Advanced Computational Techniques for Renewable Energy Systems 3031212150, 9783031212154

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Advanced Computational Techniques for Renewable Energy Systems
 3031212150, 9783031212154

Table of contents :
Preface
Contents
About the Editor
Advanced Computational Techniques
Solar Radiation Forecasting Based on Artificial Neural Network: A Case Study of Bechar City, Southwest Algeria
1 Introduction
2 Material and Models
2.1 Study Area and Data Collection
2.2 The Artificial Neural Network (ANN) Based Model to Estimate Global Solar Energy
2.3 ANN Model Architecture
3 Statistical Assessment Indices
4 Results and Discussion
5 Conclusion
References
Machine Learning KNN Classifier for Forecasting Hourly Global Solar Irradiance over Adrar City
1 Introduction
2 Methodology
2.1 Study Area and Datasets
2.2 K Nearest Neighbors Classification
3 Results and Discussions
4 Conclusion
References
A Multicore and Multithreaded Microcontroller
1 Introduction
2 The Performance Comes from Parallelism
2.1 Multicore and Parallelism
2.2 Multithreading and Parallelism
3 The Microcontroller Market and the State-of-the-Art
4 A New Microcontroller
4.1 A Multihart Pipeline
4.2 A Multicore Processor
4.3 Independent Codes
4.4 Parallelized and Distributed Code
5 Experimental Evaluation of the Proposed Microcontroller
5.1 Evaluation of the Efficiency of the Multithreading Technique on the CPI
5.2 Evaluation of the Speed-Up on a Multicore Design
6 Conclusion
References
Brain Tumor Classification Using Convolutional Neural Networks and Transfer Learning
1 Introduction
2 Brain Tumors
3 Classification Using Convolutional Neural Networks
4 Experements and Results
4.1 Dataset Acquisition
4.2 Training
4.3 Evaluation Metrics
4.4 Experiments
5 Conclusion
References
Design, Implementation, and Deployment of IoT/M2M Smart City Applications Based on MCNs
1 Introduction
2 The Proposed IOT/M2M Smart City Using Raniso App
2.1 Architecture Design
2.2 System Hardware
2.3 System Software
3 Implementation of Smart City Applications
3.1 Smart Safety
3.2 Smart Agriculture
3.3 Smart Parking System
3.4 Implementation of IoT/M2M Applications in the Final Model of the Smart City
4 Conclusion
References
Control of Three Phase Cascaded H Bridge Multilevel Inverter Supplied by a Photovoltaic System
1 Introduction
2 Mathematical Model
3 Multilevel Three-Phase SVM
4 Simulation Results
5 Conclusion
References
A Proposal of Blockchain and NFC-Based Electronic Voting System
1 Introduction
2 Related Work
2.1 Preliminaries
3 Proposed System
3.1 System Components and Actors
3.2 Block Diagram
3.3 Voting Process
4 Conclusion and Future Works
References
Application of CRM Method for Reservoir Fluid Dynamic Characterization in Haoud Berkaoui Petroleum Field
1 Introduction
2 CRMT Method
3 Results and Discussion
4 Conclusion
References
Embedded Machine Learning for Fault Detection and Diagnosis of Photovoltaic Arrays Using a Low-Cost Device
1 Introduction
2 Dataset and Features Selection
2.1 Dataset
2.2 Features Extraction
3 Machine Learning and IoT Technique
3.1 Machine Learning
3.2 Internet of Things
3.3 Embedded System
4 Results and Discussion
4.1 Simulation Results
4.2 Experimental Results
5 Conclusion and Future Work
References
Planar Micro-thermoelectric Generators Based on Cu55Ni45 and Ni90Cr10 Thermocouples for IoT Applications
1 Introduction
2 µTEG Design
3 Results and Descussion
3.1 3D Thermal Modeling
4 Characterization of the µTEGs
5 Conclusion
References
Edge Detection of MRI Brain Images Based on Segmentation and Classification Using Support Vector Machines and Neural Networks Pattern Recognition
1 Introduction
2 Overview Support Vector Machine (SVM) et Neural Pattern Recognition App
2.1 Support Vector Machine (SVM)
2.2 Neural Networks Pattern Recognition
3 Methodology
3.1 Image Database Binary Image
3.2 Pre-treatment
3.3 Segmentation and Region Boundaries in a Binary Image
3.4 Extraction of Characteristics
3.5 Classification Using SVM
3.6 Classification Using Neural Networks Pattern Recognition
4 Results
5 Conclusion
References
Determination of Intrinsic Parameters of PV Module Using Pattern Search
1 Introduction
2 Effect of Variation of the Mainly PV Cell/Module Parameters
2.1 Effect of Series Resistance (Rs) Due to Changing Materials on the Performance of PV
2.2 Effect of Changing Number of Serial Cells on the Performance of PV
2.3 Effect of Changing of Material Type on the Performance of PV
2.4 Effect of Changing Number of Cells in Parallel on the Performance of PV
3 Extarction of Intrinsec Parameters of PV Module – Results and Discussion
4 Conclusion
References
Sensing and Communication in Renewable Energy
Development of a Supervision/Control Interface for an Experimental Wind-Storage-Grid-Diesel Microgrid System
1 Introduction
2 The Proposed Algorithm
2.1 The Experimental Micro Grid Description
2.2 Energy Management Algorithm
2.3 The Supervisor/Controller Interface Development
3 Results and Discussion
3.1 The Principal Technical Program
3.2 Economic Study
3.3 Wind Resource Information
3.4 Temperature Information
3.5 The Statistical Study
4 Conclusion
References
Design of Smart Irrigation System in the Greenhouse Using WSN and Renewable Energies
1 Introduction
2 Related Works
3 Our Suggested Approach
3.1 The System Design
3.2 Control System
3.3 Advantages of the Proposed System
4 Conclusion
References
Application of Metamaterials Based on Resonators -e- for the Design of Miniature Planar Antennas
1 Introduction
2 Extraction of Effective Parameters
3 Design of the Resonators Constituting the Metamaterials
4 Patch Antenna Design
5 Design of Patch Antennas Based on Metamaterials
5.1 Antenna Based on a Single Resonator -e-
5.2 Sensitivity of the Response of the Resonator-Based Patch antenna -e- to a Variation in the Geometric Parameters
5.3 Antenna Based on Two Resonators -e- of Different Dimensions
5.4 Antenna Based on Three Resonators -e- of Different Dimensions
6 Conclusion
References
Aspect Oriented Web Service Composition Based Petri Net Model
1 Introduction
2 Related Work
3 Conceptual Architecture as Formel Fondation
3.1 Concepts and Definitions
3.2 Web Services Composition as Petri Nets
4 Recognized Petri Nets Overview of Aspect Oriented Model for Petri Extention
4.1 Aspect oriented model
5 Conclusion and Perspectives
References
High-Efficiency 60-GHz Printed Antenna Using a Triple-Layer Metasurface
1 Introduction
2 Antenna and Metasurface Unit Cell Design
2.1 60-GHz Antenna Geometry
2.2 Metasurface Unit Cell Design
3 Periodic Structures Application and Discussion
3.1 Cross-slot Periodic Structure
3.2 DCSRR periodic structure
4 Conclusion
References
Mobile User Profile in the Context of Mobile Crowd Sensing
1 Introduction
2 Background
2.1 Mobile User Profiling
2.2 Mobile Crowd Sensing of Internet of Thing
2.3 Privacy Protecting and Security Methods
2.4 Hardware Security ModuleS (HSMS)
2.5 Communication Profile Handler (CPH)
3 Mobile User Profiling & Privacy Protecting and Security (MUP & PPS)
3.1 Mobile User Profiling and Privacy Protecting Methodology
3.2 Mobile User Profiling Model
3.3 Architecture for Processing the Mobile User Profiling and Protecting Privacy
4 Implimentation and Result
4.1 Data Collection
4.2 Privacy Security
4.3 Processing
4.4 Recommendation
5 Conclusion
References
Reconfigurable and Ecological Intelligent Antenna for Satellite Communication
1 Introduction
2 Design of Reconfigurable Antenna
3 Results and Discussions
3.1 Reconfigurability Using Graphene
3.2 Reconfigurability by Using Pin Diode
3.3 Reconfigurability by Using Pin Diode and Graphene
4 Conclusion
References
Mutual Coupling Reduction Between Two Closely Spaced Microstrip Antennas Using Electromagnetic Band Gap (EBG) Structure for IoT Applications
1 Introduction
2 Antenna and EBG Design
3 Simulation Results and Discussions
4 Conclusion
References
A New Design of Patch Antenna Array for IoT Applications
1 Introduction
2 Antenna Design
2.1 Design Equations
2.2 Proposed Design
2.3 2 × 1 Array Antenna
3 Conclusion
References
Blind Sources Separation and Cryptography for Secure Remote Reading of Sonelgaz Smart Meters
1 Introduction
2 Smart Grid Concepts and Standards
3 Smart Grid Vunerabilities and Cyber-Attacks
3.1 Attack via the Optical Link
3.2 Attack via the OTA Channel
3.3 Attack via the Cellular Network
4 Proposed Security Protocol for Remote Reading of Sonelgaz Smart Meters
4.1 Proposed Security Protocol
4.2 Emission: Encryption and Mixing Operation
4.3 Reception: Decryption and Denoising
4.4 Mixture Matrix Choice
5 Experimental Results
6 Conclusion
References
Connected Sensors for a Smart Green Farm
1 Introduction
2 Smart Farming Systems
3 The Proposed System
3.1 Design Project
3.2 System Operating Principle
3.3 General Operating Flowchartneral Operating Flowchart
3.4 Experimental Model
3.5 Web Interface and Data Display
4 Conclusion
References
A Mini Review of the Literature of Fractional-Order Chaotic Systems and Its Applications in Secure Communications Schemes During the Last Three Decades (1990–2020)
1 Introduction
2 Literature Review of Chaotic System and Its Application in Secure Communication
3 Literature Review of Fractional-Order Chaotic System and Its Application in Secure Communication
4 Conclusion
References
A Wideband Millimeter Wave Antenna for 5G Application Resonate at 3.5 GHZ
1 Introduction
2 Antenna Structure and Design
3 Conclusion
References
One-Layer and Dual-Polarized Metamaterial Inspired Antenna Using Dodecagon Split Ring Resonator Mushroom and Metasurface for Terahertz Applications
1 Introduction
2 Structure Design of Dodecagon SRR
3 Antenna Design: Technique
4 Simulation Results and Discussion
4.1 Reflection Coefficient
4.2 2D-Directivity
4.3 Radiation Pattern
5 Conclusion
References
2.4 GHz Semi-textile Wearable Antenna for Off- and On-Body Communications
1 Introduction
2 Antenna Analysis and EBG Design
2.1 Antenna Design
2.2 Spiral EBG Design
2.3 Integrated Antenna with Spiral EBG
3 Analysis on Body Loading
4 Conclusion
References
Energy Efficiency and Management
Grey Wolf MPPT Controller for Grid Connected Residential Wind System Operating Under Low and High Variations in Wind Speed
1 Introduction
2 Residential Wind Turbine System
3 Grey Wolf and Its Application in MPPT Tracking
3.1 Grey Wolf Optimization Technique
3.2 Grey Wolf MPPT Controller
4 Simulations Results
5 Conclusion
References
Energy-Efficient and Traffic-Aware Function Analysis of Network Service Orchestration
1 Introduction
2 Background
2.1 Cloud Computing
2.2 SDN
2.3 Virtualized Network Functions (NFV)
2.4 Server less Computing
2.5 Orchestration: Overview
2.6 Security Orchestration
3 Applications Scenarios
3.1 5G
3.2 Transport Networks
3.3 Cloud Data Canter’s
3.4 Internet of Things
4 NSO and Standardization
4.1 MEF
4.2 ETSI
4.3 T/RM Forum
4.4 3GPP
5 Projects
5.1 T-NOVA
5.2 UNIFY
5.3 SONATA
5.4 5GEx
6 Conclusion
References
A Global MPPT Controller Based on an Improved Particle Swarm Optimization Algorithm
1 Introduction
2 Photovoltaic System
2.1 Photovoltaic Panel Model
2.2 Partial Shading Conditions
3 MPPT Techniques
3.1 Classical Techniques
3.2 PSO Algorithm
3.3 Proposed Algorithm
4 Results and Discussions
5 Conclusion
References
A Comparative Study of MPPT Algorithms to Control DC-DC Converters in PV Systems
1 Introduction
2 PV System
2.1  PV Panel Characteristics
3 Synchronous Buck Converter Modeling
4 MPPT Methods
4.1  Perturb and Observe
4.2  Incremental Conductance
4.3  Neural Networks
4.4  Adaptive Neural Fuzzy Inference Systems 
5 Results and Disscussion
6 Conclusion
References
Developing a Real-Time Monitoring System in View to Analyze and Assess the Performance of Standalone PV System Along with Its Two PV Module Technologies Located in Northern Algeria
1 Introduction
2 PV System Description
3 PV Monitoring System Description
3.1 Monitoring System Overview
3.2 Sensors and Measured Parameters
3.3 Data Acquisition Systems
3.4 Graphical User Interface (GUI)
4 Results and Discussions
4.1 Solar Radiation and Ambient Temperature Variation
4.2 Solar Radiation, Module Temperature, and Output Power
4.3 Load Curve, Battery Energy Flow, and State of Charge
5 Conclusion
References
Influence of Dust Particles Deposition on the Reflection Loss of a Photovoltaic Module
1 Introduction
2 Reflectivity at Air/Dusty Coated Glass Interface
3 Numerical Simulation Results
4 Conclusion
References
An Improved Fuzzy OTC MPPT of Decoupled Control Brushless Doubly-Fed Induction Generator
1 Introduction
2 Modeling of Wecs‐Based BDFIG
2.1 Modeling of the Turbine and Gearbox
2.2 Mathematical Model of the BDFIG
3 Field Oriented Control of a BDFIG
3.1 Control of PW Current
3.2 Control of CW Current
3.3 PW Power and Torque Control
4 MPPT with Optimal Torque Control
5 Fuzzy Logic Control of BDFG
5.1 Design of Fuzzy PI Controller for the BDFIG
5.2 BAT Algorithm
6 Simulation and Discussion
7 Conclusion
References
Choosing the Adapted Artificial Intelligence Method (ANN and ANFIS) Based MPPT Controller for Thin Layer PV Array
1 Introduction
2 Material and Method
2.1 PV Cells Modeling:
2.2 Proposed Design for Module 3
2.3 Boost Converter Design
2.4 The ANN Based MPPT Controller
2.5 Adaptive Neuro-Fuzzy Inference System
3 Result and Discussion
3.1 The Simulated Model
3.2 The Results Discussion
4 Conclusion
References
Methods Improving Solar Power System Efficiency Based on Geographical Coordinates and Sun Position Calculators
1 Introduction
2 Methods and Materials
3 Control Methods
3.1 Postal Code Method
3.2 GPS Method
3.3 User Interface Method
4 Simulation
4.1 MATLAB Software
4.2 Proteus Software
5 Results and Comparisons
6 Conclusion
References
Aerial Forest Smoke’s Fire Detection Using Enhanced YOLOv5
1 Introduction
2 Methodology
2.1 Convolutional Neural Networks
2.2 YOLOv5 Model
3 Experiments and Results
3.1 Data Preprocessing
3.2 Evaluation Metrics
3.3 YOLOv5 Model Implementation
3.4 Training
3.5 Testing
3.6 Discussion
4 Conclusion
References
Sizing, Modeling and Energy Flow Management of PV-Diesel-Batteries Microgrid for Agricultural Application
1 Introduction
2 Material and Method
2.1 Case Study
2.2 Microgrid Sizing
2.3 Microgrid Modeling
2.4 Microgrid Energy Flow Management [9]
3 Result and Discussion
3.1 Microgrid Sizing
3.2 Microgrid Modeling
3.3 Microgrid Energy Flow Management
4 Conclusion
References
Machine Learning-Based Techniques for False Data Injection Attacks Detection in Smart Grid: A Review
1 Introduction
2 History of Cyber-Attacks in the Energy Sector
3 Cyber Security in Smart Grids
3.1 Smart Grid Architecture
3.2 Impact of False Data Injection Attacks
4 False Data Attacks Detection Using Machine Learning
4.1 Supervised Machine Learning-Based Algorithms
4.2 Unsupervisedsed Machine Learning Algorithms
4.3 Reinforcement Learning-Based Algorithms
4.4 Deep Learning-Based Algorithms
5 Conclusion
References
Artificial Intelligence in Renewable Energy
Pervasive System in Smart Houses
1 Introduction
2 General Description of a Proposed Package
2.1 Hardware Implementation
2.2 Software Implementation
3 Project Implementation
4 Conclusion
References
Control and Power Management of Microgrid Supplied a Domestic and Industrial Loads
1 Introduction
2 Presentation of the System
2.1 The Configuration of the System
2.2 The Residential Load
2.3 The Industrial Load
2.4 The Sizing System
3 The Control of the System
