strategies with a variety of tuning methods have been employed and tested.

4 Overview of Tuning Techniques In this section, three optimization techniques are discussed. Those discussed optimization techniques are Ziegler and Nichols (ZN) method, Nelder–Mead simplex method, and Grasshopper evolutionary technique (GET).

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4.1 Ziegler and Nichols (ZN) Method This simple mathematical procedure was proposed by J. Ziegler and N. Nichols. This is a very old and enormously popular PID tuning method. Applying the ZN method, it is easier to tune all three PID controller gains. This rule provides an estimated value for all gain parameters. Therefore, the success of the PID controller obtained using the ZN rule is checked through the AVR system. This method was also applied for tuning the gain parameters (K p , K i , K d ) of the FO-PID controller. The other two parameters (λ, μ) of FO-PID were tuned manually and observing the outcome on the desired conditions. However, this classical method gives an unacceptable closed-loop performance such as a higher overshoot and control signal. It is also not suitable to implement for large nonlinear and complex systems like the test system considered in this study. To promote simple approaches such as hit and trial method, optimization methods are followed by many researchers in recent times [15–17]. Looking to the above challenges, both the approaches are followed for the conventional PID controller and also compared with the proposed approach. An alternate approach is discussed in the next section which reduces the issues related to the ZN method.

4.2 Nelder–Mead Simplex (NM) Method The idea of employing the NM method for a dynamic system belongs to Nelder and Mead [7]. This simplex algorithm is deterministic in its formulation and is a direct search multidimensional unconstrained minimization method. The method aims to reduce a scalar-valued nonlinear function of real variables with simple function variables, not including any explicit or implicit information. The NM process is computationally simple and is used for parameter optimization. In this study, the parameters of the controllers are tuned using this optimization approach by searching the best parameters of controllers for use with the AVR system. The tuning of controllers has been done by MATLAB simulation and the standard MATLAB function “fminsearch” has been used for simulating the Nelder–Mead algorithm. Though this method has been employed successfully to solve problems that occurred by the ZN method, it could not give better performance and research has also identified some deficiencies in this method. However, to overcome the drawbacks of these tuning rules, several artificial intelligence algorithms are proposed to tune the controller parameters.

4.3 Grasshopper Evolutionary Technique (GET) GET is a “nature-inspired” swarm algorithm which is based on the behavior of grasshopper. The life process of grasshoppers consists of three steps: egg, nymph, and adulthood. In the nymph stage, they slowly move with a minor increase (known as exploitation) and have plants get within their pathway. During the adulthood stage,

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they migrate with long-range and abrupt movements (known as exploration). Based on these behaviors, mathematical model is formulated by considering the grasshopper position and is written as follows: X n = Sn + G n + An

(5)

where X n denotes the position of the nth grasshopper, Sn denotes the social interaction, G n denotes the gravitational force on the nth grasshopper, and An denotes the wind speed. The Sn in Eq. (5) is defined as: Sn =

M 

  s(dn j )dˆn j , dn j = xn − x j 

(6)

j=1,n= j

where dn j denotes the distance connecting the n-th and j-th Grasshoppers. However, Eq. (5) cannot able to resolve the optimization difficulty, so after reformulating the equation is: ⎛

⎞  x j − xn  a − b ⎠ + Tˆd s x j − xn  X n = c⎝ c 2 d n j j=1,n= j M 

(7)

where a and b denotes the upper and lower bounds of the search space, respectively; Td denotes the best solution value originate. However, in Eq. (7) G i is not measured, and the Ai is measured to be Tˆd . Here c is a shrinking coefficient to balance exploration and exploitation. The shrinking parameter c decides the exploration or global search (when c becomes larger) and exploitation or local search (when c becomes smaller) and is given as follows:

c = cmx − t

cmx − cmn tmx

 (8)

where cmx denotes the maximum value of C , i.e., 1 and cmn denotes minimum value of C, i.e., 0.00001; t denotes to the present iteration, and tmx denotes the maximum number of iterations which has been used for this study. The optimization method starts with a set of random solutions. Grasshopper positions are updated by Eq. (7). In each iteration, the best target is updated and the position is updated iteratively till it reaches to stop criterion. This recently proposed algorithm has been applied to various areas for its simple implementation and can efficiently solve all the global optimization problems. GET gives better results in terms of convergence speed and accuracy over various well-known nature-inspired evolutionary techniques such as GA, PSO, DE, GSA, etc. It can utilize all the search agents in the population to search for the target. So, all agents involved in the optimization process and the

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search efficiency become higher. Therefore, in the search process of GET, each grasshopper updates the position by its existing position, global best, and also by all other grasshoppers’ positions.

5 Simulation Results and Analysis This section analyzes the effectiveness of the GET-FO-PID controller of the AVR model along with its implementation in MATLAB (Simulink). Based on three different tuning techniques, results of FO-PID are compared with PID controllers. The performance of the GET-PID and GET-FO-PID controller with AVR has been investigated. Figure 5 exhibits the block diagram of AVR using the GET-FO-PID controller. The discussed techniques refer to the precise optimal design that improves the dynamic response of the AVR model. The significant observations and analysis of this study are presented in the following subsections.

5.1 Performance Evaluation of PID and FO-PID Controller To validate the performance and superiority of FO-PID by using the conventional and meta-heuristic tuning approaches over the PID controller with the same tuning methods, a typical first-order plant is taken. The transfer function of the plant is: C(s) =

1 s+1

(9)

Applying MATLAB simulation to the above transfer function, the best possible values of the PID and FO-PID parameters derived by using different tuning methods are presented in Table 2. It has been very clearly seen that in the case of the PID controller, GET-PID gives better result than ZN and NM-PID. This is caused by the natural arbitrariness which assists GET in coming out of local minima. This is not feasible in the case of both ZN and NM technique. Therefore, the metaheuristic GET technique can be used as a suitable option for the solution of complex problems. Fig. 5 AVR model with GET-FO-PID controller

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Table 2 Comparison of performance indices between different tuning methods of PID and FO-PID controllers Type of controller

Value of Kp

Value of Ki

Value of Kd

μ

λ

Peak Time (S)

Rise Time (S)

Settling Time (S)

ZN-PID

1.02

1.78

0.128

–

–

2.749

0.653

7.286

NM-PID

1.17

1.32

0.241

–

–

1.905

0.511

6.859

GET-PID

1.37

1.45

0.355

–

–

1.471

0.226

5.623

ZN-FO-PID

1.46

1.82

0.244

1.12

1.051

1.934

0.574

7.514

NM-FO-PID

1.35

1.07

0.187

1.07

1.026

1.842

0.472

4.882

GET-FO-PID

0.32

0.12

0.023

0.18

0.146

1.197

0.007

3.884

Fig. 6 Steady-state response of first-order system using different controllers

Similarly, in the case of the FO-PID controller, GET-FO-PID ensures a finer result than ZN and NM-FO-PID. Therefore, the performance of GET-FO-PID outperforms the rest of the discussed methods. Using the gain parameters given in Table 2, the improved transient behavior of the test model with controllers is shown in Fig. 6. From the above figure, it is noticeable that steady-state behavior of the GET-FOPID approach produces improved outcome over the rest of the approaches.

5.2 Analysis Through Transient Response This section deals with the study of the transient response analysis of the GET optimized FO-PID controller. The discussed controller tested on the AVR model provides an improved transient response and also compared with various types of other tuned PID controllers. In this work, the MATLAB FOMCON toolbox is used. Using the calculated gain values for FO-PID controller obtained by GET optimization, the modified transfer function of the AVR model is as follows: 0.5834s 2.34 + 2.751s 1.24 + 1.1772 Vto (s) = Vro (s) s 1.24

(10)

Value of K p

1.021

1.323

0.65

Type of controller

GET- PID-AVR

FO-PID -AVR

GET-FO-PID- AVR

0.51

0.44

1.872

Value of K i

0.25

1.32

0.139

Value of K d

0.95

1.25

–

μ

0.75

0.95

–

λ

1.082

1.420

1.644

Peak overshoot (pu)

Table 3 Transient response and performance indices of the different controllers using AVR model

0.464

0.142

0.445

Rise time (s)

1.227

3.7

4.152

Settling time ( s )

0.671

0.238

0.78

Peak time ( s )

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Fig. 7 Step response of the AVR model using different controllers

In this work, gain parameters of PID and FO-PID are tuned with GET and are also given in Table 3. The GET-FO-PID-AVR system uses minimal gain parameters and provides a better dynamic response. The result of GET with the fractional controller in the AVR system gives superior performance than the existing methods. To justify the performance efficiency of the approach, a comparative analysis has been shown in Fig. 7 with GET-PID and FO-PID controller considering similar conditions of the same system undertaken. It has been found that the maximum overshoot, settling time, rise time, and peak time in grasshopper evolutionary technique in FO-PID-AVR are better than other two types of AVR models. Here, the controller gain is adjusted by the change in the system parameters in a very quick approach.

5.3 Analysis Through Stability The performance of the GET-FO-PID-AVR is also tested based on four stability criterion, i.e., root locus, bode plot, pole/zero, and Nyquist plot are shown in Fig. 8a– d. According to the stability analysis results of these plots, it can conclude that the system is stable and has a very good frequency response. Above four plots of the FO-PID-controlled AVR model tuned by the GET are compared with that of the AVR model given in Fig. 4. Hence, the efficiency of the proposed (GET-FO-PID) controller for the AVR system is confirmed.

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Fig. 8 a Root locus, b bode, c pole-zero, d Nyquist representation of AVR model using GET-FOPID controller

6 Conclusion In this paper, an optimized fractional order-PID controller based on the GET is presented and examined on the AVR model to justify its implementation in realtime conditions. It is found that the optimized FO-PID can be a replacement to conventional PID for enhanced performance of the AVR operation. FO-PID itself results as an option for the alternative to conventional PID as reflected from the result analysis. However, it is found that the robustness and accuracy of the FO-PID can be further enhanced by optimally setting the gain parameters. Due to this reason, it is suggested as an outcome of the study that the GET can be an option among many possibilities due to its simple approach and satisfactory performance in computing optimal parameters. To confirm the effectiveness and robustness of the GET-FO-PID approach, some results of this approach are compared with ZN-FO-PID and NM-FOPID are presented. The closed-loop responses are also compared with the classical PID controller. The GET-FO-PID technique gives a better response in terms of less overshoot, stable transient responses, stability with suitable gain and phase margin, and noise and disturbance rejection ratio.

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Demand Side Management by PV Integration to Micro-grid Power Distribution System: A Review and Case Study Analysis Subhasis Panda , Pravat Kumar Rout, and Binod Kumar Sahu

Abstract Photovoltaic (PV) renewable energy integration to the demand-side distribution system is exponentially increased over the last two decades to deal with the challenges related to growing electric demand. Many reasons like cost reduction, environmental and social improvement, reliability and network issues, and market improvement forced to put forward for promoting and undertaking demand-side management (DSM) parallel. Looking at the enormous possibility, this paper presents a comprehensive overview with real-time test case discussion on the incorporation of PV as a DSM tool to justify the fruitful implementation of both in the distribution system. A brief review of DSM techniques for energy savings and PV renewable energy integration is provided. To substantiate the above possible benefits this paper presents evidence with various measurement indexes through the real-time data of Odisha grid system in India. The test case results demonstrate large-scale PV adaption with proper coordination tool. DSM can affect the load pattern making the load demand graph smooth at the time of peak hour. Keywords Demand response · Demand-side management · PV integration · Load curve · Peak saving · Performance index

S. Panda (B) · B. K. Sahu Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751003, Odisha, India e-mail: [email protected] B. K. Sahu e-mail: [email protected] P. K. Rout Department Electrical and Electronics Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751003, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_35

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1 Introduction Traditionally, power system networks are designed to maintain the balance between the energy generations to the instantaneous power demand at the demand side. Previously the basic need from the power system network is to get energy, but the current situations demand the quality and the uniform availability of the power. Electricity demand at the consumer side varies rapidly throughout the day, to meet that frequent changes and to stabilize the demand and supply. Demand Side Management (DSM) techniques introduced in the present days as a novel solution to the associated issues arise due to this imbalance and to provide the energy with better reliability, security, cost-effectiveness, and load factor. The major driving forces for the extensive application of DSM are the integration of Distributed Energy Resources (DERs) like Solar PV, Small hydro, Wind Energy Conversion System (WEDES), Biomass energy, Electric Vehicles (EVs) and Energy Storage Systems (ESSs), etc. Apart from that the Time of Use (ToU) pricing schemes, and the various optimization techniques to control the power system network are also play a major rule in enhancing its performance. The above opportunities motivate this study to explore the PV integration as one of the possible DERs into the distribution system and analyzed a few aspects with a case study performance and result. The power system network, all around the globe, tends to be decentralized nowadays to meet the growing demand and the challenges in the transmission network. Different economic supports like subsidies, co-certification, feed-in tariff, and the tax exclusion make huge impacts on the PV generations. PV generation is substantially accepted as a major resource in the distribution planning process as it’s availability at peak hour. Peak hours are considered when the average load demand reaches its maximum value in a day. To reduce the expensive operation of the baseload generators, PV integration at the demand side has a great opportunity for the DSM operations [1]. First, in the instantaneous term, the PV integration benefits the electricity systems like reduction of losses, peak loads, and environmental impacts. Second, in terms of the medium long term, the integration of PV helps to electricity in deferral of future capacity investments more if the establishments of PV are near to electricity consumption points. Initially, due to random inherent uncontrollable dynamic characteristics of the PV based generators, the attraction towards PV establishment was less in comparison to wind-based generators specifically. However, the situation changes at present due to technological developments to provide better control and energy management through a true dynamic interaction between the PV systems and networks [2]. The possible added values of PV to electric grid systems have been least exploited and assessed to date. In this view, an attempt has been made in this study on the PV based generation particularly and investigates the benefits with a real-time data on peak load saving, better load factor, and efficiency through demand-side management [3]. The main purpose of the study is to analyze the effect of only the PV generation on the demand side with real-time data. Along with this, an analysis of performance

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of a 176.6 kWp grid-tied solar PV generating station situated in Bhubaneswar of India under local climate conditions throughout the year. The major out coming from this study can be mentioned below. • Various techniques related to PV integration and DSM is analyzed in a case study to focus on DSM opportunity to the PV. • The different inheritance potential of PV renewable energy with reverse power flow in a grid-connected mode of operation is identified under different load profiles and variations. • The performance is evaluated through a various measuring index to justify various possibilities to enhance the overall performance in a PV and DSM integrated micro-grid system. The remaining part of this manuscript is presented as follows. In Sect. 2, the DSM methodology with its possible benefits for the distribution system is presented. In addition to that how active DSM due to its various factors affect the PV integration levels is discussed. In Sect. 3 the system description considered as a test case for the study is described. Problem formulation and performance evaluation are presented in Sect. 4. Section 5 provides the results and analysis with the evaluation of the system performance. In the end, conclusions with the future scope of the present work are presented in Sect. 6.

2 Demand Side Management Reliable operation of power flow in a network out of many reasons, it is majorly dependent upon the balance between supply and load. Looking at the complexity of the smart micro-grid distribution system and random variation of nonlinear loads, maintaining the balance between both sides of production and consumption of power needs to be focused on its power management aspects on generation and load end. These issues further exaggerated with the integration of various types of DGs integration along with energy storage devices and electric vehicle charging systems. Furthermore, as renewable generations are dependent on weather conditions, it is hard to modulate the output of renewable to maintain a particular load shape. To overcome these issues Demand Side Management (DSM) concept is very much accepted for the past decade as a promising solution to the above issues. Apart from the above issue, DSM supply support to the smart grid in various prospects such as electricity market control, market management, framework construction, management of distributed energy resources (DERs), controlling and influencing energy demand to reduce the overall peak demand. DSM is rightly defined as the planning, implementation, and monitoring of utility activities that are planned to consumer/customer use of electricity in a way that will produce desired changes in the time pattern and magnitude of load shape. There are various DSM techniques and some of the most used DSM techniques are load shifting, peak clipping, conservation, load building, valley filling, and flexible load, etc., as illustrated in Fig. 1 [4].

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P(MW)

P(MW)

Time

Load Clipping Effect

Conservation Effect

P(MW)

P(MW)

Time Load Building Effect

Time

Time

Load Shifting Effect

P(MW)

P(MW)

Time Valley Filling Effect

Time Flexible Load Effect

Fig. 1 Various DSM techniques

• Peak Clipping: It is the process of reducing the load demand at the time of peak hours. • Valley Filling: It is the process of building the load at off-peak hours to improve the system load factor. • Load Shifting: It is the process of reducing the load demand at the time of peak demand and building the load at off-peak hours. • Conservation: It is the process of reducing the utility loads throughout the day. • Load building: It is the process of increasing or building the load at the time of surplus electricity. • Flexible load shape: It this process of allowing the customers to use energy on an as-need basis. Based on the requirement and objective, DSM can choose any of the load shape patterns to formulate the methodology to enhance the power regulation to result in a better performance. The major objective of DSM in a PV integrated distribution system as considered as the major objective of this study is to reduce the use of peak power in high demand hours and shift it towards the off-peak hours by encouraging pro-consumer participation. Apart from this, there are minor DSM objectives those need be to integrate into DSM technologies are as follows: (1) to reduce the financial benchmark to set new generating plant, (2) to improve the power system efficiency and reliability, (3) to minimize the adverse environmental impacts, (4) to reduce the power shortage and power cuts, (5) to improve the sustainability and quality of the power system, (6) to reduce the cost of power delivering to the customers [5].

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2.1 Role of DSM in India Demand for the electricity is substantially growing around 8% for the last 8–10 years, but the generation of electricity is far behind of deficit to 8–10% every year in India [2]. Figure 2 gives the idea of the energy consumption in various sectors across India. The major problem of the power sector in India can be stated as an unacceptably high amount of T&D losses due to old electrical equipment, large commercial losses due to poor billing, metering, collection, and energy theft, and, low-end use efficiency of energy [6]. In such a situation, DSM can add supplements to the traditional methods to face the growing challenges in power system networks. The reason a power system network in India undertake DSM includes: (i) to provide peak supply at the time of energy demand is high, (ii) to improve quality and reliability of power supply, (iii) to improve cash flow revenues of the utility, (iv) to mitigate the impact of rising tariff to help the customers. DSM is highly beneficial to the user as it provides lots of opportunities such as bill saving by changing their load pattern, market efficiency increase by the active participation of both generation companies (GENCO’s) and distribution companies (DISCOM’s) in the wholesale electric market, and higher penetration of DERs. To boost the implication of DSM, the Government is continuously introducing different policies and program in various sectors as smart grid, industries, smart building, and end-use-technology, etc. Despite these policies and steps towards DSM applicability in the Indian power system network, the potential remains highly unrealized due to various technical, financial, economic, and institutional barriers. Ahmadabad Electricity Company (AEC) in the state of Gujarat has established a DSM cell in the year 1994, which is the first initial step towards the DSM program in India. This worked with the energy consumers to develop load research data, the implication of energy efficiency improvement projects, and provide new technology through subsidies to the bulk consumers to integrate some renewable for supplementation of their energy needs. The Energy service companies in India (ESCOs) have worked Energy consumption by various sectors in india Industrial

35%

44%

Residential and Commercial Agriculture

7%

14%

Others

Fig. 2 a Energy consumption by various sectors in India, b Solar Power Generation in India (MW)

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with AEC to implement efficient lighting and reactive power compensation by capacitor installation at the HT (High Tension) and LT (Low Tension) consumers led to peak load saving about 10% [7]. This success pushed Indian electricity agencies like IREDA (Indian Renewable Energy Development Agencies) and various state agencies like State Electricity Regulatory Commissions (SERCs), DISCONs to reform the traditional networks. After the Energy Conservation Act 2001, the Government of India set up the Bureau of Energy Efficiency(BEE) in March 2001 to assist in developing policies and strategies to answer the issues and developments related to the Indian electricity market [4]. Now, the National Smart Grid Mission (NSGM) starts its operation from January 2016 objecting towards the development of the grids by the integration of IoT and various latest DSM techniques [6]. After the NSGM the Ministry of New and Renewable Energy (MNRE) started various DERs integration schemes at demand-side objecting towards overcoming the peak deficit and increasing the renewable generation to decrease the carbon emissions. From various schemes, the grid-connected Photo Voltaic(PV) in the rooftop of government buildings, which are mainly working on-peak hours, is the best technique to reduce the peak demand [8]. This DSM technique under Peak Saving is used nowadays which will be discussed in detail in later sections. Looking to the above potential application of PV integration to the Indian power sector and initiation of Government, a detailed case study is considered in this study.

2.2 Active Demand Side Management with PV Technology Earth receives about 1.05 × 1018 kWh of solar energy on an average which is 6600 times more of total consumption around the globe. Solar energy is a surplus, free of cost and zero polluting the production of energy from solar is growing rapidly across the globe as well as in India. In the present scenario, solar energy shares 36.7% of total renewable energy produced in India and targeted to reach the landmark of 100 GW solar generation up to the year 2022. Apart from that, the various schemes of the Government (National Solar Mission, Grid Connected Solar PV, Solar Park, etc.) supplement substantially to the production. The growth of solar production in India can be approximately estimated as shown in Fig. 2a. Energy generation from the sun by photo-voltaic is the most efficient way for the conversation. Depending on the connectivity to the grid, the PV system can be described as follows [8]: Direct PV systems: In these types of configuration PV panels and directly connected to the load only operate at the presence of sunlight. Due to fewer additional components, the system is low in cost. Solar pumps, motors, and fans are some examples of this system. Stand-alone PV systems (Off-Grid PV): These types of systems are mainly useful in the places where the grid is far away or at the matter of cost-effectiveness. OffGrid PV systems are designed to operate independent of the electric utility grid and

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are generally designed and sized to supply certain DC and/or AC electrical loads. These systems are frequently built on residential households with batteries as energy storage (ES) to provide the energy to the load even in the night and stormy weather conditions. A charge controller is used in the system to prevent overcharging and deep discharge of the batteries. Grid-Connected PV system: It is designed in a way to run in parallel with and interconnected with the utility grid. The main components of this system are the inverters. The inverter converts the DC voltage from the PV panel into AC voltage to directly use on appliances or send to the utility grid to earn the feed-in tariff compensation. This topology of the PV connection to the grid is considered in this study. A grid-connected PV system with an ES: These systems are similar to stand-alone systems except for the connection of the system to the utility grid. Due to the interconnection with the utility grid, a system can reap several benefits like selling the excess PV electricity production to the grid, battery system charging at off-peak hours, and buying power whenever the PV and battery power are deficient to feed the loads. Even though there is an extra investment cost for the battery system, by smartly scheduling the battery operation, the overall benefits of the system can get increased.

3 System Description As per the discussion in Sect. 2, the Govt. of India allowed some PPP (Public Private Participation) operations to boost solar production. Here, in this case, the study of a solar grid-tied system with the various parameters used to connect with the grid is described in this section. Government office, educational organizations, and all other non-residential load are the main consumer during peak hour, so to reduce the peak hour load the Govt. has planned to establish a solar grid-tied system in its rooftops which will supplement the load demand with productively using the non-usable area [9]. A project of 4 MW Solar PV grid-tied systems on the non-residential Govt. buildings in the twin city of Cuttack-Bhubaneswar through PPP mode is sanctioned by MNRE in Odisha. This project covers 199 numbers of buildings (126 in Bhubaneswar and 73 in Cuttack) for taking up Roof-top installations. The site location is of latitude 20.2671 and longitude of 85.8106. As the geography of the city provides an opportunity to draw solar irradiance from 4.69 to 6.9 energy per day per square meter, many investors invest in the power distribution market. Along with this 4 MW power project, various other projects like solar grid in OPTCL, the solar park in unusable land are executed and applied to DSM, which will be discussed in the next section. Here the case study of one of the grid-tied solar plants which are situated in OUAT (Odisha University of Agriculture and Technology), Bhubaneswar, is considered to study the grid side demand management.

424 Table 1 Tech. Spec. of the 176.6 kW plant and solar panel specifications

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Description

Parameters

Specifications

Number of modules

570

V OC

45.20 V

PV technology

Multi crystalline Silicon

I SC

9.14 A

Module Manufacturer

WAREE

V MP

36.70 V

Series connected

19

I MP

8.45

Parallel connected

30

Power thermal coefficient

−0.3845

Plant rated power

176.7

Efficiency

15.98

Tilt

10°

Fill factor

0.75

Dimension

1960 mm × 990 mm × 40 mm

Solar cells per module (units)

72 Nos. (12 × 6)

m2

Area

1760

Tracking system

P&O

Number of inverters (PCU)

3

Inverter manufacturer

SUN GROW

3.1 Technical Specifications of the Plant The test PV system undertaken for the study was installed on the rooftop of the institute of OUAT building, Bhubaneswar, Odisha, India. The construction consists of 570 modules covering a total area of 1760 m2 . The total capacity of generation is 176.6 kWp considering the total range of campus load requirements. The WAREE WS-310 modules are a multicrystalline Silicon of 310 WP capacity and having an efficiency of conversion is 15.98% under standard test condition used as the main component this grid [9]. All the panels are fixed with the base at an angle of 100 inclinations to draw maximum power. Table 1 shows the technical specification of the plant and the solar panel specification undertaken in this study.

3.2 Basic Schematic Diagram The schematic diagram of the grid-tied rooftop system is shown in Fig. 3. This generating system mainly consists of Solar Panels, String Combiner Box, and DC

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Fig. 3 Schematic diagram of grid-tied system

to AC converters (PCU), AC Distribution Box, Plant AC Energy Meter and Data Acquisition System. The technical specifications of the system are given in Table 1. The associated components are shortly described as follows. PV Array: The system has the multi-crystalline solar PV installed in the rooftop as mentioned specification in Table 1. A specific number of panels are connected in string then it is connected to inverters. PV panels are kept an angle of 10° angle with the rooftop surface facing south to capture maximum solar irradiance. AC Distribution Box: AC Distribution box generally known as ACDB panel are the connectors of inverter output to the distribution grid. Miniature Circuit Breaker (MCB), Module Case Circuit Breaker (MCCB), or Fuse is used to control the output of the inverters. Suitable powder-coated metals enclose to cover the entire component used in the ACDB panel. The schematic diagram of the ACDB panel is given in Fig. 4. Solar Radiation and Environment Monitoring: To monitor the real-time solar irradiance and environmental changes a solar reference cell is installed in that area. The reference cell is the same as poly-crystalline silicon solar cells. A Resistance

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AC ENERGY METER

TRANSFORMER CIRCUIT BREAKER INVERTER END

CT

MAIN DISTRIBUTION BOARD

SURGE PROTECTION

Fig. 4 Schematic diagram of ACDB

Temperature Detectors (RTD) are mounted at the backside of the solar panel to transfer good heat and monitor the temperature perfectly. Each reference cell gives the real-time data to deliver a calibration report showing the current–voltage (IV) -curve and the following parameters: I SC , V OC , I MPP , V MPP , Fill Factor, and Efficiency. Also by such type of arrangement, we can measure global and diffuse solar irradiance continuously. Connection to the Grid: From the AC distribution box the power flow line connected to the grid through the transformer in front of the Agriculture main building as shown in Fig. 3.

4 Performance Evaluation This part of the paper describes the methodology to determine the performance analysis of a grid-integrated system. There are certain parameters, given by the International Energy Agency (IEA), which helps to measure the performance of the system [6]. These performance parameters are (1) Total energy generated by the PV system (E AC ), (2) Final yield (Y F ), (3) Reference yield (Y R ), (4) Performance ratio (PR), (6) Capacity factor (CF), (7) System efficiency (ηsys ). These performance parameters act as an index to provide the overall system performance concerning energy production, solar resource, and the overall effect of system losses [10–12]. Total energy generated by the system: The total daily (E AC,d ) and monthly (E AC,m ) energy generated by the PV system is given as where N is the number of days in the month, E AC,t is the instantaneous measured value. The instantaneous energy output was obtained by measuring the energy generated by the PV system after the DC/AC inverter on 30 min intervals.

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Final yield (Y F ): It can be defined as the total AC energy generated by the PV system (E AC ) for a defined period (day, month, or year) divided by the rated output power of the installed PV system (E PV ). This can be stated as: E AC PPV

YF =

(1)

Reference yield (Y R ): It can be defined as the ratio of the total solar insolation in-plane H t (kWh/m2 ) to the reference irradiance G (1 kW/m2 ). This parameter represents an equal number of hours at the reference irradiance. Performance ratio (PR): It is defined as the ratio of the final yield (Y F ) to the reference yield (Y R ). This normalizes performance parameter for the incident solar radiation is a dimensionless quantity and provides important information on the overall effect of losses while converting DC to AC. This can be denoted as PR =

YF YR

(2)

Capacity factor (CF): The capacity factor (CF) is defined as the ratio of the actual annual energy output of the PV system to the amount of energy the PV system would generate if it operates at full rated power (PV, rated) for 24 h per day for a year and is given as: CF =

E AC PPVrated ∗ 8760

(3)

System efficiency: The computation of the system efficiency reflects the overall contribution of the PV integration to the system in terms of energy utilization. The monthly system efficiency is represented as follows: ηsys,m =

E AC Ht ∗ Aa

(4)

Performance parameters described above are evaluated from the measured power generation data of the solar power plant at OUAT for the year 2019. Table 2 shows the performance analysis in terms of all system parameter indexes. From Table 2, it can be concluded that the efficiency of the cased power plant is improved by 2.1%. This variation is dependent mostly on the area used for the establishment of the plant. The production curve of the 176.6 kW peak power plant is shown in Fig. 5, where the monthly average solar irradiance with the install peak with the actual average solar generation is shown and subsequently, the efficiency of the generated energy from the sun is computed from this. In this section, the performance of the cased power plant is measured by the parameters listed above. The values indicate a substantial

428 Table 2 Values of the performance parameters

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176.6 kW peak

Total energy generated by the system

497 kWh per day

Final yield (Y F )

2.814

Reference yield (Y R )

4.79

Performance ratio (PR)

0.586

Capacity factor (CF)

0.32

System efficiency

2.1%

Fig. 5 Production curve of the cased power plant

improvement in the overall system with PV integration. In Sect. 5, the DSM effect on the load curve by the PV integration is mentioned with the analysis.

5 Results and Discussion From the performance analysis, the result shows the system efficiency of the cased grid-connected solar PV is 2.1% which is approximately the same in the region. The system efficiency mainly depends on the solar irradiance, total roof-top area used, climatic change, the efficiency of the solar panel, etc. As the cased power plant, there are many grid-tied solar power plant can be installed to reduce the peak-demand up to 25 MW. Energy generation from solar is the best way to reduce the demand at peak hours if it is managed properly. From Table 3, it is measured the average percentage reduction in peak load demand as 6.9% in summer and 8% in winter. Figure 6, 7 shows the general average load pattern of the Bhubaneswar in summer and winter season, respectively. These data are collected from the OPTCL (Odisha Power Transmission Cooperation Limited) Chandaka Grid. Figure 8, 9 demonstrates the PV generation at the demand side during the specified generating hours in summer and winter season respectively. Figures 10, 11 shows the reduction of the load demand by the effect of PV generation at the distribution on demand-side in summer and winter season

Demand Side Management by PV Integration … Table 3 Reduction in peak load

429

Type of system

Summer

Winter

Peak load without DSM in (MW)

316

235

Peak load with DSM in (MW)

294

216

Peak reduction in (MW)

22

19

Percentage reduction

6.9%

8%

Fig. 6 Average load demand in summer

Fig. 7 Average load demand in winter

respectively. The results indicate the reduction of peak demand in all cases to reduce the stress on the grid energy regulation. Apart from the percentage reduction of peak demand energy-saving, the other factors like peak load with DSM in comparison to peak load without DSM in both the seasons are quite impressive, and substantial

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Fig. 8 PV Generation at demand side in summer

Fig. 9 PV Gen. at demand side in winter

improvement reveals the fruitful establishment of PV integration. Peak reduction in both the seasons is also quite encouraging for further initiation of the PV integration. These results can be further improved in many ways of DSM applications with PV integration along with energy storage systems (ESSs), dynamic and cost-effective tariff structure, and installing other types of DGs.

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Fig. 10 Peak reduction in summer

Fig. 11 Peak reduction in winter

This investigation and analysis provide an insight view to identify the possibilities and opportunities of PV integration for large-scale deployment in India. Although one aspect of DSM that is peak load saving or reduction of peak demand is analyzed, other DSM methods can be explored similarly. These findings as a result of this investigation are useful to justify and evaluating the operational benefits by PV integration to the existing grid system. This will bring the confidence and guideline for future projects of government, even for large-scale projects. So, there is an urgency and requirement of optimal PV management coordinated with an effective DR planning for the Indian power system scenario.

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6 Conclusion and Future Aspects This study reflects the finding that the integration of generation stations at the demand side has become a new landmark which can improve the efficiency of the overall power system. The integration of the PV generation can add a supplement to the peak load demand, doing that here the data analysis showing the reduction of 7–9% peak demand at the current situation. With the effect of the local weather condition, the total efficiency of the grid-tied PV system is found to be between 2–4%. Significant outcomes and benefits are found with regards to peak load saving, load factor improvement, load curve shape change, and overall performance with better efficiency for both utility and customer. Furthermore, the effect of the DSM in the load curve by the PV generation at the Demand side is studied in Section-VI enumerating its positive impact. For further study, implementing various optimization techniques with real-time data can make the load curve more stable. With PV various DERs implementation can make the power system network more flexible, reliable, and efficient.