3.1 MPPT Control
3.2 Synchronization and DC Bus Control
4 The Operating System
5 Results and Discussion
5.1 The Residential Side
5.2 The Industrial Side
5.3 Results Interpretations
6 Conclusion
References
Survey on Artificial Intelligence Algorithms Application for Alzheimer’s and Elderly People Safety in Smart Homes
1 Introduction
2 Smart Homes for Alzheimer’s Patients: Monitoring and Assisting
3 Survey of AI-Based Monitoring and Assistive Systems
3.1 ML Algorithms-Based Systems
3.2 DL Algorithms-Based Systems
4 Discussion and Limits
5 Conclusion and Future Directions
References
A New Transformer Condition Monitoring Based on Infrared Thermography Imaging and Machine Learning
1 Introduction
2 Experimental Data
3 Feature Extraction
4 Feature Classification Using Least Square Support Vector Machines (LS-SVM)
5 Classification Results Analysis and Discussion
6 Conclusion
References
A Robust Decoupled Control of Electric Vehicle Using Type-2 Fuzzy Logic Controller
1 Introduction
2 Modeling of Six-Phase PMSM
3 Fuzzy Logic Control
4 Structure of T2FLC
5 Dynamic Model of Electric Vehicle
6 Simulations Results
7 Discussion
8 Conclusion
References
Analysis Techno-Economic of a Stand-Alone Photovoltaic System Using a Specialized Advanced Simulation Software for Different Zones in Adrar Region
1 Introduction
2 Methodology
2.1 Description of the Sites
2.2 The Meteorological Data
2.3 House Electric Load Demand
2.4 Structure of the System
2.5 Economic Model
3 Results and Discussions
4 Conclusion
References
Convolution Neural Network Deployment for Plant Leaf Diseases Detection
1 Introduction
2 Related Work on Plant Diseases Recognition
3 Plant Leaf Diseases Recognition Using Convolutional Neural Networks Methodology
3.1 Convolutional Neural Networks
3.2 Transfer Learning
4 Experiments and Results
4.1 Experiment 1: Plant Disease Detection Using CNN and TensorFlow
4.2 Experiment 2: Plant Disease Detection Using Transfer Learning and TensorFlow
4.3 Experiment 3: Plant Disease Detection Using CNN and PyTorch
4.4 Experiment 4: Plant Disease Detection Using Transfer Learning and PyTorch
4.5 Experiment 5: Model Deployment on an EDGE Device
4.6 Discussion
5 Conclusion
References
Study and Implementation of U-Net Encoder-Decoder Neural Network for Brain Tumors Segmentation
1 Introduction
2 Medical Image Segmentation Overview
3 Brain Tumors Segmentation Using U-Net
4 Experiments and Results
4.1 Data Visualization
4.2 Tumor Segments Visualization
4.3 Model Building and Training
4.4 Model Evaluation and Testing
5 Conclusion
References
Bayesian Regularized Backpropagation Neural Network Model to Estimate Resilient Modulus of Unbound Granular Materials for Pavement Design
1 Introduction
2 Materials and Methods
2.1 Data Preparation
2.2 Bayesian Regularization Algorithm
2.3 Statistical Indicators of Performance
3 Results and Discussion
3.1 Effect of the Number of Neurons in the Hidden Layer on Model Performance
3.2 Performance Analysis of the Proposed Model
4 Conclusions
References
Optimal Placement of Phasor Measurement Units Considering the Topology Transformation Method
1 Introduction
2 The Formulation of PMUs Issue
2.1 PMUs Placement Criteria
3 Impact of Zero Injection Bus (ZIB)
4 System Observability Redundancy Index (SORI)
5 The Proposed Transformation Method
6 Grey Wolf Optimization Algorithm (GWO)
7 Application of the Suggested Algorithm
8 Results and Discussion
9 Conclusion
References
The Effect of the Intelligent Control System on the Tram Timetable Efficiency and Its Influence on the Road Capacity at Signalized Intersections
1 Introduction
2 Case Study
2.1 The Study Area
2.2 The Intelligent Control System of Constantine Tram
2.3 The Tram Timetable
3 Methodology
4 Results
4.1 Presentation of Intersections Data
4.2 Results of Palma Intersection
4.3 Results of Fadhila Saadane Intersection
5 Conclusion
References
Soil-Structure Interaction Effects on the Vibration Control of Building Structures
1 Introduction
2 Controlling Algorithm
3 Mathematical Formulation
4  Results and Discussion
5 Conclusion
References
Robust Control of Multiphase Induction Generator Equipped with Fuzzy Flywheel Energy Storage System
1 Introduction
2 Mathematical Model of All Parts of the System
2.1 Induction Machine Model
2.2 DSIG Model
3 Combined Synergetic-Vector Control Applied to DSIG Machine
4 Design of Fuzzy Logic Algorithm of the Fess Machine
5 Design of PSO Algorithm to Tuning Synergetic Control Parameters
6 Simulation Results
7 Conclusion
References
Urban Flood Risk; Diagnosis and Proposed Management. A Case Study in Bechar City, South Western Algeria
1 Introduction
2  Presentation of the Study Area
3 The Hydrographic Network
4  Background on the Flooding of Wadi Bechar
4.1 Consequences of the Floooding in Bechar (2008)
5 Climatic Overview
5.1  Precipitation
6 Flood Study
6.1 Hydrometric Data
7  Methodology of the Hydraulic Modelling
8  Discussions and Proposals for Different types of Development
8.1  Direct Protection
8.2 Indirect Protection
8.3  Proposed Development of the Wadi Bechar Basin:
9 Conclusion
References
Electromagnetic Converter for Electric Vehicles Integrated with Renewable Energy Sources for Sustainable Mobility
1  Introduction
2  Recognized Rules for Papers Submitted for Communication in IC-AIRES2022
2.1 Situation of the automotive sector in Algeria
2.2 Solar Cars
2.3 Presentation of the Initiative
3 Electric Vehicles, A Promising Market in Algeria
4 Motorization for Solar Cars
4.1 Characteristics required for an electric traction motor
4.2 Choice of Axial Flux Traction Motor for Solar Cars
4.3 2D digital model at mean radius
5 Conclusion
References
Power Electronics and Grid Connected
Variability of Solar Radiation Received on Tilted Planes in Adrar Region in the South of Algeria
1 Introduction
2  Study Area
3  Methodology
3.1 Extraterrestrial Solar Radiation Variation
3.2 Variations of Solar Radiation at the EARTH'S Surface
4  Results and Discussion
5  Conclusion
References
Environmental and Financial Impact Analysis of a Tubular 850 KW Wind Turbine Tower
1 Introduction
2 Methodology
3 Results and Discussion
4 Conclusion
References
Modeling of Two Five-Phase Induction Machines Connected in Series with an Open Phase
1 Introduction
2 Five-Phase Series-Connected Two Motor Drive
3 Five-Phase Series-Connected Two Motor Drive in Open-Phase Fault Operation
4 Experimental Results
5 Conclusion
References
Influence of Geometric Parameters on the Performance of a Vortex Type Cooling Tower
1 Introduction
2 Presentation of Vortex Tower
3 Operating Principale
4 Modeling of Vortex Tower
5 Results And Discussion
6 Conclusion
References
PEM Fuel Cell Emulator Based on a Buck Converter
1 Introduction
2 Fuel Cell Modeling
3 Dynamic Model of the Fuel Cell
4 Fuel Cell Simulation
5 Fuel Cell Emulator
6 Results and Discussions
7 Conclusion
References
Parameters Estimation Methods of Thin-Film Solar Module Using Numerical Algorithms and Artificial Neural Networks
1 Introduction
2 PV Module Models
3 Modelling of PV Modules by Artificial Neural Networks
4 Conclusion
References
An approach for Power Reserve Control (PRC) Strategy Based on a Novel ANN Model
1 Introduction
2 Proposed PV Model and Strategy
2.1 Array Model
2.2 Proposed Strategy
3 System Description
4 Simulation Tests and Discussion
4.1 Irradiation Variations
4.2 Temperature Variations
5 Conclusion
References
Frequency Enhancement of Power System with High Renewable Energy Penetration Using Virtual Inertia Control Based ESS and SMES
1 Introduction
2 Modeling of Multi-area Power System
2.1 The Principle of Inertia Frequency Stability Control
2.2 Frequency Control Mechanism
3 Derivative Control Technique-Based Virtual Inertia Control Design
4 Simulation and Results
4.1 Multiple Tests on the Interconnected Power System
4.2 Random Load and RESs Power Disturbances
5 Conclusion
References
Topology Analysis of Multi-cellular Converters in a Wind Energy System
1 Introduction
1.1 The Elementary Switching Cell
1.2 The Role of Capacitors in Serial Multicell Converter
2 Three-Cell Inverter
3 Modeling and Control of a Multicell Converter
3.1 Mathematical Model
3.2 Model with Instantaneous Values
3.3 Inverter Model
3.4 Simulation of the Model at Instantaneous Values
3.5 Control of Switches
3.6 Open Loop Simulation
3.7 Medium Model of a Multicellular Arm
3.8 Closed Loop Control Without Current Regulation
4 Decoupling Command
5 Sliding Mode Control of Multicell Converter
6 Conclusion
References
Modeling and Simulation of an Operating BLDC with Bidirectional Rotation Configurations
1 Introduction
2 Simplification Assumptions
3 Analysis in the Reference System (ABC)
3.1  The Electromotive Force
4 The Mechanical Equation of Motion 
5 Motor-Inverter Model
6 Simulation Results and Interpretation
7 Operation in clockwise direction
8 Analysis and Comments
9 Operation in counter-clockwise direction
10 Results and Discussion
11 Conclusion
References
BLDC Speed Control Based on Fractional PID Controller
1 Introduction
2 Fractional Calculation
3 Modeling Fractional Order Systems
4 Fractional Order PID Controller
5 Oustaloup Method
6 Optimisation Criteria ITAE (Integral of Time Multiplied Absolute Error)
7 Model of the BLDC When Two Phases are Energized
8 Open Loop Transfer Function
9 Simulation
10 Conventional PID Controller
11 Simulation Result Discussion
12 Conclusion
References
Application of the Prognostic and Health Management to Industrial Systems
1 Introduction
2 Prognostic Methods Categorization
2.1 Model-Based Prognostics
2.2 Data-Driven Prognostics
2.3 Hybrid Prognostics
3 Conclusion
References
Characterization and Simulation of the Power IGBT Module Used in VFD for Drilling Applications
1 Introduction 
2 AC Drives for Drilling Applications
2.1   Variable Frequency Drive
2.2 Power Module
3 Method of Parameter Extraction and Model Characterization
4 Simulations and Results
5 Conclusion 
References
Power Management Strategy of a Hybrid PV-Battery System Connected to the Grid
1 Introduction
2 Description of the Hybrid Energy System and Modeling
2.1 PV Energy Conversion System
2.2 Perturb and Observe (P & O) Technique
2.3 Energy Storage System (ESS)
2.4 Modeling and Control of the Grid
3 Energy Management System
4 Results and Discussion
5 Conclusions
References
Optimisation, Control and Power Conversion
Natural and Mixed Convection in Solar Drying Process
1 Introduction
2 Experimental Setup
3 Experimental Results
3.1 Natural Convection Flow
3.2 Mixed Convection Flow
4 Conclusion
References
Super Twisting Fuzzy High-Order Sliding Mode Control of Variable-Speed Wind Turbine
1 Introduction
2 Wind Turbine Based on DFIG Model
2.1 Model of DFIG
3 Super Twisting Fuzzy Strategy of DFIG
3.1 Control Laws
4 Results of Simulation
5 Conclusions
References
Hydrogen Diffusion Study via Phosphorus Deactivation in n-Type Silicon
1 Introduction
2 Experimental Procedure
3 Results and Discussion
4 Conclusion
References
Moth-Flame Optimizer Algorithm for Optimal of Fuzzy Logic Controller for Nonlinear System
1 Introduction
2 background Knowledge of Moth-Flame Optimizer Algorithm (MFO)
3 Fuzzy Logic Controller for Nonlinear System
4 Simulation and Results
5 Conclusion
References
Optimal Location and Sizing of Capacitor Banks in Distribution Systems Using Grey Wolf Optimization Algorithm
1 Introduction
2 Problem Formulation
2.1 Load Flow Analysis
2.2 Objective Function
2.3 Constraints
3 Grey Wolf Optimization (GWO)
4 Simulation Results
5 Conclusion
References
Estimation on the Potential of Dimethyl Ether (DME) as Clean Alternative Fuel by CFD
1 Introduction
2 Governing Equations and CFD Models
2.1 Combustion Modelling - Eddy Dissipation Concept (EDC)
2.2 The k - ε Turbulence Model
2.3 The Radiative Heat Transfer
3 Results and Discussion
3.1 Model Validation
3.2 Temperature Profile
3.3 Carbon Dioxide Emission
4 Conclusion
References
Symmetrical Voltages Dips Analysis in a Wind Turbine Based on DFIG for High Power Conversion
1 Introduction
2 Description System
3 Methodolgy
4 Dynamic Modeling System
4.1 Variable WT Based DFIG Modeling System
4.2 Control Solutions for Grid Disturbances
5 Simulation Results and Discussions
5.1 Crowbar Protection and Filter Dips for DFIG
5.2 DFIG’S LVRT Capability Analysis Under Symmetrical 3- Fault Condition
5.3 DFIG’S LVRT Capability Analysis with Crowbar at RSC
5.4 DFIG’S LVRT Capability Analysis with Crowbar at GSC
6 Conclusion
References
Optimal Placement Using Moth Flame Optimization in Radial Distribution
1 Introduction
2 Problem Formulation
2.1 Objective Function
2.2 Equality Constrains
2.3 Inequality Constrains
3 Proposed Approach
3.1 Creating the Initial Population of Moth’s
3.2 Updating Moth Location
3.3 Updating Number of Flames
4 Simulation and Results
4.1 Discussion Results
5 Conclusion
References
Analytical Study Between Fuzzy Logic and Sliding-Mode Control Applied to PV Systems
1 Introduction
2 Description of The PV System
3 Fuzzy Logic Controller
3.1 Fuzzification
3.2 Base Rule
3.3 Defuzzification
4 Sliding Mode Control
5 Simulation
6 Exprimental Realisation
7 Conclusions
References
Experimental Analysis on Performance of a Solar Photovoltaic/Thermal (PV/T) Air Collector with a Single Pass
1 Introduction
2 Description of Solar Hybrid PVT Hybrid Air Collector
3 Results and Discussion
4 Conclusion
References
Simulation of a Cavity Ventilated by Air Displacement Using the Lattice Boltzmann Method
1 Introduction
2 Materials and Methods
3 Results and Discussion
4 Conclusion
References
Application of New Optimization Algorithm for Parameters Estimation in Photovoltaic Modules
1 Introduction
2 Mathematical Model
3 Simulation Results
4 Conclusion
References
Techno-Economical Optimization of Electrical Production from Wind Power Plant Connected to the Electrical Grid in LAGHOUAT Region
1 Introduction
2 Presentation of the Study Area
3 Mathematical Formulation
3.1 Temporal Characterization of the Wind Speed
3.2 Spatial Characterization of the Wind
3.3 Wind Potential
3.4 Sizing of a Wind Farm
3.5 Economic Analysis
4 Application and Calculation
4.1 Wind Parameters
4.2 Adaptation and Choice of Wind Turbines
5 Conclusion
References
H2 Model Reduction of Nonlinear Optimal PEMFC Using Artificial Ecosystem Optimization
1 Introduction
2 H2 Optimal Model Reduction for MIMO Nonlinear Dynamic of PEMFC System
2.1 Dynamic and Neural Network Modeling of PEM Fuel Cell
2.2 Optimal Model Order Reduction
3 Artificial Ecosystem-Based Optimization Algorithm (AEO)
4 Simulation Results
5 Comparison Between EAO and MRFO
6 Conclusion
References
Control of Permanent Magnet Synchronous Machine Using Speed Estimation
1 Introduction
2 Modeling of the PMSM
3 FOC Contorl of PMSM
4 Kalman Filtre
5 Simulation Results and Discussions
5.1 Result Discussions
6 Conclusion
References
Exploring the Performance of CZTS Solar Cells Using BSF Layers
1 Introduction
2 Computational Details
3 Results and Discussion
4 Conclusion
References
Investigation and Improved Performance of MASnI3 and  MASnBr3 Perovskites Solar Cells with Porous Silicon Layer
1 Introduction
2 Methodology and Materials
3 Results
3.1 J-V Characteristics of MASnBr3 and MASnI3 Solar Cell Device
3.2 Influence of the Variation of PSi Acceptor Doping Concentration on the Performance of the Device
3.3 Impact of Variation Thickness Layer Absorber on the Performance of the Cell
4 Conclusions
References
Author Index