References 1. Gelazanskas L, Gamage KA (2014) Demand-side management in the smart grid: a review and proposals for future direction. Sustain Cities Soc 11:22–30 2. Uddin M, Romlie MF, Abdullah MF, Halim SA, Kwang TC (2018) A review on peak load shaving strategies. Renew Sustain Energy Rev 82:3323–3332 3. García-Garre A, Gabaldón A, Álvarez-Bel C, Ruiz-Abellón MDC, Guillamón A (2018) Integration of demand response and photovoltaic resources in residential segments. Sustainability 10(9):3030 4. Bayram ˙IS, ¸ Koç M (2017) Demand-side management for peak reduction and PV integration in Qatar. In 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), pp 251–256. IEEE 5. Harish VSKV, Kumar A (2014) Demand side management in India: action plan, policies and regulations. Renew Sustain Energy Rev 33:613–624 6. Warren P (2014) A review of demand-side management policy in the UK. Renew Sustain Energy Rev 29:941–951 7. Bayram IS (2019) Demand-side management for pv grid integration. In Solar resources mapping, pp 313–325. Springer, Cham 8. Caamaño-Martín E, Masa D, Gutiérrez A, Monasterio F, Castillo M, Jiménez-Leube J, Porro J (2009) Optimizing PV use through active demand side management. In Proceedings 24th European PV solar energy conference, pp 3149–3155 9. Green Energy Development Cooperation of Odisha Limited, 2019, projects 10. Kumar BS, Sudhakar K (2015) Performance evaluation of 10 MW grid-connected solar photovoltaic power plant in India. Energy Reports 1:184–192 11. Bharathkumar M, Byregowda HV (2014) Performance evaluation of 5 MW grid-connected solar photovoltaic power plant established in Karnataka. Int J Innov Res Sci Eng Technol 3(6) 12. Ayompe LM, Duffy A, McCormack SJ, Conlon M (2011) Measured performance of a 1.72 kW rooftop grid-connected photovoltaic system in Ireland. Energy Convers Manage 52(2):816–818

Machine Learning Based Efficiency and Power Estimation of Circular Buffer Praveen Kumar Yethirajula, Trailokya Nath Sasamal, and Divya Parihar

Abstract Shift register to Circular buffer (Sr2Cb) conversion contributes imperative power savings in ASIC design optimization. This conversion includes time and resources exhausting complex calculations of Circular buffer (CB). Minimizing these calculations, it is important to estimate the efficiency and power of circular buffer using a machine learning approach. In this paper, we introduce a novel approach for predicting the power and efficiency of ASIC circuits using machine learning (ML) techniques on real-time designs. This model is trained on parameters obtained from numerous different configurations of Shift Register (SR). Using these parameters, the model is trained with minimal error. The proposed model uses the SR parameters and configuration to estimate the CB efficiency and power reduction with the conventional tool process of generating the CB Netlist and its complex calculations. We evaluated the performance on various established machine learning models and compared them with Mentor PowerPro tool. The trained model can be used to predict the efficiency and power for CB thus reducing its effort in the Sr2Cb conversion in tool. Keywords Machine learning models · Efficiency and power estimation · Shift register to circular buffer conversion

P. K. Yethirajula (B) School of VLSI Design and Embedded System, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana 136119, India e-mail: [email protected] T. N. Sasamal Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana 136119, India e-mail: [email protected] D. Parihar CSD Calypto—PowerPro, Mentor Graphics Corporation, Noida, Uttar Pradesh 201305, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_36

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1 Introduction Modern VLSI design requires tremendous optimization in enormous designs with no trade-off in area, power, and performance. Existing EDA tools like Mentor PowerPro [1] provides great optimization from register transfer level (RTL), behavioral level, or even gate level (GL) Netlist with the assistance of high-level synthesis (HLS). In total power analysis of designs, dynamic power plays a key role. Dynamic power consumption is proportional to the switching activity of digital logic design. In a Serial-In-Serial-Out (SISO) shift register, all the registers toggle for every cycle even though data is read/written in only one register. CB is the functional equivalent of SISO SR with additional combinational logic—multiplexer and counters to reduce switching activity. Sr2Cb conversion is a Micro-Architectural optimization technique of PowerPro tool which reduces the SR switching activity, thus minimizing the dynamic power consumption. This optimization is saving dynamic power at the cost of complex conversions and calculations of CB. Hence it is required to use machine learning approach to estimate CB efficiency and power without the above process. Figure 1a shows, with existing technique RTL designers must trust the resourceconsuming process to calculate efficiency and power. This flow is used in the existing methodology which needs to be mitigated. Machine learning is a discipline which contains ample techniques to handle complex situations of next-generation EDA tool developments [2, 3]. In this paper, we tried a few of such techniques to bringing out the solution to reduce the run-time in Sr2Cb conversion in the RTL design. To deploy Sr2Cb conversion in design, we developed a model to predict the profiles of CB. Figure 1b characterizes the proposed workflow using an ML-based training model. With the proposed ML-based model, the tool will estimate the efficiency and power of CB in Sr2Cb conversion in the given RTL/GL using the predictive analysis methods, thus enhancing the run time. The significant contributions of this work include:

(a) Traditional Flow Fig. 1 Traditional Sr2Cb flow versus proposed flow

(b) Proposed Flow

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• An ML-based model for quick efficiency and power profile estimation of CB for the SR chain present in the design. This enhances the run time involved in the Sr2Cb conversion. • Investigated over a handful of well-established ML models [4, 5] estimation and summarized prediction accuracies. • Used feature scaling to reduce the effect of the outliers on the training model and the results are scaled back. • Demonstrated that the proposed model will bring faster runtime than the conventional method using in the PowerPro with an approximate < 5% error rate. • Verified the model on designs with more than 5 lakh registers to check the model performance on large designs. We found reasonably good results and reduced run time around. The rest of the paper is organized as, Sect. 2 presents the related work, Sect. 3 introduces the existing method in brief, Sect. 4 introduces the proposed work. Experimental results and comparative analysis of various models are presented in Sect. 5. Section 6 gives concluding remarks.

2 Related Work Estimation of parameters is emerging as a vital research topic with the increased demand for EDA tool computation speed. Many interesting techniques were proposed to model the early estimations with reasonable accuracy. Plethora of research work [6–11] includes estimating the power profiles using Netlist level parameters and standard cell parameters. Authors in [12] focused on gating sizes to estimate the switching capacitance and activity factors using the lower-level abstraction parameters and ultimately power profiles. In [13], the model is estimating the power using gate-level parameters. The approach of simple linear model relation works well for architecture level, but are slow to use in real-time power monitoring. The models discussed so far are developed based on the parameters obtained from the transistor level. The motivation of the proposed approach is observed from the similarities of SR and CB, i.e. to generate CB, EDA tool is using SR parameters. We used the SR parameters to estimate the efficiency and power of CB without implementing its Netlist. This approach has proven prominent results in saving resources and enhancing the run time.

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3 Shift Register to Circular Buffer Optimization In this section, we will introduce the existing shift register to Circular Buffer optimization technique. The block-level representation of SR and its equivalent CB are depicted in Fig. 2a and 2b, respectively. Figure 2a represents the RTL SR chain of depth four, without any gating conditions. Though the implementation of SR is effortless, dynamic power consumption is more due to high switching activity. Power α Switching activity

(1)

In SR the flop toggle rate is proportional to the depth of the chain. To minimize power consumption switching activity need to be reduced. Replacing SR with equivalent CB model in the RTL design is one of the best techniques to optimize the power. In Fig. 2b, CB is a functional equivalent sequential circuit whose toggling rate is reduced with the combination of counter and decoder, so that only one flop toggle each time a read/write is performed. The features chosen for the data set collection are mentioned in Table 1.

(a) SR chain

(b) CB chain

Fig. 2 RTL level SR chain and its equivalent CB chain

Table 1 Parameters used in the Model

Parameters

Variable

SR flops properties (switching activity, toggle density)

Input features (Independent variables)

Flop bit width SR chain length Initial efficiency of SR Initial power of SR CB efficiency CB power

Target (dependent variable)

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The complex conversions involved in this optimization paved the way for the model is covered in detail in the following section. The mathematical relation to find CB efficiency using SR metrics is mentioned below. CB s Flop Efficiency = 100−TFlop

(2)

Here TFlop = TSRFlop /NSR

(2a)

TFlop = Flop s ON time percentage TSRFlop = Shift Register Flop ON time percentage NSR = No. of Shift Register Flops

4 Proposed Model This section describes the techniques and methodology used to estimate the CB power and efficiency.

4.1 Motivation The dynamic power of a typical circuit [14] is given by Pdyn =

1 αCeff V 2 f 2

(3)

where α is a transition probability constant, Ceff is the effective capacitance which can be computed using .lib files, V is the supply voltage, and f is the switching frequency. Toggling activity and capacitance can be linked as switching capacitance, voltage and frequency will be constant in a design. For a given register i, si will be switching capacitance and γi will be constant operating conditions(Vi f i ). Hence Pdyn can be rewritten as Eq. (4) for a design of N registers. Pdyn =

N 

si γi

(4)

i=1

Here dependant variable, Pdyn holds a linear relationship with its independent variables of si and γi .

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In a similar way, the efficiency of CB is proportional to SR toggle probability and other constant parameters. Hence it can also model using a linear relation.

4.2 Overview of Dataset and Models This section introduces data set, ML techniques, and ML model architecture.

4.2.1

Data Set

Dataset is divided in the ratio 70:30 for training and testing the model. A portion of the training dataset is utilized as a validation dataset to tune the hyperparameters in the model. We have chosen various linear and non-linear models, out of which we are discussing only those two models which performed well on the dataset. The first model is the eXtreme Gradient Boost, a boosting ML technique [15].

4.2.2

EXtreme Gradient Boosting Technique

The decision tree is the basic unit in the tree boosting. For a given training data set: D = {(x11 , x12 , . . . , x1M , y1 ), (x21 , x22 , . . . , x2M , y2 ), . . . , (x N 1 , x N 2 , . . . , x N M , y N )}

(5)

The output of XGBoost model is a function of linear superposition of an array of regression trees. It is an improved tree boosting technique based on Gradient Boosting Decision trees. For n samples and m features in the data set D, the tree boosting model can be written as yi =

T 

f t (xi ), f t ∈ F

(6)

t=1

where F is the set of Classification and Regression Trees(CART) and T is the number of CART. The objective of XGBoost is given by Obj(Θ) =

n  i=1

L(yi , y i ) +

T 

Ω( f t )

(7)

t=1

where the term L(yi , yi ) represents the loss function, measures the error between the predicted y i and the target yi . And the Ω is a regularization to avoid overfitting of the model. To build a tree, it finds the best splitting point recursively until it reaches

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maximum depth. It associates weight to the new leaves reweights them iteratively so that inaccurate points have relatively high weights. Then it prunes the nodes with negative gain in a bottom-up-order. XGBoost uses parallel processing which makes it faster in execution and it handles missing values without any data processing. The second model which has given better result is the Random Forest model [16], which is an ensemble learning method which can perform both regression and classification tasks.

4.2.3

Random Forest Technique

Random Forest is a collection of tree-structured regressors h(x, Θk ) k = 1, 2, … Where the Θk is an independent identically distributed random vector. Each tree is developed in an accompanying manner: If the training set has N features, then the N features are sampled at random, called bootstrap sample. This sample is used for growing the tree. The tree structure will be created based on the information gain in each feature. The root node has the highest information while the leaf node has the lowest information. Each tree is grown to the largest extent possible and this can be tuned. The trees thus generated from the random vector of parameters holds a unique vote for the input x. The new input vector runs down through all the trees and each tree casts a vote. Finally, the Random Forest combines the output of individual trees to generate the final output.

4.2.4

Work Flow

We collected the data set containing SR chain, CB information in both vectored and vector less flow using the PowerPro tool. Here vectored flow refers to designs with FSDB/SAIF simulation profile. The dataset contains SR flops switching activity information, capacitance of pins read from the library files, slew rate, various powers of each flop, SR chain length. Figure 3a depicts the flow of dataset collection. Flow of model preparation is depicted in Fig. 3b. Designs ranging from unit test cases to ASIC designs are used in this process of data collection. For those designs FSDB/SAIF file does not exist, Questa simulator is used to generate timing profile. Using the training set the model is trained. Though training data prepares the model well, validation data helps in unbiased evaluation of the model and tunes hyperparameters.

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(a) Data collection using tool

(b) Flow for Model preparation

Fig. 3 Process involved in model preparation

4.3 Feature Engineering (1) Standard preprocessing techniques like filling in missing values, removal of variables if it contains null values, or replacing null values with the mean of corresponding feature column. (2) Outliers play a key role in model fitting, Outliers are common in any dataset and need to be handled appropriately. K Means clustering is deployed to for outlier identification and these act as an additional feature in the dataset. The dataset contains information of SR chain parameters which can be categorized using the clustering technique, the number of clusters is determined using the elbow technique. This will add an additional feature to the dataset and helps in fitting the best possible line. (3) Correlation between the features and target holds the key in choosing the prominent features. We have figured out the correlation matrix among them and used corresponding features that control the target.

n 

¯ i − y¯ ) (xi − x)(y Correlation =   n n  2  ¯ (xi − x) (yi − y¯ )2 i

i

(8)

i

where x¯ = mean of feature x, y¯ = mean of the target y 4) The range of data present in the features may have a high deviation between its maximum and minimum, scaling technique is a handy technique to drag them into a range so that model will fit with minimum error. Min-Max scaler is used in the process of scaling features that are beyond the range (0, 1). The mathematical relation of Min-Max scaler is

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xi − xmin xmax − xmin

(9)

5 Experimental Results This section presents regression-based evaluation metrics and simulation results.

5.1 Evaluation Methods We have opted for prediction accuracy as one of the evaluation metrics, it is calculated on the estimation results, which is the number of predictions within the acceptable region out of whole test samples. RMSE (root mean squared error) is a popular metric to evaluate the model, which is a standard deviation of prediction errors. If the RMSE of test data is very high than that of train data, it implies that the model is overfitted.

RMSE =

  n 2   (yi − yi )  i=1

(10)

n

Along with RMSE, an adjusted R2 score is used to evaluate the model. R2 gives a score about the amount of information present in the dataset is explained by the model. In R2 score, with the increased uncorrelated independent variables it also increases leading to misconception. Whereas adjusted R2 adds a penalty for the new variables if they are uncorrelated with the target variable. 



N −1 Adjusted R = 1 − 1 − R N − P −1 Where P = number of predictors 2

2

N = Total sample size



(11)

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5.2 Results and Discussion First, the data is preprocessed using the standard techniques as discussed in earlier sections. Prior to splitting the data to train and test set, the dataset is refined to few important features based on the correlation of the independent variables and target. This filtering gives an advantage of involving only those features which govern the target to participate in model training. Twenty per cent of trained data is used in hyper-parameter tuning, train data contains sixty per cent and rest of the refined dataset belongs to the test set. We have tried the models starting from statistical linear model to the bagging model. Since the relation between the SR parameters and target variables efficiency and power have non-linear relation, we tried adding polynomial features to meet the non-linear relation. Though the increasing complexity and regularization methods like lasso have given reasonable accuracy in predicting the targets, ensemble techniques create a robust model to a wide range of test data since these techniques are meta learners and use a number of weak learners. By tuning the hyper-parameter including tree depth, number of trees, number of lead nodes using cross-validation we have figured tree count and correlation limit to tune the tree. From the experiments on various models, we have observed notable prediction accuracy with the Random Forest model. Figure 4 depicts the results of targets using RF regressor, where accurate and inaccurate predictions are calculated based on the deviation from the threshold limit set. Figure 4a contains plots depicting the predicted values and line of fit, deviation of predicted values from the actual values and comparison of actual and predicted values on the same plot for the dependent variable “Efficiency savings” on the conversion of Sr2Cb. Figure 4b represents all the plots for the variable “Power savings” on Sr2Cb conversion. The savings metric is obtained from the difference between SR and CB parameters. Table 2 summarizes the metric comparison between significance using other ensemble models. To emphasize prediction accuracy we represented it alone in the table. Since the basic standard regression-based models found difficult in learning the non-linearity present in the dataset we concentrated much of meta-learning models which makes the best use of weak learners for an improved performance. Out of model performance presented above, RF algorithm has good prediction accuracy so we have chosen it to enhance the conventional technique. With the model, we have achieved the prediction accuracy of 97% and 93% for efficiency improved and power savings of Sr2Cb.

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(a) Plots for the target ‘Efficiency Improved’

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(b) Plots for the target ‘Power Savings’

Fig. 4 Plots of best line of fit and deviations from the truth value with RF regressor Table 2 Comparison of results among models

ML algorithm

Prediction accuracy Efficiency (%)

Power (%)

Random forest

96.7

93.8

XG boost

96.5

89.1

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6 Conclusion We have proposed a novel model that makes use of simulation profiles and design parameters to enable quick and accurate efficiency and power estimation for the circular buffer without the necessity of its netlist. The trained model is applied to real-time designs and it is observed less than 3 and 6% error for efficiency and power, respectively. Compared with the Mentor PowerPro tool, the proposed model achieves speedup and consumes less memory. In future work, Neural networks can be tried to increase speed and accuracy. Acknowledgements We sincerely thank Mahima Jain and Mohie Pokhriyal for their generous support for this work, Mentor Graphics, Inc. for permission to use their designs and software for our experiments.

References 1. PowerPro Inc https://www.mentor.com/hls-lp/powerpro-rtl-low-power/ 2. Pandey M (2018) Machine learning and systems for building the next generation of EDA tools. In: 23rd Asia and south pacific design automation conference (ASP-DAC), pp 411–415 3. Jeff Dyck, “Machine Learning for Engineering”, 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), pp 422–427, 2018 4. Pedregosa F et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 2825–2830 5. https://scikitlearn.org/stable/supervised_learning.html 6. Resul T, Sule GO (2017) Demand prediction using machine learning methods and stacked generalization. In: 6th international conference on data science, technology and applications (DATA 2017), pp 216–222 7. Allesandro B, Luca B, Giovanni De M (2000) Regression-based RTL power modeling. In: Transactions on design automation of electronic systems, pp 337–372 8. Mel Stockman et al., “A Novel Approach to Memory Power Estimation Using Machine Learning”. International Conference on Energy Aware Computing, pp. 1–3, 2010 9. Ahuja S, Mathaikutty DA, Singh G, Stetzer J, Shukla SK, Dingankar A (2009) Power estimation methodology for a high-level synthesis framework. In: 10th international symposium on quality electronic design, pp 541–546 10. Xu M, Wu L, Zhang X (2018) Power analysis on SM4 with boosting methods. In: 12th IEEE international conference on anti-counterfeiting, security, and identification (ASID), pp 188–191 11. Gourishetty S, Mandadapu H, Zahra A, Abbas Z (2003) A highly accurate machine learning approach to modelling PVT variation aware leakage power in FinFET digital circuits. In: Asia Pacific conference on circuits and systems (APCCAS), pp 61–64 12. Zhou Y, Ren H, Zhang Y, Ben K, Brucek K, Zhang Z (2019) PRIMAL: power inference using machine learning. In: 56th annual design automation conference (DAC’19), pp 1–6 13. Yang J, Ma L, Zhao K, Cai Y and Tin-Fook Ngai. “Early Stage Real-Time SoC Power Estimation Using RTL Instrumentation”, 20th Asia and South Pacific Design Automation Conference, pp. 770–784, 2015 14. Chanda R, Bhasker J (2013) An ASIC low power primer: analysis, techniques and specification. Springer, pp 9–65 15. Chen T, Carlos G (2016) XGBoost: a scalable tree boosting system. In: Proceedings international conference on knowledge discovery and data mining (KDD’16) 16. Geron A (2017) Hands-on machine learning with scikit-learn and tensorflow. O’Reilly Media ch. 4,6,7, pp 105–133, 167–195

Binary Dragonfly Algorithm-Designed Fuzzy Cascade Controller for AGC of Multi-area Power System with Nonlinearities Prakash Chandra Sahu, Subhadra Sahoo, Ramesh Chandra Prusty, and Binod Kumar Sahu

Abstract The paper has addressed on design of robust fuzzy cascade controller for automatic generation control (AGC) of a multi-area power system under both step load and stochastic load perturbations. The AGC aims to maintain proper balance between net generation and active demand to pertain stability over system frequency and tie-line power effectively. A control action is highly essential to make stability over system frequency under any uncertainties. The paper proposes a robust fuzzy cascade controller to make necessary control action in the multi-area system for getting desired frequency and scheduled tie-line power under different load dynamics. Further, a novel binary dragonfly algorithm (BDA) is suggested to optimally design the proposed fuzzy cascade controller under different operating conditions. To show superiority of the proposed fuzzy cascade controller, a comparative study is synthesized over fuzzy PID and PID controllers under above uncertainties. Finally, it has been conferred that the proposed binary dragonfly algorithm-designed fuzzy cascade controller is found to be more effective in regard to AGC of multi-area power system. Keywords Automatic · Generation control (AGC) · Fuzzy cascade controller · Binary dragonfly algorithm · Objective function · Governor dead band (GDB) · Tie-line power, load frequency control

P. C. Sahu Department of Electrical Engineering, Silicon Institute of Technology, Sambalpur 768212, India e-mail: [email protected] S. Sahoo (B) · B. K. Sahu Department of Electrical Engineering, IITER, Siksha O Anusandhan (Deemed to be University), Sambalpur, Bhubaneswar 751030, Odisha, India e-mail: [email protected] B. K. Sahu e-mail: [email protected] R. C. Prusty Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur 768018, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_37

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1 Introduction The electrical power system plays a vital role for electrifying various geographical areas efficiently. The objective of the power system is to generate and deliver electrical power smoothly to different end consumers. The generation part is associated with different synchronous generators to generate electrical power for different electrical loads. The generating units such as thermal power plant, hydro power plant, nuclear power plants are built in different geographical area as per the availability of respective resources. The concern generating units constitute one control area by comprising governor, turbine, generating unit and power system and able to electrify its local area effectively. Nowadays, individual control areas are interconnected to build huge interconnected power system as well. The interconnected power system though electrifies various geographical areas efficiently; however, it includes loss issues in the power system. So care should be taken by power engineers while generating electrical power to meet various load demands. For reliability, the net generation should satisfy both loss and active demand efficiently. The phenomena which controls net generation irrespective of load demand is referred as automatic generation control (AGC) [1–4]. AGC always plays an important role in power system to keep desired frequency and tie-line power irrespective of load demand. To obtain AGC in interconnected system, a highly control action is necessary for the stability of system frequency. The researchers proposed various controllers as secondary frequency control loop for AGC of multi-area power system. Panda et al. [5] proposed conventional PID control strategy for AGC of multi-area power system efficiently. Sahu et al. [6] addressed over fuzzy droop control strategy for frequency control of a multi-area power system under governor dead band (GDB) and generation rate constraint (GRC) nonlinearities. Further, Sahu et al. [7] suggested a type-II fuzzy control strategy for AGC of multi-area power system under different load dynamics. Again to design various proposed controllers optimally, design engineers proposed different optimization algorithm for AGC study. Pilla et al. [8, 9] have implemented conventional and fuzzy logic-based controller to study the frequency stability issues in power system. The present research paper proposes a fuzzy cascade controller for AGC of multi-area power system under both random and step load perturbations. The contributions of the present research paper are as follows: (a) A hybrid three-area power system is modelled for AGC study under various load dynamics. (b) A robust fuzzy cascade controller is proposed to constitute secondary frequency control loop of the multi-area power system for AGC study. (c) A novel binary dragonfly algorithm (BDA) is preferred to tune the parameters of the proposed fuzzy cascade controller. (d) An assessment over comparative analysis has been synthesized to confer superiority of proposed fuzzy cascade controller and BDA algorithm.

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2 System Investigated The power system model proposed for this study comprises three different control areas of which each carries thermal power plant as power generating unit and is given in Fig. 1. The thermal generating unit is modelled with governor, turbine, synchronous generator and power system [10]. All individual components are represented through their simple transfer function expressions and are addressed as followings. The governor is modelled as: G g (s) =

Pgo 1 = Pgi 1 + s.Tg

(1)

The non-reheat turbine is modelled as: G t (s) =

Pv 1 = Pgo 1 + s.Tt

The reheat turbine is modelled as

Fig. 1 Proposed three-area power system model

(2)

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G r (s) =

Pm 1 + s.Tr K r = Pv 1 + s.Tr

(3)

The generator is modelled as Pe (s) Pm (s)

G gen (s) =

(4)

The power system is modelled as G p (s) =

ω(s) KP = PO (s) 1 + s.TP

(5)

Further, some physical constraints such as governor dead band (GDB), boiler dynamics and generation rate constraints (GRC) are considered in the power system model for realization of nonlinear and realistic.

3 Fuzzy Cascade Controller The conventional PID controller is well known and has been implemented in various engineering problems for its simple structure. This conventional controller gives improved performance in linear systems but looks inferior to perform under nonlinear systems. To mitigate such drawbacks, researchers proposed fuzzy-based PID controller in most of nonlinear systems [11]. In such conventional fuzzy PID controller, the unwanted activation of integral gain during transient period degrades the performance of fuzzy PID controller. Owing to such limitations, the research paper proposed a fuzzy cascade controller for AGC of three-area power system. The membership function structure and block diagram of proposed fuzzy cascade controller are depicted in Figs. 2 and 3, respectively. Rule base of the fuzzy logic system is depicted in Table 1. HN

LN

Z

HP

LP

1

0.8 0.6 0.4 0.2 0

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

Fig. 2 Membership functions of proposed fuzzy cascade controller

0.6

0.8

1

Binary Dragonfly Algorithm-Designed Fuzzy Cascade Controller … 1

KP

K1 Fuzzy Logic Controller

e(t)

+ +

d dt

K2

449

K PP

d dt

KD

KI

+

+ +

u(t)

1 s

Fig. 3 Fuzzy cascade PD-(1 + PI) Controller

Table 1 Rule base of the fuzzy logic system e/e

HN

LN

Z

LP

HP

HN

HN

HN

LN

LN

Z

LN

HN

LN

LN

Z

LP

Z

LN

LN

Z

LP

LP

LP

LN

Z

LP

LP

HP

HP

Z

LP

LP

HP

HP

4 Objective Function In frequency and tie-line power control concern, the research work employs an integral of time multiplied absolute error (ITAE) as objective function and is addressed in Eq. (6). T ITAE =

(| f i | + |Pi− j |) .t.dt

(6)

0

where  f i = deviation of frequency of ith area, Pi− j = tie-line power between area i and j, T = simulation time.

5 Proposed Binary Dragonfly Algorithm (BDA) The proposed binary dragonfly algorithm is inspired from the swarming activity of dragonfly in nature. This algorithm is originated from the concept of swarming feature of dragonflies in nature. The important exploration and exploitation attributes of proposed BDA technique are initiated from the static and dynamic nature of dragonfly. Researcher Sayedali Mirjaili has developed original dragonfly algorithm to propose various engineering problems [12]. The flow chart of proposed BDA algorithm is illustrated in Fig. 4.

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Fig. 4 Flow chart of DBA algorithm

6 Results and Analysis This section extracts the outcomes of proposed AGC work and has addressed the suitable dynamic responses and numerical results. The proposed power system model was developed in SIMULINK environment, and coding of proposed optimization technique is written in .m file of 2018 MATLAB software. The result analysis is progressed with two different case studies. Case 1: Controller validation, Case 2: Technique validation.

6.1 Case 1: Controller Validation This case study deals with validation of the proposed fuzzy cascade controller over fuzzy PID (FPID) and PID controllers in response to AGC of multi-area power

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system. The gain parameters of the controllers are optimized by using a novel BDA algorithm under different loadings such as step load, random load and stochastic load perturbations. The gain values of the proposed fuzzy cascade controller are addressed in Table 2. The performance validation is performed under three different scenarios. Scenario 1: Step load; Scenario 2: Random load; Scenario 3: Stochastic load (Table 2). Scenario 1: Performance Study Under Step Load A step load of 2% as shown in Fig. 5 is applied at area 1 as disturbance for frequency and tie-line power regulation study under BDA-optimized fuzzy cascade, FPID and PID controller. Figure 6 shows transient response of frequency deviation of area 1 for the same disturbance. The resulted frequency response depicts superiority of proposed fuzzy cascade controller in response to least settling time and reduced oscillation of the responses. Scenario 2: Performance Study Under Random Load A randomly varied load as shown in Fig. 7 is applied at area 1 as disturbance for frequency and tie-line power regulation study under BDA optimized fuzzy cascade, Table 2 BDA-optimized optimal parameters of fuzzy cascade controller Loadings

BDA-designed optimal parameters of fuzzy cascade controller

Step load

K 11 = 1.0062; K 21 = −0.8000; K P1 = −0.0812; K D1 = 0.1242; K PP1 = − 1.0088; K I1 = −0.1628; K 12 = 1.0028; K 22 = 0.2254; K P2 = −1.0062; K D2 = −1.0442; K PP2 = −1.1010; K I2 = 0.1244; K 13 = 1.6626; K 23 = 0.0808; K P3 = −1.1122; K D3 = -0.1460; K PP3 = −0.9802; K I3 = −1.5642

Random load

K 11 = −0.4322; K 21 = −1.7664; K P1 = −1.0432; K D1 = 1,0042; K PP1 = − 1.5628; K I1 = −1.1654; K 12 = −1.4428; K 22 = −1.2254; K P2 = −0.4982; K D2 = −0.4424; K PP2 = −0.2242; K I2 = 0.1244; K 13 = −0.7862; K23 = − 1.8732; K P3 = −0.7076; K D3 = −1.5520; K PP3 = −1.6580; K I3 = −0.2254.

Stochastic load K 11 = −0.2212; K 21 = −1.8760; K P1 = −0.8712; K D1 = 0.9878; K PP1 = − 1.0024; K I1 = −0.9878; K 12 = 1.9020; K 22 = 0.7852; K P2 = −1.8720; K D2 = −1.7866; K PP2 = −1.9080; K I2 = 0.870; K 13 = 1.8810; K 23 = 0.9008; K P3 = −1.0002; K D3 = −0.5076; K PP3 = −0.0342; K I3 = −1.8080;

Fig. 5 Step load response

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in pu

0.03 0.02 0.01 0 -0.01 -0.02

0

10

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30

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Fig. 6 Frequency deviation of area 1

F

1

in Hz

0.05 0 PID

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fuzzy PID fuzzy cascade

-0.1 0

10

20

30

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50

Time in Second

FPID and PID controllers. Figure 8 shows the dynamic response of frequency deviation of area 2 for the same disturbance. The frequency response confers supremacy of proposed fuzzy cascade controller in response to reduced oscillation and least settling time of the responses. Scenario 3: Performance Study Under Stochastic Load A real-time validation stochastic load shown in Fig. 9 is applied at area 1 as disturbance for frequency and tie-line power regulation study under BDA-optimized fuzzy cascade, FPID and PID controllers. Figure 10 shows the dynamic response of frequency deviation for the same disturbance. The frequency response suggests Fig. 7 Response of random load

P L in pu

0.02 0 -0.02 -0.04

0

5

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Fig. 8 Deviation of area 2 frequency

F 2 in Hz

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PID fuzzy PID fuzzy cascade

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Time in Second

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0.7 0.65 0.6 0.55

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Fig. 10 Deviation of area 3 frequency

0.4 PID

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in Hz

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0 0.1

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supremacy of fuzzy cascade controller in response to least settling time and reduced damping of the responses.

6.2 Case 2: Technique Validation This section validates the performance of proposed BDA technique in regard to optimal design all implemented controller such as fuzzy cascade controller, FPID controller and PID controller. The performance is validated under both step load and random load perturbations. Both the step load and random load responses are illustrated in Figs. 11 and 13, respectively. Under such disturbances, the responses of frequency and tie-line power deviation are illustrated in Figs. 12 and 14, respectively. It has been conferred from frequency and tie-line power response that proposed BDA algorithm is more effective in regard to optimal deign of proposed fuzzy cascade controller for frequency and tie-line power control of three-area power system.

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Fig. 11 Step load response

P L in pu

0.03 0.02 0.01 0 -0.01 -0.02

10

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Time in second Fig. 12 Frequency deviation of area 1

F 1 in HZ

0.05 0 GA(fuzzy cascade)

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PSO(fuzzy cascade BDA (fuzzy cascade)

-0.1

20

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40

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Time in Second Fig. 13 Random load response

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Fig. 14 Frequency deviation of area 1

0

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Time in Second

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7 Conclusion The research paper has addressed on AGC issue of a three-area power system with various approaches. The work proposed a robust fuzzy cascade controller to regulate frequency and tie-line power under various loadings such as step load, random load and stochastic load perturbations. The performance of fuzzy cascade controller over FPID and PID controllers has been synthesized through suitable comparative studies. It has been noticed that for same BDA methodology, the proposed fuzzy cascade control technique results more stability in frequency and tie-line power of three-area power system. The BDA-designed fuzzy cascade controller is able to obtain least settling time and reduced overshoot/undershoot oriented responses. Finally, it has been conferred that the proposed BDA-designed fuzzy cascade controller exhibits improved performance in response to the AGC of the three-area power system.

Appendix T g1 = T g2 = T g3 = Thermal system governor time constant = 0.3 s; T t1 = T t2 = T t3 = Thermal system non-reheat turbine time constant = 0.8 s; T r1 = T r2 = T r3 = Thermal system reheat turbine time constant = 10 s; K r1 = K r2 = K r3 = Thermal system reheat turbine gain constant = 10; K P = Power system gain = 120; T P = Power system time constant = 20 s.