Citation preview

Lecture Notes in Networks and Systems 591

Mustapha Hatti   Editor

Advanced Computational Techniques for Renewable Energy Systems

Lecture Notes in Networks and Systems Volume 591

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. For proposals from Asia please contact Aninda Bose ([email protected]).

More information about this series at https://link.springer.com/bookseries/15179

Mustapha Hatti Editor

Advanced Computational Techniques for Renewable Energy Systems

123

Editor Mustapha Hatti UDES/EPST-CDER Bou Ismail, Tipasa, Algeria

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-21215-4 ISBN 978-3-031-21216-1 (eBook) https://doi.org/10.1007/978-3-031-21216-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

To ensure a smooth energy transition and energy security, under the pressure of environmental pollution, the intermittent renewables considered as suitable alternates and energy shortage multiple high supporting technologies should now be used for more sustainability. Renewable energy generation, IoT, 5G, intelligent algorithms and clean, non-polluting electric vehicles have developed rapidly. However, the deployment and exploitation of renewable energy is still falling short of our expectations and faces insurmountable constraints and difficulties. The need for advanced computing technologies remains as relevant as ever and brings great challenges. With the advent of big data, the Internet of Energy (IoE) structure will take into account the intermittency of dispersed renewable generations. Micro-grid with interconnected loads and distributed generation will be beneficial for the multiplication of smart cities. To reduce the effect of intermittency in the performance of energy systems, several optimization strategies including particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization (GWO), and modified grey wolf optimization (MGWO) have been proposed dans ce livre. Utilities and advanced computing are contributing to the transformation of the electricity grid by renewable energy. As the electricity grid moves from quiescent to intelligent and from centralised to decentralised, blockchain technology will bring significant improvements in the monitoring and management of all facilities, from individual contributions to clean and economic energy loans. To meet the highly dynamic user demand and to ensure effective power management, an efficient computing technique methodology is proposed that performs various load scheduling and power management plans with the integration of renewables for geographically distributed cloud datacenters. Accordingly, this book proposes a set of intelligent programming system and advanced computing techniques for future IoE to maximise the use of distributed renewable energy and reduce the carbon emissions caused by traditional power generation. The use of artificial intelligence today makes it possible to improve decentralised energy management

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by optimising flows. The authors of this book have sought to clarify the issue of renewable energy related to the development of information and communication technologies, especially to the Industrial Internet of Things (IIoT) which is becoming increasingly important.