References 1. Elgerd OI, Fosha CE (1970) Optimum megawatt-frequency control of multi-area electric energy systems. IEEE Trans Power App Syst 89(4) 2. Sahu PC, Prusty RC, Sahoo BK (2020) Modified sine cosine algorithm-based fuzzy-aided PID controller for automatic generation control of multiarea power systems. Soft Comput 1–18 3. Debbarma S, Saikia LC, Sinha N (2014) Automatic generation control of two degree of freedom controller fractional order PID controller. Electr Power Energy Syst 58:120–129 4. Sahu PC, Prusty RC, Panda S (2019) A gray wolf optimized FPD plus (1 + PI) multistage controller for AGC of multisource non-linear power system. World J Eng 5. Pradhan PC, Sahu RK, Panda S (2016) Firefly algorithm optimized fuzzy PID controller for AGC of multi-area multi-source power systems with UPFC and SMES. Eng Sci Technol Int J 19(1):338–354 6. Sahu PC, Prusty RC, Panda S (2019) Stability analysis in RECS-integrated multi-area AGC system with SOS algorithm based fuzzy controller. In: Computational intelligence in data mining. Springer, Singapore, pp 225–235 7. Sahu PC, Prusty RC, Panda S (2020) Approaching hybridized GWO-SCA based type-II fuzzy controller in AGC of diverse energy source multi area power system. J King Saud Univ-Eng Sci 32(3):186–197 8. Pilla R, Azar AT, Gorripotu TS (2019) Impact of flexible AC transmission system devices on automatic generation control with a metaheuristic based fuzzy pid controller. Energies 12(21):4193

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9. Gorripotu TS, Pilla R (2020) Golf-worm swarm optimized 2DOF-PIDN controller for frequency regulation of hybrid power system. In: Das A, Nayak J, Naik B, Dutta S, Pelusi D (eds) Computational intelligence in pattern recognition. Advances in intelligent systems and computing, vol 1120. Springer, Singapore 10. Pathak N, Bhatti TS, Verma A, Nasiruddin I (2017) AGC of two area power system based on different power output control strategies of thermal power generation. IEEE Trans Power Syst 33(2):2040–2052 11. Sahu PC, Prusty RC, Panda S (2017) MFO algorithm based fuzzy-PID controller in automatic generation control of multi-area system. In: International conference on circuit, power and computing technologies (ICCPCT). IEEE, pp 1–6 12. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053– 1073

Verilog Implementation of High-Speed Wallace Tree Multiplier Sandeep Kumar and Trailokya Nath Sasamal

Abstract The design of a high-speed Wallace tree multiplier has been always challenging on a system design level. In this paper, we proposed a high-speed Wallace tree multiplier using 7:3 and 5:3 counter. Instead of the traditional architecture of the Wallace tree multiplier, we focused on the arrangement of partial product matrix and optimization technique in the reduction tree matrix. The proposed work starts with generation of a partial product matrix and continues until achieves two rows of the partial product matrix. The last two rows of Wallace tree multiplier is added with the help of parallel prefix Ladner-Fischer adder. Results show efficiency of Wallace tree multiplier in terms of hardware and speed. The synthesis and verification of the design is done using Xilinx ISE 14.7. For implementation, Spartan 3E FPGA device xc3s500e-5fg320 is used. Keywords Wallace tree multiplier · Counter · Parallel prefix adder · Xilinx ISE 14.7 · Ripple carry adder

1 Introduction Multiplication is a basic building block in a digital system and essential operation in most of the high-performance signal processing algorithms, FIR filter and microprocessors [8]. Presently, almost all multipliers had suffered from long latency, large area, and high-power consumption for the design of good multipliers. The system designer has a challengeable task of optimizing power and latency of multiplier [2, 9]. In the current scenario, only Wallace tree multipliers provide high-speed operation [1]. So, it is used in most computation to fulfill high-performance applications. S. Kumar (B) School of VLSI and Embedded System Design, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana 136119, India e-mail: [email protected] T. N. Sasamal Department of Electronic and Communication Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana 136119, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_38

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458 Fig. 1 Sub-module of tree-based Multipliers

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Partial Product Generator

Reduction Tree

Final Adder

Wallace tree multiplier has mainly three types of operations which are shown in Fig. (1). The Wallace tree multiplier uses carry-save adder to compress the N-row matrix into the two-row matrix. Then, after, we used conventional adder module to achieve the product of multiplication [1]. The part of a conventional partial product matrix is divided into two groups. The first group used a full adder to generate and carry propagate signal, and the second group used half adder to generate and carry propagate a signal. Conventional Wallace tree multiplier process starts from using half adder and full adder with two input and three input, respectively. The above operation is carried out, until two rows in partial product tree is achieved. On the other hand, for multiplication operation, a large number of authors proposed different-different types of multiplication architecture. One of the common multiplication architectures is basic multiplication, also called add and shift multiplication. Other types of multiplication architectures are Booth multiplication, Vedic multiplication, etc. In the conventional add and shift multiplication, basic mathematical operation performed is simply adding an integer to itself for a specific number of times, i.e., multiplicand (a) is added to itself for number of times equal to multiplier (x) to get the final product (p). On the other hand, the beauty of the Vedic multiplier is that they can be used to solve mathematical operations orally compared to other methods for finding the result. We know that Veda means knowledge of storehouse [13] whereas the Booth algorithm was developed by Andrew Donald Booth at Bloomsbury, London in 1951. Booth encoding consists of two major steps, the first one is taking one bit of multiplier, and then, to choose whether to add the multiplicand to the current or previous bit of multiplier. Booth encoding also reduces the number of addition operations to enhance the performance of multiplier [8]. This paper proposed a 16-bit Wallace tree multiplier with the help of AND-gate to generate the partial product. The generation of the partial product method is the traditional method. But in the part of the reduction tree, we have replaced some full adders with the help of 7:3 counter and 5:3 counter to reduce the number of internal steps in the reduction tree. By this technique, we did a compression task just in four steps instead of six steps and saved two steps delay. By the above approach, we are able to enhance the speed of the Wallace tree multiplier. Then, finally we used the

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Ladner-Fischer adder to add the last two matrices of the multiplier to achieve the final result. The remaining parts of the paper are organized as follows: Sect. 2 discusses tropology of the two counters, the full adder module and the half adder module which are used in the implementation of the proposed multiplier. In Sect. 3, we described the module of Wallace multiplier using counter and adder. In Sect. 4, we discussed the simulation and the RTL diagram of proposed high-speed Wallace tree multiplier along with a comparative study of different architecture of multiplier. In last and final arrangement of this paper, we described the work’s conclusion in Sect. 5.

2 Background Our objective is to construct the Wallace tree multiplier using the counter to replace some full adder and half adder module in the reduction matrix. We have used two types of the counter as 7:3 and 5:3 counter to achieve our target. So brief detail of counter implementation is as follows:

2.1 7:3 Counter 7:3 counter module has seven input and three output ports [5]. The seven input of this counter is further separated into two groups [A, B, C, D] and [E, F, G] and generate three output as sum, C1 and C2 as shown in Fig. (2). The architecture of the 7:3 counter is targeted for our proposed Wallace multiplier to achieve high-speed operation. We used this counter to reduce number of rows in partial product matrix. We have implemented the 7:3 counter by using four slices and four LUT with 8.792 ns delay. Compared to a full adder, this counter needs 2.556 ns more delay, but saved an internal step in reduction tree. Here, the Boolean expression for three output pins are sum, Cout1 and Cout2 for implementation of the 7:3 counter can be written by following Eqs. (1), (2), and (3), respectively. Sum = m2 ⊕ m4 ⊕ D

(1)

Cout1 = m1 ⊕ m5 ⊕ m3

(2)

Cout2 = (m1 · m5) + m3 (m1 + m5)

(3)

where m1 = (A · B) + C · (A + B)

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Fig. 2 7:3 counter

m2 = A ⊕ B ⊕ C m3 = (E · F) + G · (E + F) m4 = E ⊕ F ⊕ G m5 = (m2 · m4) + D (m2 + m4)

2.2 5:3 Counter 5:3 counter module is discussed in [4]. Gate level RTL diagram of the 5:3 counter is presented in Fig. (3). Implementation of 5:3 counter used less resources, specifically number of slices and four-input LUT are 3 and 6, respectively. The total delay of 5:3 counter is 6.779 ns. The 5:3 counter utilizes the propagate signal to generate signals as given in the following equations. Here, we have generated output for 5:3 counter which helps in implementation of efficient multiplier. A Boolean expression for this counter is given in following Eqs. (4), (5), and (6), respectively. Sum = P0 ⊕ P1 ⊕ E

(4)

Cout1 = [(G 0 ⊕ G 1 ⊕ H0 ) ⊕ (H1 ⊕ H2 )] ⊕ ((P0 · P1 ) ⊕ H3 )

(5)

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Fig. 3 5:3 counter RTL

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Cout2 = [(G 0 · G 1 + (G 0 · H2 ))] + (G 0 · H3 ) + [(G 1 · H0 ) + ( G 1 · H1 )]

(6)

where, P0 = A ⊕ B P1 = C ⊕ D H0 = A · E H1 = B · E H2 = C · E H3 = D · E

2.3 Full Adder Full adder is the adder module which adds three one-bit different variable inputs and gives output as sum and carry. The first two inputs are X 1 and X 2, and the third input is a previous carry (C_IN ). The output carry is designated as C_OUT . Normally output is designated as S which is sum and C_out which is output carry. We have implemented this module using one slice and four LUT resources with 6.236 ns delay. The full adder is shown in Fig. (4).

2.4 Half Adder The addition of a 2-bits module is called “half adder.” One-bit input variables of half adder are A and B; the output variables are S (sum) and C out (carry) bits. This module is implemented using one slice and two LUT resources with 6.236 ns delay. The half adder is shown in Fig. (5).

2.5 Ladner –Fischer Adder Ladner–Fischer adder is a parallel prefix carry look-ahead adder proposed by Ladner– Fischer [15] in 1980. Out of all parallel prefix adders, Ladner-Fischer adder is one of the fastest adders and used for high-performance applications [16]. The construction of the adder has three internal steps as following: • Pre-processing step • Carry generation network step • Post-processing step.

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Fig. 4 Full adder RTL

3 Conventional and Proposed Wallace Tree Multiplier In conventional design of 16-bit Wallace tree multiplier architecture, only full adders and half adder module are used in reduction tree [11, 14]. After generating the product matrix of 16 × 16 multiplier, we need to reduce 16 rows into 2 rows. A conventional reducing technique takes six internal steps for performing this operation as shown in Fig. (6). Each internal step takes some delay up to six unit. Therefore, we proposed improved Wallace tree multiplier instead of conventional Wallace tree multiplier. Observing the architecture of the conventional Wallace tree multiplier, we focused on reducing the internal part of the reduction tree. Here, we discussed the design of proposed improved Wallace tree multiplier. Our proposed algorithm starts by generating a partial product tree in the first stage of design similar to conventional Wallace tree multiplier which is constructed as the Dadda pyramid [3, 6, 12]. Then,

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Fig. 5 Half adder RTL

the reduction is performed using the 7:3 and 5:3 counters along with the full adders and half adders. The dot compression architecture of multiplier is completely based on the two types of implemented counters in this paper at Figs. (2) and (3). Figure (7) shows the dot representation of a 16 × 16-bit proposed multiplier. The maiden step generates partial product matrix of 16 row and 16 column for 16-bit multiplier and matrix arranged as Dadda pyramid simultaneously. Then, we need to minimize rows in the internal operation of the reduction tree matrices until we get two rows matrices only. To minimize number of rows, we have used the counter and full adder which are discussed in the topology section of this paper. At the time of the compression of dot notation, we have used 23 modules of 7:3 counter and 3 modules of 5:3 counter in place of full adders. By this approach, we are able to reduce number of internal steps from 6 to 4 in this task. Then, the final stage of the proposed multiplier needs the addition of the remaining two rows. For the addition of remaining two rows, we are using a parallel prefix adder instead of a ripple carry adder to boost overall speed of multiplier. For boosting up this operation, we have some most efficient adder. Some of the author proposed parallel prefix adders for fast response time like Brent–Kung adder [7], Sklansky adder [8], Brent–Kung adder [7], Han–Carlson adder [8], and Ladner–Fischer [10]. All these adders are only differed in terms of design logic level and fan out but all are similar in network topology. By

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Fig. 6 16-bit conventional Wallace tree multiplier

depth analysis of all the parallel prefix adders one by one, we found that LadnerFischer is best suited to our aim. So, in this paper, we used Ladner-Fischer adder to complete the task.

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Fig. 7 Dot represent of 16-bit Wallace tree multiplier

4 Result and Simulation The synthesis and verification of the design are done using Xilinx ISE 14.7. For implementation, Spartan 3E FPGA device xc3s500e-5fg320 is used. Figures (8) and (9) show the RTL schematic and simulation diagram of 16-bit Wallace tree multiplier, respectively. For verification of proposed high-speed Wallace tree multiplier, we have considered three random input variables. In first input given, A = 65,535, B = 65,535 and result as product = 4,294,836,225. In the second sample, we have taken A = 65,535, B = 1 and generated product = 65,535. The last sample was taken as A = 65,535, B = 43,690 and generated product = 2,863,224,150. The simulation result of three samples is given in Fig. (9). Results show the correct functioning of the proposed multiplier. Further, comparative analysis is done for proposed Wallace tree multiplier with 16-bit Array, Dadda, Wallace tree conventional, Wallace tree using BEC-1, Wallace tree using CSLA multiplier, and the corresponding result is listed in Table 1. The comparative result is also presented graphically in Fig. (10). It is worth noticing that proposed design requires less hardware resources and better speed when compared to the previous work [13].

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Fig. 8 RTL schematic diagram of 16-bit Wallace tree multiplier

5 Conclusion A novel architecture for an efficient high-speed 16-bit Wallace tree multiplier is proposed in the paper. We thoroughly studied various multiplication architectures like Array, Booth, Wallace, and Booth Wallace multiplier considering parameters such as speed, area, and power consumption. It has been found that Wallace tree multiplier is more efficient than other multipliers. The design and implementation of the proposed multiplier are done using Verilog HDL code and simulated successfully

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Fig. 9 Simulation result of proposed Wallace tree multiplier

Table 1 Comparative details of proposed and conventional multipliers Multiplier

No. of slice registers

No. of four-input LUTs

Delay (ns)

Array 16 × 16 Multiplier [13]

493

844

61.39

Dadda 16 × 16 Multiplier [13]

493

889

55.65

Wallace 16 × 16 Multiplier [13]

493

1000

36.35

Wallace 16 × 16 Multiplier using BEC-1 [13]

493

1019

24.95

Wallace 16 × 16 Multiplier using CSLA [13]

493

1021

25.59

Wallace 16 × 16 Multiplier using Counter [Proposed]

383

668

23.64

on Xilinx ISE 14.7 synthesis tool. Results indicate total delay of the proposed 16bit high-speed Wallace tree multiplier is 23.636 ns. In addition, proposed design is 61.49, 57.52, 34.97, 05.41, and 07.65% faster than the existing 16-bit Array, Dadda, Wallace tree conventional, Wallace tree using BEC-1, and Wallace tree using CSLA multiplier, respectively [13].

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1200 1000 800 600 400 200 0 No. of Slice Register

No. of 4 input LUTs

Array mulplier Wallace using BEC-1

Dadda mulplier Wallace using CSLA

Wallace convenonal Wallace using counter

Fig. 10 Bar graph of slice and LUTs used in varies multiplier

References 1. Wallace CS (1964) A suggestion for a fast multiplier. IEEE Trans Electron Comput 13(1):14–17 2. Parhami B (2000) Computer arithmetic, algorithms and hardware designs. Oxford University. Press, London, U.K. 3. Waters RS, E Swartzlander (2010) A reduced complexity wallace multiplier reduction. IEEE Trans Comput 59(8):1134–1137. https://doi.org/10.1109/TC.2010.103 4. E Swartzlander (1973) Parallel counters. IEEE Trans Computers C-22(11):1021–1024. https:// doi.org/10.1109/t-c.1973.223639 5. Mehta M, Parmar V, E Swartzlander (1991) High-speed multiplier design using multiinput counter and compressor circuits. In: Proceedings 10th IEEE symposium on computer arithmetic. Grenoble, France, pp 43–50 6. Dadda L (1976) On parallel digital multipliers. Alta Frequenza 45:574–580 7. Dadda L (1965) Some schemes for parallel multipliers. Alta Frequenza 34:349–356 8. Purohit D, Joshi H (2014) Comparative Study and Analysis of Fast Multipliers. Int J Eng Tech Res (IJETR) 2(7) ISSN: 2321-0869 9. Sureka N, Porselvi R, Kumuthapriya K (2013) An efficient high speed Wallace tree multiplier. In: 2013 international conference on information communication and embedded systems (ICICES). Chennai, pp 1023–1026. https://doi.org/10.1109/icices.2013.6508192 10. George JP, Ramesh P (2015) Wallace tree multiplier using compressor. Int J Curr Eng Technol 5(3):2347–5161 E-ISSN 2277–4106, P-ISSN 2347–5161 11. Swee KLS, Hiung LH (2012) Performance comparison review of 32-bit multiplier designs. In: 2012 4th international conference on intelligent and advanced systems (ICIAS2012), Kuala Lumpur, pp 836–841. https://doi.org/10.1109/icias.2012.6306130 12. Mishra RS, Khan T, Singh AP High speed hybrid wallace tree multiplier. Int J Electron Comput Sci Eng 13. Challa Ram G, Sudha Rani D, Balasaikesava R, Bala Sindhuri K (2016) Design of delay efficient modified 16 bit wallace multiplier. In: IEEE international conference on recent trends in electronics information communication technology. India 14. Momeni A, Han J, Montuschi P, Lombardi F (2015) Design and analysis of approximate compressors for multiplication. IEEE Trans Comput 64(4):984–994. https://doi.org/10.1109/ TC.2014.2308214 15. Chakali P, Patnala MK (2013) Design of high speed ladner-fischer based carry select adder. Int J Soft Comput Eng (IJSCE) 3(1). ISSN: 2231-2307, 16. Payal R, Goel M, Manglik P (2015) Design and implementation of parallel prefix adder for improving the performance of carry lookahead adder. Int J Eng Res Technol (IJERT) 04(12)

Multi-objective Optimal Location and Size of Embedded Generation Units and Capacitors Using Metaheuristic Algorithms Subrat Kumar Dash, Laloo Ranjan Pati, Sivkumar Mishra, and Prashant Kumar Satpathy Abstract Weighted sum-based multi-objective metaheuristic methods for simultaneous location, sizing of shunt capacitors, and embedded generators with optimal power factor selection of these embedded generators in radial distribution networks are presented in this paper. The metaheuristic algorithms are simple genetic algorithm (SGA), particle swarm optimization (PSO), and differential evolution algorithm (DEA) for which the results are presented and compared to meet the three objectives such as real power loss reduction, voltage deviation reduction, and enhancement in branch current carrying capacity. To validate the effectiveness of the proposed approaches, a 51-node radial distribution network is considered. Keywords Radial distribution networks · Embedded generators · Sensitivity analysis · Optimal selection of location and size

1 Introduction Embedded generators (EGs) or distributed generators (DGs), referred to the onsite power generating modules, are connected directly to the local loads at medium voltage level [1]. Positive benefits of EGs include a reduction in real power loss, improvement in voltage profile, decrease in substation real and reactive power import, postponing the extension of new transmission & distribution infrastructures, S. K. Dash Government College of Engineering, Kalahandi, Bhawanipatna, Odisha, India e-mail: [email protected] L. R. Pati (B) · S. Mishra Centre for Advanced Post Graduate Studies, BPUT, Rourkela, Odisha, India e-mail: [email protected] S. Mishra e-mail: [email protected] P. K. Satpathy College of Engineering and Technology, Bhubaneswar, Bhubaneswar 751003, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_39

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extending the margin of voltage stability limit, increased reliability, and reduced environmental impacts [2]. Major issues of integrating EGs into the network account for voltage rise at the connecting node and its neighboring nodes, reversal of power flow, (may lead to) rise in real power loss, and require complex protection coordination. This has inducted a lot of research in recent years in the area of optimal selection of location and size of EGs, which have been thoroughly reviewed in [3–5]. Various metaheuristic methods are quite popular to solve this optimization problem as these have proven to produce the best results. Some of the recent metaheuristic methods proposed for this optimization are reported in [6–12]. In light of above developments, the objective of this paper is to use a simple but effective sensitivity-based method to find the best locations for a set of EGs and three metaheuristic methods are implemented on a comparison basis for simultaneous optimal sizing and power selection of EGs and shunt capacitors for various objectives considering four different cases. In Sect. 2, the problem has been formalized by discussing the sensitivity method and the objective function of the optimization problem. In Sect. 3, the algorithms for the three metaheuristic methods are presented for the sizing problem. In Sect. 4, the detailed results and its analysis are presented.

2 Problem Formulation In this paper, EGs and capacitors interconnection nodes are modeled as PQ node [2]. Further, EGs & capacitors are modeled as negative active & reactive power load, respectively, in the load flow algorithm.

2.1 Sensitivity Analysis To reduce the burden on optimization techniques, a sensitivity analysis is carried out to determine the best locations for installing compensating devices (EGs, or capacitors). In this paper, voltage sensitivity to real and/or reactive power injection by compensating devices is utilized to determine candidate nodes for placing shunt capacitors, EGs operating at unity power factor, lagging power factor as well as EGs with optimized power factor. The nonlinear power flow equation representing the power system can be linearized as:      P J1 J2 δ (1) = J3 J4 V Q For shunt capacitors, P equals to zero and for UPF, EGs Q equals to zero. Substituting these conditions into Eq. (1) and solving it for V yields

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V = Jcap Q

(2)

−1  Jcap = J4 − J3 J1−1 J2

(3)

V = J DG, p P

(4)

−1  J DG, p = J2 − J1 J3−1 J4

(5)

where

&

where

Now, EGs operating at a specified power factor contributes both real and reactive powers to the nodes to which they are connected. If power factor angle is represented as φ, then Q = P tan φ

(6)

Substituting Eq. (4) in Eq. (1) and solving for V, Eq. (1) becomes V = J DG, pq P tan φ

(7)

−1    I − J1 J3−1 J DG, pq = J2 − J1 J3−1 J4

(8)

where

2.2 Objective Functions The inclusion of EGs into the network greatly affects the node voltages & also causes reversal power flow. Therefore, the size of EG is constrained by the amount of allowable voltage rise in the network and thermal limit of the feeders. Considering the above facts, the following objective function is proposed which will optimize the sizes of EGs and capacitors to reduce the real power loss of the network while maintaining the required voltage profile and keeping feeder current of each lateral within acceptable limits. Minimize,

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  n    plossdg Ii + w J = w1 ∗ + w ∗ max ∗ (Vi − Vmin )2 + (Vi − Vmax )2 2 3 o ploss Ici i=1 (9) where plossdg and plosso represent the total real power loss in presence of EGs and without EGs, respectively. I i and I ci are the branch currents in the presence of EGs and rated feeder capacities, respectively. In this paper, V min and V max are set to 0.95 pu and 1.05 pu, respectively. In this paper, w1 , w2 , and w3 are set to 1/3 to give equal importance to all the three objectives. However, network operators can choose different weighting factors as per their preference.

3 Optimization Methods Three popular metaheuristic-based optimization algorithms are considered for the effective sizing of the EGs in this section; these are: simple genetic algorithm (SGA), particle swarm optimization (PSO), and differential evolution algorithm (DEA).

3.1 Simple Genetic Algorithm (SGA) It is one of the most popular metaheuristic optimization techniques which is used in this paper for optimizing the sizes of EGs and shunt capacitors. The underlying principle of GA is inspired by Darwin’s Theory of Evolution [13]. In GA, design variables are first encoded to binary strings of a fixed dimension where each binary bit is regarded as gene, and the entire string is called a chromosome. Each chromosome represents a potential solution in the search space. A set of chromosomes forms a population. The population converges toward the solution when it is acted upon by genetic operators (selection, crossover, and mutation) detected by the fitness of the objective function to be optimized. First, a population is generated randomly where each design variables are well in agreement to their specified ranges. To find the goodness of the quality of solution, the entire population is evaluated by the objective function formulated for optimization. As per the Roulette Wheel selection method, a chromosome is selected for crossover based on its cumulative fitness and total fitness of the population. A random number is generated between zero and the total fitness value of the population. Chromosome, whose cumulative fitness is just greater than or equal to the random number, is selected and is called as a parent. In the process of crossover, a random number between zero and the size of the chromosome is generated. The remaining bits after the random number of pairs of selected chromosomes are swapped to form offspring. Finally, the mutation is performed by flipping some bits of the population detected by the probability of mutation.

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The steps for implementing SGA to determine optimal sizes of EGs & shunt capacitor and optimal power factor EGs are described below. STEP-1: Initialize of SGA parameters STEP-2: Form initial population (sizes & power factor of EGs & sizes of capacitors). STEP-3: Determine the fitness of each chromosome by evaluating the objective function using Eq. (9) by running a load flow program. STEP-4: Do selection, crossover, and mutation & increment generation. STEP-5: Repeat STEP-4 until the stopping criterion is met. STEP-6: Print the optimal DGs sizes, capacitor sizes, and power factor of EGs.

3.2 Particle Swarm Optimization (PSO) PSO is also a population-based intelligence technique developed by Kennedy and Eberhart [14], which is inspired by the social behavior of birds flocking. This algorithm starts with initializing a population with random positions and velocities as defined in Eq. (10) and (11). t t t t X (t) = xi1 xi2 xi3 . . . xim

(10)

t t t t V (t) = vi1 vi2 vi3 . . . vim

(11)

where xit j and vit j represent position and velocity of ith particle in jth dimension at iteration t, respectively. The fitness of each particle is determined by evaluating the objective function for its position vector (10). The best fitness value of each particle in the population up to tth iteration is stored as Pibest and its corresponding position as Pij . Similarly, the best fitness value of the whole population is stored as G ibett and its corresponding position as gij . Weight, velocity, and position of the population for the next iteration are modified using Eqs. (12–14), respectively. 

 maxiter − t = wn + (wo − wn ) w t



 t+1 t vit+1 vi j + c1r1 pit j − xit j + c2 r2 git j − xit j j =w ∗

t+1

t+1 t xit+1 j = x i j + vi j

(12) (13) (14)

where W o , W n … and maxiter are initial weight, final weight, and maximum number of iteration, respectively. c1 , c2 represents acceleration coefficients and r 1 and r 2 are random numbers between (0, 1). Complete procedures for implementing PSO for optimizing sizes of EGs are enumerated below. Step 1: Initialize PSO parameters

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Step 2: Form initial population (sizes & power factor of EGs & sizes of capacitors) by assigning random positions and velocities equal to Npop. Step 3: Determine the fitness of each particle by evaluating the objective function using Eq. (9) by running a load flow program. Step 4: Update Pbest and Gbest. Step 5: Increment iteration, t = t+1, update weight, velocity, and position of the population using Eqs. (12–14). Step 6: Repeat steps 2–5 until the stopping criterion is met. Step 7: Print the optimal DGs sizes, capacitor sizes, and power factor of DGs.

3.3 Differential Evolution Algorithm (DEA) [15] Storn Price (1995) first proposed a differential evolution algorithm (DEA) that stems from the natural evolution of species. It is a population-based metaheuristic technique that can solve both continuous as well as mixed-integer optimization problems elegantly. The basic operations performed in DEA are mutation, crossover, and selection. DEA starts with initializing a population of target vectors uniformly distributed over the search space as given by the following Eq. (15). ∗

= X min j + X max j − X min j r X i(G=0) j

(15)

where I Np J D R

1, 2, 3 … N p , Number of Population. 1, 2, 3 … D, Number of decision variable. random no uniformly distributed between the range [0, 1]

Mutation: Mutation is performed on target vectors to generate mutant vectors. This begins with randomly drawing three target vectors Xr 1 , Xr 2 , and Xr 3 from the population for each target vector X i such that r1 = r2 = r3 = i and then modifying target vectors as per Eq. (16). where r 1 , r 2 , and r 3 are the indices of target vectors belongs to the range of [1 … N p ].

 ViG = X rG1 + F ∗ X rG2 − X rG3

(16)

For i = 1, 2, 3 … N p . Where F is a user-defined constant called as scaling mutation factor often selected within a range of [0, 1]. Crossover: In each generation, trial vectors (U i ) are then produced by the crossover operation performed on each pair of target vectors (X i ) and mutant vectors (V i ). The content of target vectors is derived from the pair of target vectors and mutant vectors

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where the composition is controlled by crossover rate (CR). CR usually has a range between [0, 1]. The following Eq. (17) describes the crossover operation. j Ui,G

=

j

Vi,G if rand ≤ C R j X i,G Otherwise

(17)

Selection: In the selection process, the fitness values of target vectors, f (X i , G ), and newly generated trial vectors f (U i , G ) are compared for the current generation. The one in each pair of target vectors and trial vectors, with the best solution, will advance to the next generation as described in Eq. (18).

j X i,G+1

=





Ui,G if f Ui,G ≤ f X i,G Otherwise X i,G

(18)

Complete procedures for implementing DEA for optimizing sizes of EGs are enumerated below. STEP-1: Read system data. STEP-2: Initialize DEA parameters. STEP-3: Initialize target vectors (sizes of EGs, sizes of capacitors, and power factor of DGs) using Eq. (15). STEP-4: Generate mutant vectors for corresponding target vectors using Eq. (16). STEP-5: Apply crossover operators on target vectors and mutant vectors to form trial vectors using Eq. (17) STEP-6: Run the load flow program to evaluate the objective function (9) called as fitness value for the EG units (target vectors and trial vectors) placed at candidate nodes. By comparing the fitness values of target vectors and trial vectors form the target vectors for the next generation. STEP-7: Check the convergence criteria: (if G < Gmax ). If yes increment iteration counter (G = G+1) and go to step 4 else go to step 8. STEP-8: Print the optimal DGs sizes, capacitor sizes, and power factor of EGs.

4 Results The proposed algorithms have been implemented on a 51-node radial distribution network comprising of a total real (Pt ) and reactive power loads (Qt ) of 2463 kW and 1569 kVAr, respectively. The line and load data of the network are given in [16]. A simple and efficient load flow algorithm [17] based on forward and backward sweep technique has been applied to compute the system performance under various operating conditions. In this work, three units of synchronous generator-based EGs capable of delivering both real and reactive powers have been optimized. Each EG unit has a maximum capacity of 600 kW. Three shunt capacitors each having a maximum reactive power output of 1000 kVAr are used. The proposed algorithm has

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Table 1 Sensitivity analysis for selection of best nodes

Ranking of node

Shunt capacitor

Unity power factor DG

0.95 lagging power factor DG

1

16

16

16

2

15

45

45

3

14

15

15

Table 2 Distribution system performance without DGs and shunt capacitors Pt (kW)

Qt (kVAr)

Active power loss (kW)

Reactive power loss (kVAr)

V min (pu)

V max (pu)

2463

1569

147.6

111.3

0.9023

1.0000

been implemented on Intel(R) Core (TM) 2 Duo, 2.20 GHz CPU in the MATLAB environment. The effectiveness of the proposed algorithm is validated by considering three cases as explained later in this section. To obtain the best locations of DGs & shunt capacitors, DGs & shunt capacitors of 0.4 p.u. capacity are placed at all nodes one at a time & Eqs. (2), (4) & (7) are evaluated and ranked in ascending order of V. Top three nodes are selected for DGs & shunt capacitor placements. Ranking of top three nodes for installation of shunt capacitor, EG units operating at unity power factor, and 0.95 power factor lagging are listed in Table 1. Distribution system performance before the installation of EGs and capacitors is shown in Table 2.

4.1 CASE-1: Optimization of Unity Power Factor DGs Only In this case network performance in the presence of only unity power factor, DGs is analyzed. The optimal DGs sizes, minimum and maximum voltages of the network and real and reactive power losses of the network are compared in Table 3 for all the three optimization methods. In the presence of DGs, voltage profile of the network is improved significantly than without any DGs and capacitor placement as shown in Table 2. The voltage profile of the network before and after the placement of EGs for three methods is shown in Fig. 1. Since optimal EGs sizes obtained by using GA, DE, and PSO yield almost identical voltage profile, the voltage waveforms in Fig. 1 are found to be overlapping. EGs installation has also helped in real power loss reduction of (40.56%, 40.29%, 40.29%) and reactive power loss reduction of (52.23%, 52.26%, 52.26%) using GA, DE, and PSO methods, respectively. It can be observed from Table 3 that the sizes of DGs obtained are identically the same when optimized using DE and PSO methods. Result confirms that DGs sizes obtained by DE/PSO correspond to better loss reduction than GA. Performance of GA, DE, and PSO methods for optimization of sizes of unity power factor EGs are depicted in Fig. 2. It is clearly observed that PSO performs

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Table 3 Performance of the system after installation of optimal UPF DG units Methods

Optimum DG values (Kw)

V min (pu)

V max (pu)

Ploss (kW)

Qloss (kVAr)

GA

P16 DG

73.90

0.9534@51

1.0 @ 1

88.89

53.20

P45 DG

561.88

P15 DG

598.24

P16 DG P45 DG P15 DG P16 DG P45 DG P15 DG

82.92

0.9537@51

1.0 @ 1

88.30

53.17

0.9537@51

1.0 @ 1

88.30

53.17

DE

PSO

560.85 600.00 82.92 560.85 600.00

1

Voltage Magnitude (pu) ----->

0.99 0.98 0.97 0.96 0.95 0.94 0.93 base case GA DE PSO

0.92 0.91 0.9

5

10

15

20

25

30

35

40

45

50

Node No ----->

Fig. 1 Voltage profile of the network with and without UPF DGs placement

better in optimization as compared to GA and DE as within 20 iterations PSO able to explore the global minima.

4.2 CASE-2: Optimization of 0.95 Power Factor DGs Only Sizes of DGs operating at 0.95 power factor is optimized at sensitive nodes as decided from sensitivity analysis. Sizes of EGs, real and reactive power losses, and maximum and minimum voltages of the network after EGs placement are compared in Table 4 for proposed methods. KVA ratings of EGs operating at lagging power factor are found to be more as compared to their counterparts operating at unity power factor.

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GA DE PSO

Objective Function Value(J) ----->

1.594 1.593 1.592 1.591 1.59 1.589 1.588 1.587 1.586

0

20

40

80

60

100

120

140

160

200

180

No of Iterations ----->

Fig. 2 Performance analysis of optimization methods for case 1

Table 4 Performance of the system after installation of 0.95 power factor DG units Methods

Optimum DG values (Kw)

V min (pu)

V max (pu)

Ploss (kW)

Qloss (kVAr)

GA

P16 DG

165.92

0.9562@51

1.0045 @ 16

80.27

47.23

P45 DG

476.83

P15 DG

573.60

P16 DG

143.14

0.956@51

1.0043 @ 16

80.32

47.20

P45 DG

476.61

P15 DG

600.00

P16 DG

143.10

0.9563@51

1.0043 @ 16

80.32

47.20

P45 DG

476.62

P15 DG

600.00

DE

PSO

It is important to note that EGs operating at 0.95 lagging power factor can now compensate for both real and reactive power demands. Therefore, it resulted in better voltage profile and improved real and reactive power losses reduction as compared to EGs operating at unity power factor. Moreover, the voltage at node no 16 is found to be more than substation voltage (though within safe limit, i.e., < 1.05 p.u.) as indicated in the 5th column of Table 4. Once again, sizes of EGs obtained using DE and PSO are identically the same, whereas that obtained using GA is slightly less which is reflected in terms of marginal improvement in voltage profile and thin improvement in loss reduction. Improvement in voltage profile before and after placement of DGs operating at 0.95 power factor is depicted in Fig. 3. Figure 4 compares the convergence characteristic of GA, DE, and PSO in optimizing the sizes of 0.95 power factor lagging DGs.

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481

Voltage Magnitude (pu) ----->

1.02 1 0.98 0.96 0.94 base case GA DE PSO

0.92 0.9

10

5

15

20

25

30

35

40

50

45

Node No ----->

Objective Function Value(J) ----->

Fig. 3 Voltage profile of the network with and without 0.95 PF DGs placement 1.35

GA DE PSO

1.345 1.34 1.335 1.33 1.325 1.32 1.315

0

20

40

60

80

100

120

140

160

180

200

No of Iterations ----->

Fig. 4 Performance analysis of optimization methods for case 2

4.3 CASE-3: Optimization of DGs Operating at the Optimal Power Factor In this case, both the sizes as well as the power factor of EGs are optimized using the proposed methodology. Optimal EGs size, optimal EGs power factor, voltage profile, and loss reduction are furnished in Table 5. Results indicate when EGs operate at their optimal power factor, it leads to better improvement in voltage profile and significant loss reduction as compared to EGs operating at a unity power factor or EGs operating at 0.95 lagging power factor. It is interesting to note that in the presence of EGs operating at optimal power factor voltage at none of the nodes have crossed substation voltage level (1.0 p.u.). Voltage profiles of the network for DGs operating at optimal power factor are displayed in Fig. 5. Convergence characteristics of GA, DE, and PSO in optimizing

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Table 5 Performance of the system after installation of optimized power factor EG units Methods

Optimum DG values (Kw)

Optimum power factor

V min (pu)

V max (pu)

Ploss (kW)

Qloss (kVAr)

GA

P16 DG

42.82

0.9792 (lead)

0.9326@51

1@1

111.26

85.09

P45 DG

119.06

0.9612 (lead)

P15 DG

239.88

0.9772 (lead)

P16 DG

337.34

0.9696 (lag)

0.9600@39

1.0233@16

72.77

40.81

P45 DG

399.82

0.9536 (lag)

P15 DG

465.00

0.9720 (lag)

P16 DG

143.17

0.9502 (lag)

0.9614@39

1.0163@16

66.52

37.07

P45 DG

476.80

0.9502 (lag)

P15 DG

600.00

0.9502 (lag)

DE

PSO

Voltage Magnitude (pu) ----->

1.04 1.02 1 0.98 0.96 0.94 base case GA DE PSO

0.92 0.9

5

10

15

20

25

30

35

40

45

50

Node No ----->

Fig. 5 Voltage profile of the network with and without DGs operating at the optimal power factor

the sizes of DGs and power factor are shown in Fig. 6.

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483

Objective Function Value (J) ------>

25

PSO GA DE

1.52

20

1.5

15

1.48

10

1.46

5

1.44

0

0

20

40

60

80

100

120

140

160

180

1.42 200

o of terations ------>

Fig. 6 Performance analysis of optimization methods for case 3

4.4 CASE-4: Simultaneous Optimization of UPF DGs and Shunt Capacitors In this case, both the sizes of unity power factor EGs, as well as shunt capacitors, are optimized simultaneously as per the proposed methodology. Results for optimal sizes of EGs and shunt capacitors, real and reactive power loss reduction, and minimum and maximum voltage of the network are represented in Table 6. The real power demand is locally contributed by the EGs whereas reactive power demand is fed from shunt capacitors. In this case, more real and reactive powers are generated Table 6 Performance of the system after simultaneous installation of optimal UPF DG units and shunt capacitor units Methods Optimum DG values (Kw) GA

DE

PSO

Optimum CAPs values (kVAr)

V min (pu)

Q16 C

0.9632@39 1.024@ 16

P16 DG

42.82

P45 DG P15 DG P16 DG P45 DG P15 DG P16 DG P45 DG P15 DG

119.06 Q15 C

300

239.88 Q14 C

300

0

337.34 Q16 C

0

399.82 Q15 C

100

465.00 Q14 C

450

143.17 Q16 C

50

476.80 Q15 C

50

600.00 Q14 C

500

V max (pu)

Ploss (kW) Qloss (kVAr)

71.65

35.96

0.9625@39 1.0183@16 68.93

35.95

0.9632@39 1.0221@16 70.87

35.72

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locally as compared to that of only EGs operating at unity power factor or only EGs operating at 0.95 power factor. Therefore, it can be seen from Table 5 that a better voltage profile is maintained as compared to the previous two cases. Voltage profiles of the network for DGs operating at optimal power factor are displayed in Fig. 7. Convergence characteristics of GA, DE, and PSO in optimizing the sizes of DGs and power factor are shown in Fig. 8. Table 7 compares the performance of optimization techniques (GA, DE, and PSO) in terms of simulation time, best, worst, and average results and number of function evaluations. The results are obtained for 30 independent runs of each optimization technique for a population size of 21 and a maximum iteration of 200. Though GA requires minimum number of function evaluations, but in terms of searching the best solution, PSO proves its supremacy over both GA and DE.

Voltage Magnitude (pu) ----->

1.04 1.02 1 0.98 0.96 0.94 0.92 0.9

5

10

15

20

base case GA DE PSO 25 30

35

40

45

50

Node No ----->

Fig. 7 Voltage profile of the network with and without simultaneous UPF DGs and shunt capacitors

Objective Function Value(J) ----->

1.44

GA DE PSO

1.42 1.4 1.38 1.36 1.34 1.32 1.3

0

20

40

60

80

100

120

140

No of Iterations ----->

Fig. 8 Performance analysis of optimization methods for case 4

160

180

200

Multi-objective Optimal Location and Size of Embedded … Table 7 Comparison of performance analysis of optimization techniques

485 GA

DE

PSO

Avg time

103.0614

108.07

49.956

Best time

101.868

108.07

29.412

Best result

1.0955

1.0952

0.7668

Worst result

1.1358

1.0984

0.8404

Average result

1.1089

1.0961

0.7995

Number of function evaluation

18,900

56,700

19,530

5 Conclusion In this paper, a sensitivity-based method has been used to find the optimum locations for DGs and shunt capacitors, which has been proven to be quite simple and effective. Three metaheuristic techniques SGA, PSO, and DEA have been used to find the optimal size of the DGs and capacitors for four different cases. The detailed analysis of the results establishes the supremacy of PSO-based optimization methods.

References 1. Patnaik B, Mishra M, Bansal RC, Jena RK (2020) AC microgrid protection–a review: Current and future prospective. Appl Energy 271:115210 2. Mishra, S., Das, D., Paul, S.: A Simple Algorithm for Distribution System Load Flow with Distributed Generation. In: International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), 1–5. India (2014) 3. Georgilakis PS, Hatziargyriou ND (2013) Optimal distributed generation placement in power distribution networks: models, methods, and future research. IEEE Trans Power Syst 28(3):3420–3428 4. Sambaiah KS, Jayabarathi T (2020) Loss minimization techniques for optimal operation and planning of distribution systems: a review of different methodologies. Int. Trans. Electr. Energy Syst. 30(2):1–48 5. Huy, P.D., Ramachandaramurthy, V.K., Yong, J.Y.: Tan, K.M., Ekanayake, J.B.: Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage. Energy. 195, Article 117011 (2020) 6. Khodabakhshian A, Andishgar MH (2016) Simultaneous placement and sizing of DGs and shunt capacitors in distribution systems by using IMDE algorithm. Int. J. Electri. Power Energy Syst. 82:599–607 7. Raut U, Mishra S (2019) An Improved Elitist Jaya algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems. Renew. Energy Focus. 30:92–106 8. Suresh, M.C.V., Edward, J.B.: A hybrid algorithm based optimal placement of DG units for loss reduction in the distribution system. Appl. Soft Comput. 91 (2020) 9. Truong, K.H., Nallagowndena, P., Elamvazuthia, I., Vo, D.N.: A Quasi-oppositional-chaotic symbiotic organisms search algorithm for optimal allocation of DG in radial distribution networks. Appl. Soft Comput. 88, 1–25 (2020) 10. Raut U, Mishra S (2020) An improved sine cosine algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems. Appl Soft Comput 92:1–25

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11. Selim A, Kamel S, Jurado F (2020) Efficient optimization technique for multiple DG allocation in distribution networks. Appl Soft Comput 86:1–20 12. Raut, U., Mishra, S.: A new Pareto multi-objective sine cosine algorithm for performance enhancement of radial distribution network by optimal allocation of distributed generators. Evol. Intell (2020) 13. Goldberg DE.: Genetic Algorithms in Search, Optimization and Learning, a Book. AddissonWesley, Reading, MA (1989) 14. Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. (ICNN), 4, 1942–1948 (1995) 15. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417 16. Gampa, S.R., Das, D.: Optimum placement and sizing of DGs considering average hourly variations of load. Electri. Power Energy Systems 66, 25–40 (2015) 17. Mishra, S.: A simple algorithm for unbalanced radial distribution system load flow. In: IEEE Region 10 Conference (TENCON) India, 1–6 (2008)

Comparative Performance Investigation of Fractional Order and Conventional PID Controller Implemented for Frequency Stability B. Begum, Prakash Chandra Sahu, and Binod Kumar Sahu

Abstract This paper presents automatic generation control (AGC) of an interconnected power system having two-area thermal reheat system. The control areas are provided with single reheat turbine with generation rate constraints (GRC) of 5%/min. In this article, proportional–integral–derivative (PID) and fractional proportional–integral–derivative (FOPID) control structure are employed in automatic generation control (AGC) loop. The parameters of the control structure are tuned with a metaheuristic optimization technique, i.e., spotted hyena optimization technique (SHO). Performance of classical controller PID is investigated and compared with FOPID controller. The results reveled in the comparison FOPID control structure, gives better response than classical PID controller. For the superiority of the given optimization technique, the results are compared with PSO technique. For sensitivity analysis, the system dynamic performances are studied with large variation of step load change in both the area. Keywords AGC · Fractional order PID controller (FOPID) · Spotted hyena optimization (SHO)

Nomenclature B Frequency response characteristics puMW/HZ R Regulation parameter of speed governor, HZ/puMW K ps Power system gain B. Begum · B. K. Sahu (B) Department of Electrical Engineering, IITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] B. Begum e-mail: [email protected] P. C. Sahu Department of Electrical Engineering, Silicon Institute of Technology, Sambalpur, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_40

487

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T ps TT Tr Ti j TG Kr

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Power system time constant Turbine time constant Reheat time constant Tie-line synchronizing coefficient Governor time constant Reheat gain

1 Introduction For reliable and secure interconnected power system, we have to keep the frequency and interconnected powers within pre-specified limits. Whenever there is change in loading condition, the frequency and interconnected powers changes from their specified values which is unenviable, and as results, it provide interrupted power supply. Therefore, for continuous operation of power system, we have to observe the behavior of load demand and generation continuously that concept is automatic generation control (AGC). AGC maintains balance between load and generation and minimize the frequency error or maintaining area control error (ACE) to zero [1]. The theory of AGC in interconnected system first proposed by Elgerd et al. [2]. Nowadays many researchers are focused on automatic generation control of multi-area system having more than three areas with various effective optimization techniques. The literature surveys show the development and strategies of research in past few years of AGC in power system with various classical and modern controllers. The mostly used classical controllers are proportional–integral (PI) and proportional– integral–derivative (PID); Khuntia et al. [3] analyzed the advantages of the controllers to maintain zero steady-state error to a step load change. Due to some certain disadvantages, like undesirable speed overshoot, the slow response due to sudden load change. After that some new control strategies are introduced in AGC to regulate the problem. Some researchers proposed the cascaded PI-PD controller for automatic generation control [4] in multi-source power system; for better performance, the two degree of freedom PID controller are introduced in two- and three- area systems with some nonlinear constraints by Rabindra et al. [5]. After conventional controllers, fuzzy logic-based controllers play a vital role in AGC; Subha [6] proposed fuzzy logic controller in two-area reheat thermal system. Sanjoy et al. [7] focused on two degree of freedom proportional–integral–derivative controller in three-area system with GRC constraints. Recently, fractional controllers are used in AGC, and it has been noticed that the fractional controllers give the better performance than integer controller. Taher et al. [8] introduced fractional PID controllers (FOPID) and Almoush et al. [9]. Besides these many more controllers have been introduced for AGC, Hybrid neuro fuzzy by Bhagyashree et al. [10], neural network-based controller by S Panda [11], adaptive fuzzy controller for AGC issues in power system; Sahu et al. [12] designed a fuzzy PID controller by teaching learning-based algorithm to study AGC problem in power system. Due to the intricacy of power system, the

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synthesis of the above controller is not possible directly. Therefore, optimization of controller parameters by different technique plays an important role. The various optimization techniques widely used in AGC problems are particle swarm optimization (PSO), differential evolution algorithm (DE), bacterial forging optimization algorithm (BFOA, genetic algorithm (GA), hybridization of (PSO-BFOA), firefly algorithm with pattern search algorithm, and flower pollination algorithm-based proportional–integral–derivative controller has been used for frequency control. Recently, golf-worm swarm optimization technique has been implemented for frequency control in hybrid power system [13] and metaheuristic-based fuzzy PID controller designed for FACTS devices [14], and many more algorithms are under process for load frequency control. Currently fractional order controllers are used to achieve the most robust performance of the system. Initially, the FOPID controller was introduced by Podlubny [15] for a fractional order system. Indranil Pan [16] focused on fractional order LFC of interconnected power system using chaotic multi-objective optimization; Ismayil et al. [17] designed FOPID for single area thermal power system for AGC. Recently, for dynamic performance a novel cascaded PI-FOPID introduced by Y.Arya [18] with a recent stochastic imperialist competitive algorithm. From the literature review, we noticed that the fractional order controller has the better performance than integer controller, and it has been shown in Almoush [8] FOPID more flexible in design and gives better performance than IO-PID controller. In this article, FOPID controller considered as a secondary controller for LFC of a two-area thermal reheat system. Here, all the parameters of control structure are tuned with spotted hyena optimization technique (SHO) [19]. Objective of the paper i. ii. iii. iv. v.

Here, a two-area thermal reheat system is considered with 5%/min. GRC, with a demand slip of 2%. The performance of FOPID controller is compared with classical or conventional PID controller. By using spotted hyena optimization algorithm, all the controller gains are optimized. For the best optimal values of the controller parameters, the gains are compared with PSO algorithm. Robustness of FOPID controller studied by large change in system parameters with large variation of step load in both area-1 and area-2.

2 System Under Study A two-area interconnected thermal system with single reheat turbine with 5%/min GRC is considered in given Fig. 1. The power rating of both system are equal,i.e.,2000 MW. All the system parameters are carried from [18] which is depicted in the appendix. The system dynamics are investigated with a demand slip of SLP 2% in area-1. The parameter of control structure is optimized with a novel

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Fig. 1 Transfer function model of two-area thermal power plant

optimization technique spotted hyena optimization (SHO). Initially, the response of the system investigated with PID controller and then with fractional PID controller, then comparison is taken. For the better performance of the system, the controller parameters are compared with the PSO technique.

3 Controller Structure and Objective Function Due to the simplicity of design and better performance at small settling time, PID controllers are widely used in many industries for process control application. In this article, an extension of PID controller is designed, i.e., fractional PID controller (FOPID). In PID controller, we tuned only three gain parameters (K p , K i , K d ) but in FOPID we are tuning five parameters (K p , K i , K d , λ and μ). If the value of extra two parameters becomes one, then FOPID becomes conventional PID controller. Therefore, the maximum value of λ and μ is one. There are various advantages of FOPID controller a. b. c. d.

No steady-state error. Robustness to variations in the gain of the plant. Robustness to high frequency noise. Good output disturbance rejection.

The nonlinear system is linearized at different operating points, and control structure is designed for different operating points, whereas one fractional PID control

Comparative Performance Investigation of Fractional Order …

491

Fig. 2 Block diagram of FOPID controller

structure is sufficient for nonlinear system. For a long time-delay system, FOPID gives better results than classical PID controller. The block diagram of fractional order controller (FOPID) is shown in Fig. 2. For better performance of the system, the controller gain parameters are properly tuned. Here, for tuning of controller gain, spotted hyena optimization technique is used with integral time absolute error (IATE) objective function. J = ITAE =   t  | f 1 | + | f 2 | + ptie 12  t · dt (1) 0

where f 1 and  f 2 are the deviations in frequency of area 1 and 2, and ptie 12 is change in interconnected power between area 1 and area 2.

4 Spotted Hyena Optimization (SHO) Algorithm It is a novel optimization algorithm inspired by Hyenas. The theory behind this algorithm is the social interaction between spotted hyenas and their collaborative behavior. The tentative results reveal that the SHO algorithm performs better than the other competitive optimization technique. The flow chart for the SHO algorithm is shown in Fig. 3.

5 Simulation Results and Discussion 5.1 Case-1: Transient Analysis of Presided System For the dynamic response of the system shown in Fig. 1, the time domain simulation is performed in MATLAB/SIMULINK for step load change in parameter variation. In Figs. 4, 5, and 6, the frequency deviation in area-1 (f 1 ), frequency deviation in area-2 (f 2 ), and the tie-line power deviation (Ptie ) are shown during a 2% step load variation in area-1 only. The dynamic response of the system is obtained with proposed FOPID controller and compared to conventional PID controller with SHO and PSO optimization technique. The optimal values of the control parameters are shown in Table 1. The overshoot, undershoot, and settling time are given in below Table 2. Settling time is determined with 0.2% band for f 1 and 0.02% band for f 2 and Ptie .

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B. Begum et al. START Obtain the initial spotted hyena inhabitants Choose the initial values Calculate the appropriate value of each search agent Define the set of best value Restore the location of each search agent Calculate the appropriate value of restored search agent Restore the location of each search agent if it is better than previous Updated the group of spotted hyena based on the restored appropriate value of search agent No

Checking the terminating point Yes Return the best solution STOP

Fig. 3 Flow chart of SHO technique

Fig. 4 Deviation of frequency in first area with 2% slip in first area

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Fig. 5 Deviation of frequency in second area with 2% slip in first area

Fig. 6 Variation of tie-line power with 2% slip in first area Table 1 Optimal values of the control parameters Controller type

K p1

K i1

K d1

K p2

K i2

K d2

λ1

λ2

μ1

μ2

PSO-PID

1.4985 1.4862 0.3110 1.4888 1.5279 0.6307 –

–

–

–

SHO-PID

1.9985 1.9887 0.4236 1.9991 1.9926 0.8262 –

–

–

–

SHO-FOPID 2.1529 2.9892 1.1491 2.8598 2.9986 1.0865 0.8611 0.4722 0.8296 0.9000

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Table 2 Transient parameters of the system Indices FOPID

PID

PSO-PID

u sh

osh

ts in

u sh

osh

ts in

u sh

osh

ts in

−3

−3

sec

−3

−3

sec

−3

−3

sec

 f1

−23.4081 0.5376

1.75

−28.7726 2.3028

2.78 −32.1694 2.9812

4.48

 f2

−15.0480 0.1740

3.55

−19.7709 0.9012

13.8

−24.0733 1.4180

14.87

−4.4792 0.0701

4.3

−6.1408 0.2983

11.1

−7.2963 0.3608

13.24

×10

Ptie

×10

×10

×10

×10

×10

5.2 Case-2: Robustness Analysis The response of the proposed model further investigated for various loading condition. The robustness of the system is carried out by variation of step load in both the areas shown in Fig. 7. From the result shown in below figures shows that the designed controller are robust and having better performance. It can be observed from the investigation of given model the proposed control strategy provides a stable and reliable system.

6 Conclusion A simplified model of interconnected power system having two-area thermal reheat system is analyzed for AGC of power system. The system is designed with PID and FOPID controller, and the controller gains are optimized with spotted hyena optimization technique (SHO) and compared with the PSO-based algorithm. From the various output waveforms and the table given, it concluded that the dynamic response of the system is enhanced. From the robustness analysis, it can be seen that there is negligible variation of frequency and tie-line power with change in system condition. The system having the better performance in FOPID controller than conventional PID controller. Further, the work can be extended to three-area multi-source system with variation of different loading condition.

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Fig. 7 a Random load in area-1 and its effects on the transient response, b random load in both the areas and its effects on the transient response

Appendix B1 = B2 = 0.425 p.u.MW/Hz, Pr = 2000 MW, R1 = R2 = 2.4 Hz/p.u, T G =0.08 s, T T =0.3 s, K r = 0.5 s, T r = 10 s, 1 = 2 = 120 Hz/p.u.MW, 1 = 2 = 20 s, 12 = 0.0866 p.u.MW/rad, a12 = −1, Pn = 50%, f = 60 Hz, Pd = 0.02 p.u.MW.

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References 1. Kundur P, Balu NJ, Lauby MG (1994) Power system stability and control, vol 7. McGraw-hill, New York 2. Fosha CE, Elgerd OI (1970) The megawatt-frequency control problem: a new approach via optimal control theory. IEEE Trans Power Appar Syst 4:563–577 3. Khuntia SR et al (2009) A comparative study of PI, IP, fuzzy and neuro-fuzzy controllers for speed control of DC motor drive 4. Padhy S, Panda S (2017) A hybrid stochastic fractal search and pattern search technique based cascade PI-PD controller for automatic generation control of multi-source power systems in presence of plug in electric vehicles. CAAI Trans Intell Technol 2(1):12–25 5. Sahu RK, Sidhartha P, Umesh KR (2013) DE optimized parallel 2-DOF PID controller for load frequency control of power system with governor dead-band nonlinearity. Int J Electri Power Energy Syst 49:19–33 6. Subha S (2014) Load frequency control with fuzzy logic controller considering governor dead band and generation rate constraint non-linearities. World Appl Sci J 29(8):1059–1066 7. Debbarma S, Lalit CS, Nidul S (2014) Robust two-degree-of-freedom controller for automatic generation control of multi-area system. Int J Electri Power Energy Syst 63:878–886 8. Taher, Seyed A, Masoud HF, Saber FA (2014) Fractional order PID controller design for LFC in electric power systems using imperialist competitive algorithm. Ain Shams Eng J 5(1):121–135 9. Alomoush MI (2010) Load frequency control and automatic generation control using fractionalorder controllers. Electr Eng 91(7):357–368 10. Shree SB, Kamaraj N (2016) Hybrid neuro fuzzy approach for automatic generation control in restructured power system. Int J Electric Power Energy Syst 74:274–285 11. Khuntia SR, Panda S (2012) Simulation study for automatic generation control of a multi-area power system by ANFIS approach. Appl Soft Comput 12(1):333–341 12. Nayak, Jyoti R, Binod S, Binod KS (2018) Application of adaptive-SOS (ASOS) algorithm based interval type-2 fuzzy-PID controller with derivative filter for automatic generation control of an interconnected power system. Eng Sci Technol Int J 21(3):465–485 13. Gorripotu TS, Pilla R (2020) Golf-worm swarm optimized 2DOF-PIDN controller for frequency regulation of hybrid power system. In: Das A, Nayak J, Naik B, Dutta S, Pelusi D (eds) Computational intelligence in pattern recognition. advances in intelligent systems and computing, vol 1120. Springer, Singapore 14. Pilla R, Azar AT, Gorripotu TS (2019) Impact of flexible AC transmission system devices on automatic generation control with a metaheuristic based fuzzy PID controller. Energies 12(21):4193 15. Podlubny Igor (1994) Fractional-order systems and fractional-order controllers. Inst Exp Phys Slovak Acad Sci Kosice 12(3):1–18 16. Pan I et al (2012) Chaos suppression in a fractional order financial system using intelligent regrouping PSO based fractional fuzzy control policy in the presence of fractional Gaussian noise. Nonlinear Dyn 70(4):2445–2461 17. Ismayil C, Kumar RS, Sindhu TK (2014) Automatic generation control of single area thermal power system with fractional order PID (PIλDμ) controllers. IFAC Proc 47(1):552–557 18. Arya Y (2019) A novel CFFOPI-FOPID controller for AGC performance enhancement of single and multi-area electric power systems. ISA Trans 19. Dhiman G, Kumar V (2018) Multi-objective spotted hyena optimizer: a multi-objective optimization algorithm for engineering problems. Knowl-Based Syst 150:175–197

Performance Analysis of Solar PV-Based Unified Power Quality Conditioner System for Power Quality Improvement Under Nonlinear Load Condition Sarita Samal, Prasanta Kumar Barik, Tarakanta Jena, and Manoj Kumar Debnath Abstract In this paper, the power quality (PQ) profile like harmonics and voltage sag is improved in the power system by integrating a solar PV-based unified power quality conditioner (UPQC). The control technique of the UPQC is designed by using a basic a synchronous reference frame technique for reference current generation, hysteresis current controller technique for switching pulse generation and conventional proportional- and integral- (PI)based DC-link voltage controller. The foremost objective of the proposed research is to control the DC-link voltage of the UPQC which is achieved by integrating a solar PV-based renewable energy source across its DC-link capacitor. The analysis is carried using MATLAB simulated results. To verify the outcomes, the proposed UPQC results are also compared with conventional PI controller. Keywords DC-link voltage · Harmonics · Power quality · Sag

1 Introduction Active power filter (APF) technology is emerged mainly for the need of reducing the harmonics in the power system. Moreover, the APFs are utilized to reimburse the reactive power consumed by the nonlinear loads, load balancing, improving stability of the power system, reducing voltage flickering and neutral current compensation, S. Samal School of Electrical Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India e-mail: [email protected] P. K. Barik (B) Department of Mechanical and Electrical Engineering, CAET, OUAT, Bhubaneswar, India e-mail: [email protected] T. Jena · M. K. Debnath Department of Electrical Engineering, ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] M. K. Debnath e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_41

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etc. [1, 2]. Earlier, passive filters were used and they were the main cause of resonance problem, frequency variations and instability of the system. Active filters overcome the problems of passive filters due to the usage of power electronic components [3]. The unified power quality conditioner (UPQC) may be connected at point of common coupling (PCC) for the compensation of harmonic currents and voltage sags and swells [4, 5]. Moreover, UPQC is enormously used for compensations of reactive power and current harmonics, sag and swell both in voltage and current waveform. In order to achieve proper compensation, the control action plays a vital role. Hence, the literatures on different control strategies are explained further. Many control strategies for generating reference currents were proposed such as instantaneous p-q theory, synchronous detection, active and reactive current, synchronous reference frame (SRF) and sine multiplication are presented in the literature. However, SRFbased control strategies for generating reference are simple and easy to implement. Hence, the author has used this scheme in this paper [6]. The authors in [7, 8] have suggested that hysteresis current control (HCC) technique is simple and easy to implement. Hence, this switching scheme has been taken in this paper. In most of the earlier reported work, the DC-link voltage control and reduction of its settling time are achieved by either proportional – integral (PI) controller or fuzzy logic controller (FLC) and PI-FLC [9]”. Previous works on UPQC tells that the PI controller has been widely used in case of UPQC connected with any load gives satisfactory result [10]. However, it fails to work when the load is variable. Therefore, this motivates the present authors to investigate further the performance of UPQC under varying load condition using solar PV across its DC-link. The foremost idea of this research is prearranged below • To develop a solar PV-based UPQC system (comprising of a conventional SRF, HCC-based switching scheme and PI controller-based DC-link voltage control) in MATLAB/SIMULINKR environment. • Performance analysis of the proposed UPQC in view of harmonics and voltage sag mitigation under the action of conventional PI controller-based UPQC and PV-based UPQC are investigated in the presence of nonlinear load. • Additionally, comparative performance analysis of proposed UPQC is investigated in view of DC-link capacitor voltage regulation in the presence of nonlinear condition Other sections are given as follows. Section 2 discusses the configurations of the UPQC with its conventional control scheme. In Sect. 3, modelling of solar PV system with boost converter is presented. In Sect. 4, performance analysis of the UPQC is carried out using the conventional as well as proposed control techniques. The paper ends with a brief conclusion in Sect. 5.

2 UPQC Design This chapter begins with system configuration and detailed description on UPQC. The basic structure of UPQC is shown in Fig. 1 which consists of two inverters

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Fig. 1 UPQC model

Diode based Load 3-Phase Source

Series Transformer

IC

UPQC

connected to a common DC-link capacitor. The series inverter is connected through a series transformer, and the shunt inverter is connected in parallel with the point of common coupling. The series inverter acts as a voltage source whereas the shunt one is acts as a current source. The main function of UPQC is to control the power flow and reduce the harmonics distortion both in voltage and current waveform. The series APF topology is shown in Fig. 2. The series APF protects load from the utility side disturbances. In case of series APF, Park’s transformation method is used for generation of unit vector signal. A PWM generator, generating synchronized switching pulses, is given to the six switches of the series converter. The control action of this SAPF is made to supply/draw a harmonic current to/from the utility, in order to cancel the harmonic currents produced by the nonlinear (i_{s} ) load along with the reactive power compensation. By doing this, the resulting current drawn from the utility grid is made as sinusoidal, which is free from harmonics. The block diagram of SAPF is shown in Fig. 3. To eliminate the harmonics from the source current i s , an equal amount of compensating current (i c ) is injected by SAPF in opposite phase to that of harmonic current. The equation shows below are the basic equation for understanding SAPF operation. i s (t) = il (t) − i c (t)

(1)

vs (t) = Vs sin ωt

(2)

Line Impedance

Load

3-phase Sorce Transformer

Inverterr

Fig. 2 Series inverter model

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Fig. 3 Basic structure of SAF

For diode-based nonlinear load il (t) = i 1 sin(ωt + ϕ1) +

∞ 

i n sin(nωt + ϕn)

(3)

n=2

The current from the filter is i c (t) = il (t) − i s (t)

(4)

Hence, for the exact compensation of reactive power and harmonics current, it is essential to determinei s (t). wherei s (t),il (t), i c (t) are the values of source, load and compensating current, respectively. Where, i 1 and ϕ1 are the amplitude of the fundamental current and its angle with respect to fundamental voltage; i n and ϕn are the amplitude of the nth current and its angle.

3 Control Strategies for UPQC There are many control methods available for UPQC; here, we design two basic methods which are easy for implementation. The SRF controller scheme for reference current generation as it works in steady-state as well as in dynamic condition exquisitely to manage the active, reactive power and reduce the harmonics in load current.

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3.1 Generation of Reference Current The compensation task has been carried out by implementing different reference current generation techniques, proposed in the literatures [11, 12], which includes an instantaneous power theory, i.e. (p-q) method, an instantaneous current theory, i.e. (d-q) method, direct testing and calculating method, Fourier transform method, sine multiplication theorem, synchronous reference frame (SRF) method.

3.1.1

SRF Method

Here, the three-phase currents in abc coordinates are transformed to d −q coordinate system. The transformed vectors in d − q are given as input to the LPF. The filtered elements are transformed back again to the stationary frame represented in terms of three-phase equivalents is shown in Fig. 4 ⎤ ⎤⎡ ⎤ ⎡ ila iq cos θ cos(θ − 120) cos(θ + 120) ⎣ i d ⎦ = 2 ⎣ sin θ sin(θ − 120) sin(θ + 120) ⎦⎣ ilb ⎦ 3 1 1 1 i0 ilc 2 2 2 ⎡

(5)

First, the circuit senses the (ila , ilb and ilc ) and converts into rotating components i d − i q as given in Eq. (5). The components i d − i q are representing the rotating reference frame of the abc reference frame. The i d − i q currents are passed through an LPF for filtering the harmonic components of the load current, which allows only

Fig. 4 SRF-based control scheme

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Reference Current isa* isa Switching scheme

isb* isb

S1 S2 S3 S4 S5 S6 VDC

isc* i sc

Actual Current

-VDC

Fig. 5 HCC switching scheme

the fundamental component. Finally, this i d −i q current is transformed to three-phase stationary frame using inverse Park transformation technique.

3.2 Generation of Switching Pulses A variety of approaches are reported in the literature [13, 14] but HCC technique has more beneficial for SAPF as it is easy to implement. Hence, it is used in this paper. The HCC scheme is demonstrated in Fig. 5. The switching pattern is derived ∗ ) of from the falling and rising current inside the band. The HCC generates the (i sa preferred magnitude and frequency, which will then be compared with the (i sa ).