Contents

Advanced Computational Techniques Solar Radiation Forecasting Based on Artificial Neural Network: A Case Study of Bechar City, Southwest Algeria . . . . . . . . . . . . . . . . . . H. Djeldjli, D. Benatiallah, K. Bouchouicha, and A. Benatiallah

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Machine Learning KNN Classifier for Forecasting Hourly Global Solar Irradiance over Adrar City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manal Y. Boudjella and Aissa Boudjella

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A Multicore and Multithreaded Microcontroller . . . . . . . . . . . . . . . . . . Bernard Goossens, David Parello, and Dushan Bikov

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Brain Tumor Classification Using Convolutional Neural Networks and Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cherifi Dalila, Cherifi Zakaria, and Belkadi Wassim

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Design, Implementation, and Deployment of IoT/M2M Smart City Applications Based on MCNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rania Djehaiche, Salih Aidel, Massinissa Belazzoug, and Nasir Saeed

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Control of Three Phase Cascaded H Bridge Multilevel Inverter Supplied by a Photovoltaic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatima Zahra Khemili, Moussa Lefouilli, Omar Bouhali, and Lakhdar Chaib A Proposal of Blockchain and NFC-Based Electronic Voting System . . . Hanane Echchaoui, Boudrali Roumaissa, and Rachid Boudour Application of CRM Method for Reservoir Fluid Dynamic Characterization in Haoud Berkaoui Petroleum Field . . . . . . . . . . . . . . Mohamed Z. Doghmane, Sid-Ali Ouadfeul, Zakia Benaissa, and Said Eladj

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Embedded Machine Learning for Fault Detection and Diagnosis of Photovoltaic Arrays Using a Low-Cost Device . . . . . . . . . . . . . . . . . . . . M. Bouzerdoum, A. Mellit, N. Djazari, and M. Laissaoui

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Planar Micro-thermoelectric Generators Based on Cu55Ni45 and Ni90Cr10 Thermocouples for IoT Applications . . . . . . . . . . . . . . . . . . . . I. Bel-Hadj, Z. Bougrioua, and K. Ziouche

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Edge Detection of MRI Brain Images Based on Segmentation and Classification Using Support Vector Machines and Neural Networks Pattern Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zouhir Iourzikene, Djamel Benazzouz, and Fawzi Gougam

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Determination of Intrinsic Parameters of PV Module Using Pattern Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 Mohamed Rezki, Ghania Ouadfel, Hamza Houassine, and Samir Bensaid Sensing and Communication in Renewable Energy Development of a Supervision/Control Interface for an Experimental Wind-Storage-Grid-Diesel Microgrid System . . . . . . . . . . . . . . . . . . . . . 115 Djohra Saheb-Koussa, Mustapha Koussa, Saida Makhloufi, Naserdine Belhaouas, Farid Hadjrioua, Azzedine Aissaoui, and Khaled Bakria Design of Smart Irrigation System in the Greenhouse Using WSN and Renewable Energies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Achouak Touhami, Sana Touhami, Nawal Touhami, Khelifa Benahmed, and Fateh Bounaama Application of Metamaterials Based on Resonators -e- for the Design of Miniature Planar Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Becharef Kada, Nouri Keltouma, Bouazza Nadjet Nadia, Daoudi Wafaa, Abes Turkiya, and Saidi Amaria Aspect Oriented Web Service Composition Based Petri Net Model . . . . 148 F. Khalifa and B. Guelta High-Efficiency 60-GHz Printed Antenna Using a Triple-Layer Metasurface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 Tarek Messatfa and Fouad Chebbara Mobile User Profile in the Context of Mobile Crowd Sensing . . . . . . . . 170 S. Ichou, S. Hammoudi, A. Benna, and A. Meziane Reconfigurable and Ecological Intelligent Antenna for Satellite Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 R. D. Taleb, M. Z. Baba-Ahmed, F. Bousalah, and M. A. Rabah

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Mutual Coupling Reduction Between Two Closely Spaced Microstrip Antennas Using Electromagnetic Band Gap (EBG) Structure for IoT Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Bachir Zoubiri, Abdelhalim Mayouf, and Mokhtar Mokhtari A New Design of Patch Antenna Array for IoT Applications . . . . . . . . . 196 M. A. Rabah, F. Bousalah, H. Benosman, F. Merad, and M. A. K. Goual Blind Sources Separation and Cryptography for Secure Remote Reading of Sonelgaz Smart Meters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 S. Chitroub, Y. Brahimi, N. Haddadi, A. Saighi, and Y. Gaceb Connected Sensors for a Smart Green Farm . . . . . . . . . . . . . . . . . . . . . 213 M. Ferroukhi, H. Saadi, R. Bendib, L. Berracheddi, and A. Cherifi A Mini Review of the Literature of Fractional-Order Chaotic Systems and Its Applications in Secure Communications Schemes During the Last Three Decades (1990–2020) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Mahedi Abdelghani Atoussi, Bachir Nail, Slami Saadi, and Maamar Bettayeb A Wideband Millimeter Wave Antenna for 5G Application Resonate at 3.5 GHZ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 F. Z. Gourari, A. Mosbah, M. E. Irid, and S. M. Meriah One-Layer and Dual-Polarized Metamaterial Inspired Antenna Using Dodecagon Split Ring Resonator Mushroom and Metasurface for Terahertz Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 A. Bendaoudi, K. Benkhallouk, M. Berka, and Z. Mahdjoub 2.4 GHz Semi-textile Wearable Antenna for Off- and On-Body Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 S. Berhab, A. Annou, F. N. Azzouz, and M. Chouya Energy Efficiency and Management Grey Wolf MPPT Controller for Grid Connected Residential Wind System Operating Under Low and High Variations in Wind Speed . . . . 261 Amel Abbadi, Fethia Hamidia, M. R. Skender, and F. Bettache Energy-Efficient and Traffic-Aware Function Analysis of Network Service Orchestration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 A. E. Dinar, S. Ghouali, M. S. Guellil, and E. M. Onyema A Global MPPT Controller Based on an Improved Particle Swarm Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 E. Mammeri, A. Ahriche, A. Necaibia, A. Bouraiou, A. Ziane, and S. Lachtar

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A Comparative Study of MPPT Algorithms to Control DC-DC Converters in PV Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 S. Della Krachai and M. Bensaada Developing a Real-Time Monitoring System in View to Analyze and Assess the Performance of Standalone PV System Along with Its Two PV Module Technologies Located in Northern Algeria . . . . . . . . . . . . . 299 A. Aissaoui, N. Belhaouas, F. Hadjrioua, B. Taghezouit, and K. Bakria Influence of Dust Particles Deposition on the Reflection Loss of a Photovoltaic Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Houria Hamouche and Mohammed M. Shabat An Improved Fuzzy OTC MPPT of Decoupled Control Brushless Doubly-Fed Induction Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 M. Hamidat and K. Kouzi Choosing the Adapted Artificial Intelligence Method (ANN and ANFIS) Based MPPT Controller for Thin Layer PV Array . . . . . . . . . . 322 Elaid Bouchetob and Bouchra Nadji Methods Improving Solar Power System Efficiency Based on Geographical Coordinates and Sun Position Calculators . . . . . . . . . . . . 332 K. Dahli and N. Cheggaga Aerial Forest Smoke’s Fire Detection Using Enhanced YOLOv5 . . . . . . 342 Dalila Cherifi, Belkacem Bekkour, Assala Benmalek, Meroua Bayou, Ines Mechti, Abdelghani Bekkouche, Chaima Amine, and Ahmed Halak Sizing, Modeling and Energy Flow Management of PV-DieselBatteries Microgrid for Agricultural Application . . . . . . . . . . . . . . . . . . 350 Salma Nait Bachir, Mustapha Hatti, and Saliha Arezki Machine Learning-Based Techniques for False Data Injection Attacks Detection in Smart Grid: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368 Benras Mohamed Tahar, Sid Mohamed Amine, and Oussama Hachana Artificial Intelligence in Renewable Energy Pervasive System in Smart Houses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 Mokhtaria Derkaoui, Mansour Abou Chemala, and Hadj Meridja Control and Power Management of Microgrid Supplied a Domestic and Industrial Loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 H. Guentri, F. Lakdja, M. Belhamidi, and A. Dahbi Survey on Artificial Intelligence Algorithms Application for Alzheimer’s and Elderly People Safety in Smart Homes . . . . . . . . . . . . 398 Wissam Benlala, Siham Bouchelaghem, and Mohand Yazid

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A New Transformer Condition Monitoring Based on Infrared Thermography Imaging and Machine Learning . . . . . . . . . . . . . . . . . . . 408 Amine Mahami, Toufik Bettahar, Chemseddine Rahmoune, Foudil Amrane, Mohamed Touati, and Djamel Benazzouz A Robust Decoupled Control of Electric Vehicle Using Type-2 Fuzzy Logic Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 Mohamed Kabir Billal Boumegouas, Katia Kouzi, and M. Birame Analysis Techno-Economic of a Stand-Alone Photovoltaic System Using a Specialized Advanced Simulation Software for Different Zones in Adrar Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 T. Touahri, B. Berbaoui, R. Maouedj, and S. Laribi Convolution Neural Network Deployment for Plant Leaf Diseases Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 Dalila Cherifi, Meroua Bayou, Assala Benmalek, Ines Mechti, Abdelghani Bekkouche, Belkacem Bekkour, Chaima Amine, and Halak Ahmed Study and Implementation of U-Net Encoder-Decoder Neural Network for Brain Tumors Segmentation . . . . . . . . . . . . . . . . . . . . . . . 448 Dalila Cherifi, Abdelghani Bekkouche, Meroua Bayou, Assala Benmalek, Ines Mechti, Belkacem Bekkour, Chaima Amine, and Halak Ahmed Bayesian Regularized Backpropagation Neural Network Model to Estimate Resilient Modulus of Unbound Granular Materials for Pavement Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 K. Sandjak, M. Ouanani, and T. Messafer Optimal Placement of Phasor Measurement Units Considering the Topology Transformation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Abdelkader Azzeddine Laouid, Aicha Djalab, and Nail Alaoui The Effect of the Intelligent Control System on the Tram Timetable Efficiency and Its Influence on the Road Capacity at Signalized Intersections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482 Mouloud Khelf and Bhouri Neila Soil-Structure Interaction Effects on the Vibration Control of Building Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Mohamed Seghir Jaballah, Salaheddine Harzallah, and Nail Bachir Robust Control of Multiphase Induction Generator Equipped with Fuzzy Flywheel Energy Storage System . . . . . . . . . . . . . . . . . . . . . . . . . 501 Derkouche Djamel and Kouzi Katia Urban Flood Risk; Diagnosis and Proposed Management. A Case Study in Bechar City, South Western Algeria . . . . . . . . . . . . . . . . . . . . 511 Bouhellala Kharfia

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Electromagnetic Converter for Electric Vehicles Integrated with Renewable Energy Sources for Sustainable Mobility . . . . . . . . . . . . . . . 526 Larbi Belkacem, Hatti Mustapha, Kouzi Katia, and Ghadbane Ahmed Power Electronics and Grid Connected Variability of Solar Radiation Received on Tilted Planes in Adrar Region in the South of Algeria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537 I. Oulimar, K. Bouchouicha, N. Bailek, and M. Bellaoui Environmental and Financial Impact Analysis of a Tubular 850 KW Wind Turbine Tower . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547 F. Ferroudji, L. Saihi, and K. Roummani Modeling of Two Five-Phase Induction Machines Connected in Series with an Open Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 Nekkaz Mohamed, Djahbar Abdelkader, and Benali Youcef Mohammed Influence of Geometric Parameters on the Performance of a Vortex Type Cooling Tower . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565 A. L. Deghal Cheridi, A. Bouaam, A. Dadda, and A. Dahia PEM Fuel Cell Emulator Based on a Buck Converter . . . . . . . . . . . . . . 574 S. Gahgouhi, A. Hadjaissa, K. Ameur, A. Rabhi, and M. Kious Parameters Estimation Methods of Thin-Film Solar Module Using Numerical Algorithms and Artificial Neural Networks . . . . . . . . . . . . . . 584 B. Benabdelkrim, A. Benatiallah, and T. Ghaitaoui An approach for Power Reserve Control (PRC) Strategy Based on a Novel ANN Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594 C. Messasma, S. E. Chouaba, B. Sari, and A. Barakat Frequency Enhancement of Power System with High Renewable Energy Penetration Using Virtual Inertia Control Based ESS and SMES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 602 H. Abbou, S. Arif, and A. Delassi Topology Analysis of Multi-cellular Converters in a Wind Energy System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614 Ahmed H. Zebboudj, Rafik Boukhelif, Mouhamed Z. Doghmane, and H. Akroum Modeling and Simulation of an Operating BLDC with Bidirectional Rotation Configurations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629 Mawloud Tellaa, Mohamed Z. Doghmane, Abderrezak Aibeche, and Aimad Ahriche

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BLDC Speed Control Based on Fractional PID Controller . . . . . . . . . . 639 Mawloud Tellaa, Abderrezak Aibeche, Mohamed Z. Doghmane, and Aimad Ahriche Application of the Prognostic and Health Management to Industrial Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652 Abdenour Soualhi, Bilal El Yousfi, Mourad Lamraoui, and Kamal Medjaher Characterization and Simulation of the Power IGBT Module Used in VFD for Drilling Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665 H. Sefsaf, B. Nadji, and Y. Yakhelef Power Management Strategy of a Hybrid PV-Battery System Connected to the Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672 Y. Bouthiba, B. Meghni, B. Benlahbib, and M. Ouada Optimisation, Control and Power Conversion Natural and Mixed Convection in Solar Drying Process . . . . . . . . . . . . 685 Samah Adjmi and Chérifa Abid Super Twisting Fuzzy High-Order Sliding Mode Control of VariableSpeed Wind Turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691 Lakhdar Saihi, Fateh Ferroudji, Khayra Roummani, Youcef Bakou, Khaled Koussa, and Mohammed Boura Hydrogen Diffusion Study via Phosphorus Deactivation in n-Type Silicon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 700 R. Ouldamer, D. Madi, and D. Belfennache Moth-Flame Optimizer Algorithm for Optimal of Fuzzy Logic Controller for Nonlinear System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 706 Ahmed Bennaoui, Aissa Ameur, Slami Saadi, and Ameur Bennaoui Optimal Location and Sizing of Capacitor Banks in Distribution Systems Using Grey Wolf Optimization Algorithm . . . . . . . . . . . . . . . . 719 A. Hachemi, F. Sadaoui, and S. Arif Estimation on the Potential of Dimethyl Ether (DME) as Clean Alternative Fuel by CFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729 Ali Bouziane, Hadj Miloua, and Mohammed Zaitri Symmetrical Voltages Dips Analysis in a Wind Turbine Based on DFIG for High Power Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 740 H. Bouregba, M. Hachemi, S. Mekhilef, and A. Ratni Optimal Placement Using Moth Flame Optimization in Radial Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751 Djedidi Imene, Naimi Djemai, Salhi Ahmed, and Bouhanik Anes