3.3 Capacitor Voltage Regulation The main purpose of connecting the SAPF is to inject the (i c ) at PCC, thereby reducing the harmonic content and required reactive power. This paper proposes solar PV-based VDC regulation under nonlinear load condition, and for a proper comparison, conventional control scheme employing the PI controller is also projected.

3.3.1

PI Controller

The control of VDC in the UPQC is normally done by the traditional PI controller. Maintaining VDC at a constant level it is important for obtaining the desired compensation performance of the UPQC. This voltage is maintained constant until the active power absorption by the converter is quite good for maintaining its losses.

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Fig. 6 PI controller scheme

If the magnitude of this active power is decreased to the level of inability of the converter to compensate its losses, VDC will deviate from its normal value. Hence, the compensation is not properly done. According to Fig. 6, the measured VDC is compared with the (VDC.ref ). The error (en ) generated is managed by the PI controller with the help of PI gain K p and K i , respectively.

4 Solar PV Systems Solar energy is a good alternative source, because of its pollution free and abundant nature for electric power production. To interconnect the solar energy with UPQC, a boost converter (BC) is essential. Moreover, they are implemented with their control loop and maximum power point tracking arrangements. To achieve this, the duty cycle of the selected converter should be controlled [15]. Perturb and Observe is selected for this research work because it is simple to implement, easy to track the prior values of current and voltage, provides better performance in the frequently changing environmental conditions and its 90% efficiency in tracking the maximum power.

4.1 Modelling of Solar Panel The PV array model of KC200GT is considered for modelling, as given in the literatures [15, 16]. This model represents the PV cell as a current source with a parallel diode and a resistance connected in series as demonstrated in Fig. 7. The scientific model of a solar array is represented as in Eq. (6) which specifies the nonlinear output characteristics of the solar cell.

  q × V P V + I P V ∗ Rse −1 I P V = N p × I Ph − N p × Io exp Ns × AKT (6)

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Fig. 7 Solar cell model

4.2 Power Tracking Algorithm for Solar Panel The main issue in the PV generation system is that the value of power generated by the solar array is always varying with weather conditions, i.e. the intensity related to the solar radiation. MPPT algorithm having a quick response characteristic is capable of generating electric power at its maximum, and in any weather condition is employed to solve the aforementioned problems [17]. Several MPPT algorithms have been reviewed in the literatures. (P & O) assure an easy and fast response to the frequently varying solar irradiance. Hence, (P & O) is used here as an effective MPPT algorithm to produce maximum energy irrespective of the irradiation level. The flowchart of (Pe & Ob) MPPT is given in Fig. 8.

4.3 Boost Converter This is otherwise called as voltage step up converter and used for stepping up the solar array output and thus making the output voltage of level of DC-link capacitor operation. This is shown in Fig. 9. In addition, boost converter (BC) is useful in tracking maximum power point also. The design of BC (voltage and current calculation) is achieved based on the following parameters [18, 19]. Using the duty ratio D, then the output DC voltage is calculated. The overall model of the PV with MPPT and BC is shown in Fig. 10 which produces an output of 700 V shown in Fig. 11 as required by the UPQC.

5 Result Analysis The proposed UPQC is connected at the PCC through filter inductor as displayed in Fig. 12. The modelling of the proposed system is carried out using

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Fig. 8 Flowchart of P and O MPPT algorithm

Fig. 9 Boost converter

L

D1 Vin

D

S

C

R

V0

MATLAB/SIMULINKR . The best chosen values of the parameters used for this design are:vs = 400 V, f = 50 Hz,Rs = 1 Ω,L s = 0.1 mH and (VDC,ref ) = 700 V. The first nonlinear load consists of an uncontrolled bridge rectifier supplying 20 W resistors in series with 50 mH inductors. The performance analysis of the considered UPQC is investigated in different scenario are explained below.

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R

Diode DC Output

25 Solar Panel C1

DC

C2

400 Insolation MPPT

Fig. 10 Block diagram of overall solar PV system

Fig. 11 Output voltage of solar PV system

3-Phase Source

Series Transformer

Non linear load

Shunt Inductor

Shunt APF

Series APF DC LINK Capacitor

Boost converter

Fig. 12 Block diagram of the proposed UPQC

Solar PV

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(a) Scenario 1: Performance analysis based on voltage sag mitigation and harmonics compensation capability without UPQC and UPQC with basic control approach under the said load. (b) Scenario 2: Performance analysis based on voltage sag mitigation and harmonics compensation capability with proposed UPQC. (c) Scenario 3: Comparative performance analysis of proposed UPQC is investigated in view of DC-link capacitor voltage regulation in the presence of nonlinear condition.

5.1 UPQC Performance Analysis Considering Scenario 1 In this case, the UPQC performance analyses in the presence of the nonlinear load. In order to validate the operation of series APF of the UPQC, voltage sag introduce in the system. The profile of load voltage is shown in Fig. 13a conforms that from 0.1 s to 0.3 s of the time axis voltage sag observed clearly. “For sag condition, the series APF detects the voltage drop and injects the required voltage through the

Fig. 13 Voltage sag reimbursement a V s without UPQC b V c injected by UPQC c V s with UPQC

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series coupling transformer. It maintains the rated voltage across the load terminal. In order to compensate the load voltage sag, UPQC (employing SRF scheme with PI controller) is turned on, which injects compensating voltage at the PCC as displayed in Fig. 13b. As a result, the load voltage is same as that of source voltage. The load voltage after compensation is shown in Fig. 13c. In order to investigate, the operation of shunt APF a rectifier-based nonlinear load is introduced into the system, and the level of harmonics is checked. It may be seen from Fig. 14a that the source current waveform is non-sinusoidal in nature and a very high total harmonic distortion (THD) of 16.60% as per the FFT analysis of the load current shown in Fig. 14b. In order to make source current to be sinusoidal, the shunt APF of the UPQC (employing SRF scheme with PI controller) is turned on, at t = 0.1 s it injects ic at the PCC as displayed in Fig. 14c. As a result, THD level comes down to 2.63% as shown in Fig. 14d, and the source current waveform is nearly sinusoidal after 0.1 s as shown in Fig. 14e.

5.2 UPQC Performance Analysis Considering Scenario 2 In this work, a PV system connected to a DC-link instead of a constant DC source connected for supplying input to three-phase VSI with a selected control strategy for extraction of harmonic current which acts as a UPQC. The use of a PV array with BC for generating a voltage of 700 V DC for retaining fixed DC-link voltage is another distinguishing feature of this system. To validate the proposed UPQC in compensating the harmonics and voltage sag, case studies were conducted on the simulated model. Initially, voltage sag introduces in the system from 0.1 s to 0.3 s of the voltage waveform as shown in Fig. 15a. For sag condition, the series APF detects the voltage drop and injects the required voltage through the series coupling transformer. It maintains the rated voltage across the load terminal. The V c injected by the series APF of the solar-based UPQC is demonstrated in Fig. 15b. The V l with UPQC is presented in Fig. 15c which conforms that the sag is completely mitigated by the injection of compensating voltage. Similarly, for harmonics mitigation, the compensating current ic generated by the SRF method of the with solar-based UPQC system is portrayed in Fig. 16a. Fig. 16a shows that the shunt APF is turned ON at 0.1 s which generates I c from 0.1 s to 0.4 s and before 0.1 s the shunt APF is in turned OFF condition. The is with UPQC is presented in Fig. 16b, and the level of THD content is presented in Fig. 16c. It is concluded from Fig. 16b that during the ON period (01–0.4 s) of the shunt APF the source current is nearly sinusoidal in nature with a very less harmonics content of 1.80% as per Fig. 16c.

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Fig. 14 Harmonics mitigation a is without UPQC b THD without UPQC c ic injected by UPQC d is with UPQC e THD with UPQC

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Fig. 15 Voltage sag reimbursement a V s without UPQC b V c injected by UPQC c V s with UPQC

5.3 UPQC Performance Analysis Considering Scenario 3 In this case, the UPQC in conjunction with SRF scheme, HCC switching scheme in regulating VDC is compared with that PI controller during nonlinear load condition. Figure 17a and b show the VDC regulation and its corresponding response time for both the cases.” Figure 17a shows that PI controller attains high voltage settling time response of 0.25 s, and from Fig. 17b for the case of solar PV-based UPQC technique, the voltage settling time reduces to 0.04 s.

6 Conclusion The proposed solar-based PV UPQC technique performs outstandingly in curbing deviation in DC-link capacitor voltage response settling time in comparison to that of conventional PI controller. From the numerical comparison of results, it is evidently noticeable that the solar PV-based UPQC can able to reduce the source current harmonics about 1.80% and also reduces the DC-link capacitor voltage response

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Fig. 16 Harmonics mitigation a is without UPQC b THD without UPQC c ic injected by UPQC d is with UPQC e THD with UPQC

Fig. 17 DC-link voltage regulation a conventional PI technique b proposed Technique

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time to 0.04 s. Investigations may be made to use the hybrid filter and to design its control loop in order to improve the power quality of the system in various aspects.

References 1. Singh B, Al-Haddad K, Chandra A (1999) A review of active filters for power quality improvement. IEEE Trans Industr Electron 46(5):960–971 2. Hosseinpour M, Yazdian A, Mohamadian M, Kazempour J (2008) Desing and simulation of UPQC to improve power quality and transfer wind energy to grid. J Appl Sci 8(21):3770–3782 3. Samal S, Hota PK, Barik PK (2016) Harmonics mitigation by using shunt active power filter under different load condition. In: 2016 international conference on signal processing, communication, power and embedded system (SCOPES), pp 94–98 4. Chen D, Xie S (2004) Review of the control strategies applied to active power filters’, in electric utility deregulation, restructuring and power technologies, 2004. In: Proceedings of the 2004 ieee international conference on, vol 2, pp 666–670 5. Mishra M (2019) Power quality disturbance detection and classification using signal processing and soft computing techniques: a comprehensive review. Int Trans Electrical Energy Syst 29(8):e12008 6. Salam Z, Cheng TP, Jusoh A (2006) Harmonics mitigation using active power filter: A technological review’. Elektrika J Electri Eng 8(2):17–26 7. Das JC (2004) Passive filters-potentialities and limitations. IEEE Trans Ind Appl 40(1):232–241 8. Barik PK, Shankar G, Sahoo PK (2019) Power quality assessment of microgrid using fuzzy controller aided modified SRF based designed SAPF. Int Trans Electr Energ Syst e12289. https://doi.org/10.1002/2050-7038.12289 9. Nabae A, Ogasawara S, Akagi H (1986) A novel control scheme for current-controlled PWM inverters. IEEE Trans Ind Appl 4:697–701 10. Mao H, Yang X, Chen Z, Wang Z (2012) A hysteresis current controller for single-phase three-level voltage source inverters. IEEE Trans Power Electron 27(7):3330–3339 11. Rong Y, Li C, Ding Q (2009) An adaptive harmonic detection and a novel current control strategy for unified power quality conditioner. Simul Model Pract Theory 17(5):955–966 12. Mikkili S, Panda AK (2014) Performance analysis and real-time implementation of shunt active filter Id-Iq control strategy with type-1 and type-2 FLC triangular MF. Int Trans Electric Energy Syst 24(3):347–362 13. Samal S, Hota A, Hota PK, Barik PK (2020) Harmonics and Voltage Sag Compensation of a Solar PV-Based Distributed Generation Using MSRF-Based UPQC. In: Innovation in electrical power engineering, communication, and computing technology. Lecture Notes in Electrical Engineering, vol 630. Springer 14. Hua C, Shen C (1998) Study of maximum power tracking techniques and control of DC/DC converters for photovoltaic power system. In: Power electronics specialists conference, 1998. PESC 98 Record. 29th Annual IEEE, vol 1, pp. 86–93 15. Hassaine L, Olias E, Quintero J, Haddadi M (2009) Digital power factor control and reactive power regulation for grid connected photovoltaic inverter’. Renew Energy 34(1):315–321 16. Rajkumar MV, Manoharan P (2013) FPGA based multilevel cascaded inverters with SVPWM algorithm for photovoltaic system. Sol Energy 87:229–245 17. Hussein K, Muta I, Hoshino T, Osakada M (1995) Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions’. IEE Proc-Gener Trans Distrib 142(1):59–64 18. Bist V, Singh B (2014) An adjustable-speed PFC bridgeless buck–boost converter fed BLDC motor drive. IEEE Trans Industr Electron 61(6):2665–2677 19. Viinamäki J, Jokipii J, Messo T, Suntio T, Sitbon M, Kuperman A (2014) Comprehensive dynamic analysis of photovoltaic generator interfacing DC–DC boost power stage. IET Renew Power Gener 9(4):306–314

Distributed Estimation of IIR System’s Parameter in Sensor Network Using Block Diffusion LMS Meera Dash, T. Panigrahi, and Renu Sharma

Abstract In literature, distributed algorithms are proposed to estimate the parameters of finite impulse response (FIR) systems wich is inherently stable. Whereas in some sensor network-based remote sensing applications like target tracking and fast rerouting, feedback systems are used along with feedforward path. The distributed estimation process improves the estimation algorithm by reducing the probability of falling into the local minima of the IIR systems cost function. But if the network is sparse, then each node interacted with less number of neighbors. This limitation is improved by employing multihop diffusion LMS algorithm which introduces more communication overhead. Now, in this paper to compensate the increase in communication overhead can be reduced by following the block diffusion method with multihop communication scenario. It is seen from the simulation results that the 2hop block diffusion LMS algorithm provides best performance by providing least steady state mean square error and mean square deviation with minimum number iterations. Keywords Block diffusion LMS · Diffusion LMS · Distributed parameter estimation · Sparse wireless sensor network · IIR system · Feedback system

M. Dash (B) Department of ECE, ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India e-mail: [email protected] T. Panigrahi Department of ECE, NIT Goa, Veling, Goa, India e-mail: [email protected] R. Sharma Department of EE, ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_42

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1 Introduction The distributed algorithm in wireless sensor network (WSN) is used to improve the estimation accuracy at every sensor node. This is feasible because of information exchange between sensor node is a common phenomenon in distributed WSN [14]. But, in sparse WSNs, where the connectivity among the sensor node is very poor, each of the node is unable to access all the information from the sensor network [8, 10]. In this scenario, the best solution is the centralized approach where each node send the data to the fusion center. But it needs more communication and is not energy efficient [4]. To overcome this limitation, multi-hop diffusion least mean squared algorithm in WSN is proposed in literature. Every sensor node in network now access the information from the immediate neighbors and h-hop nodes as well. In literature, distributed algorithms for to estimate the parameters of finite impulse response (FIR) system are proposed [12]. If the system is modeled as infinite impulse response (IIR), then the conventional algorithm has its own limitation as the cost function is multimedia. But it requires less number of parameter to model the system which can leads to energy efficient algorithm in estimating them in distributed scenario. In [9], the authors used incremental approach where a senor nodes estimates its parameter by utilizing own observed data and the estimated information received from its predefined neighbour using particle swarm optimization. This incremental mode of operation needs ring topology among the sensor nodes. It may not feasible in a large WSNs. It is impossible when the network is sparse [7]. Whereas in the diffusion cooperation yields better performance over incremental [19]. Therefore, diffusion cooperation between the nodes in the sparse sensor network is applied here to determine the parameters of the unknown IIR system [18]. In this approach, Every node in the network locally update the system parameters and then it sends the estimated parameter with their immediate neighbors [18]. The diffusion LMS method is extended to determine the parameters of the feedback system in [2, 3]. Further, the diffusion LMS algorithm for IIR system is extended for sparse network by using multi-hop communication [5, 8]. That is, each sensor node exchanges their estimated parameters among one and two-hop neighbors. In fact, in multihop diffusion the communication overheads increased by h times compared to the conventional diffusion LMS. In this paper, block diffusion LMS [12] is proposed to compensate the increase in communication overhead by conventional diffusion LMS (DLMS). In block DLMS each sensor updates own information after processing block of data together and then shares with the neighbours. This is different from the conventional method. Because in conventional method, each sensor nodes send their updated information to the neighbours after each iteration. By doing this the performance slightly decreased but the communication overhead decreased by block length times with respect to DLMS algorithms. Therefore, In this paper a multihop block diffusion LMS algorithm is proposed in sparse sensor network for determine the parameters of the feedback system. By doing this the proposed new

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Fig. 1 Estimation of parameter in distributed sparse WSN at kth nodes

algorithm will be energy efficient as it required block length time less communication overhead than the DLMS algorithm.

2 Problem Formulation Let us consider a sparse sensor network of K sensor nodes which are distributed in a geographical region. Each sensor nodes associated with and IIR system and the focus is to determine the parameters of interest. An undirected graph is used to develop the topology of the sparse WSN. S = {s1 , s2 , . . . , s K } represents the set of sensors. In compact form kth sensor is denoted as sk . Any two nodes l and l are linked then called immediate neighbouring nodes. The links between sensor nodes are to be bidirectional in nature. The neighbourhood of a sensor node k is defined as the set of sensors connected to it via a bidirectional link [12]. The degree of every sensor is very less in sparse WSN, may the edge one connected hardly to one node. A time varying feedback system (having both feedback and feed forward path) is associated with every sensor in the sparse sensor network. The block diagram for adaptive estimation of an time varying IIR system at kth sensor is shown in Fig. 1. Each node in the sparse sensor network is measured noisy output dk (i) to the input data vector xk (i) at ith time instant. The noise is assume to be additive and identically distributed. The noise is also assumed as spatially and temporally independent. Each node sk has desired data dk (i) to the input vector xk (i) at time instant i [16]. The model of the adaptive system has N and M number of poles and zeros respectively. In a standard IIR system, the input-output measured data is related as [2]

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dk (i) =

M 

bm xk (i − m) +

m=0

N 

an dk (i − n) + vk (i)

(1)

n=1

N M where {an }n=1 and {bm }m=0 are the pole and zero of the feedback system which are unknown and can be determined, vk (i) denotes the additive noise at senor node sk and at ith time instant. The nro,ally distributed additive noise has zero mean and 2 variance σv,k . The standard noise is assumed to be normalized white Gaussian and is uncorrelated with the input data xk (i). Equation (1) is written in compact form as T

T

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(2)

where a = [a1 , a2 , . . . , a N ]T , b = [b0 , b1 , . . . , b M ]T , dki−1 = [dk (i − 1), dk (i − 2), . . . , dk (i − N )]T and w T = [a T bT ]. In order to identify the feedback system associated at each sensor node, the adaptive system here is also modeled as feedback kind of system with similar structure. At each sensor sk , the adaptive system has the estimated output yk (i) for the same input xk (i) can be written as recursive equation which is similar to (1) given as [2] yk (i) =

N 

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(3)

m=0

The above equation represents the adaptive systems output at sensor sk . The estimated N M and {bˆk,m (i)}m=0 at each node k are coefficients of the adaptive system {aˆ k,n (i)}n=1 adjusted during training period in order to minimize the mean squared error.

3 Determining IIR System’s Parameter in Distributed Scenario In order to estimation the vector w, optimum least square global cost function is to be optimized which is given as [6]: J (w) =

K 

Jk (w)

(4)

k=1

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(5)

is the cost function at each sensor sk . The solution which is optimal may be obtained by solving (5) [16].

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ˆ = arg min w w

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(6)

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In literature, mean squared error (MSE) approximation is used to estimate the parameters of the FIR filter. Now the same algorithm can be applied to estimate the coefficients of IIR filter [17]. The natural recursive steepest-descent algorithm may be used to optimize (5) which is given as ˆ i−1 − μ ˆi = w w

K 

ˆ i−1 )] ∇w [Jk (w

(7)

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ˆ i is the estimated parameter in the ith iteration, μ > 0 is defined as the where w ˆ i−1 ) with respect to w ˆ i−1 is defined step size. Whereas the gradient vector of Jk (w i−1 ˆ )]. In fact the Eq. (7) is not distributed since it requires the global as ∇w [Jk (w ˆ i−1 . Adapt and then combine mode of diffusion LMS algorithm is information w introduced for (7) here as [1] ˆ k0 = winit k = 1, 2, . . . , K i = 1, 2, . . . ; w ek (i) = dk (i) − uki wki−1 ˆ ki = w ˆ ki−1 + μk ek (i)uki w  ˆ ki = ˆ li w clk w

T

(8)

l∈Nk

Every sensor node in the sparse WSN cooperate by following distributed algorithm (8) to find the parameter of IIR system.

3.1 Determining the Parameter of IIR System Using Multihop Communication in Sparse WSN In a sparse sensor network, the conventional diffusion cooperation strategy is unable to give desired performance. Multihop diffusion LMS is proposed in [5, 10]. The distributed estimation algorithm has energy constraints. Whereas a multihop diffusion method is proposed here without constraints to determine the parameters of IIR system. In the this strategy, every sensor node is accessible to h-hop nodes unlikely only to 1-hop neighbours in most of the distributed algorithms. Now, Nkh is defined as the set of sensors present to h-hop distance from node sk . This is also called the h-hop neighbours of sensor k. The topology for 1-hop and 2-hop neighbours is described in [10].

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The weight update strategy for h-multihop diffusion is defined as ˆ k0 = winit i = 1, 2, . . . ; k = 1, 2, . . . , K ; w ek (i) = dk (i) − uki wki−1 ˆ ki = w ˆ ki−1 + μk ek (i)uki w  ˆ ki = ˆ li w clk w

T

(9)

l∈Nkh

where μk is the step size parameter of sensor sk , and clk are combiner weights (metropolis weights) satisfying clk ≥ 0,



ckl = 1, and ckl = 0 if l ∈ / Nkh

(10)

l∈Nkh

Here in the combiner step, each node has used more number of neighbors data. This step is difference from the conventional diffusion algorithm (That is in between (8) and hATC (9)). In h-hop adapt then combine (hATC) every node k has accessed information from h-hop neighbors Nkh . The immediate neighbor Nk is a subset of the h-hop neighbours. If the combinational coefficient clk > 0, one can say that sensor l is connected to sensor k. The combiner coefficients are same for both the strategies [12].

3.2 Estimation of IIR System’s Parameter Using Block LMS for Multihop Diffusion It is observed that in multihop diffusion, the communication overheads increases because each node accumulates information from the h-hop sensors unlike only from the next neighbours in DLMS. In the mean time, block diffusion LMS for FIR system identification is proposed in [12] to reduce the communication overhead by block length time from the conventional DLMS algorithm. In block LMS, the weight is to be updated after processing blocks of data unlike after each data in conventional LMS. If block DLMS is used, then sensors interact among themselves after processing block of data which leads to the reduction in the communication overhead. Therefore, here the block diffusion LMS is incorporated in multihop scenario for infinite impulse response system’s parameter estimation in a sparse network. By doing this, the increase in communication overhead due to multihop will be compensated by the block technique. Let L is the block size and j represents the block index. The time index is related to the block index as i = j L + n; n = 0, 1, 2, . . . , L − 1

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For block LMS of IIR system, the input data matrix of size L × (N + M + 1) at node k in jth block index is given as j

Uk = [ukJ L ; ukJ L+1 ; . . . ; ukJ L+L−1 ]

(11)

where each data vector uki is defined in (?). The desired output for the block of data at kth node in jth block index is defined as j

dk = [dk ( j L), dk ( j L + 1), . . . , dk ( j L + L − 1)]T

(12)

The error is calculated for block of data at kth node is j

j

j

ek = [ek ( j L), ek ( j L + 1), . . . , ek ( j L + L − 1)]T = dk − Uk wˆk j−1

(13)

Now, the block multihop n diffusion LMS (BhDLMS) algorithm for IIR system is defined as [10, 12] ˆ k0 = winit j = 1, 2, . . . ;k = 1, 2, . . . , K ; w L−1  μbk  j L+q  j j−1 j L+q j−1 ˆk = w ˆk ˆk + w dk ( j L + q) − uk w uk L q=0  j j ˆk = ˆl w clk w

(14) (15)

l∈Nkh

where μbk is the step-size parameter of node k for BhDLMS algorithm. In compact form the adaptive step can be written as j

j−1

ˆk = w ˆk w

+

μbk j j j j−1 ˆk ) X (d − X k w L k k

(16)

Comparing (15) with (9), it is observed that the information update algorithm is modified into block format. It has been analyzed that the performance of both LMS and Block LMS algorithms are same if μbk = Lμk [11].

4 Simulation Results and Discussions Here, the performance for the different distributed approaches used to determine the parameters of feedback system in sparse WSN is evaluated through a simulation study. To minimize the number of message transmission in the sparse network, block muti-hop DLMS (BhDLMS) algorithm is proposed here. The desired data for the simulation are generated as per the literature [13].

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The nodes are collecting data through a correlated process [13]. For the simulation purpose, the standard IIR systems are consider. The system function of 2nd, 3rd and 4th order infinite impulse response system is given as [9, 15] 1.25 − 0.25z −1 1 + 0.3z −1 − 0.4z −2

(17)

0.6 − 0.4z −1 1 + 0.2z −1 − 0.5z −2 + 0.1z −3

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1 − 0.9z −1 + 0.81z −2 − 0.729z −3 1 + 0.04z −1 + 0.2775z −2 − 0.2101z −3 + 0.14z −4

(19)

H2 (z) = H3 (z) = and H4 (z) =

We have used 2nd, 3rd and 4th order adaptive IIR filter at every sensor to model the same order feedback system respectively. Consider a sparse sensor network with twenty nodes. The network topology is shown in Fig. 2. Three thousand iterations is taken to simulate for each adaptive system. Length of the optimum vector is chosen as the block size in block multihop DLMS. The block length L is four, five and eight for IIR systems I, II and III respectively has chosen in present simulation. The corresponding step size μ = 0.075 ∗ L is taken as per the block length of the system. As per the literature, 0.075 step size is chosen for nonblock algorithms to get similar performance as block algorithm[12]. The average results are plotted over five hundred Monte-Carlo simulations. The MSE and MSD curves for 1-hop DLMS called as DLMS-IIR, 2-hop (multihop) DLMS which is called hDLMS-IIR and their block counterpart are presented. From

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the simulation results, 2-hop algorithms are providing better performance compared to the one-hop and non-cooperative algorithms. In Figs. 3, 4 and 5 the transient MSD and MSE for the sparse sensor network are plotted for systems I, II and III respectively. It has been observed from the figures that the 2-hop hDLMS-IIR and 2-hop hBDMS-IIR algorithms are outperforms over the 1-hop algorithms. Multihop algorithms are providing less steady state values and convergences fast with respect to the conventional DLMS algorithm which follows 1-hop communication. The proposed block multihop diffusion algorithm algorithms are performing in the similar way as that of multihop DLMS without block processing. In fact, in block processing, we are saving the communication between the nodes by block times. Therefore the proposed new algorithm has less communication overhead (block length time) than the non block counter part. This makes the algorithm energy efficient.

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5 Conclusion Here, a multihop diffusion method is incorporated to estimate feedback systems parameter in sparse sensor network. The objective is to achieve good estimate by diffusion LMS algorithm especially for the edge nodes. In order to overcome the more communication overhead problem in multihop diffusion strategy, block multihop diffusion method is employed here. This reduces communication overhead by the block length times. All the algorithms are simulated for a network with 20 sensor nodes. There is a improvement of 10 dB and 5 dB in steady state MSD and MSE performance respectively for 2-hop diffusion algorithms with respect to one-hop counterpart.

References 1. Cattivelli FS, Sayed AH (2010) Diffusion LMS strategies for distributed estimation. IEEE Trans Signal Process 58(3):1035–1048. https://doi.org/10.1109/TSP.2009.2033729 2. Dash M, Panigrahi T, Sharma R (2020) Distributed estimation of IIR system’s parameters in sensor network by multihop diffusion LMS algorithm. In: Mohanty MN, Das S (eds) Advances in intelligent computing and communication. Springer, Singapore, pp 20–29 3. Dash M, Panigrahi T, Sharma R (2017) Distributed parameter estimation of IIR system using diffusion particle swarm optimization algorithm. J King Saud Univ Eng Sci Elsevier 4. He P, Fan T (2016) Distributed fault-tolerance consensus filtering in wireless sensor networkspart I: communication failure. Int J Sens Netw 22(2):127–142 5. Hu W, Tay WP (2015) Multi-hop diffusion LMS for energy-constrained distributed estimation. IEEE Trans Signal Process 63(15):4022–4036 6. Khalili A, Rastegarnia A (2016) Tracking analysis of augmented complex least mean square algorithm. Int J Adapt Control Signal Process 30(1):106–114 7. Khalili A, Rastegarnia A, Bazzi WM, Rahmati RG (2017) Incremental augmented complex adaptive IIR algorithm for training widely linear arma model. Signal Image Video Process 11(3):493–500

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8. Kong JT, Lee JW, Kim SE, Shin S, Song WJ (2017) Diffusion LMS algorithms with multi combination for distributed estimation: formulation and performance analysis. Digital Signal Process 71(Supplement C):117–130 9. Majhi B, Panda G (2013) Distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization. Digital Signal Process 23(4):1303–1313 10. Nayak M, Panigrahi T, Sharma R (2015) Distributed estimation using multi-hop adaptive diffusion in sparse wireless sensor networks. In: International conference on microwave, optical and communication engineering (ICMOCE), pp 318–321. https://doi.org/10.1109/ICMOCE. 2015.7489756 11. Panda G, Mulgrew B, Cowan CFN (1986) A self-orthogonalizing efficient block adaptible filter 34(6):1573–1582 12. Panigrahi T, Pradhan PM, Panda G, Mulgrew B (2012) Block least-mean square algorithm over distributed wireless sensor network. J Comput Netw Commun (Hindawi Publishing Corporation) 13. Panigrahi T, Panda G, Mulgrew B (2014) Error saturation nonlinearities for robust incremental LMS over wireless sensor networks. ACM Trans Sens Netw 11(2):27:1–27:20 14. Sayed AH (2014) Adaptive networks. Proc IEEE 102(4):460–497 15. Scarpiniti M, Comminiello D, Parisi R, Uncini A (2015) Nonlinear system identification using IIR spline adaptive filters. Signal Process 108:30–35 16. Schizas I, Mateos G, Giannakis G (2009) Distributed LMS for consensus-based in-network adaptive processing. IEEE Trans Signal Process 57(6):2365–2382. https://doi.org/10.1109/ TSP.2009.2016226 17. Shynk JJ (1989) Adaptive IIR filtering. IEEE ASSP Mag 6(2):4–21 18. Wilson AM, Panigrahi T, Dubey A (2020) Robust distributed lorentzian adaptive filter with diffusion strategy in impulsive noise environment. Digital Signal Process 96:102589 19. Zhao X, Sayed AH (2012) Performance limits for distributed estimation over LMS adaptive networks. IEEE Trans Signal Process 60(10):5107–5124

Krill Herd Algorithm-Tuned IDN-FPI Controller for AGC in Interconnected Reheat Thermal–Wind Power System Priyambada Satapathy, Jyoti Ranjan Padhi, Manoj Kumar Debnath, Pradeep Ku Mohanty, and Sarita Samal

Abstract A novel integral derivative filter (IDN)—fractional proportional integral controller (IDN-FPI) is projected for load frequency control (LFC) over a reheat-based thermal system and wind power system. The constraints of the IDN-FPI controller are achieved by using krill herd optimization technique by projecting an Integral of time multiplied absolute error (ITAE) as cost function. The supremacy of the IDN-FPI controller is proved by doing a comparative analysis of the dynamic responses with the krill herd algorithm-tuned proportional integral derivative controller. Investigations show that introduced IDN-FPI controller delivers outstanding performances as compared to PID controller in terms of time response indices of frequency as well as tie-line power oscillations subjecting instabilities. Lastly, it is scrutinized that the proposed IDN-FPI controller tuned by krill herd optimization method is dynamic and complete reasonably to fix the problem of the load frequency control with the presence of SMES. Keywords Integral derivative filter with fractional proportional integral controller (IDN-FPI) · Automatic generation control (AGC) · Krill herd optimization algorithm · Reheat thermal–wind power system

1 Introduction In the present era, the necessity of distributed generation (DG) is growing rapidly to meet the energy disaster in the power sector due to the excess of power demand. The approach of DG can be broadly interconnected to the customer load sites, distribution feeder or in the substation with an approximate limited size of 10 MW. The resources of DG typically comprise of renewable energy resources like wind power, biomass, solar power, biogas, small hydro and geothermal power. Generally, the cost P. Satapathy · J. R. Padhi · M. K. Debnath (B) · P. K. Mohanty ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] S. Samal School of Electrical Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_43

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of electricity is more expensive to meet high peak load demand of the consumer. At that time, distributed generator not only reduces the economical cost but also delivers reliable power to the consumer [1–4]. In 2014, researchers introduced electric vehicles and distributed resources for optimal design of regulation of frequency [5]. Droop-controlled AC micro-grids along with agent-based distributed system for automatic generation control is introduced by Li et al. [6]. A hybrid wind and diesel power resources introduced for automatic reactive power control by authors in 2006 [7]. Authors employed battery energy storage device for frequency regulation in an isolated power system [8]. Later in 2011, several traditional controllers are implemented for thermal hybrid power system for LFC [9]. A new concept of discrete mode of automatic generation control of a two-area reheat thermal system along with new area control error is introduced by Kothari et al. [10]. Classical controllers tuned by grey wolf optimization technique applied for the study of load frequency control over a hybrid solar–thermal system [11]. In 2006, the researchers applied the impact of fuel cells in power distribution system to control the frequency during the period perturbation [12, 13]. The dynamic structure of power distribution system is changed radically by the installation of small size distributed generators. Alleviation of delivered power by the grid to the consumer is the main purpose of using DG. In 2013, researchers have done a literature survey on distributed generator to regulate the frequency [14]. A novel profound robust Hinfinity is employed to regulate the frequency for distributed generation power system [15]. In 2008, adaptive decentralized-droop controller was used to preserve power sharing stability in interconnected system with distributed generation [16]. Innovative MPC strategies was implemented in interconnected power system for automatic generation control [17]. Researchers have done inverter-interfaced distributed generators in a micro-grid system [18]. In 2011, several classical controllers are incorporated with hybrid thermal system to control the frequency [9]. The combine features of RFB and hydrogen electrolyser units are implemented over reheat thermal system along with PI controller using BWNN approach [19]. The hybrid of optimization techniques (bacterial foraging and particle swarm optimization) are introduced to tune PI controller for multi-objective load frequency control [20]. The performance of PI controller is enhanced by adding the features of fuzzy logic optimized by PSO-PS methodology in a hybrid interconnected system [21]. The PI controller is implemented along with fuzzy gain scheduling to regulate the frequency [22]. In 2011, a profound dual-mode-based PI controller to meet the problems on LFC [23]. Further in 2018, hybrid network of power system is designed with dual-mode controller [24]. In [25], decentralized biased dual-mode controller is implemented in a hybrid network. Bat inspired algorithm is employed to tune dual-mode gain PI controller over a interline network [26]. The nonlinearities GDB and GRC have been considered over a decentralized dual-mode controller for AGC [27].