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Analytical Study Between Fuzzy Logic and Sliding-Mode Control Applied to PV Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 762 Antar Beddar, Issam Abadlia, Fateh Abdoune, and Linda Hassaine Experimental Analysis on Performance of a Solar Photovoltaic/ Thermal (PV/T) Air Collector with a Single Pass . . . . . . . . . . . . . . . . . . 770 Saadi Zine, Kouki Nadjat, Boukhlef Djedjiga, Allali Malika, Amina Bekraoui, and Abdelkrim Rouabhia Simulation of a Cavity Ventilated by Air Displacement Using the Lattice Boltzmann Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 777 A. Arab, N. Himrane, Z. Hireche, Y. Halouane, and D. E. Ameziani Application of New Optimization Algorithm for Parameters Estimation in Photovoltaic Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785 Lakhdar Chaib, Abdelghani Choucha, Mohammed Tadj, and Fatima Zahra Khemili Techno-Economical Optimization of Electrical Production from Wind Power Plant Connected to the Electrical Grid in LAGHOUAT Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 794 O. Bouchiba, M. Hamidat, S. Chettih, and K. Kouzi H 2 Model Reduction of Nonlinear Optimal PEMFC Using Artificial Ecosystem Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805 Zohra Touati, Khaled O. M. Touati, Slami Saadi, and Mecheri Kious Control of Permanent Magnet Synchronous Machine Using Speed Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 814 Abdeldjalil Dahbi, Miloud Benmedjahed, Abderrahman Khelfaoui, Omar Ouledali, Ahmed Bouraiou, Hocine Guentri, Messaoud Hamouda, Abdelghani Harrag, Abdeldjalil Slimani, Nouar Aoun, Boualam Benlahbib, Samir Mouhadjer, Ahmed Boutadara, and Mohammed Lemchachaa Exploring the Performance of CZTS Solar Cells Using BSF Layers . . . 820 A.-A. Kanoun, Z. Kourdi, F. Merad, and M. A. Rabah Investigation and Improved Performance of MASnI3 and MASnBr3 Perovskites Solar Cells with Porous Silicon Layer . . . . . . . . . . . . . . . . . 826 B. Bachiri and K. Rahmoun Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 833

About the Editor

Dr. Mustapha Hatti was born in El Asnam (Chlef), Algeria. He studied at El Khaldounia School, then at El Wancharissi High School, obtained his electronics engineering diplomat from USTHB Algiers, and his post-graduation studies at USTO-Oran. He worked as a research engineer, at CDSE, Ain Oussera, Djelfa, CRD, Sonatrach, Hassi Messaoud, CRNB, Birine, Djelfa, and a senior scientist at UDES/EPST-CDER, Bou Ismail, Tipasa. Now, he is a researcher director, he leads the “Tipasa Smart City” initiative, he is an IEEE senior member, he is the author of several scientific papers and chapter books, and his areas of interest are smart sustainable energy systems, artificial intelligence, innovative, fuel cell, photovoltaic, optimization, intelligent embedded systems, green hydrogen, sustainability, and electric vehicle.

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Advanced Computational Techniques

Solar Radiation Forecasting Based on Artificial Neural Network: A Case Study of Bechar City, Southwest Algeria H. Djeldjli1(B) , D. Benatiallah2 , K. Bouchouicha3 , and A. Benatiallah1 1 Laboratory of Energy Environment and System Information (LEESI), Faculty of Sciences and

Technology University Ahmed Draia, 01000 Adrar, Algeria [email protected] 2 Laboratory of Sustainable Development and Computer Science (LSDCS), Faculty of Sciences and Technology University Ahmed Draia, 01000 Adrar, Algeria 3 Centre de Développement des Energies Renouvelables, CDER, 01000 Algiers, Algeria

Abstract. Estimating solar irradiance is an essential step in the design of solar systems and the performance evaluation of their various applications. This work has the purpose of developing a model based on an Artificial Neural Network (ANN) to anticipate the global solar irradiance on a daily basis in the city of Bechar. The models were given seven input data. We developed four models using different training algorithms. Correlation coefficient (R) and mean absolute percentage error (MAPE) were used to assess these models’ efficiency. The results over 6 years demonstrated that Model1, provides significantly better forecasts with (R = 0.9198 and MAPE = 7.57). Therefore, in the Multi-Layer Feed Forward Neural Network (MLF), using the Levenberg-Marquardt back-propagation training algorithm provides the best accuracy for estimating daily solar radiation and may be considered one of the fastest and most accurate algorithms. This model is useful for sizing and designing solar systems in Algeria. Keywords: Solar energy systems · Neural networks · Gradient descent · Levenberg-marquardt · Solar radiation

1 Introduction In the last century, the environmental problems caused by burning fossil fuels are becoming severe. Therefore, solar energy is increasingly demanded as an alternative [1]. Previous research shows that having renewable energy sources, specifically wind and solar, can be effective alternatives to traditional energy sources for fulfilling global demand while also protecting the planet. [2]. The knowledge of accurate global solar radiation data is extremely important for the proper selecting and designing of the solar energy system. However, Many countries do not have easy access to these data due to the high cost of measurement and difficulty [3]. Machine-learning (ML) techniques, in general, are capable of solving highly nonlinear problems, it have numerous potential applications, and became of great interest to academics worldwide [4]. A variety of statistical models © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 3–12, 2023. https://doi.org/10.1007/978-3-031-21216-1_1

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have been presented for modeling solar irradiance using existing variables. Along with linear, nonlinear, and multivariate modeling techniques, Artificial Intelligence (AI) and fuzzy approaches have been used to forecast the amount of solar radiation at specific locations throughout the world. The models are developed for both smaller and larger site scales [5]. ANNs are widely used in solar radiation forecasting because they provide promising solutions using only few available parameters as inputs. There are different ANN architectures, such as Multilayer Perceptron, Radial Basis Function network and Recurrent Neural Network. [6] From a general overview on ANN modeling of solar radiation, a reader observes easily that there is a large volume of published studies in this field. ANN have been considered in several researches such as the work in [7] where Benkaciali et al. (2016), They created eight models to assess the daily global solar irradiation (GSI) on the horizontal surface in south Algeria, using four weather data inputs obtained from a radiometric station implemented at the Ghardaia site, the ANN model provided the best performance compared to the developed empirical models. Furthermore, it was demonstrated that the duration of sunshine is a key element for forecasting the GSI. While, in Algeria, Benatiallah et al. (2020), [8] presented a neural model for estimating global hourly solar irradiation, according to some parameters of solar geometry and astronomical data for the Adrar region. Nine models and three activation functions and several combinations of the input data were used, it has been concluded that the logistic Sigmoid function of 15 neurons of the hidden layer, to be preferable for estimating global solar radiation values for the research site and other places with comparable climatic conditions. In Northern Greece, empirical equations, Neural Networks (ANN) and multilinear regression methods (MLR), has been developed in order to estimate solar irradiation by [9]. Different combinations of input variables were examined. Extraterrestrial radiation is used in the ANN and MLR models to increase the accuracy of the findings. The findings of ANN are consistent when compared to MLR models with the same input parameters. Generally, when compared to alternative techniques and model parameters, the artificial neural network technology performed better. The objective of this research is to create an artificial neural network (ANN)-based model for forecasting daily global solar radiation considering multiple input characteristics in Bechar, Algeria’s south-west area. Four proposed models are developed and tested to select the most suitable model, using six input parameters: temperature average, relative humidity, declination, hour angle and extraterrestrial solar irradiation.

2 Material and Models 2.1 Study Area and Data Collection The research region is situated in Algeria’s south-west region, with the coordinates (Longitude: 2.13, Latitude: 31.37, Altitude: 780.0 (m)). This area is noted for its hot summer days and cold winters [10]. It also has a lot of solar energy potential. The highest insolation time in the southern area is 3900 h [11]. On a horizontal surface, the average solar energy collected is 5 kWh/m2 for the most of the national area (Fig. 1), or roughly 2263 kWh/m2 /year in Algeria’s south [12].

Solar Radiation Forecasting Based on Artificial Neural Network

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Bechar

Fig. 1. The location of the study city (Bechar) and solar radiation potential in Algeria.

We collect a large database, enough to be utilized for training and testing the model. We used daily data from the SODA (Simple Ocean Data Assimilation) database for six (06) years (June 2015 - June 2020) in this study [13]. We forecasted daily global sun irradiation (DGSI) using astronomical and meteorological characteristics (see Table 1): Table 1. The parameters used. Parameters

Abbreviation

Unit

Type Numerical (digital)

Average temperature

Tavg



Atmospheric pressure

Bp

Hpa

Relative humidity

Rh

%

Wind speed

Ws

M/s

Declination

De

Degree (°)

Hour angle

Ah

Degree (°)

Extraterrestrial solar irradiation

H0

Wh/m2

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2.2 The Artificial Neural Network (ANN) Based Model to Estimate Global Solar Energy The neural network is an artificial computing model that simulates human brain functioning [14]. For i = 0,1,…n hidden layers, each neuron in the network calculates a weighted by Wij sum of its p input signal yi, and then applies a non-linear activation function to create an output signal uj. It takes the following form uj =

ni−1 

wij yi

(1)

The multi-layer feed forward neural network (MLF) using the back propagation (BP) algorithm, which has always been the most frequently used ANN approach for predicting solar radiation [15]. This approach is advantageous since it can represent problems which are not linearly separable. The MLF is made up of three levels: an input layer (i), an output layer (k), and one or more hidden layers (j). In practice, a three-layer feed forward neural network (FFNN) is frequently sufficient (see Fig. 2).

Fig. 2. Basic ANN structure with seven inputs.

Each layer is interconnected by weights Wij and Wik, and every unit adds a bias or threshold term to the sum, and nonlinearity transforms it to produce an output. This transformation is called the node activation function. Where the output layer nodes often have linear activations. The tangent-sigmoid transfer function (Eq. (2)), or logistic sigmoid function (Eq. (3)), and linear function (Eq. (4)) are generally used in the hidden and output layer respectively [16]: f (W ) =

2 −1 1 + e−2W

(2)

1 1 + e−W

(3)

f (W ) =

f (x) = x

(4)

Solar Radiation Forecasting Based on Artificial Neural Network

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W represents the input weighted sum, while x represents the output layer input. Backpropagation (BP) is a process of updating synaptic weights. It refers to the manner in which error determined at the outcomes is propagated backward eventually to the input layer [17]. In conjunction with the BP procedure, we used the algorithms shown in Table 2, as a second training algorithm. Each of the training algorithms has its own specific characteristics that must be adjusted according to a particular model. Table 2. Different training algorithms Function

Algorithm

Trainlm

Levenberg-Marquardt

Trainbr

Bayesian regularization

Traingdm

Gradient descent with momentum

Traingd

Gradient descent

2.3 ANN Model Architecture This investigation used a three-layer or FFNN for daily solar radiation estimation. This architecture has demonstrated its capacity to simulate a wide range of real-world operational issues [18]. Choosing the hidden neurons is considered the most challenging aspect of ANN modeling. Table 3 displays the ANN characteristics of our models. Backpropagation (BP) was used to train the neural networks, employing the algorithms indicated in Table 2. We performed 1000 learnings for this architecture, then we will save the value of the synaptic weights, which gives the minimum RMSE on the training basis. The results obtained are summarized in Table 5. Table 3. The models Features City Bechar

Model ID

Activation functions Hidden layer

Output layer

Number of hidden layer units

Traning algorithm

Model1 Model2

Tansig Logsig

Pureline

5

trainlm

Model3 Model4

Tansig Logsig

Pureline

5

traingd

The structure and architecture of the multilayer neural network (MLP) generally depends on the database, which is made up of input and output pairs. In our study, we will predict daily global solar irradiation, so we only use a single neuron in the output layer, with a limited number of neurons in the hidden layer. The challenging aspect of ANN modeling is the selection of hidden neurons, which is related to the complexity

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of the system being represented. The optimal number of neurons in the hidden layer in this study was achieved using a simple trial and error method. A range of 2–25 neurons was tested until a minimal accepted error between anticipated and observed output was attained. In this case, we retain the architecture that gives the minimum amount of error on the basis of the test, to avoid the problem of overfitting. All models were trained with the training data set, and the trained models were tested with the validation data set. We developed MATLAB-based computer program for this ANN method in Bechar using a script file written in the MATLAB software® V.R2020.

3 Statistical Assessment Indices The efficiency of the models under consideration was evaluated using measures widely utilized in assessment scores [19]. Table 4 below presents the definitions of such indices, where actual and estimated values are represented as GAct and GSim , respectively, while N is the observations total number. Table 4. Statistical assessment indices Indice

Ideal

Equation 

RMSE

Zero

RMSE =

MBE

Zero

MBE = N1

MAPE R

Zero One

N

2 i=1 (GSim,i −GAct,i )

N

N  

GSim,i − GAct,i



(7) (8)

i=1

MAPE = 100 N

 N  G  −GAct,i   Sim,i  XAct,i

i=1   N  G G −G −G R =   i=1 Sim,i 2Sim Act,i Act 2 N N i=1 GSim,i −G Sim × i=1 GAct,i −G Act

(9) (10)

4 Results and Discussion The ANN-based prediction is performed, resulted in a very efficient model for estimating global solar radiation. This is based on the statistical assessment indices (MAPE, MBE, RMSE, and R) of the models for training and testing outcomes, as given in Table 5 and Fig. 4. Overall, it has been shown that Model1 presents better estimation results. According to statistical assessment indices, Model1 is the most accurate model when compared to the outcomes of other models in the training phase (R = 0.9342, RMSE = 631.44 Wh/m2/day, and MBE = −0.000036 Wh/m2/day), second by Model2 (R = 0.9327, RMSE = 646.27 Wh/m2/day, and MBE = 0.00119 Wh/m2/day). Model1 is likewise the most accurate model in the testing phase with (R = 0,9198, RMSE =

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Table 5. Statistical scores for each model during training and testing phases in Bechar city. Training score Model1

Testing score

Model2

Model3 Model4

MBE(Wh/m2 /day)

−0.00003 0.00119 −5.49

MAE(Wh/m2 /day)

441.83

RMSE(Wh/m2 /day) R

Model1 Model2 Model3 Model4

−21.6

−4.53

−1,05

81.0

−177

437.32

674.51

825.86

462.19

472,96

627.63

908.93

631.44

646.27

923.97

1055.65 693.78

719,79

842.06

1174.09

0.9342

0.9327

0.8545

0.8025

0.9177

0.9198

0.7795

0.9198

MODEL1

MODEL2

MODEL3

15.30

13.46

rRMSE 10.03

11.99

7.88

11.36

7.57

MAPE

19.76

693,78 Wh/m2/day and MBE = −4,53 Wh/m2/day), followed by Model2 (R = 0.9177, RMSE = 719,79 Wh/m2/day and MBE = −,05 Wh/m2/day). In addition, Fig. 4 shows the estimated daily solar global radiation estimated by Model1 (MAPE = 7.57; rRMSE = 11.36), during the testing phase, where Model1 performed better than the rest of the models.

MODEL4

Fig. 4. MAPE (%) and rRMSE (%) during the testing phase.

Similarly, scatter plots (Fig. 5) of Model1 estimated daily solar global radiation show the estimated data dispersed as a sequence of points near to the linear (blue) perfect fit, illustrating the relation between collected and estimated values at Bechar city across the research period. Model1 is the most effective model throughout the training phase. Figure 6 illustrates the observed and predicted values of the best model (Model1) for the research period. The models with Levenberg-Marquardt training algorithm have the best performance compared to the models using Gradient Descent training algorithm, and to the other training algorithms. Furthermore, the findings are largely consistent with those of recent studies. This led to the conclusion that in MLF (class of FFANN) using Levenberg-Marquardt training algorithm provides the best accurate estimation of the daily solar radiation. Levenberg-Marquardt back-propagation (trainlm)] is widely regarded as one of the fastest and most accurate algorithms.

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Fig. 5. Scatter plot for model1 between collected and predicted of daily global solar radiation in the training phase.

Fig. 6. Training phase for model1 between collected and values of daily global solar radiation in the training phase.

5 Conclusion This research was carried out for Bechar city in Algeria, where four models were examined in terms of accuracy. We have chosen ANNs because they provide promising solutions using only few available parameters as inputs. It has been proven that ANNs are simpler, faster, and give high accuracy. There are different ANN architectures such as multilayer perceptron and radial basis function network. To estimate daily global solar irradiance in all sky conditions, we used a multi-layer neural network model, various training algorithms, the basic neurons connection architecture, the feed-forward neural network, and using tangent sigmoid/logistic sigmoid transfer function in hidden layer combined with linear transfer function in output layers, and several statistical indicators (R, MBE, RMSE, and MAE). Relative humidity, temperature average, hour angle, and declination, pressure, wind speed, in addition to the extraterrestrial solar irradiance, were used as input data. In comparison to other models, the findings showed that Model1 using tangent-sigmoid transfer function in hidden layer and linear transfer function in output layers and trained by Levenberg-Marquardt algorithm, is better suited for the forecast. Therefore, the developed model can be used to estimate daily global solar radiation in arid climate regions, and in other areas similar to these climatic conditions whenever data is available. Additionally, solar-energy system installation and building thermal condition valuations. Moreover, we found out that the second training algorithms have the biggest impact on models accuracy, while changing the transfer function has smaller effect on the results. Undoubtedly, in MLF (class of FFANN) using LevenbergMarquardt back-propagation training algorithm provides the best accuracy, it could be considered as one of the more accurate and fastest algorithms. The ANN model’s important features include the selection of a suitable training algorithm, transfer function, and number of neurons in the hidden layer. Each training algorithm has unique properties that must be adapted to a specific model. One of the most important conclusions of this study is that it can help planners and decision makers establish early plans and identify prospective candidate locations for solar power plants, which will be vital in decreasing future dependency on fossil fuel-based energy.

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References 1. Zhang, J., Zhao, L., Deng, S., Xu, W., Zhang, Y.: A critical review of the models used to estimate solar radiation. Renew. Sustain. Energy Rev. 70, 314–329 (2017) 2. Zendehboudi, A., Baseer, M.A., Saidur, R.: Application of support vector machine models for forecasting solar and wind energy resources: A review. J. Clean. Prod. 199, 272–285 (2018) 3. Despotovic, M., Nedic, V., Despotovic, D., Cvetanovic, S.: Review and statistical analysis of different global solar radiation sunshine models. Renew. Sustain. Energy Rev. 52, 1869–1880 (2015) 4. Zhou, Y., Liu, Y., Wang, D., Liu, X., Wang, Y.: A review on global solar radiation prediction with machine learning models in a comprehensive perspective. Energy Convers. Manage. 235, 113960 (2021) 5. Baser, F., Demirhan, H.: A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation. Energy (2017). https://doi.org/10.1016/j.ene rgy.2017.02.008 6. Kabouche, N., Chellali, F., Recioui, A.: A review on solar radiation assessment and forecasting in algeria (part 2: solar radiation forecasting). Algerian Journal Of Signals And Systems (AJSS) 6(3) (September-2021). ISSN: 2543-3792-EISSN: 2676-1548 7. Benkaciali, S., Haddadi, M., Khellaf, A., Gairaa, K., Guermoui, M.: 2016, Evaluation of the global solar irradiation from the artificial neural network technique. Revue des Energies Renouvelables 19(4), 617–631 (2016) 8. Benatiallah, D., Benatiallah, A., Bouchouicha, K., Nasri, B.: Prediction du rayonnement solaire horaire en utilisant les reseaux de neurone artificiel. Algerian J. Env. Sc. Technology 6(1), 1236–1245 (2020) 9. Antonopoulos, V.Z., Papamichail, D.M., Aschonitis, V.G., Antonopoulos, A.V.: Solar radiation estimation methods using ANN and empirical models. Comput. Electron. Agric. 160, 160–167 (2019). https://doi.org/10.1016/j.compag.2019.03.022 10. Benatiallah, D., Benatiallah, A., Bouchouicha, K., Hamouda, M., Nasri, B.: An empirical model for estimating solar radiation in the Algerian Sahara. American Institute of Physics 7, 710–727 (2018) 11. Benatiallah, D., Bouchouicha, K., Benatiallah, A., Harrouz, A., Nasri, B.: Forecasting of Solar Radiation using an Empirical Model. Algerian Journal of Renewable Energy and Sustainable Development 1, 212–219 (2019) 12. Benatiallah, D., Benatiallah, A., Harouz, A., Bouchouicha, K.: Development and modeling of a geographic information system solar flux in adrar, Algeria. Int. J. Sys. Model. Simul. 1, 15–19 (2016) 13. SODA data: Available at: www.soda-pro.com/web-services#meteodata 14. Haykin, S., Lippmann, R.: Neural networks, A Comprehensive Foundation. Int. J. Neural Syst. 5, 363–364 (1994) 15. Yadav, A.K., Chandel, S.S.: Solar radiation prediction using artificial neural network techniques: a review. Renew. Sustain. Energy Rev. 33, 772–781 (2014). https://doi.org/10.1016/ j.rser.2013.08.055 16. Ata, R.: Artificial neural networks applications in wind energy systems: a review. Renew. Sustain. Energy Rev. 49, 534–562 (2015). https://doi.org/10.1016/j.rser.2015.04.166 17. Esmaeelzadeh, S.R., Adib, A., Alahdin, S.: Long-term streamflow forecasts by adaptive neurofuzzy inference system using satellite images and K-fold crossvalidation (case study: Dez, Iran). KSCE J. Civ. Eng. 1–9 (2014)

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Machine Learning KNN Classifier for Forecasting Hourly Global Solar Irradiance over Adrar City Manal Y. Boudjella1(B) and Aissa Boudjella2 1 Department Physics Engineering, Laboratory of Analysis and Application of Radiation,

LAAR, University of Sciences and Technology of Oran Mohamed Boudiaf, Oran, Algeria [email protected], [email protected] 2 Department Electrical Engineering, Bircham International University, Miami, USA [email protected]

Abstract. In this investigation, simulations based on the K-Nearest Neighbor (KNN) classifier were performed to examine the performance metric characteristics in estimating the hourly global solar irradiance received at Adrar city/Algeria. The system is implemented and simulated in Anaconda, and its performance is evaluated using real unsupervised dataset with seven (07) features and 44872 instances for classifying the hourly global solar irradiance. For the classification, four (04) classes (4 target name labels) were created based on the captured global irradiance magnitude. The simulation results show that the performance metrics depends on both the test size and the number of neighbors k. The model perform very well in predicting the class label of hourly global irradiance magnitude when k is in the range of [7–11] and the training size is less than 25%. The model prediction accuracy is about 86%. Keywords: Hourly global irradiance · Meteorological parameters · K-Nearest neighbor classifier · Test size · Accuracy

1 Introduction The sun is the fundamental source of energy for the planet Earth. Precise knowledge of the amount of solar energy reaching the ground surface and its temporal and spatial variability is of a prime importance in different research area related to: solar energy, climatology, renewable energy …etc [1, 2]. Surface solar energy can be determined using ground observation, satellite based observation or numerical methods. During the past decade, variety of numerical methods has been established for the estimation of the surface solar irradiance such as: empirical model, physical model, and those based on the use of machine learning algorithms [3–5]. Recently, Due to the prominent progress in artificial intelligence framework, many machine learning (ML) models have been applied for forecasting the amount of solar radiation reaching the ground surface, such as the artificial neural network(ANN), regression decision tree(DT),genetic programming (GA), support vector regression (SVR), data mining, and fuzzy logic [6]. Based on the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 13–21, 2023. https://doi.org/10.1007/978-3-031-21216-1_2

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time interval for measuring surface solar radiation, models that use machine learning algorithms to estimate the global irradiance can be divided into three categories: Monthly Average Global Solar Radiation [7], Daily Average Global,Solar Irradiance [8, 9], and Hourly Average Global Solar Radiation [10, 11]. Liexing Huang et al. [12] used 12 machine learning algorithms to predict and compare global irradiance in the Ganzhou city in China. The results showed that meteorological parameters: Such as sunshine duration and land surface temperature are essential in machine learning models. More recently, Guher et al. used four (04) different machine learning algorithms such as and KNN and a Library for Support Vector Machine (libsvmlibrary) to evaluate Hourly Average Global Solar Radiation over two geographical provinces in Turkey [11]. Note that artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) are the most commonly used machine learning algorithms for predicting and forecasting the amount of surface solar radiation, whereas only a few studies have investigated the use of KNN machine learning algorithm, such as Pedro et al.’s [13] application of the K Nearest Neighbors KNN algorithm for forecasting the intra-hourly horizontal global irradiance and the direct normal irradiance. Chen and Kartini proposed a model for estimating hourly global irradiance using a combination of KNN and ANN algorithms [14]. In this study, we propose to build a supervised machine learning algorithm, applied to a dataset without any label information (Unsupervised learning algorithms) that can learn from seven (07) input variables (features) which consist of date (month and day number ), time variables,and four (04) meteorological parameters whose features are known so that we may predict the range of the hourly horizontal global irradiance received at Adrar city for a new instance of seven (07) input parameters of Adrar city. We want to predict one of several options of the hourly horizontal global irradiance as classes with four (04) target label names that define the hourly horizontal global irradiance magnitude received at Adrar city. The possible outputs with different horizontal global irradiance ranges are called classes (class 1, class 2, class 3, and class 4). This investigation aims to implement a test platform for measurement and verification of the horizontal global irradianc based on the statistical performance metrics by defining a boundary analysis defined by the independent parameters such as K Nearest Neighbors KNN, test sizes. These primary simulations results can be useful to design a graphical user interface with the best performance metrics maximizing the accuracy prediction.