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2 Proposed System The investigating model of interconnected two-area network is studied to deal with the execution of AGC. For the research purpose, this system is incorporated with two-area power system having various generating sources. The generating resources like thermal power plant (in area-1) and wind power plant (in area-2) have considered with the purpose of making the system practical and genuine. In this research article, profound innovative hybrid controllers IDN-FPI is introduced to recover the steadiness of the examined system. The finest constraints are achieved by optimizing with method by considering ITAE as cost function. Figure 1 represents the linear model of the two-area reheat thermal and wind power system with SMES. The minimal values of the constraints of this examined model are grouped in Appendix. The signal of the area controller error carries the combine error signal of frequency and tie-line power for a particular control area. A brief description about all the resources is elaborated in below t (| f 1 | + | f 2 | + |Ptie |) · t · dt

ITAE = 0

Fig. 1 Reheat thermal–wind power system transfer function model along SMES

(1)

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3 Proposed Controller Fractional Order Calculus and System Dynamics: The simplification of the ordinary calculus is considered as fractional calculus. The appearance for fractional order derivative and fractional order integral along with fractional order operative is signified in Eqs. (2) and (3) separately. α α Dt

dn 1 f (t) = (n − α) dt n α α Dt

1 f (t) = (α)

t

t (t − )n−α−1 f ()d

(2)

α

(t − )α−1 f ()d

(3)

α

where n −1 ≤ α ≺ n, n, is an integer. (.) is Euler’s gamma function. The fractional order differential equations describing the dynamic behavior of a system turns to transfer functions with fractional orders of “s” with zero initial conditions. The mathematical appearance for the transfer function of fractional calculus in control system is defined below. G C (s) = K p +

Ki sμ

(4)

Here μ is the fractional order of integrator and the values can be taken any real number. Conventional ID Controller with derivative filter. Basically, the inappropriate distribution of the sensor in a power system produces large transient error on the output side of the controller. A derivative controller helps to increase the life span of the actuator. An appropriate control input signal is generated by introducing the endowed features of integral and derivative controller represented in Fig. 2. The gain constraints tuned by krill herd optimization algorithm of traditional PID controller. T FP I D N =

Ns Ki + Kd s s

(5)

4 Krill Herd Optimization Algorithm In this research study, a novel profound biological and environmental enthused krill herd algorithm is employed to resolve the optimization issues. The distance from food

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Fig. 2 Structure of IDN-fractional PI controller

and the utmost density of flock are defined the fitness function of each krill. Different parameters are responsible to create the krill herd after predation. The density of krill automatically rises up in the process of searching food here and there. The objective function becomes less as the distance from the food is less and the density of folks is high. The three main factors which affect to find the time-dependant function of individual krill are • Individual krill movements • Foraging activity • Random diffusion. Generally, the ability of optimization algorithm is to search arbitrarily. The generalized Lagrangian model to a n dimensional space is represented as follows dMi = X i + Yi + Z i dt

(6)

Here. X i Individual krill movement. Yi Foraging motion. Z i Random diffusion of ith krill individuals.

5 Individual Krill Movement: The mutual effects within the krill individuals tend to keep a high density at the time of searching of food. The movement of krill individuals can be represented as follows X inew = X max βi + wn X iold Here

(7)

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βi = βilocal + βi

(8)

In equation. X max wn X iold βilocal target βi

Maximum speed of the krill herd. Inertia weight of the movement within [0, 1]. The last movement of the krill herd. The local effect carried by the neighbourhoods. The target path carried by the best krill individual.

The local effect of neighbours may be either attractive or repulsive and it can be represented as follows βilocal =

XX 

Aˆ i j Bˆ i j

(9)

j=1

here Aˆ i j =

Ai − A j Aworst − Abest

B j − Bi  Bˆ i j =   B j − Bi  + ε

(10) (11)

where Ai and Bi are the fitness function value of ith krill individuals. A j and B j are the fitness of the jth neighbour. B represents the associated locations and X X defines the amount of neighbours. ε is a optimistic number which is additionally included to avoid the singularities. For each iteration, the distance of every krill can be find out by following equation ds,i =

X  1   Bi − B j  5X j=1

(12)

Here ds,i the detecting displacement of the ith krill individuals. X the amount of krill individuals. The global optima with the effect of individual krill with the best fitness function is defined as = C best Aˆ i,best Bˆ i,best

(13)

  J = 2 random + Jmax

(14)

target

βi C

best

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In this equation, C best is the co-efficient of the individual krill with the best fitness value to the ith Krill individuals. The random values have been considered within 0–1 to enhance the exploration. J is the actual iteration number and Jmax is the maximum iteration number.

6 Foraging Motion: The location of food and the past experiences to search the location of food are two main effective parameters of foraging motion. The expression for ith krill individuals for foraging motion is defined as F Mi = V f αi + w f F Miold

(15)

αi = αifood + αibest

(16)

In the above Eq. (15)

Here Vf wf F Miold αifood αibest

The foraging speed Inertia weight of the foraging motion Last foraging motion The attraction of food Effect of the best fitness of the ith krill.

The centre of food is formulated at first to find out the food attraction. The expression for centre of food can be represented as N B

food

1 i=1 Ai

= N

Bi

1 i=1 Ai

(17)

The expression for the ith individual krill is given below. αifood = C food Aˆ i,food Bˆ i,food

(18)

In the above equation, the food coefficient is represented as   I C food = 2 1 − Imax

(19)

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7 Random Diffusion The motion of the individual krill can be expressed in terms of the maximum diffusion speed and random directional vector which is represented in below Di = D max δ

(20)

The upper bound and lower bound of the maximum diffusion speed (D max ) are considered within 0.002 and 0.010 ms−1 . The random directional vector (δ) can be taken between −1 and 1.

8 Simulation Result and Discussion Reheat thermal system and wind system are simulated with the help of MATLAB circumstances. The gains of the modelled reheat wind power system under research study are described in Appendix. The parameters of IDN-FPI controllers are kept within [0–5] and 0 to 500 for derivative filter significant N. The dimensions of the population and maximum repetitions are both considered as 100. In the research article, the fluctuation of frequency and tile-line power deviations is accomplished by IDN-FPI and PID controller. The six gains of IDN-FPI controller and three gains of PID controllers are tuned by newly invented krill herd optimization method by considering ITAE as cost function. At first, the optimization process has been carried out with the presence of disturbances in one area as well as both the areas and the gains of IDN-FPI and PID controllers are depicted in Table 1. The system frequency fluctuations in first area (f1), second area (f2) and tie-line power alternations (Ptie) are presented in Fig. 3, Fig. 4 and Fig. 5, respectively, during the disturbances in area 1. To prove the superiority of the implemented controller, the outcomes of the power system are compared with a PID controller by introducing SMES. A comparative analysis of several time response indices like peak overshoot, peak undershoots and settling time is presented in Table 2 during above described perturbations. Figs. 6, 7 and 8 reveal that the proposed IDN-FPI controller shows superior behaviour as compared to PID controller with the disturbances on both the sides. Further, the inclusion of SMES is examined with the presence of IDN-FPI and it is compared to the responses of only IDN-FPI controller. Figures 9 and 10 show Table 1 Krill herd optimization algorithm-tuned parameter of IDN-FPI and PID controller Controller

KI1

Kd1

Kp1

KI2

L1

N

IDN-FPI

1.0000

0.2776

1.0000

0.8637

0.0203

236.1715

PID

Kp2

KI3

Kd2

0.5861

0.5426

0.2654

Krill Herd Algorithm-Tuned IDN-FPI Controller for AGC … Fig. 3 Frequency oscillation in area one due to perturbation in area one

533

0.005 0 PID IDN-FPI

∆ f1 in hertz

-0.005 -0.01 -0.015 -0.02 -0.025 -0.03

0

5

10

15

20

25

30

25

30

25

30

Time in Sec

Fig. 4 Frequency oscillation in area-2 due to perturbation in area-2

10

5

—3

∆ f2 in hertz

0 PID IDN-FPI

-5 -10 -15 -20

5

0

10

20

15

Time in Sec

Fig. 5 Tie-line power oscillation due to perturbation in area one

10

2

—3

1

∆ Ptie12 in PU

0 -1

PID IDN-FPI

-2 -3 -4 -5 -6

0

5

10

15

20

Time in sec

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Table 2 Time response indices during the presence of disturbances and SMES Deviations f1

f2

Ptie

Time s Ts in s

SLP in area 1

SLP in both areas

IDN-FPI

PID

IDN-FPI

PID

24.3300

30.00

24.3300

30.00

Osh in Hz

0.0016

0.0046

0.0080

0.0510

Ush in Hz

−0.0264

−0.0031

−0.0455

−0.1647 13.6800

Ts in s

9.9200

15.0200

9.3200

Osh in Hz

0.0011

0.0014

0.0626

0.0482

Ush in Hz

−0.0184

−0.0055

−0.1360

−0.2200

Ts in s

28.7800

17.4900

22.2300

17.5800

Osh in PU

0.0011

0.0007

0.0144

0.0317

Ush in PU

−0.0051

−0.0031

−0.0008

−0.0112

Fig. 6 Tie-line power oscillation due to perturbations in both areas

0.035 0.03

∆ Ptie12 in PU

0.025 0.02 PID IDN-FPI

0.015 0.01 0.005 0 -0.005 -0.01 -0.015

0

5

10

15

20

25

30

25

30

Time in sec 0.1

Fig. 7 Frequency oscillation in area-1 due to disturbance in both areas

∆ f1 in hertz

0.05 0 PID IDN-FPI

-0.05 -0.1 -0.15 -0.2

0

5

10

15

20

Time in sec

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0.01

Fig. 8 Frequency oscillation in area-1 due to the presence of SMES ∆ f1 in hertz

0 IDN-FPI WITHOUT SMES IDN-FPI WITH SMES

-0.01 -0.02 -0.03 -0.04 -0.05

0

5

10

15

20

25

30

25

30

25

30

Time in sec 0.1

Fig. 9 Frequency oscillation in area-2 due to disturbance in both areas ∆ f2 in hertz

0.05 0 -0.05

PID IDN-FPI

-0.1 -0.15 -0.2 -0.25

0

5

10

15

20

Time in sec

Fig. 10 Frequency oscillation in area-2 due to the presence of SMES

0.1

∆ f2 in hertz

0.05 0 IDN-FPI WITHOUT SMES IDN-FPI WITH SMES

-0.05 -0.1 -0.15

0

5

10

15

20

Time in sec

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that the presence of SMES helps to increase the stability of the modelled network. The simulation results for case-2 are exposed in Figs. 6, 7, 8 and Table 2.

9 Conclusion An innovative IDN-FPI cascaded controller is being optimized over a reheat-based thermal and wind power system over interconnected power system network by krill herd optimization technique. The procedure and evaluations of the dynamic responses have been executed by doing simulation in MATLAB. The responses of the IDN-FPI controller are compared with the PID controller under 0.01 p.u step load disturbance in area 1 as well as in in both the areas to establish the supremacy of the implemented controller. Hence, it specified that the implemented controller can provide stability with the presence of SMES also. The execution of the IDN-FPI controller is also superior in the aspects of supreme overshoot along with least undershoots and settling time.

Appendix The nominal parameters of the reheat and wind power system are given below. K W 2 = 1.25; K W 3 = 1.3; T W 1 = 0.6; T W 2 = 0.041; K P S = 68.9566, T P S = 11.49; T SG = 0.008, T T = 0.3; T 12 = 0.043; R1 = R2 = 2.4; B = 0.4321; K R = 0.3; T R = 10, F = 60.

References 1. Kundur P, Balu NJ, Lauby MG (1994) Power system stability and control, vol 7. McGraw-hill, New York 2. Boyle G Renewable energy (2004) Oxford University Press, p 456. ISBN-10: 0199261784. ISBN-13: 9780199261789(2004) 3. Pepermans G et al (2005) Distributed generation: definition, benefits and issues. Energy Policy 33(6):787–798 4. Patnaik B et al (2020) AC microgrid protection—a review: current and future prospective. Appl Energy 271:115210 5. Battistelli C, Conejo AJ (2014) Optimal management of the automatic generation control service in smart user grids including electric vehicles and distributed resources. Electric Power Syst Res 111:22–31 6. Li Z et al Agent-based distributed and economic automatic generation control for droopcontrolled AC microgrids. IET Gener Transm Distrib 10(14):3622–3630 (2016)

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7. Bansal RC (2006) Automatic reactive-power control of isolated wind–diesel hybrid power systems. IEEE Trans Industr Electron 53(4):1116–1126 8. Aditya SK, Das D (1999) Application of battery energy storage system to load frequency control of an isolated power system. Int J Energy Res 23(3):247–258 9. Saikia Lalit Chandra J, Nanda, Mishra S Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system. Int J Electric Power Energy Syst 33(3):394–401 (2011) 10. Kothari ML et al (1989) Discrete-mode automatic generation control of a two-area reheat thermal system with new area control error. IEEE Trans Power Syst 4(2):730–738 11. Sharma Y, Saikia LC Automatic generation control of a multi-area ST-thermal power system using grey wolf optimizer algorithm based classical controllers. Int J Electric Power Energy Syst 73:853–862 (2015) 12. Sedghisigarchi K, Feliachi A (2006) Impact of fuel cells on load-frequency control in power distribution systems. IEEE Trans Energy Convers 21(1):250–256 13. Huang X, Zhang Z, Jiang J (2006) Fuel cell technology for distributed generation: an overview. In: 2006 IEEE international symposium on industrial electronics, vol 2. IEEE 14. Pandey SK, Mohanty SR, Kishor N A literature survey on load–frequency control for conventional and distribution generation power systems. Renew Sustain Energy Rev 25:318–334 (2013) 15. Singh VP et al Robust H-infinity load frequency control in hybrid distributed generation system. Int J Electr Power Energy Syst 46:294–305 (2013) 16. Mohamed YA-RI, El-Saadany EF Adaptive decentralized droop controller to preserve power sharing stability of paralleled inverters in distributed generation microgrids. IEEE Trans Power Electron 23(6):2806–2816 (2008) 17. Venkat Aswin N et al (2008) Distributed MPC strategies with application to power system automatic generation control. IEEE Trans Control Systems Technol 16(6):1192–1206 18. Chung I-Y et al (2010) Control methods of inverter-interfaced distributed generators in a microgrid system. IEEE Trans Ind Appl 46(3):1078–1088 19. Francis R, Chidambaram IA (2015) Optimized PI+ load–frequency controller using BWNN approach for an interconnected reheat power system with RFB and hydrogen electrolyser units. Int J Electr Power Energy Syst 67:381–392 20. Dhillon SS, Lather JS, Marwaha S Multi objective load frequency control using hybrid bacterial foraging and particle swarm optimized PI controller. Int J Electric Power Energy Syst 79:196– 209 (2016) 21. Sahu RK, Panda S, Chandra Sekhar GT (2015) A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems. Int J Electric Power Energy Syst 64:880–893 22. Chang CS, Fu W, Wen F (1998) Load frequency control using genetic-algorithm based fuzzy gain scheduling of PI controllers. Electric Mach Power Syst 26(1):39–52 23. Chatterjee K (2011) Design of dual mode PI controller for load frequency control. Int J Emer Electric Power Syst 11(4) 24. Mohanty B, Acharyulu BVS, Hota PK (2018) Moth flame optimization algorithm optimized dual-mode controller for multiarea hybrid sources AGC system. Opt Control Appl Methods 39(2):720–734 25. Velusami S, Chidambaram IA (2006) Design of decentralized biased dual mode controllers for load–frequency control of interconnected power systems. Electric Power Comp Syst 34(10):1057–1075 26. Sathya MR, Mohamed Thameem Ansari M (2015) Load frequency control using Bat inspired algorithm based dual mode gain scheduling of PI controllers for interconnected power system. Int J Electric Power Energy Syst 64:365–374 27. Velusami S, Chidambaram IA (2007) Decentralized biased dual mode controllers for load frequency control of interconnected power systems considering GDB and GRC non-linearities. Energy Convers Manage 48(5):1691–1702

Performance Evaluation of Region-Based Segmentation Algorithms for Brain MR Images Tapasmini Sahoo, Rohit Kumar Pradhan, Kunal Kumar Das, and Sibasish Sahu

Abstract Segmentation of an image is crucial for dividing it into different classes so that it can be made useful for information extraction for image classification and dissemination in medical imaging and diagnosis. There are various region-based segmentation algorithms that are used to detect the required region of interest in MR image. The hard and soft clustering algorithm along with an unsupervised classification method based on expectation maximization for image segmentation is illustrated in this paper. These methods are used on the brain MR image to confer the result for medical diagnosis purposes. The best method is chosen by computing certain performance evaluating parameters such as rand index, variation of information and global consistency error. As per the performance measure, expectation maximization result proved to be the best when it was compared with the ground truth image. Thus, it propels to be used in medical diagnosis. Keywords Hard and soft clustering · Expectation maximization · Region of interest · Unsupervised classification

1 Introduction In digital image processing, a digital image is transformed into its integer containing elements, known as pixels, having a particular location and value, for example, scene luminance, stored in an advanced memory, and primed by any advanced machinery T. Sahoo (B) · R. K. Pradhan · K. K. Das · S. Sahu ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] R. K. Pradhan e-mail: [email protected] K. K. Das e-mail: [email protected] S. Sahu e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_44

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like digital computer. The representation of an image is done by f (x, y), i.e., a twodimensional function [1], where the amplitude of f is known as the intensity of the image at any pair of coordinates (x, y). An image is said to be digital when x, y and intensity values of f (positive scalar quantity) are all finite and discontinuous in nature [1]. Differentiating amid image processing and its various domains and equivalent area computer vision is found to be challenging. The study related with the science of artificial systems that has high level of understanding and unique feature to extract information from digital images is known as computer vision [2]. The intensity of MRI is similar to that of gray images in the range between 0 and 255. The GM, WM and CSF are the three main tissue types of brain MRI’s elements [3]. The GM encompasses of cortex, which shapes brain’s external surface, the deep internal gray nuclei, basal ganglia and thalami. The WM encompasses of neural axons, which interlinks various sections of the brain, serving like a connection to the body. The CSF is a watery fluid that flows within and around the brain, functioning as a physical shock absorber [4]. The clustering method in region-based image segmentation is done to accomplish the idea behind the paper. Clustering method helps in classification, where in an image, a vector of N measurements explaining every pixel and pixel groups (i.e., regions), resembling measurement vectors and clustering in N-dimensional measurement space infers resemblance of the analogous pixel and group of pixels [5]. Hence, an indicator of resemblance of image regions in measurement space is acted by clustering, and then image segmentation may be done using it [6]. Feature vector is also known as the vector of measurements that describes essential features of an image. In the feature space, resemblance between image pixels or pixel groups infers small separation distances, i.e., clustering. In data segmentation, clustering approaches were some of the earliest developed techniques. There are various region-based segmentation methods defined according to their specification and requirement of parameters. Clustering methods such as k-means (KM) clustering [7], fuzzy c-means clustering (FCM) [8, 9] and expectation maximization algorithm (EM) [10, 11] are some of segmentation algorithms based on segregation of regions of the MR images are illustrated in this paper.

2 Segmentation by Clustering Approach An image can be divided into a meaningful finite number of separate homogeneous objects by segmentation. There are various image segmentation methods according to the number of classes it has to divide. This paper concerns on image segmentation using two main clustering algorithms and an unsupervised method. Clustering algorithms used are (a) k-means (b) fuzzy c-means.

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2.1 k-Means Clustering In k-means hard clustering algorithm, input variable k is taken, the numeral of clusters and a group of n objects is subdivided into k clusters giving a high intra-cluster and a low inter-cluster similarity resultant. Defining k centroids is the main idea, i.e., present for each cluster. Placement of centroids is done in a crafty way to avoid varying results due to different locations. To resolve this problem, we have assigned distant locations to the centroids [12]. Then association of the closest centroid is done with each taken point that fits to a specified data set. Hence, an early grouping is done, when the first step is accomplished with no unresolved points. In the next instance, k new centroids are recalculated, and in between the nearest new centroid and the same data set points, a new bond has to be created. Then we may observe the generation of a loop resulting in, the change of location of k centroids, in a step wise manner, till no further changes. Hence, there is no more movement of centroids. Therefore, the algorithm minimizes a squared error or an objective function expressed as follows in Eq. 1: J=

k   n j=1

j

i=1

j

2

xi − c j 

2

(1)

j

Here, xi − c j  denotes the chosen measured distance between a data point, xi is the center of the cluster and c j indicates the distance of n data points from the center of their respective clusters. k-means algorithm: Step 1: An input image in the form of pixels is transformed to a RGB feature space. Step 2: Data points having similar colors are assembled together. k-means clustering is used to form clusters and the distances are measured by Euclidean method. Step 3: Mean of the clusters is calculated. Then calculation of the mean color in each cluster is done to remap the image using it. Therefore, k-means clustering method creates an exclusive number of nonhierarchical, disjoint and global clusters. This clustering approach is iterative, numerical, unsupervised and non-deterministic in nature.

2.2 Fuzzy c-Means Clustering (FCM) In this paper, fuzzy c-means algorithm is applied over some images to analyze the drawbacks of k-means algorithm. As the name suggests, fuzzy c-means clustering allows each pixel to be in more than one cluster rather than in single cluster since; each element is associated with some membership levels [13]. The membership values allow that data element to be in more than a single cluster. This method is also known as soft clustering because it allocates the membership levels which are then used to allocate data elements to a single or additional cluster.

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On the basis of the measured distance among the data point and the cluster center, fuzzy c-means allots membership to every data point analogous to every cluster center. The data membership increases with decrease in the distance between the data point and the particular cluster center [14]. The summation of all membership of every data point has to be unity. Cluster centers are newly calculated according to the formula for every iteration membership [10]. As compared to the hard clustering, this algorithm is more natural. Membership values are given between 0 and 1 without the boundary objects being forced to belong to one class only. This clustering method was first highlighted by Joe Dunn for a case (m = 2) in the year 1974. The development of the general case (m > 1) was done by Jim Bezdek in the year 1973 [8, 9]. The above algorithm is frequently used in data mining, pattern recognition, etc. Membership is directly proportional to the confidence in the pattern allocation to the cluster [15]. Thresholding of membership value is done to get hard clustering from a fuzzy partition. In recent decades, generalization and modification of the fuzzy c-means algorithm has become an established technique in clustering. This algorithm partitions countable n elements X = {x 1 …xn }, into a group of fuzzy c clusters with respect to a criterion. A countable data set is given where the algorithm returns a list of a partition matrix (i.e., J = U =u i j ∈ [0, 1], i = 1 … n, j = 1 … c) and c cluster centers (i.e. c = {c1 …cc }). This algorithm partitions n objects X = {x, x, …,xn } in Rd dimensional space of R d in many fuzzy c (1 < c < n) clusters having y = {y1 , y2 , y3 , …,yc } centroids or cluster centers. The fuzzy matrix is denoted by ‘u’ that consists of (n, c) (i.e., rows, columns) which denotes the numeral of data objects and numeral of clusters, respectively. The element u i j , present in the i gth row and jgth column in ‘u’ indicates the membership function or the degree of association between the i gth object and the jgth cluster. Minimization of the objective function given below in Eq. 2 is the basis of fuzzy c-means clustering: J=

N  c 

2

u i j xi − c j  , 1 ≤ m < ∞ m

(2)

i=1 j=1

This paper describes the implementation of FCM through some images and its application in medical imaging’s (m > 1) scalar weighting exponent. The variable di j denotes the distance from object xi to the cluster center c j by Euclidean method. The variable m controls the resulting cluster’s fuzzy characteristic. FCM Algorithm: Step 1: The value of k is being either chosen randomly, manually or heuristically. Step 2: k clusters are being generated and the cluster centers are calculated by using the following way depicted in Eq. 3: N u i j m xi c j = i=1 N m i=1 u i j

(3)

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Step 3: Variance value is minimized between the center of the cluster and the pixel allocated to the cluster. Step 4: The cluster centers are recomputed by averaging all the pixels in the clusters as defined below in Eq. 4 = Ui(k+1) j

c



1

(xi −c j ) k=1 k (xi −ck )

2  m−1

(4)

Step 5: The above steps were checked, if u k+1 − u k  < , then the algorithm is stopped. Here, u k+1 is the new iterated membership matrix after the computation of cluster centers and is the stopping criterion. FCM has the ability to make bound for a data to be in each cluster by membership function, representing the fuzzy behavior of this algorithm. The principle behind fuzzy partition is very essential for analysis of cluster, and hence, it is used in identification techniques on the basis of fuzzy clustering.

3 Segmentation by Expectation Maximization In statistical models, where the problem depends on unknown latent variables, expectation maximization algorithm is used [18, 19]. It works on iterations alternatingly between performing an expectation (E) and maximization (M) step and finding out maximum likelihood estimates of parameters. A function is formed by step E to evaluate the expectation of log likelihood for the parameters using the recent estimates and step M calculates the maximizing parameters of the expected log likelihood, i.e., in step E. Hence, the final results of the parameter estimates are used to find out the distribution of the latent variables in the next step of E. It was an old idea (late 50s) that was formalized by Dempster, Laird and Rubin in the year 1977 [4]. In the cases where the equations cannot be solved directly, EM algorithm is used. Involvement of concealed variables with unidentified parameters and identified observations of data makes these models, which can be formulated either by lost values of the given data or by the assumption about the presence of other undetected data points. As E and M steps consist some sets of equations, the EM algorithm proceeds by solving the above two. We basically picked random values for a single set out of the two unknowns and then we estimated the second one; we used these estimated values for finding the first set’s improvised estimation, and then we kept on alternating the two steps until both their resultants meet at fixed points. EM is an algorithm that uses the actual probabilistic distribution to describe our approach. It computes the probability that each pixel goes to one or the other cluster. It never quantizes the probability to 0 and 1; rather, it gives a number between 0 and 1. The probability distribution function for a function is defined as in Eq. 5 (Figs. 1 and 2):

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(5)

Here, σ2 is the variance, and μ is the mean of the Gaussian curve. Figure 3 shows an illustration of the probability distribution curves for different tissue classes present in an image by virtue of the algorithm. Expectation Maximization Algorithm: Step 1: The algorithm is started with Gaussian values μn and σn 2 being randomly placed according to the required number of clusters. Step 2: Then (μj , σj ) and P(C j ) for each cluster j is initialized. Step 3: For each point; the Aposteriori probability is calculated by using Bayes theorem. Expectation Step. Estimates of the cluster of each data are calculated as:

(a)

(b)

(g)

(c)

(h)

(k)

(d)

(e)

(i)

(f)

(j)

(l)

Fig. 1 a input image_1, b ground truth of input image_1, c input image_2, d ground truth of input image_2, e input image_3, f ground truth of input image_3, g k-means result with k = 3 for input image_1, f k-means result with k = 2 for input image_1, g k-means result with k = 3 for input image_2, h k-means result with k = 2 for input image_2, k k-means result with k = 3 for input image_3, l k-means result with k = 2 for input image_3

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(a)

(b)

545

(c)

Fig. 2 FCM result for input image 1, 2 and 3, respectively

p(C j |xi ) =

p(xi |C j ) · p(C j ) p(xi |C j ) · p(C j ) = . p(xi ) j p(x i |C j ) · p(C j )

(6)

Maximization Step. The cluster parameters are re-estimated for each cluster j as:   p(C j |xi ) · (xi − μ j ) · (xi − μ j )T i p(C j |x i ) · x i   μj = , j = i i p(C j |x i ) i p(C j |x i )

(7)

Here, (xi − μ j )T is the transpose matrix. Step 4: Mean and covariance is adjusted accordingly to fit points assigned to them:  p(C j ) =

i

p(C j |xi ) N

(8)

Step 5: The program is iterated until convergence and all the above steps defined by Eqs. 6, 7 and 8 are repeated.

4 Quantitative Analysis and Evaluation The three algorithms of segmentation are applied upon a set of images. Since there are many segmentation techniques that are used to evaluate the exact location of the desired ROI, it becomes hard to conclude which technique should be chosen to apply on the source image to get the required result. The performance measurement of segmentation process is difficult to be extracted from the outputs since no mutual algorithm is there for segmenting an image [16]. Thus, the performance of different segmentation algorithm is evaluated using some statistical parameters such as rand index (RI), variation of information (VOI) and global consistency error (GCE) [17]. The dimension of all the resulted images is made equal to avoid the size mismatch in creating the contingency matrix. The segmented output images were compared with their respective ground truth images.

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(a)

(b)

(c)

(d)

Fig. 3 a Probability distribution curves for input image_1 for k = 2. b Probability distribution curves for input image_1 for k = 3. c Probability distribution curves for input image_2 for k = 2. d Probability distribution curves for input image_2 for k = 3. e Probability distribution curves for input image_3 for k = 2. f Input image_3 for k = 3

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(e)

(f)

Fig. 3 (continued)

4.1 Rand Index It deals with counting of the fraction of pairs of pixels which are more likely to be consistent between the obtained segmented result and the ground truth image. It defines the resemblance in the two evaluated clusters of data [18]. For a set of n elements M = {m1 , m2 , …, mn } and two segments of M for comparison P = {P1 , P2 , …, Px }, a segment of M with x number of subsets and Q = {Q1 , Q2 , …, Qy }, segment of M with y number of subsets define the following. • s, the number of pairs of elements in M that are in the same subset in P and in the same • subset in Q. • t, the number of pairs of elements in M that are in different subsets in P and in different • subsets in Q. • u, the number of pairs of elements in M that are in the same subset in P and in different • subsets in Q. • v, the number of pairs of elements in M that are in different subsets in P and in the same subset in Q. Then, RI is defined as:

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RI =

s+t s+t = s+t +u+v m/2

(9)

Here, P and Q defines the ground truth image and the output image to be compared, respectively, where ‘s + t’ is the number of agreements and ‘u + v’ is the number of disagreements between the two images. RI has a value between 0 and 1 where a higher value indicates a better segmentation process.

4.2 Variation of Information (VOI) The VOI for two disjoint segments P and Q of a single set X can be determined using the following equations: V O I (P, Q) = −

 m,n



tmn [log(

tmn tmn ) + log( )] um vn

where P = {P1 , P2 , …, Px }, Q = = {Q1 , Q2 , …, Qy }. Let k =

(10) 

|Pm | =

m

|Q n | = |R|,

n

um =

|Pm | |Q n | |Pm ∩ Q n | , vn = , tmn = k k k

(11)

P and Q indicates the ground truth image and the segmented output image.

4.3 Global Consistency Error The extent of refinement among various segmentation methods is measured by GCE. Associated segmentation methods can display the same segmented image at unlike scales and are well-thought-out to be consistent. The segmentation results different sets of pixels of an image, so a pixel which falls in an area of refinement if the segment is a proper subset of the other with zero error. Two regions deprived of subset link will converge with each other in an uneven manner. The formula for GCE is depicted in Eq. 12 as follows: GC E =

  1 min{ (P, Q, pi x j ), E(Q, P, pi x j )} n j j

(12)

Here, the two segmentation results P and Q are taken as input for calculating the error measure. The obtained error is a real value in the range [0:1] with a specification of no error justified by the error value as zero. It is considered over here that a given pixel pix j is contained in both the segments P and Q.

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5 Results and Discussions On the basis of k value, we have acquired diverse segmented images from the source images. The RI and VOI value is found to be slightly more in case of EM-segmented images, and similarly, the GCE value for the EM algorithm is comparatively less as compared to other segmented images. The three brain MR images were taken from research gate database for being utilized in the analysis as shown in Fig. 1 [18]. All the segmented images were obtained and the EM segmentation method displayed more desirable outputs than the others. The segmented images with respect to the different methodologies are illustrated in Figs. 1, 2 and 4, respectively. Hence, a comparison between EM, k-means and fuzzy c-means is evaluated on the basis of above mentioned statistical parameters and is presented in Table 1. It is observed that with increasing the cluster size to from k = 2 to k = 3, the RI value increases

(a)

(b)

(c) Fig. 4 a EM algorithm result with k = 2, 3 and 4 for input image_1. b EM algorithm result with k = 2, 3 for input image_2. c EM algorithm result with k = 2, 3 for input image_3

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Table 1 Statistical measure of simulation results of different segmentation methodologies Image

Methodology

Input image_1

k-means

k=2 k=3

FCM

Input image_2

VOI

GCE

5.7715

0.4005

0.7844

5.2893

0.6275

0.8156

5.9195

0.5376

0.8394

6.0253

0.2604

EM

k=2 k=3

0.8906

6.6819

0.3708

k-means

k=2

0.7988

5.6568

0.4106

k=3

0.7743

5.2328

0.6337

FCM EM Input image_3

RI 0.7658

k-means

0.7965

5.8392

0.5480

k=2

0.8096

6.2257

0.2993

k=3

0.8879

6.6942

0.3357

k=2

0.7925

5.4396

0.3770

k=3

0.7457

5.3952

0.6091

0.8061

5.5909

0.4570

k=2

0.8482

6.1276

0.2493

k=3

0.9008

6.4684

0.3376

FCM EM

and the value for VOI decreases. In case of GCE, for higher number of clusters, the value decreases but for EM, increasing the number of cluster increases the GCE value consequently resulting misclassification.