2 Methodology 2.1 Study Area and Datasets In this work, Adrar city was selected as the study area. Adrar is located in the south of Algeria Latitude/Longitude (27.8815°, −0.2767°).This city is characterized by a desert climate with a very high temperature exceeding 45 °C, and a very low precipitation (average annual precipitation ~ 11mm). Adrar is one of the sunniest cities in Algeria. Table 1 shows maximum hourly Global Horizontal Irradiance GHI for the years: 2016,2017,2018,2019 and 2020 as estimated by Merra-2.

Machine Learning KNN Classifier for Forecasting Hourly Global Table 1. Maximum hourly global years:2016,2017,2018,2019 and 2020.

irradiance

received

at

Adrar

city

15 for

Year

2016

2017

2018

2019

2020

MaximumGHI (W/m2 )

1046.83

1043.81

1058.8

1051.8

1049.45

the

The data that we will use in this investigation is a set of hourly meteorological data and hourly global horizontal irradiance (w/m2 ) received at Adrar city for the period of time ranging from the 1th January 2016 to 1th January 2021. [https://power.larc.nasa.gov/dataaccess-viewer/]. The dataset contains seven(07)features: X1 : Month, X2 :Day, X3 :Hour, X4 :Relative humidity(%), X5 :Temperature at two (02) meters above the surface of the earth (°C), X6 :Pressure at the surface of the earth (kPa), and X7 : Wind speed at two (02) meters above the surface of the earth (m/s), and one Output: denoted Y1 :Hourly Global Irradiance (W/m2 ). Figure 1 illustrates Hourly Global Irradiance received at Adrar city for the years 2017 and 2020. Table 2 presents maximum and minimum of the output (Hourly Global Irradiance) and of each feature from the dataset used in this study. Table 2. Minimum and maximum of data features (X1 , X2 , X3 , X4 , X5 , X6 and X7 )and output (Y1 ) X1

X2

X3

X4

X5

X6

X7

Minimum

1

1

0

2.94

0.33

96.42

0.2

Maximum

12

31

23

100

49.05

99.85

14.37

Y1 0 1058.8

Fig. 1. Hourly global irradiance received at Adrar for the year 2017 and 2020.

The aim of this study is to use the seven (07) features to predict the magnitude of hourly global horizontal irradiance indicated by each class target name label. Each class target name label is defined by the authors. We want to build a machine learning model from this unlabeled data that can forecast the Hourly Global Surface Irradiance magnitude in a specific range of new set of measurement metrological features variables in

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machine learning. Based on the magnitude of the output Y1 , Authors proposed categorizing the data into four (04) classes named: class1, class2, class3 and class4. Each class is defined by the minimum and maximum of global irradiance magnitude. Class 1 is null global irradiance (observed at noon time). Minimum, maximum and number of instances of each class are given in Table 3. The system is implemented and simulated in Anaconda, and its performance is tested on a real dataset that contains 7 features and 44 872 instances to classify the global irradiance magnitude into four (04) classes (4 target name labels). Table 3. Hourly global irradiance range for each class. Classes labels

Minimum globalirradiance (W/m2 )

Maximum globalirradiance (W/m2 )

Range of each class

Number of instances

Class 1

0

0

Class0 = [0]

21241

Class 2

2.39

352

Class1 = [2.39– 352]

8539

Class 3

352.12

704.35

Class2 = [352.12–704.35]

7252

Class 4

704.44

1058.8

Class3 = [704.44–1058.8]

6840

2.2 K Nearest Neighbors Classification The K-Nearest Neighbor algorithm KNN is a simple and non-parametric method used for classification and regression. In classification problems, the KNN classifier determines the k closest neighbors to the query data point and uses a majority vote of these k neighbors to predict a class label. In this study the Euclidean distance has been used to compute the distance between a sample x to estimate its class label and the training samples T j with a known class label:  N       j 2 xi − Ti d x, T j =  i=1

N : is number of inputs for a given sample. xi : is the ith input of the sample to estimate its class label. j Ti : is the ith input of the training sample.

3 Results and Discussions Figures 2, 3 and 4 show the variation of training accuracy, test accuracy, precision score, recall, f1 _score, and Kappa versus the number of neighbors k, n_neighbors for fixed test

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sizes in the range of [10%–50%] by keeping the random state constant at 66. For all the cases of test sizes considered in this study, the training accuracy reaches its maximum (100%) for k = 1 while the highest test accuracy (Test accuracy = 84.7%) was observed at k = 11 for test size of 10%. The variation of test accuracy and training accuracy are not monotonic, they increase or decrease when k increases depending on the test size. For k > 11The test accuracy remains approximately constant. The training accuracy is always higher than the test accuracy as illustrated in details in Figs. 2, 3, 4 and 5.

Fig. 2. Training accuracy, test accuracy, precision score, recall, f1_score, and Kappa vs k for test sizes = 10% and 15%.

Fig. 3. Training accuracy, test accuracy, precision score, recall, f1 _score, and Kappa vs k for test sizes = 20% and 25%.

Figure 5 indicates that training accuracy is in the range of (84.99%–100%) while the test accuracy varies between 78.44% and84.7%.Minimum train accuracy and test accuracy are observed under a test size of 50% at k = 20 and k = 2,respectively. When k = 1, train accuracy is unaffected by test size, the train accuracy is equal to 100%. While the test accuracy varies between 79.38% and 79.99% for k = 1. When k is in the range of [2–11], the training accuracy and the test accuracy is in the range of [87%–92.88%] and [78.44%–84.7%], respectively. When k is greater than 11, the train accuracy and test accuracy remains relatively constant in the range of [88.025% 84.99] and [84.36%–83.99%], respectively.

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Fig. 4. Training accuracy, test accuracy, precision score, recall, f1 _score, and Kappa vs k for test sizes = 40% and 45%.

Fig. 5. Train and test accuracy versus k undervarious test sizes(10%–50%).

It has been found out that Precision and Recall increase or decrease in the range of (73.5–80.96%) and (69.67–79.2%), respectively. Figure 6 shows that the highest magnitude of precision and recall are observed at k = 11 for the test size of 10% and 15%, respectively, precision_score (k = 11, test size = 10%, 80.96%), recall(k = 11,testsize = 15%,79.21%). They take lowest value under test size of 50% at k = 1 and k = 2,respectively; precision(k = 1,Test_size = 50%,73.5%), and Recall(k = 2,Test_size = 50%,69.67%). For k in the range of [3–11], the precision score and recall varies in the range of [75.43–80.96%] and [74.33%–79.21%],respectively. When k is greater than 11, the precision is in the range of [77.15%–80.65%] and recall is in the range of [74.07%–78.6%]. Figure 7 shows that F1 _score and Kappa increase or decrease when increasing the number of neighbor k.F1 _score varies between 71.5% and 79.8%; Kappa varies between 67%and 77.2%. The variation of F1 _score and Kappa depends on both k and test size. The highest F1 _score is observed when k = 11 under the test size of 15%.Kappa reaches its maximum when k = 10 for the test size 15%. For k in the range of [3–11] F1 _score and Kappa are in the range of [74.68%–79.94%] and [71.09%–77.2%], respectively.While for k higher than 11, F1 _score varies between 75.57% and 79.5%, and Kappa is in the range of [72.24–76.56%].

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Fig. 6. Precision_Score and Recall versus k under various test sizes (10%–50%).

Fig. 7. F1 _score and Recall versus k under different test sizes (10%–50%).

Figure 8 illustrates the variation of the ratio of the training accuracy to Test accuracy. The ratio of the training accuracy to the test accuracy varies (1.028–1.25). It takes its maximum at k = 1 for the test size of 50% and its lowest value for a test size of 15% at k = 20. A small value of k could lead to overfitting. The model under overfitting condition performs well on the training data but has poor performance when new data is coming. In the present simulations, to prevent overfitting, we can reduce the boundary by K nearest neighbors KNN in the range of [2–20] which gives a better ratio of the training accuracy to test accuracy in the range of [1.04–1.15]. Based on the simulation results, this model is able to make accurate predictions from the training set to the test for the whole the test size and the range of k = [2–20]. When considering k in the range of [2–20] the model performs well for the training set in the range of [85%–93%]while the test accuracy is in the range of [79.38%–79.99%].When k is in the range [3–11] an improvement in test accuracy is observed in the range of [80.54%–84.77%] with a train accuracy in the range of [87%–92.88%]. For the same range of k in the range of [3–11], the precision_score, recall, f1 _score and kappa can take highest magnitude. The best performance for this model is observed when k is in the range of [7–11] and the training size less than 25%, with the training set and the test set accuracy larger than 88.5% and 83.6%, respectively. The precision, recall, kappa and f1 _score are greater than 79%, 77%,75% and 79%, respectively. We see that our model is about 86% accurate, which might still be acceptable.

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Fig. 8. Ratio training accuracy to Test accuracy.

4 Conclusion In this study, we develop a machine learning model that is applied to data that does not contain any label information (Unsupervised learning algorithms).A quantitative estimation classification based on a statistical machine learning tool based on the KNN classifier is provided to forecast the hourly horizontal global irradiance received at Adrar city by creating four(04)classes. The implemented system is tested successfully, which is able recognize the hourly horizontal global irradiance magnitude on real dataset features consisting of 7 features and containing 44872 instances. It has been found out that the model performance depends on the test size and the number of neighbor k. Based on the evaluation of the model the performance in predicting the hourly horizontal global irradiance magnitude,we conclude that the prediction of four (04) classes can be optimized in the range of k [7–11] andby keeping the test size less than 25%. With this combination, the model prediction performs well where the test and training accuracy are larger than 88.5% and 83.6%, respectively.