6 Conclusion In this paper, three methods were used to segment a given set of images and the resulting segmented outputs were found out by varying the number of class k of the clustering technique. To confer the best possible segmentation method, the results were compared to its ground truth image and the performance metric was obtained. The obtained results show that the RI values of k-means and fuzzy c-means for the given image set are lower than that of expectation maximization process. The analysis with the respective ground truth image of each individual image of the data set suggests that the segmented output of expectation maximization is much closer to their respective ground truths with lower values of VOI and GCE. Thus, EM algorithm is proved to be better as compared to the former two clustering algorithms.

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Reference 1. Gonzalez RC, Woods RE Digital image processing, 2nd edn. Pearson Education, ISBN: 81– 7808–629–8 2. Song L, Gao M, Wang S (2015) An image segmentation method by combining fuzzy C-means clustering and graph cuts optimization for multiphase level set algorithms. In: 2nd international conference on information science and control engineering 3. Moreno JC, Surya Prasath VB, Proenca H, Palaniappan K (2014) Fast and globally convex multiphase active contours for brain MRI Segmentation. Comput Vis Image Understand 125:237–250 4. Clustering very large databases using EM mixture models. In: Proceedings of 15th international conference pattern recognition, vol 2, pp 76–80 (2000) 5. Clustering with evolution strategies. Pattern Recogn 27(2):321–329 (1994) 6. Boujemaa N (2000) Generalized competitive clustering for image segmentation. In: Proceedings of 19th international meeting north American fuzzy information processing society (NAFIPS’00). Atlanta, GA, pp 133–137 7. Kanungo, Mount DM (2002) An efficient K-means clustering algorithm: analysis and implementation. Pattern Anal Mach Intell IEEE Trans Pattern Anal Mach Intell 24(7) 8. Backer E, Jain A (1981) A clustering performance measure based on fuzzy set decomposition. IEEE Trans Pattern Anal Mach Intell PAMI-3(1):66–75 9. Bezdek J (1980) A convergence theorem for the fuzzy isodataclustering algorithms. IEEE Trans Pattern Anal Mach Intell 2(1):1–8 10. Clustering very large databases using EM mixture models. In: Proceedings of 15th International conference on pattern recognition, vol 2, pp 76–80 (2000) 11. Ravindraiah R, Tejaswini K (2014) IVUS image segmentation by using expectation—maximization approach. IJARCCE 3(2) 12. Zha H, Ding C, Gu M, He X, Simon HD (2001) Spectral relaxation for k-means clustering. In: Neural information processing systems, vol 14 (NIPS 2001). Vancouver, Canada, pp 1057–1064 13. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B (Methodological) 39(1):1, 38 14. Huang H-C, Chuang Y-Y, Chen C-S Multiple kernel fuzzy clustering. IEEE Trans Fuzzy Syst 15. Ghosh S, Dubey SK (2013) Comparative analysis of k-means and fuzzy c-means algorithm. IJACSA 4(4) 16. Daniel Ratna Raju P, Neelima G, Prasada Rao K (2011) Image segmentation-MR images segmentation with a modified Gaussian mixture model. (IJCSIT) Int J Comput Sci Inform Technol 2(6):2573–2578 17. Ghosh S (2013) Comparative analysis of k-means and fuzzy c-means algorithms. (IJACSA) Int J Adv Comput Sci Appl 4(4) 18. Nagarajan BM, Prem Paul G, Senthil Babu P (2014) Image segmentation using unsupervised technique, ICIET 19. Ethan S, Brown Tony F, Bresson CX (2012) Completely convex formulation of the Chan-Vese image segmentation model. Int J Comput Vis 98:103–121

Optimal Allocation of Distributed Generators Using Metaheuristic Algorithms—An Up-to-Date Bibliographic Review Subrat Kumar Dash, Sivkumar Mishra, Laloo Ranjan Pati, and Prashant Kumar Satpathy Abstract Optimal allocation of distributed generators (OADG) in power distribution systems is a very popular power optimization problem and many useful contributions can be found in the literature. Metaheuristic methods are very effective for solving this problem, and many recent developments have been there adding several dimensions to the approach. In this paper, an up-to-date bibliographic review (most of the reviewed papers are published in 2019 and 2020 with some in 2018 or 2016), on the various metaheuristic approaches proposed for the OADG problem, is presented. The review is also augmented with a survey of results considering several test distribution systems as reported in recent papers. This will be very helpful to researchers for any ready reference. Keywords Optimal allocation · Distributed generators · Radial distribution systems · Metaheuristic algorithms

1 Introduction Distributed generators (DGs) play a very important role making the electrical power grid smarter because with the growing penetration of DGs in power distribution systems along with several energy storage devices and embedded customer power devices the performance of these systems is going to change in a big way than ever S. K. Dash Government College of Engineering, Kalahandi, Bhawanipatna, Odisha, India e-mail: [email protected] S. Mishra (B) · L. R. Pati Centre for Advanced Post Graduate Studies, BPUT, Rourkela, Odisha, India e-mail: [email protected] L. R. Pati e-mail: [email protected] P. K. Satpathy College of Engineering and Technology, Bhubaneswar, Bhubaneswar 751003, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_45

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[1]. Consequently, the allocation of these devices, i.e., both optimal placement as well as sizing of these devices, has become a challenging task as these allocations are in some way linked to benefits as well as shortcomings. Hence, the optimal allocation of DGs (OADG) has emerged as an important power system optimization problem in the smart grid scenario with many additional features being added from time to time. Initially, the OADG problem was mainly based on a single objective of loss minimization, but gradually several other objectives based on cost, power quality, and reliability were introduced making it a multi-objective one. The uncertainty involved with renewable DGs and smart loads makes this problem more challenging as well as interesting. In present times, the literature is flooded with many techniques to solve this OADG problem. Researchers also have proposed algorithms for the simultaneous allocation of other optimizing tools such as network reconfiguration, placement of shunt capacitors, D-FACT devices, and energy storing devices along with DGs. Another important observation is that various population-based metaheuristic methods are used to solve these optimization problems as these have proven to produce the best results particularly with multi-objectives with Pareto fronts and dealing DG and load uncertainty. New metaheuristic methods are being proposed to solve these problems with each reporting improvisations. Some review papers have also been written to discuss these problems [2–11]. In all papers [2, 3, 5–8, 11] the various exclusive solution methods meant for the OADG problem are reviewed. However, keeping in view of the fast-changing developments in this area, an up-to-date bibliographic review is necessary which can be used as a ready reference by the researchers. Considering all the above facts and developments, the objective of this paper is to present an up-to-date bibliographic review of the optimization problem highlighting the state-of-the-art research directions. As most of the papers included in this review are either from 2020 or 2019, it can certainly be considered as an up-to-date survey on the concerned topic. In Sect. 2, the bibliographic survey of various categories of the OADG problem is presented. In Sect. 3, the various approaches and objectives adopted in most recent papers are discussed, which is also augmented with a review of results for various test distribution systems. The review concludes in Sect. 4 with some observations.

2 Various Categories of OADG Problem The OADG problem has been solved by researchers adopting several metaheuristic algorithms and can be categorized as per the following.

2.1 Exclusive OADG Allocation of DGs refers to the optimization of both placements of sizing. For placement of DGs, mostly analytical methods such as sensitivity methods are preferred, and in those cases, the sizing optimizations are generally carried out using metaheuristic searches [12, 13]. The other subcategory is the simultaneous placement and sizing of DGs using metaheuristic algorithms [14–21].

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2.2 OADG with Shunt Capacitor Allocation Optimal placement and sizing of shunt capacitors in a power distribution system are an older optimization problem than OADG. However, simultaneous DG and shunt capacitor allocation are also very effective, and related recent papers involving metaheuristic approaches are discussed in [22–30].

2.3 OADG with Network Reconfiguration Network reconfiguration (NR) has emerged as a very effective smart grid tool for network optimization so also the simultaneous OADG and NR. Many recent papers based on this are presented in [31–38].

2.4 OADG with D-STATCOM Allocation D-STATCOM is an important custom power device for power distribution systems, and optimal allocation of this plays a significant role in improving various network performances. In recent years, many important papers based on simultaneous OADG and D-STATCOM allocation can be found in the literature [39–45]. Gholami et al. [46] proposed a particle swarm optimization (PSO)-based algorithm to optimally size DGs with unified power quality controllers (UPQCs) with NR. Recently, Sedighizadeh et al. [47] have presented a multi-objective pareto algorithm to allocate inverter-based DGs with passive filters.

2.5 OADG with Energy Storage The integration of energy storage devices is fast increasing in the power distribution system for the overall enhanced dynamic behavior of the system. In [48], simultaneous allocation of OADG and energy storage devices is presented. In [49, 50], simultaneous reconfiguration is added with the other two. Recently, Gampa et al. [51] proposed a grasshopper optimization algorithm to allocate DGs with shunt capacitors and electric vehicle charging stations. In [52], a PSO-based allocation algorithm for photovoltaic (PV)-DG allocation and battery energy storage system (BESS) with NR is presented.

3 Review of Approaches and Objectives in OADG Problem A review of some of the various approaches and objectives used in recently published papers is presented in Table 1.

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Table 1 Comparison of performance analysis of optimization techniques References

Approach

Objective(s)

Nguyen and Vo [14]

Novel stochastic fractal search algorithm

Power loss minimization (PLM), voltage profile improvement (VPI), increase of voltage stability (IVS)

Prasad et al. [17]

Elephant herding optimization algorithm (EHOA)

PLM, VPI, maximization of economic benefits (MEB)

Suresh and Edward [17]

Hybrid of grasshopper optimization algorithm (GOA) and cuckoo search (CS) technique

PLM, minimization of voltage deviation (MVD), DG cost minimization (DGCM)

Truong et al. [20]

Quasi-oppositional chaotic symbiotic search (QOCSS) algorithm

PLM, VPI, and IVS

Sharma et al. [23]

Quasi-oppositional swine influenza model-based optimization with quarantine

PLM, VPI, IVS

Gampa and Das [26]

Fuzzy multi-objective genetic algorithm (GA)

PLM, improvement of branch current carrying capacity (IBCC), VPI, feeders load balancing (FLB)

Mahmoud and Lehtonen [26]

Generic closed-form analytical expressions for calculating optimal sizes of DG units and capacitors

PLM

Kumar et al. [28]

multi-objective opposition-based chaotic differential evolution

Pareto-optimal solution of PLM, MVD, minimization of yearly economic loss (MYEL)

Siahbalaee et al. [32]

Improved shuffled frog leaping (ISFL) algorithm

PLM, minimization of no. of switching (MNS) in NR

Raut and Mishra [33]

Improved elitist JAYA algorithm (IEJA)

PLM & IVS

Nagaballi and Kale [34]

Improved raven roosting optimization (IRRO) algorithm

Active and reactive PLM, VPI, IVS, minimization of line loading ( MLL), DGCM

Teimourzadeh and Ivatloo [35]

Three-dimensional group search optimization (3D-GSO)

PLM

Tolabi et al. [36]

Thief and police algorithm (TPA)

PLM, IVS, minimization of operating cost (MOC)

Tran et al. [37]

Stochastic fractal search (SFS) algorithm

PLM

Raut and Mishra [38]

Improved sine cosine algorithm PLM and IVS (ISCA)

Gampa et al. [51]

Grasshopper optimization algorithm (GAO)

PLM, VPI, improvement of substation power factor (ISPF)

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A review of results of unity power factor (UPF) ODGA for some of the above methods is also included in Table 2. The review reveals that most of these results are centered around the two most popular test systems, i.e., 33 bus and 69 bus. Table 2 A review of results References

Test distribution systems used

Nguyen and Vo [14] 33b

Prasad et al. [17]

Some important results DG (UPF) placements (at bus no.) and sizes (MW)

Base case—Optimized‚ Active power loss (kW)

13,24 and 30/0.802, 1.092 and 1.0537

210.988–72.785

69

11,18 and 61/0.5273, 0.3805 and 1.7198

225.001–69.428

118

50,73, 80,91,110/3.1813, 2.3241, 2.0428, 2.0156, 2.8686

15

15/681.1

33b

30/1544.5

211–125.2 225–83.22

1298.091–578.741

61.7933–42.24

69

61/1873.6

Suresh and Edward [18]

33b

24/926.99

69

12/1890.6615

Truong et al. [20]

33b

13,24 and 30/0.8017, 1.0913 and 1.0537

210.998–72.7869

69

11,18 and 61/0.5261, 0.3803 and 1.7190

225.001–69.4284

118

30,42, 50,73,80,96,109/3.7083, 1.1543,2.3338, 2.4170 2.1072, 1.6877, and 3.1199

33b

14,24 and 30/0.7708, 1.0965 and 1.0655

210.9–72.8

69

9,8 and 61/0.8336, 0.4511 and 1.5000

224.9–71

33a

14,25 and 30/0.668, 0.426 and 0.905

202.677–79.02

69

18,61 and 64/0.387, 1.313 and 0.298

202.677–73.22

33b

13,24 and 30/0.801, 1.09 and 1.054

211–72.78

69

12,50, 61/0.784,0.863 and 1.780

225–71.14

33a

11,18 and 32/0.766, 0.2852 and 0.9033

Sharma et al. 2016 [23]

Siahbalaee et al. [32]

Nagaballi and Kale [34]

Teimourzadeh and Ivatloo [35]

210–139.5915 225.1789–151.6664

1298.092–516.2658

202.6–79.87 (continued)

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Table 2 (continued) References

Tran et al. [37]

Raut and Mishra [38]

Test distribution systems used

Some important results

69

27,61 and 64/0.388,1.464 and 0.289

224.96–73.477

33a

14,24 and 30/0.7540, 1.0994 and 1.0714

202.68–71.47

69

11,18 and 61/0.5268,0.3804 and 1.7190

225.03–69.44

84

7,72 and 80/3.1389,2.8350 and 3.5847

531.99–359.73

119

50,71 and 109/2.8833, 2.9786 and 3.1199

1298.09–667.29

136

11,29 and 106/2.3284, 2.0639 and 2.8411

320.66–169.22

33a

16,25 and 33/0.743, 0.743 and 0.743

202.66–80.15

69

12,61 and 62/0.760, 0.760 and 0.760

DG (UPF) placements (at bus no.) and sizes (MW)

Base case—Optimized‚ Active power loss (kW)

225–73.477

However, there are two topologically same types of 33-bus test systems with very little variations in system data [53], hence are referred to as 33a and 33b test systems in this paper.

4 Conclusion In this paper, an up-to-date review of papers related to the OADG problem has been presented including the most recent papers. The review reveals that the OADG problem has emerged as a modern power system optimization problem with the simultaneous combinatorial allocations such as that of D-FACT devices and energy storage devices with particular emphasis on uncertainty modeling of renewable DGs and loads.

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Quality of Power Enhancement of Distribution Network System Using DSTATCOM in Simulink Tool of MATLAB Jay Prakash Keshri and Harpal Tiwari

Abstract Power quality is now a major issue in homes, industry and agriculture as an inductive form such as single-phase and three-phase induction engines, pump station, computer fan engine, etc. The load is needed in this reactive current and the source provides the lagging current requested. Thus, that reactive power requirement increases and that active power also depends on it. So that, the power factor also decreases which causes the distribution system to increase the burden. Therefore, This paper improved power quality performance using Distribution Static Compensator (DSTATCOM) using the MATLAB Simulation tool which improves quality of power issues in the distribution network system. DSTATCOM is Facts device which is used in a distribution network system for quality of power conditioning. DSTATCOM is source and sink of reactive power demanded to a different type of non-linear load also Power quality is enhanced, Total Harmonic Distortion of source current (THD) of source current is reduced. DSTATCOM mitigates voltage flicker, swell, sag and spikes, which provides reactive power current to the distribution system. That reactive power control using the current injection from source to DSTATCOM. Working of DSTATCOM by taking the theory of IRP for nonlinear and unbalanced is evaluated by using the result of simulation done in the MATLAB. Keywords Instantaneous reactive power (IRP) · Distribution static compensator (DSTATCOM) · Total harmonic distortion (THD) · Flexible ac transmission system (FACTS)

J. P. Keshri (B) · H. Tiwari Malaviya National Institute of Technology, Jaipur, India e-mail: [email protected] H. Tiwari e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_46

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1 Introduction Power quality is a big issue nowadays in homes, industry, and agriculture are an inductive type like a single phase and three phase induction motors, pump station, computer fan motor etc. In this reactive current is demanded by the load and source provides that demanded lagging current. So, that reactive power requirement increases and active power also depends on that so that’s way power factor also reduces which causes an increase in the burden of the distribution system. Power electronics devices which having an increase in power electronics devices, non linear loads increasing day by day, that is rectifiers, UPS, inverter, computers, etc. Thus, they reduce the quality, introduce harmonics content sag and swell components in the system. So theses harmonic component present in currents that effect on power quality [1]. Generators operation and transformers affected by unbalancing. Development of gate turn-off capability of semiconductor switches opened the way of FACTs controller using VSC. VSC provides faster response. STATCOM is shunt connected device for compensating. Zyl et al. (1996) gives an idea of power quality issue on radial line system. This was first power converter compensator.STATCOM concept was given by Hanson et al. (2002), Chun et al. (2000), Dong et al. (2004), Bantai Public Bahut hard and Chen et al. (2006) [2, 3]. In 1995, first STATCOM of 100 MVA was established in Tennessee VAlley Authority (TVA) in Tennessee. This is used to regulate daily load variation of 160 kv bus to reduce operation of tap changer 1.3 GVA, 160 kv/500 kv transformers. DSTATCOM is having two different modes. It is also working in VCM (Voltage control mode) and CCM (Current control mode), which provides power quality improvement using voltage and current injection mod. Which control reactive power [4] in the distribution network system and improve power factor. Inverter is used for dc input to ac output by [3] using single phase and three phase converters , which is required for load demand if input is current then it is known as current source inverter (CSI) and if input is voltage then it is known as voltage source inverter (VSI) and in both case input will in dc form. VSI is used for controlling the output voltage and in CSI, output current is control. VSI having parallel and series combinations of power electronics device for converting input dc to output ac. Load may be R (resistive), RL (resistive and inductive), RLC (RL with capacitor). The power quality enhanced using like DSTATCOM. The Controlling scheme that used in this chapter for the working of DSTATCOM is the theory of synchronous frame of reference (SRF), Compensation of current using regulation using DC bus, and theory of IRP, a scheme which depends on the neural network from these techniques mostly used the SRF and IRP. This chapter based on the MATLAB simulation model of DSTATCOM which used IRP theory for controlling of reactive power of the system, unbalancing, THD and it also improves the quality of power factor in the distribution system. This chapter shows the interactive performance for addressing an issue related to load unbalancing [15, 17] using DSTATCOM and controlling algorithm used for different type of mode. The research in this quality improvement area is now focusing on using the PWM based SVM technique for DSTATCOM oper-

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Fig. 1 DSTATCOM in distribution system

ation in addition to the PI controller in the dq coordinate systems. This chapter also show the application of the SVM based HCC from the inverter to the DSTATCOM. The main objective of the chapter is to enhance the power quality of the system. The Controlling scheme used in this chapter for the working with DSTATCOM is the theory of synchronous frame of reference (SRF), Compensation of current using regulation using DC bus, and theory of IRP, a scheme which depends on the neural network from these techniques mostly used the SRF and IRP. This chapter based on the MATLAB simulation model of DSTATCOM which used IRP theory for controlling of reactive power of the system, unbalancing and also improves the quality of power factor in the distribution system. This chapter shows the interactive performance for addressing an issue related to load unbalancing using DSTATCOM and controlling algorithm used for different type of mode.

2 System Configuration A Distributor feeder with non-linear and unbalanced load is shown in Fig. 1. The operational performance of DSTATCOM taken by IRP theory is evaluated by DSTATCOM using PI controller system. STATCOM is known as static synchronous compensator or static synchronous condenser. It is based on power electronics devices and which is having voltage source converter. STATCOM is used in distributor system for improving and maintaining quality of power which is distributed static synchronous compensator.

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This system configuration is shown in Fig. 1 here Ls and Rs are shown which is source inductance and resistance. The nonlinear load [12] is obtained by linking the 3-ph uncontrolled rectifier with R-L type of. An unbalanced load is obtained by changing the impedance value. DSTATCOM having voltage source inverter (VSC) and six pulses IGBT with an antiparallel diode. VSI having some important advantages over CSI for controlling of DSTATCOM • Voltage source inverters use with constant voltage but current source inverters are fed with constant current. Hence, the output of a VSI drive is adjustable, threephase AC voltage frequency and magnitude, while a CSI drive is having adjustable current only. • VSI and CSI having different energy storage techniques, CSI use inductive terminology for storing while VSI uses capacitor for storing, therefore voltage fluctuation can be made smooth by using VSI. • CSI output contain high harmonics so that for reducing requires extra filter on both input and output side while in VSI no extra component are required. • VSI is more efficient than CSI. This is due to switching devices, IGBT switching used in VSI while GTO switching used in CSI that is less efficient.

3 Control Algorithm The objective of DSTATCOM is to maintain the reactive power of source and sink that is demanded by variable load. So that source current is maintain at UPF and burden is also reduced. For implantation of the proposed approach Fig. 2 is use for the simulation in Matlab platform. This Theory is used for calculating reference currents using Instantaneous Reactive Power which was developed by Akagi [10]. This theory defines the 3 − φ load currents are transformed into 2 − φα − β coordinates. These α − β coordinates are orthogonal. According to this theory, the instantaneous both P and Q are calculated by these α − β co-ordinates. The source current [11] is generated by load current which is provided by load voltage. The generated reference currents are given to the PWM controller to generate pulses for IGBT switches. Three phase voltage and three phase current [11] into 2 − φα − β frame are completed by using Clark’s transformation method as shown in Eq. (1). ⎡ ⎤    1 1  v La 1− − vα √2 √2 ⎣v Lb ⎦ = (1) vβ 0 23 − 23 v Lc

⎡ ⎤    1 1  i La 1 − − iα √2 √2 ⎣i Lb ⎦ = iβ 0 23 − 23 i Lc

(2)

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So instantaneous power for three phase p = v La i La + v Lb i Lb + v Lc i Lc

(3)

Similarly for two phase this power can be as p = vα i α + vβ i β

(4)

So instantaneous reactive power can calculated by p = −vβ i α + vα i β

(5)

This upper Eqs. (4) and (5) can be written in matrix form      p vα vβ i α = −vβ vα i β q

(6)

The reference currents of the source must be calculated to compensate using the theory of IRP, and some active power is used to balance the switching loss of IGBT at time of operation of the VSC and grid controller as DC for the voltage and it plays an important role in maintaining a constant DC voltage. The voltage at the capacitor is matched with source voltage (DC) and that error is obtained is giving to the PI controller. By using these current axis [10] components we can calculate source current [13] so, that in the α − β frame format reference source current calculated

Fig. 2 DSTATCOM simulation model

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i α∗ i β∗

 =

   1 vα −vβ p¯ + Vd 0 Δ vβ vα

(7)

where, Δ = vα2 + vβ2

(8)

So in abc frame reference source current given by Reverse Clark’s transformation, ⎤⎡ ⎤ ⎤ ⎡ √1 1 0 ∗ i∗ 2 i sa √ ⎢ √1 − 1 3 ⎥ ⎣ 0∗ ⎦ ∗ ⎦ ⎣ i sb i =⎣ 2 2 2 ⎦ α √ ∗ i β∗ i sc √1 − 1 − 3 2 2 2 ⎡

(9)

Zero sequence current does not present in three phase three wire type of system so = 0 in Eq. (9). The PI controller is used here for controlling purpose P for proportional and also for current value and I for the integrator and for past value for finding the error generated for fixing through the controller. The mathematical form which is used for the controlling [14] purpose is defined as e(t) = S P − P V

(10)

t

u(t) = u bias + K p e(t) + K i

e(t)dt

(11)

0

Here SP stands for set point and PV for process variable which define error in the system. The variable and [9] for proportional and integrator component constant for controller. In VSI we can done harmonic analysis by using following equations these six step waveform free from 3rd harmonic V AN =

VB N =

VC N =



2E dc sin(nwt) nπ n=1,5,7...∞ 

2π 2E dc sin(nwt − ) nπ 3 n=1,5,7...∞ 

2π 2E dc sin(nwt + ) nπ 3 n=1,5,7...∞

(12)

(13)

(14)

For balanced three phase load, the magnitude of any phase can be determined by using superposition of different harmonic current of particular phase, for simple RL

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Fig. 3 Simulation model in MATLAB of DSTATCOM in distribution system

load the current equation for phase A can be express as follow. iA =

 n=1,5,7...∞

nπ

√

2E dc R2

+

n 2 ω2 L 2

sin(nwt − tan−1 (

nωL )) R

(15)

Same as phase-A we can also calculate for phase-B and phase-C by replacing by angle, that for phase-B it will be 120◦ lag respect to phase-A and for phase-C it will be 120◦ lead respect to phase-A.

4 Simulation Model of DSTATCOM Simulink model of DSTATCOM [15] represents with power electronic device IGBT converters shown in Fig. 3. For achieving an acceptable perfection at 1.68 KHz switching frequency, Simulink model discretized at a short time (5 µs). Simulink model shown is improving harmonics and voltage sag [8] and swell occurred in the system so that power quality is maintained. Model is used here for mitigating voltage fluctuation and harmonic reduction etc. DSTATCOM voltage is used for maintaining this according to the DSTATCOM terminology supply voltage is greater or less is major factor for controlling the reactive power. Reactive power is major function for harmonics and power factor if power factor value is less then burden on line will be more so we require high value of power factor for reducing burden on line, it is also reduce overall system cost. Distributed static synchronous compensator (DSTATCOM) is at 25 KV in the distribution system and two feeders are used which is 21 km feeder and 2 km feeder for transmitting instantaneous power given to load which is at bus B2 and bus B3. The capacitor at shunt is correcting the power factor in the distribution network system at bus B2. The connected load is nonlinear like arc furnace type.

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Fig. 4 Source voltage and current (pu)

The load current is the module for 5 Hz so apparent power [7] is varied between 1 and 5.2 MVA and power factor is 0.9 lagging. This combination allows voltage flicker mitigation. DSTATCOM output voltage is compared with input if it is high then DSTATCOM works reactive power source else it absorbs [5] reactive power, it acts like capacitor generating reactive power. This Simulink model contains voltage source PWM inverter which reduces harmonics in the system and inverter modulation frequency is 1.68 KHz so first harmonic [6] around at 3.36 KHz. A 0.1 F capacitor at dc voltage source and antialiasing filter of current acquisition.

5 Simulation Results DSTATCOM is controlling the power quality like harmonic and voltage flicker, sag, swell. Source voltage [16] and current waveform are represented in the Fig. 4 at before 0.2 s source voltage at 1 pu and current is zero because no current [4] is flowing through DSTATCOM from 0.2 to 0.4 s nonlinear load gives interruption to the distribution system so reactive current flow through to mitigate the reactive power in the system.

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Fig. 5 Simulation result of DSTATCOM

At the time of starting load kept constant and after some time which is 0.2 s load change to nonlinear and which varying according to modelling done in the load, we have chosen 1.077 pu voltage for keep DSTATCOM voltage at 1 pu. Three steps is given to the system at the different time setting at 0.2, 0.3, and 0.4 s.There voltage is increased by 6%, decrease by 6% and then at 1.077 pu. THD of the load current at time of disturbance is 90% and after compensation with control strategies of voltage and current signal of DSTATCOM is about 8% shown in Figs. 4, and 5. That shows the applicability of the proposed strategy with the permissible limit. We conclude that in MATLAB simulation different scope shows different waveform at a distinct time period which is given to the system for operating DSTATCOM in different modes first when variation in voltage decrease and increase so that 0.96 and 1.04 pu change in voltage after compensating this variation is reduced up to -/+0.7% of initial. DSTATCOM, When the voltage is high this behave as 0.6 pu inductive and when it is low so behave as 0.6 pu capacitive. Therefore, from results shown describe the importance of the proposed strategy.

6 Conclusion Power quality is improved by using DSTATCOM. Power quality is a combination of voltage, frequency, harmonics, and reliability of power supply. The proposed control strategy of DSTATCOM improves quality after voltage change by using arcing load. DSTATCOM is here to mitigating the voltage fluctuation by using IRP theory. This

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also reduces harmonic contain, voltage sag, swell and also improves power factor maintained at unity. Although, DSTATCOM is fast and for a higher rating so it is used in the distribution network system. Therefore, the proposed control strategy for power quality enhancement is shows their feasibility in the distribution system.

References 1. Shekar LR, Sumana S, Dhanalakshmi R (2018) Voltage regulation using DSTATCOM in distribution grid for enhancement of power quality. In: 2018 2nd international conference on trends in electronics and informatics (ICOEI), pp 298–303 2. Mohapatra M, Chitti B (2010) Fixed and sinusoidal-band hysteresis current controller for PWM voltage source inverter with LC filter. In: Students’ technology symposium (TechSym), 2010 IEEE, pp 88–93 3. Keshri JP, Tiwari H (2019) Fault classification in VSC-HVDC transmission system using machine learning approach. In: IEEE 2019, 8th international conference on power systems, pp 1–6 4. Siegfried H (2014) Grid integration of wind energy: onshore and offshore conversion systems. Wiley 5. Panda AK, Penthia T, Mangaraj M, Dash AR (2018) Power quality refinement by executing icos control algorithm in fuel cell based DSTATCOM. In: 2018 IEEMA engineer infinite conference (eTechNxT), pp 1–6 6. Yan C, Yongjun Z (2015) Application of DSTATCOM on distribution networks with small hydropower injected. In:2015 modern electric power systems (MEPS), pp 1–5 7. Mangaraj M, Panda AK (2015) Traveling-wave-based line fault location in star-connected multiterminal HVDC systems. In: 2015 international conference on industrial instrumentation and control (ICIC), pp 1069–1073 8. Sharad MW, Aware, Mohan V (2008) Power quality issues & it’s mitigation technique in wind energy generation, In: Harmonics and quality of power, 2008. ICHQP 2008. 13th international conference on IEEE, pp 1–6 9. Hou Y, You G (2010) Integral sliding mode variable structure control for DSTATCOM. In: 2010 international conference on measuring technology and mechatronics automation, pp 476–478 10. Lei E, Yin X, Zhang Z, Chen Y (2018) An improved transformer winding tap injection DSTATCOM topology for medium-voltage reactive power compensation. IEEE Trans Power Electron 13(3):2113–2126. ISSN: 0885-8993 11. Paul K, Oleg W, Scott D, Steven P (2014) Analysis of electric machinery and drive systems. Wiley 75:508–510 12. Padiyar KR (2017) FACTS controllers in power transmission and distribution, New Age International 13. Singh B, Solanki J (2009) A comparison of control algorithms for DSTATCOM. IEEE Trans Industr Electron 56(7):2738–2745 14. Farshad M, Sadeh J (2008) Understanding ionic liquids at the molecular level: facts, problems, and controversies. Angewandte Chemie Int Edn Wiley Online Library 47(4):654–670 15. Keshri JP, Tiwari H (2018) IFault detection, classification in multiterminal HVDC transmission system with MC-SVM. J Intell Fuzzy Syst IOS Press 5:1–11 16. He ZY, Liao H, Li XP, Lin S, Yang JW, Mai RK (2018) Detection and classification of transmission line faults using modified F-SVM. In: 2018 IEEE international conference on environment and electrical engineering and 2018 IEEE industrial and commercial power systems Europe (EEEIC/I & CPS Europe), pp 1–8 17. Keshri JP, Tiwari H et al (2013) A review on active power filter. In: Proceeding of international conference on advance trends in engineering & technology (ICATET-2013), pp 214–223

Technical Proposal for Sizing of Equipment and Designing of Solar Photovoltaic System Manasi Pattnaik, Harish Sharma, Manoj Badoni, Yogesh Tatte, and Manoj Kumar Debnath

Abstract Electricity from solar power PV system generates zero raw fuel cost and no environmental issues. Designing is the process of analysis and determining the size of each component of the system to meet the load requirement. This chapter is based on sizing of basic elements required for designing a stand-alone solar PV system to get the energy without environment degradation at low cost. This approach involves survey of a site installed with solar photovoltaic. For recommendation, conformation the hypothesis, reality and future utility a 3 kW solar PV system is designed and implemented. Solar PV system must have installed according to National Electric Code (NEC-690), IEC-62548, IEC-61215 and MNRE in order to determine load requirements, sizing of PV module, charge controller, battery bank, and inverter. Solution of energy demand at low cost is decentralized Solar PV system without degradation of environment. This paper includes the designing and analysis of 3 KW Solar PV system for electrification. Keywords Survey of proposed site · Solar PV system equipment sizing · Designing of stand-alone solar PV system · Renewable energy

M. Pattnaik (B) · M. Badoni · Y. Tatte Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, India e-mail: [email protected] M. Badoni e-mail: [email protected] H. Sharma TUV-SUD, Asia, Gurgaon, India e-mail: [email protected] M. K. Debnath ITER, Siksha O Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_47

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1 Introduction Among renewable energy options, solar electric systems are a popular choice due to requirement of low maintenance and long life time. There are no moving parts so less chance of mechanical failure. Most of solar system will continue to produce power for 25 years or more. Solar cell produces electricity through photovoltaic effect [1, 2]. The classifications of solar PV system are stand-alone, grid-connected and hybrid system [3, 4]. Increasing population increases the need for energy and its related service [5]. Steps used for designing and sizing of Solar PV System are as follows. • • • • • •

Site Survey. Determine the solar power plant capacity according to area and load. Calculate number of PV module used and its arrangement. Calculate size of charge controller. Calculate the size of the inverter Calculate the battery size.

2 Survey of Proposed Site Google earth image of H-Block of Thapar University, Patiala, Punjab, India is shown in Fig. 1. The details about the location are as follows. Location: Thapar University, Patiala, Punjab. Latitude: 30.3362◦ N Longitude: 76.3922◦ E Altitude: 255 m. Roof Top Area of H-Block of Thapar University = 1551.55 m2 (Approximate). Size of solar system depends on the available area of roof. For panel installation, 70% of roof top area can be used. With higher cost, 90% of rooftop area is also used for certain solar panel. Area 10 m2 is required for 1 kW power from solar panel as a thumb rule. Size of solar PV system = (Rooftop area × Panel’s rated output × 70%) /Each panel area. The typical weight of solar panel with structure is 15 kg per square meter. With technology and type of structure, this weight is varying. The system output depends on solar radiation and panel efficiency. These two factors define capacity utility factor (CUF) for solar system. In India, only 19% CUF is taken for estimation. As a thumb rule, 1kw solar system generates 1600–1700 kwh of electricity per year. The annual number of units generated by solar system can be calculated as follows. Annually Generated Units in kWh = System size in kW × CUF × 365 × 24.

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Fig. 1 Google earth image of H-block of Thapar University, Patiala, Punjab, India

The system output depends on solar radiation and panel efficiency. For recommendation, conformation of the hypothesis, reality and future utility a 3 kW Solar PV System is designed and implemented. Solar Path of Institute of Engineering and Technology, Patiala, Punjab is presented in Fig. 2.

3 Design Methods for 3 kw Solar PV System Method of designing is a procedure or set of rules and intellectual principle of operations to achieve result using analysis [6]. Designing of PV System is the process of calculating the size of each component with the purpose of meeting load requirements. Following steps are used for designing [7]. i. Survey of site. ii. Calculation of Load requirement. iii. PV module sizing: calculate the following: (a) (b) (c) (d)

Total power used/day Total energy consumption per day The total energy requirement from Solar Panel The minimum requirement of PV module.

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Fig. 2 Solar Path of Institute of Engineering and Technology, Patiala, Punjab

For India, the panel generation factor is 3.43, which is used to determine sizing of PV module used to determine the sizing of PV modules. Array capacity is determined by array nameplate. Power rating under standard test conditions (STC), meaning at 1000 w/m2 , and 25◦ cell temperature and reference solar spectral irradiance of air mass 1.5. iv. Charge controller (DC-DC Converter) sizing: Charge controllers are used to convert variable voltage module output to a fixed voltage output, which required to charge the battery or that is used as an input for an inverter in a grid-connected system. Also, its function is to regulate the charge going into battery bank. It is used to prevent overcharging and reverse current flow at night. Normally PWM and MPPT type Charge Controller are used [8–10].

Charging controller rating = Isc × 1.5 × number of PV string. where Isc = Short circuit current of panel. 1/Battery charging and Discharging efficiency = 1.5. v.

Battery bank sizing: This step is also very important for reliability of our system because during night and cloud days’ sufficient energy is required to operate the appliances. Battery Capacity in Ah = (Total WH/day × DOD) /(0.85 × 0.6 × nominal battery voltage).

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where 0.6 is depth of discharge and 0.85 is battery loss. vi. Inverter Sizing: Inverter converts DC into AC. System will be overloaded if the rating of inverter is less than load power. So for safe and efficient operation the rating of inverter should be 25–30% greater than the power of appliance. vii. Cost Estimation: Rooftop Solar system without battery and grid connection is 125 rupees per Watt power. A system with battery 5 h. Backup typical cost 200 rupees/WP. Price per watt WP reduces for large system in the range of 100 rupees/WP for MW Size system.

4 Mathematical Calculation of 3 KW Implemented Solar PV System To provide the required electricity, implementation of 3 KW Solar PV system is required. To confirm the reality and future utility, the result of this implementation is helped. Determination of client needs, size of PV modules, inverter, battery bank and solar charge controller sizing and cost estimation depends on use of appliances client needs. Implementation of 3 kw Solar panel is displayed in Fig. 3. Implementation of 3 kw Solar Power System with battery and inverter is displayed in Fig. 4. A. Determination of Power Consumption Demands Table 1 shows estimated energy demand for load per day. B. Sizing of Solar PV Module: The technical data of 300 W Vikram solar panel is tabulated in Table 2. The following computation is performed to obtain the technical data. i. Solar Power Plant Capacity • Power Consumption Demand = Total Energy used = 7.805 kWh/day, • 30% energy lost in the Solar PV System,

Fig. 3 Implementation of 3 kw solar panel

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Fig. 4 Implementation of 3 kw solar power system with battery and inverter Table 1 Following electrical appliance are used S. No.

Equipment name

Load per equipment (W)

Quantity

Operating hours

Total load (W)t

Total energy (Wh)

1

Fluorescent lamp

40

4

5

160

800

2

CFL/LED

10

4

2

40

80

3

Fan

90

2

5

180

900

4

Computer or laptop

100

2

5

200

1000

5

Phone charger 10

4

5

40

200

6

Printer

50

1

0.5

50

25

7

Room A.C (1 Ton)

1400

1

3

1400

4200

8

Television

120

1

5

120

600

Total load = 2190 W/day

Total energy = 7805 wh/day

Table 2 Technical data of 300 W Vikram solar panel

Peak power

Pmax

300 W

Peak power

V mpp

37.05 V

Voltage (Max)

I mpp

8.1A

Current (Max)

V oc

45.58 V

Voltage (Open circuit)

I sc

8.58 A

Current (Short circuit)

α

−0.310%

Temperature coefficient (Voltage)

β

0.052%

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• Total Solar PV Panel Energy required = Power consumption × 1.3 = 10.15 kWh/day, • Solar Power Plant Capacity = Total Solar PV Panel Energy required/ (Peak Sunny Hour × Plant Performance). 10.15/(5 × 0.7) = 3 kW. ii. Sizing of Solar PV Panel. Number of PV Panels Required = 3000/300 = 10 Modules. So the system is powered by at least 10 modules of 300 W each. iii. Calculation of number of modules in series in a Series in a string and total number of strings. Max Temperature Condition Voltage (NOCT @ 50 ºC) Vmin = Vmp [1 + α(T2 − T1 )] = 34.17 V Min Temperature Condition Voltage (NOCT @ 5 ºC) Vmax = Voc [1 + α(T2 − T1 )] = 48.4 V where α T1 T2 DC voltage Range

Voltage Temperature Coefficient. STC condition Temperatures (Reference Temp). Actual Temperatures (Cell Temp). MPPT Voltage = 300–950 V.

• DC voltage Range = MPPT Voltage = 65–130 V from Inverter Data sheet • Max number of Series connected modules is = (Max. MPPT Voltage)/(Vmax ) = 130/48.4 = 2.6 • Min number of Series connected modules is = (Min. MPPT Voltage)/(Vmin ) = 65/48.4 = 1.34. Therefore, number of modules in a string is 2. Total Number of module required for generation. = DC Capacity/Module Capacity = 3000/300 = 10. Total Number of strings. = Total Number of module/Number of module in a string = 10/2. Total No. of String = 5. So results from above calculation are: • Total number of Module = 10 • Module connected in series = Number of module in a string = 2 • Number of Strings connected in parallel = 5. C. Inverter Sizing Luminous Solar NXT 3 kW off-grid hybrid solar inverter is used in this project. It is a power full inverter with advanced MPPT technology based solar charge controller [11–15]. Table 3 shows the various data reflecting inverter sizing.

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Table 3 Inverter data

Capacity of the inverter

3 kW

Solar Panel current

46 A

Solar panel voltage (V oc )

65–130 V

Full battery recharge time

10–12 h

• Total load in Watt of all Appliances = 2190 W. • The inverter should be 25–30% bigger size for safety. • Size of the inverter = 2190 × 1.25 = 2737.5 W or bigger. D. Battery sizing Power Consumption = 7805 Wh/day. Nominal Battery voltage = 12 V. Days of Autonomy = 1 day (for 24 h). Battery Capacity= (Energy Consumption × Design Margin × Day of autonomy)/(Charge Controller Voltage × Depth of Discharge × Battery efficiency). Battery capacity = 593.63 Ah. • So the rating of battery bank is 12 V, 546.5 Ah for 1 day Autonomy. • No of battery required = V max of panel/12 = 64/12 = 4 One battery capacity = 546/4 = 136 Ah = 150 Ah (Approximately). So rating of each battery is 12 V, 150 Ah and battery bank is consisting of 4 batteries, which are connected in series. E. Solar Charge Controller Sizing Charging controller rating = Isc × 1.5 × number of PV string = 8.6 × 1.5 × 5 = 64.5 A or little bit bigger. Figure 5 depicts 3 kW Standalone Solar PV System with Equipment Sizing.

Fig. 5 3 kW standalone solar PV system with equipment sizing

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5 Conclusion In this chapter, 3 kW Solar PV System is studied. Design and Performance of the system has been evaluated. The method and techniques used are scientifically recognized so finding this research is reliable. The technique and methods used are recognized scientifically so finding of this paper is reliable. The design result showed that a load estimation of 7800 WH per day required 3000 W capacity of 10 PV modules, 65 A charge controller, 4(12 V, 150 Ah) batteries, 3 KVA, 48 V inverter for installation. The objectives cited within this study have been achieved and the hypothesis has been confirmed. So the solution of energy demand at low cost without environment degradation is decentralized PV energy system.

References 1. Pillai DS, Natarajan R (2019) A compatibility analysis on NEC, IEC, and UL standards for protection against line-line and line-ground faults in PV arrays. IEEE J Photovolt 9(3):864–871 2. Kurtz S (2015) International PV quality assurance task force (PVQAT). No. NREL/PR-500065121. NREL (National Renewable Energy Laboratory (NREL)) 3. Skoczek A et al (2008) Electrical performance results from physical stress testing of commercial PV modules to the IEC 61215 test sequence. Solar Energy Mater Solar Cells 92(12):1593–1604 4. Urja A (2013) Ministry of new and renewable energy. Government of India, New Delhi 7.1 5. Beniwal N, Hussain I, Singh B (2019) Vector-based synchronization method for grid integration of solar PV-battery system. IEEE Trans Industr Inf 15(9):4923–4933 6. Badoni M, Singh A, Singh B (2016) Comparative performance of wiener filter and adaptive least mean square-based control for power quality improvement. IEEE Trans Industr Electron 63(5):3028–3037 7. Gungor VC et al (2011) Smart grid technologies: communication technologies and standards. IEEE Trans Industr Inf 7(4):529–539 8. Ma L, Ran W, Zheng TQ (2010) Modeling and control of three-phase grid-connected photovoltaic inverter. IEEE ICCA 2010. IEEE 9. Deng R et al (2015) A survey on demand response in smart grids: mathematical models and approaches. IEEE Trans Industr Inf 11(3):570–582 10. Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Industr Inf 7(3):381–388 11. Beriber D, Talha A (2013) MPPT techniques for PV systems. In: 4th international conference on power engineering, energy and electrical drives. IEEE 12. Verma D et al (2016) Maximum power point tracking (MPPT) techniques: recapitulation in solar photovoltaic systems. Renew Sustain Energy Rev 54:1018–1034 13. Pathare M et al (2017) Designing and implementation of maximum power point tracking (MPPT) solar charge controller. In: 2017 international conference on nascent technologies in engineering (ICNTE). IEEE 14. LokeshReddy M et al (2017) Comparative study on charge controller techniques for solar PV system. Energy Procedia 117:1070–1077 15. Bhattacharjee A et al (2018) Development and validation of a real time flow control integrated MPPT charger for solar PV applications of vanadium redox flow battery. Energy Convers Manage 171:1449–1462

A Comparative Study of Muscle Artifacts Removal in Single Channel EEG Binapani Pal and Karmila Soren

Abstract The Electroencephalogram (EEG) takes a vital role to point out brain activity and behavior. However, the EEG recordings are always contaminated with artifacts. It may be physiological or non-physiological artifacts. The muscle artifacts (Physiological Artifact) are very laborious to remove because they are every time contaminated with the EEG (Electroencephalogram) signal, which gives the EEG signal analysis more difficult. The muscular artifacts arises from different type of muscle groups. To inscription this issue, achieve a method for a comparative study of muscle artifact removal in single channel EEG, which is easy but effective. The methods involving for removal of muscular artifacts from single channel EEG are based on the combination of Joint blind source separation (JBSS) techniques and Ensemble empirical mode decomposition (EEMD) technique. These two methods are more successful in comparison to other methods to removal of muscle artifacts in single channel EEG. It is clearly removes the muscle artifacts from EEG and gives a well healthcare system. Keywords EEG · Muscular artifact · Single channel EEG

1 Introduction The different neural states of the brain correlate with different electrical activity. Some activity of brain will generate electrical signal due to all physical activity of person [1]. The transiting system of measurement of physiological signals of human are capable of better monitoring the diseases like therapy of chronic and diagnosis continuously. It also getting very low-quality signals to increases the likelihood by taking clinical environment out of environment system. The recording B. Pal (B) · K. Soren Department of Instrumentation and Electronics Engineering, College of Engineering and Technology, Bhubaneswar, Bhubaneswar 751003, Odisha, India e-mail: [email protected] K. Soren e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9_48

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signal is the linear combination of different bioelectrical signals like ECG (Electrocardiogram), EMG (Electromyogram), EEG (Electroencephalogram) and EOG (Electrooculogram) [2]. A standard recording to measure the electrophysiological activity of the brain is called an EEG (electroencephalogram) signal. Now a day, it is used for identifying different cognitive and pathological states of brain for its technical simplicity, low cost and temporal resolution behavior [3]. A German scientist Hans Berger, was done the first EEG (Electroencephalogram) recording in 1920 from the Human scalp. The EEG signals frequency and amplitude content give brain dynamic ample information. In dissimilar frequency band, the EEG signal can be divided starting from 1 to 100 Hz [4]. Depending on their origin the EEG artifacts are divided into two part (Physiological and non-physiological). The size of amplitude of artifacts is generally larger than the size of amplitude of signals of interest [5]. EEG is used in many medical diagnosis, therapies and prognosis like brain death, comma and sleep disorder, etc. It always corrupted by two artifacts; one is physiological (eye, muscle and cardiac activities) and non-physiological (line interference and electrode noise) [6]. By neuromuscular activities like contraction and stretch of muscle which produced the electrical current in the muscle is measured by the EMG signal. This signal depends upon physiological and anatomical properties of muscle completely, and nervous system is also controlled by this signal. Its characteristics play a vital role in diagnosis of neuromuscular disease like Amyotrophic lateral sclerosis (ALS) and myopathy [7]. Using adaptive filter and Blind Source Separation (BSS) techniques the EOG signals are effectively removed. But muscular activity (EMG) like biting, chewing, frowning involving artifacts are difficult to remove because the EEG signal always contaminated with the muscle artifacts(Electromyogram signal) [8]. When recorded the Electroencephalogram signal it is always misleading because muscle artifacts (Electromyogram signal) are always contaminated with the Electroencephalogram signal. The muscle artifacts are generated by contraction of scalp and neck muscles of body [9]. The range of EMG signal is 0 to >200 Hz. Amplitude and waveform of the artifacts depend upon the degree of contraction and stretch of the muscle. The aim of the muscle artifact is to totally delete the recorded data, so it is very difficult to remove the muscle artifacts in single channel EEG than the other signal like Electrooculogram and Electrocardiogram [10]. The details about the EEG signals [1–5] and application EEG signal in medical field [6] can be referred from the literature [1–6]. Idea about EMG signal, different artifacts and its removal technique can be referred from the literature [6–10]. Concept of moving average filters is presented in [11]. Concept of JBSS and CCA can be extracted from literature [10]. Concept of EEMD and CCA-EEMD are detailed in [12, 13].

2 System Overview The Temple University hospital database NEDC THU for EEG signals selected for experiment. The 5 s signals of muscle artifact which are contaminated with EEG

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EEG signal

Noisy EMG signal

Moving Average Filter or CCA-EEMD method

Muscle Artifacts are extracted

Performance analysis of Moving Average Filter and CCA-EEMD

EMG signal

Fig. 1 Proposed block diagram for a comparative study of removal of muscle artifacts in single channel EEG

recordings are selected. These are randomly chosen from the 20 channels of EEG signal, which are annotated for muscle artifacts. The goal is to remove the muscle artifacts from EEG signal to get the correct leading or clean EEG signal.

3 Proposed Method In this proposed method basic of removing the muscle artifacts is the step by step process to identifying the muscle artifact and passing through the different models. And then calculate the error and comparison between them.

3.1 Block Diagram The block diagram for a comparative study of removal of muscle artifacts in single channel EEG can be seen in Fig. 1. In this artifact removal technique gives a better reading of EEG signal. Here, the contaminated signal is passed through some methods one by one to calculate the clear EEG signal.

3.2 Methods Used The details about the method used in this task are presented as follows. Moving Average Filter The moving average filter structure is a very simple Low Pass FIR (Finite Impulse Response) filter. It removes a unnecessary noisy data from the original data. To generate a single output point, the moving average filter takes the input size of M samples at a time and calculates the average of that M samples [11]. In case of moving average filter, to increase in the length of the filter the smoothness of output also increases.

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 X m = (Z m−2 + Z m−1 + Z m ) 3 or X m−1

(1)

 = (Z m−2 + Z m−1 + Z m ) 3

In above Eq. (1) Z m is the input signal and X m is the output signal. CCA Canonical correlation analysis (CCA) is a commonly technique in BSS. It brings less computational time and use second-order statistics. It also separates uncorrelated sources from components. Between the two multi-dimensional random variables, it identifies the linear relation by maximize pairwise correlation through the two data set [10]. Let with C channel and T observations A1 (t) be the observed data matrix A(t) and let the original data matrix A2 (t) = A(t − 1) with its delayed version temporally X 2 (t). Suppose A1 and A2 are the mean of each row that has been removed. CCA target to seek two pairs of basis vector, one foe A1 and another for A2 for mutually maximized the correlation among the projection of variables onto these basis vectors. Maximizing the correlation between the linear combination of components in A1 and A2 this take the following objective function: max  X 1, X 2

X 1T

 12 X 1   T 11 X 1 X 2 22 X 2

X 1T 

(2)

Here 1 and A2  X 1 and  X 2 are the weight vectors. The auto covariance matrix of A are 11 and 22 respectively, the cross covariance matrix of A1 and A2 is 12 . By settings the derivatives of Eq. (2) with respect to X 1 and X 2 to zero, the maximizing problem can solve. The following Eigen decomposition problem can be formulated after some manipulation: −1  −1  11

12

22

−1  −1  22

21

11

21 12

X 1 = Q1 X 1

(3)

X 2 = Q2 X 2

where the eigenvalues are Q 1 and Q 2 . X 1 and X 2 are the corresponding Eigenvectors. It can be solved that Q 1 = Q 2 = ρ 2 with ρ as the correlation coefficient among X 1T A1 and X 2T A2 which is the first pair of canonical variants. By solving the same Eigen decomposition problem with constraint the second pair is obtained and the second pair is uncorrelated with the first pair. −

−

Suppose all the canonical variants derived from A1 and A2 are M1 and M2 respec−

−

tively. The corresponding rows are highly correlated among M1 and M2 , while the rows within each separate matrix are uncorrelated with each other [12].

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EEMD EMD is a nonlinear and non-stationary single channel technique. It decomposes time series into multiple intrinsic mode functions (IMFs) [13]. In IMFs properties, the number of zero crossing and the number of extrema must either be differing or equal at most by one and defined the local maxima and local minima is zero by the mean value envelope, at any point. A shifting process is implemented to get the IMF from a time series signal p and need to be identify all extrema in p. By a cubic spline line as the upper envelope eα all local maxima are connected, with the same process repeated  lower  for the envelope eβ of local minima. Their average s is calculated as s = eα + eβ /2. The new signal h = p − s. Until the new signal h satisfies the two above conditions, the aforementioned steps are repeated and it is identified to an IMF. Then =z i h as the first IMF component and c1 = p − z 1 is the residual signal to obtain the second IMF componentz 2 . When the residual signal c N becomes a monotonic function, the shifting process will stop. To reconstruct the original signal p the IMFs can be added together. X=

N 

z j − cN

(4)

j=1

Therefore, the original signal p is decomposed into N empirical modes z j s and a residue c N . In the ensemble mean of sufficient trail, the noise can be cancelled out [12]. CCA-EEMD CA-EEMD is a novel single channel artifact removal technique. From single channel EEG and FNIRS it removes motion artifacts. Above all, the single channel techniques achieved the best performance because of similar properties among the muscle and motion artifacts. It combined with EEMD and CCA and it is two-step method of modeling. Using the EEMD algorithm the single channel signal p is decomposed with a multichannel signal P. P matrix contains the final residual and the derived N IMFs. (N + 1) × T is the size of P [12]. A(θ ) = AEEG + θ × AEMG

(4)

where θ is the muscle activity and A(θ ) is the synthesized data of last. When θ parameter changing, the Signal to Noise Ratio (SNR) can be adjusted, i.e. SNR = The RMS is denoted as,

RMS(X EEG ) RMS(θ × X EMG )

(5)

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 RMS(A) =

1 × AT T

(6)



RMS(AEEG − A) RRMS = RMSAEEG

(7)



whereafter muscle artifact removal A is the estimated EEG signal [14].

4 Results and Discussion The EEG signal added with the EMG signal with Signal to Noise ratio (0.5) gives the contaminated (Noisy) EMG signal as shown in Fig. 2. Then passing through the moving average filter, and then passed through the CCA-EEMD method. Then

Fig. 2 Noisy EMG signal with SNR: 0.5

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Fig. 3 Clean EEG signal after passing through the moving average and CCA-EEMD with SNR: 0.5

the calculation and comparison of the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE) and Correlation Coefficient (CC) of both the Moving average filter and CCA-EEMD, respectively, are carried out to show the clean EEG is better than the moving average filter (refer Fig. 3). The EEG signal added with the EMG signal with SNR: 1 dB gives the contaminated (Noisy) EMG signal shown in Fig. 4. Then passing through the moving average filter and processed through the CCA-EEMD method, the MAE, MSE, RMSE, RRMSE and CC are calculated by both moving average filter and CCA-EEMD. The obtained result shows that the clean EEG signals obtained through CCA-EEMD are better than the moving average filter (refer Fig. 5). The EEG signal added with the EMG signal with SNR: 1.5 gives the contaminated (Noisy) EMG signal shown in Fig. 6. Then passing through the moving average filter and processed through the CCA-EEMD method, the MAE, MSE, RMSE, RRMSE and CC are calculated by both moving average filter and CCA-EEMD. The obtained result shows that the clean EEG signal obtained through CCA-EEMD is better than the Moving average filter (refer Fig. 7). After performance measure of both of the Moving Average filter and CCA-EEMD, we conclude that relative root mean square error and correlation coefficient are more important to compare between two signals. In Table 1 the RRMSE is more than the CCA-EEMD method and the Correlation Coefficient is less than the CCA-EEMD

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Fig. 4 Noisy EMG signal with SNR: 1

Fig. 5 Clean EEG signal after passing through the moving average and CCA-EEMD with SNR: 1

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Fig. 6 Noisy EMG signal with SNR: 1.5

method for every case. In case of moving average filter with an increase in SNR value, increases the RRMSE and decreases the CC. But in case of CCA-EEMD with increase in the SNR, decreases the RRMSE and increases the CC.

5 Conclusion and Future Scope The comparative analysis on Canonical Correlation Analysis-Ensemble Empirical Mode Decomposition (CCA-EEMD) and moving average filter are experimented for a comparative study of muscle artifacts removal from single channel EEG signal. Performance is analyzed with real-time signals obtained for authenticated resources. Validation is carried with standard performance measures such as MAE, MSE, RMSE, RRMSE and CC. The obtained results over varying SNR, strongly suggest the CCA-EEMD for muscle artifact removal from Electroencephalogram signal. Further testing of these methods on other artifacts may be future work for global validation of CCA-EEMD approach.

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Fig. 7 Clean EEG signal after passing through the moving average and CCA-EEMD with SNR: 1.5 Table 1 Comparison study of error in moving average filter and CCA-EEMD method SNR

Error

Moving average filter

CCA-EEMD

0.5

MAE (Mean absolute error)

0.8938

0.5736

MSE (Mean square error)

1.4153

0.5729

1

1.5

RMSE (Root mean square error)

1.1896

0.7569

RRMSE (Relative root mean square error)

1.1899

0.7570

CC (Correlation coefficient)

0.6283

0.7135

MAE (Mean absolute error)

0.4552

0.3756

MSE (Mean square error)

0.3654

0.2438

RMSE (Root mean square error)

0.6044

0.4938

RRMSE (Relative root mean square error)

0.6046

0.4939

CC (Correlation coefficient)

0.8476

0.8781

MAE (Mean absolute error)

0.3126

0.2920

MSE (Mean square error)

0.1709

0.1475

RMSE (Root mean square error)

0.4134

0.3841

RRMSE (Relative root mean square error)

0.4135

0.3841

CC (Correlation coefficient)

0.9197

0.9262

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References 1. Sulaiman N, Ying BS, Mustafa M, Jadin MS (2018) Offline labview-based EEG signals analysis for human stress monitoring. In: 2018 9th IEEE control and system graduate research colloquium (ICSGRC). IEEE, pp 126–131 2. Zhang Z, Li H, Mandic D (2016) Blind source separation and artefact cancellation for single channel bioelectrical signal. In: 2016 IEEE 13th international conference on wearable and implantable body sensor networks (BSN). IEEE, pp 177–182 3. Chavez M, Grosselin F, Bussalb A, Fallani FDV, Navarro-Sune X (2018) Surrogate-based artifact removal from single-channel EEG. IEEE Trans Neural Syst Rehabil Eng 26(3):540–550 4. Ahmad RF, Malik AS, Kamel N, Amin H, Zafar R, Qayyum A, Reza F (2014) Discriminating the different human brain states with EEG signals using fractal dimension: a nonlinear approach. In: 2014 IEEE international conference on smart instrumentation, measurement and applications (ICSIMA). IEEE, pp 1–5 5. Roy V, Shukla S (2014) Artifacts removal of EEG Signals by the application of ICA and double density DWT algorithm. Int J Eng Manuf 4(2):42 6. Khatun S, Mahajan R, Morshed BI (2016) Comparative study of wavelet-based unsupervised ocular artifact removal techniques for single-channel EEG data. IEEE J Transl Eng Health Med 4:1–8 7. Singh A, Dutta MK, Travieso CM (2017) Analysis of EMG signals for automated diagnosis of myopathy. In: 2017 4th IEEE Uttar Pradesh section international conference on electrical, computer and electronics (UPCON). IEEE, pp 628–631 8. Chen X, Liu A, Peng H, Ward RK (2014) A preliminary study of muscular artifact cancellation in single-channel EEG. Sensors 14(10):18370–18389 9. Narasimhan SV, Dutt DN (1996) Application of LMS adaptive predictive filtering for muscle artifact (noise) cancellation from EEG signals. Comput Electr Eng 22(1):13–30 10. Jiang X, Bian GB, Tian Z (2019) Removal of artifacts from EEG signals: a review. Sensors 19(5):987 11. Magsi H, Sodhro AH, Chachar FA, Abro SAK (2018) Analysis of signal noise reduction by using filters. In: 2018 international conference on computing, mathematics and engineering technologies (iCoMET). IEEE, pp 1–6 12. Chen X, Liu A, Chiang J, Wang ZJ, McKeown MJ, Ward RK (2015) Removing muscle artifacts from EEG data: multichannel or single-channel techniques? IEEE Sens J 16(7):1986–1997 13. Mishra M, Sahani M, Rout PK (2017) An islanding detection algorithm for distributed generation based on Hilbert-Huang transform and extreme learning machine. Sustain Energy Grids Netw 9:13–26 14. Gao J, Zheng C, Wang P (2010) Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis. Clin EEG Neurosci 41(1):53–59

Author Index

A Alam, Kamran, 91 Ali, S. K. M., 35

B Badoni, Manoj, 573 Barik, Prasanta Kumar, 497 Begum, B., 487 Behera, Hirak Keshari, 377 Bhatter, Siddharth, 81 Bhoi, Sushil Kumar, 327 Bhol, Subrat, 169 Bhowmik, Pritam, 69 Bhuyan, Satyanarayan, 257 Biswal, Anil Kumar, 43 Biswal, Sunita S., 401 Biswaranjan, 257

C Chandra Sekhar, Ch, 289 Chopra, Namarta, 91 Choudhury, S., 225

D Dandpat, A., 207 Dash, Meera, 514 Dash, Subrat Kumar, 471, 553 Dash, T. P., 225 Das, Kunal Kumar, 539 Das, Sanghamitra, 225 Das, Sushovit, 196 Das, Uma Shankar, 196 Debnath, Manoj Kumar, 23, 129, 497, 525, 573

Dinesh, Paidi, 1

G Gadanayak, Debadatta Amaresh, 299 Goel, Sonali, 157 Gupta, Bhupendra Kumar, 184

H Hajra, Sugato, 35 Hota, Sarbeswara, 217

J Jena, Adarsh Kumar, 169 Jena, Bibekananda, 265 Jena, Narendra Kumar, 277 Jena, Priyansh P., 257 Jena, Tarakanta, 497

K Kar, Durga Prasanna, 257, 377 Kar, Sanjeeb Kumar, 23, 355 Kasturi, Kumari, 327 Keshri, Jay Prakash, 564 Kumar, Sandeep, 457

L Lal, Deepak Kumar, 311

M Maiti, C. K., 225 Mali, Sabita, 233

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 R. Sharma et al. (eds.), Green Technology for Smart City and Society, Lecture Notes in Networks and Systems 151, https://doi.org/10.1007/978-981-15-8218-9

595

596 Maneesha, B., 289 Mishra, Manohar, 1 Mishra, R. K., 207 Mishra, Shuvangi, 196 Mishra, Sivkumar, 471, 553 Mohanta, Kalyani, 35 Mohanty, Debidasi, 117 Mohanty, Kanungo B., 277 Mohanty, Pradeep Ku, 525 Mohanty, Pritish Kumar, 311 Mohapatra, E., 225 Mohapatra, Gayatri, 129 Mohapatra, Karisma, 105 Mohapatra, Subhashree, 1 Mohapatra, Tapas Kumar, 196

N Naik, Amiya Kumar, 277 Naik, Bighnaraj, 1 Nanda, Amar Bijay, 169 Nanda, Anuja, 355 Nanda, Pradipta Kumar, 339 Nayak, Ajit Kumar, 105 Nayak, Janmenjoy, 1 Nayak, Mamata, 105 Nayak, Manas Ranjan, 327 Niazi, Khaleequr Rehman, 247

P Padhi, Jyoti Ranjan, 525 Pal, Binapani, 583 Pal, Vijayeta, 35 Panda, Jyotiranjan, 339 Panda, Nibedan, 289 Panda, Sidhartha, 117 Panda, Sobhit, 265 Panda, Subhasis, 417 Panigrahi, Basanta K., 35 Panigrahi, T., 514 Parhi, Manoranjan, 184 Parihar, Divya, 433 Pati, Laloo Ranjan, 471, 553 Pati, Sarada Prasanna, 217 Patra, Akshaya Kumar, 355 Patro, Ashutosh, 257 Pattanayak, Binod Kumar, 43, 184 Pattnaik, Manasi, 573 Pilla, Ramana, 367 Pradhan, Rohit Kumar, 539 Prusty, Ramesh Chandra, 445 Purohit, Varsha, 35

Author Index R Raiguru, P., 207 Ramana, B. V., 289 Ratnam, Jayashree, 233 Rawat, Tanuj, 247 Rout, Bidyadhar, 355 Rout, Prabhash, 169 Rout, Pravat Kumar, 69, 143, 385, 401, 417 Routray, Sangram Keshari, 143 Rout, Susanta Kumar, 55 Roy, Abhijit, 257

S Sahani, Mrutyunjaya, 55 Sahoo, Asish Kumar, 196 Sahoo, Buddhadeva, 143 Sahoo, Pradyumna K., 257 Sahoo, Subhadra, 277, 445 Sahoo, Tapasmini, 539 Sahu, Binod Kumar, 277, 385, 417, 445, 487 Sahu, Nakul Charan, 169 Sahu, Prakash Chandra, 445, 487 Sahu, Sibasish, 539 Samal, Sarita, 497, 525 Sarangi, Swetalina, 385 Sasamal, Trailokya Nath, 433, 457 Satapathy, Pranati, 217 Satapathy, Priyambada, 525 Satpathy, Prashant Kumar, 471, 553 Satpathy, Priya Ranjan, 265 Sharma, Harish, 573 Sharma, Lalita, 91 Sharma, Renu, 55, 81, 157, 257, 265, 514 Sharma, Sachin, 247 Singh, Debabrata, 43 Sinha, Sayantan, 81 Soren, Karmila, 583 Srivastava, A. K., 207 Subudhi, Dillip Kumar, 355 Swain, 257 Swain, Bhanja Kishor, 55 Swain, D. R., 401 Swarnkar, Tripti, 1

T Tatte, Yogesh, 573 Tiwari, Harpal, 564 Tripathy, Sabita, 23 Tulasichandra Sekhar, G., 367

Author Index V Vakula, Kanithi, 1 Vandana, S., 289 Verma, Kusum, 247

597 Y Yethirajula, Praveen Kumar, 433