References 1. Gueymard, C.A., Lara-Fanego, V., Sengupta, M., Xie, Y.: Surface albedo and reflectance: Review of definitions, angular and spectral effects, and intercomparison of major data sources in support of advanced solar irradiance modeling over the Americas. Solar Energy 182, 194–212 (2019) 2. Zhang, J., Zhao, L., Deng, S., Xu, W., Zhang, Y.: A critical review of the models used to estimate solar radiation. Renewable and Sustainable Energy Reviews 70, 314–329 (2017) 3. Besharat, F., Dehghan, A.A., Faghih, A.R.: Empirical models for estimating global solar radiation: A review and case study. Renewable and Sustainable Energy Reviews 21, 798–821 (2013) 4. Kumari, P., Toshniwal, D.: Deep learning models for solar irradiance forecasting: A comprehensive review. Journal of Cleaner Production 318, 128566 (October 2021) 5. Ruiz-Arias, J.A., Gueymard, C.A.: Worldwide inter-comparison of clear-sky solar radiation models: Consensus-based review of direct and global irradiance components simulated at the earth surface. Solar Energy 168, 10–29 (2018)

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6. Voyant, C., Notton, G., Kalogirou, S., Nivet, M.L., Paoli, C., Motte, F., Fouilloy, A.: Machine learning methods for solar radiation forecasting: A review. Renewable Energy 105, 569–582 (2017) 7. Martinez-Castillo, C., Astray, G., Mejuto, J.C.: Modelling and prediction of monthly global irradiation using different prediction models. Energies 14(8), 2332 (2021) 8. Feng, Y., Hao, W., Li, H., Cui, N., Gong, D., Gao, L.: Machine learning models to quantify and map daily global solar radiation and photovoltaic power. Renewable and Sustainable Energy Reviews 118, 109393 (2020) 9. Benghanem, M., Mellit, A., Alamri, S.N.: ANN-based modelling and estimation of daily global solar radiation data: A case study. Energy conversion and management 50(7), 1644– 1655 (2009) 10. Hasni, A., Sehli, A., Draoui, B., Bassou, A., Amieur, B.: Estimating global solar radiation using artificial neural network and climate data in the south-western region of Algeria. Energy Procedia 18, 531–537 (2012) 11. Guher, A.B., Tasdemir, S., Yaniktepe, B.: Effective estimation of hourly global solar radiation using machine learning algorithms. International Journal of Photoenergy 2020 (2020) 12. Huang, L., Kang, J., Wan, M., Fang, L., Zhang, C., Zeng, Z.: Solar radiation prediction using different machine learning algorithms and implications for extreme climate events. Frontiers in Earth Science 9, 202 (2021) 13. Pedro, H.T., Coimbra, C.F.: Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances. Renewable Energy 80, 770–782 (2015) 14. Chen, C.R., Kartini, U.T.: K-nearest neighbor neural network models for very short-term global solar irradiance forecasting based on meteorological data. Energies 10(2), 86 (2017)

A Multicore and Multithreaded Microcontroller Bernard Goossens1,2(B) , David Parello1,2 , and Dushan Bikov1,2 1 DALI/UPVD, Université de Perpignan Via Domitia, 52 avenue Paul Alduy, 66860 Perpignan,

France {goossens,parello,dushan.bikov}@univ-perp.fr 2 LIRMM: UMR 5506, 860 rue St Priest, 34095 Montpellier, France

Abstract. This paper presents a new type of multithreaded and multicore microcontroller. The aim is to provide more computing power for embedded applications, like the ones developed for smart cities. Through parallelism, our microcontroller is able to run multiple tasks, either independent or cooperating, like getting data from sensors, analysing them, taking decisions and activating actuators. Parallelism is handled through multithreading, which is better suited to real-time constraints than more traditional interruptions. The microcontroller Instruction Set Architecture (ISA) is the open source RISC-V RV32I. A prototype of the microcontroller has been implemented through High-Level Synthesis (HLS) tools on a Field Programmable Gate Array (FPGA). The implementation has been tested and evaluated on a set of benchmarks from the embedded application domain. This evaluation shows that multithreading is an effective technique to hide latencies with an average 1.09 Cycle Per Instruction (CPI) on a set of 16 benchmarks taken from the embedded application domain. The microcontroller speed-up has been measured on a distributed version of the matrix multiplication, varying the number of threads to distribute the computation. This measure shows that a 4 core processor running a total of 8 threads is 5.73 times faster than a sequential run. Keywords: Smart city · Microcontroller · Parallelism · RISC-V · High-Level synthesis · FPGA

1 Introduction For wikipedia, a smart city is “a technologically modern urban area that uses different types of electronic methods, voice activation methods and sensors to collect specific data. Information gained from that data is used to manage assets, resources and services efficiently; in return, that data is used to improve operations across the city”. A rather striking aspect of this definition is that the data collection is clearly separated from the utilization of the data. It seems implied that there should be at least 2 different systems, one being devoted to collect the data and the second to act intelligently from the collected data. However, there are many situations in which collecting data and using them should be done within a unique system. For example, autonomous vehicles observe their environment, and from this data take the correct decisions regarding the control of the vehicle. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 22–36, 2023. https://doi.org/10.1007/978-3-031-21216-1_3

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Usually, the data collection is done through sensors connected to a microcontroller. The microcontroller reads the sensors periodically and sends the read values to some centralized gathering station, where they are saved in a data base. Some nomadic devices like smartphones may access the data base through their installed applications. These nomadic devices themselves embark System-on-Chip (SoC) processors which are a combination of multiple components including processors and microcontrollers. For some reasons related to real-time constraints, the microcontrollers available on the market are mainly built around a single processing unit (single core). The constructors do sell a few high-end multicore microcontrollers. They are more SoCs linking multiple single core processors than true multicore. Moreover, they are too expensive to be used when millions of them are needed. In this paper, we propose a multicore multithreaded microcontroller to allow running a full smart city application, from the data acquisition on multiple sensors to their exploitation through the launching of actuators. The multiple concurrent or cooperating tasks are handled through the multithreading mechanism [1] embedded in the microcontroller pipeline design. As we show in this paper, multithreading is a way to run concurrent tasks better suited to real-time constraints than traditional interrupt based systems. The proposed microcontroller design aims to keep simple to become the new reference in the domain of the low-end, high volume microcontrollers. Instead of using n parts, each running a single task, our design allows to use n/8 parts, each running 8 tasks. The proposed microcontroller should be more expensive than actual single core ones probably but maybe less expensive than the needed set of components to run an equivalent set of tasks (multiple single core microcontrollers, printed circuits and cables).

2 The Performance Comes from Parallelism 2.1 Multicore and Parallelism Parallelism is not very popular in the domain of microcontrollers. To our opinion, the reason is probably that safety is difficult to ensure when multiple concurrent real-time tasks are run. It is very difficult, if not impossible, to precisely bound the time between a sensor read and an actuator write when other tasks may interfere through interrupts. The universal method to implement concurrency is through processor interrupts. In classical MicroProcessor Units (MPU), a single core may run multiple tasks through interrupt based interleaving. The interruption mechanism is also used to deal with I/Os. While a first task is waiting for an I/O controller, the core runs a second task. A multiple core processor also relies on interrupts. The cores pick their tasks from a shared pool. When a task is waiting, it is placed back in the pool. When a core needs to suspend a task, it picks up a replacing one from the pool. This parallelizing method is convenient on traditional Operating Systems like Linux, Windows or MacOS. However, for real-time applications, the interrupt technique has a major drawback. As the running task can be interrupted, it is impossible to associate a fixed running time to it, as illustrated on Fig. 1.

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Fig. 1. Interrupts make timing non deterministic.

The lw load instruction reads a data from a sensor. The data is saved into register s0. Then, some computation is done using the data as a source and producing a result into register s1. This result is sent to an actuator with the sw store instruction. On Fig. 1a, the computation time is fixed and can be deduced from the machine instructions latencies. On Fig. 1b, the run may be interrupted multiple times at any moment between the sensor read and the actuator write, leading to an unpredictable delay. Timing is no more deterministic. The uncertainty on Worst Case Execution Time (WCET) is not acceptable in many safety critical real-time applications like autonomous vehicle driving. This is why, on microcontrollers running time critical tasks, interrupts are mainly used to do asymmetric I/Os rather than doing time-sharing for multiple tasks. The processor runs a single time-critical task which sleeps while waiting for an I/O completion. When this is done, the I/O controller interrupts the processor to resume the waiting task, which may not be interrupted. During the wait, a low priority task may run but should be replaced by the time-critical one as soon as the waiting condition is over. 2.2 Multithreading and Parallelism In this paper, we propose to replace the interrupt mechanism by multithreading. Multithreading is both a software and a hardware technique. In software, multithreading divides an application into multiple concurrent threads of computations. In hardware, multithreading is the interleaving of multiple HARdware Threads, or harts, in the processor pipeline. A multihart processor is composed of multiple hart slots. On Fig. 2, there are 2 hart slots. Each hart slot has a program counter (pc0 and pc1) and a register file (register file 0 and register file 1). The execute stage has 1 instruction register per hart (ir0 and ir1). The memory stage has 1 result register per hart (re0 and re1). The write back stage has 1 value register per hart (va0 and va1). The stage selects one of the harts to provide the instruction to be treated (on Fig. 2, the vertical lines joining the pcx, irx, register file x, rex, and vax boxes, with x ranging

A Multicore and Multithreaded Microcontroller

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from 0 to 1, represent the selection mechanism). Hence, at each cycle, each stage runs 1 instruction of any hart (e.g. fetch from pc0, execute from ir1, accesses the data memory with execution result re1 and writes back final value va0 to register file 0). All the harts are run in parallel. A multihart processor, instead of interleaving threads through interrupts, runs all its harts in true parallelism. The major difference is that the hart parallelism gives deterministic timings (because they do not depend on any external factor) but the interrupt based thread interleaving does not (because of unknown added timings due to external interrupts).

Fig. 2. A pipeline built for 2 harts.

Multithreading is not only a technique ensuring deterministic timings. It is also a cheap way to tolerate latencies. In a multicycle pipeline, while the execution stage is running a long latency instruction for a given hart, instead of staying idle until the computation completion, it selects other harts. With multithreading, even though there are long latency pipeline stages, the CPI can keep close to its 1.0 peak value. For example, the multithreading mechanism can be used to replace caches because the processor runs other harts during multiple cycle memory accesses. The same remark applies to branch predictors and complex arithmetic operators. If at first sight multithreading seems a costly technique because of the register file duplication, it turns out to be cheap because many units necessary to keep the pipeline filled in a single thread processor can be removed in a multithreaded one (e.g. caches, predictors and pipeline improvements like bypassing).

3 The Microcontroller Market and the State-of-the-Art The MicroController Units (MCU) market was about 20 billion dollars in 2021, with a CAGR (Compounded Annual Growth Rate) of 14% [2]. This is to be compared to the 90 billion dollars MicroProcessor Units (MPU) market with a CAGR of 4% [3]. In terms of unit sales, in 2021 more than 30 billion MCUs were sold [4], to be compared to the 2.5 billion of sold MPUs [5]. 46% of the MCUs are general purpose (smartphones and consumer products) and 40% are used in the automotive industry. The remaining 14% are used in smartcards. In the automotive segment the demand is clearly for high-end MCUs: 77% of 32 bit MCUs, versus 18% for 16 bits MCUs and 6% for 8 bit MCUs [6]. 64 bit MCUs are still a niche.

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The main producing companies for 32 bit MCUs are NXP Semiconductors (PowerPC e200, ARM Cortex M), Microchip Technology (PIC32), Renesas Electronics (ARM Cortex M, RISC-V), STMicroelectronics (PowerPC e200, ARM Cortex M) and Infineon Technologies (TriCore). ARM Cortex M MCUs are the most popular ones, followed by Microchip PIC32. There is a rising market around RISC-V based microcontrollers (Renesas RZ/Five, Andes Technology HPM6000, Gigadevice GD32VF103). High-performance pipelining techniques used in microprocessors like out-of-order execution are carefully avoided in microcontrollers. Moreover, none of the actual MCUs are multithreaded (except for the XMOS Xcore-200 [7]). When they are claimed to be multicore, they are in fact built around 2 rather independent cores, being more SoCs than multicore processors (the Parallax Propeller is an 8-core MCU though) [8]. The PIC32MX is a single core MCU [9]. It has a 5 stage in-order pipeline. The ARM Cortex M family [10] ranges from the low-end M0 with a 2 stage in-order pipeline to the high-end M7 with a 6 stage, dual issue, branch prediction, in-order pipeline. Concerning multicore MCUs, the NXP Mac57D5 [11] is a 3 core MCU combining a Cortex A5 (used as the application processor), a Cortex M4 (defined as a vehicle processor) and a Cortex M0 + (I/O processor). The STMicroelectronics STM32H7 [12] is a dual core combining a Cortex M7 (480MHz) and Cortex M4 (240MHz) processors. RISC-V based microcontrollers represent a small proportion of the market. It was 1.3% of the total shipments in 2021 but should reach 3.8% in 2022 [13]. The growth is expected to be high in the next few years. RISC-V based microcontrollers are competitive for at least 2 reasons. First, new companies may easily enter the market because the RISC-V architecture is open source [14, 15]. Second, the RISC-V ISA is designed as a combination of various purpose subsets. For example, the “E” subset (“E” stands for “Embedded”) restricts the number of registers to 16 instead of 32 in the base “I” subset. The “E” subset does not include floating-point instructions, nor integer multiplication or division. Hence, a constructor may develop a product line with increasingly complex processors, starting from the most basic ones using the “E” subset to the most general ones using the “G” subset.

4 A New Microcontroller 4.1 A Multihart Pipeline In this paper, we propose a new microcontroller design to improve performance through parallelism without sacrificing safety through the elimination of interrupts, replacing them with multithreading. To further allow the quick adoption of our proposition by microcontroller producing companies, our design is built around the open source RISC-V architecture.

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The base microcontroller pipeline has a 6 stage multicycle structure. This structure is vectorized to implement multithreading, as shown on Fig. 3.

Fig. 3. A 4-hart pipeline.

On Fig. 3, the pcx, dx, ix, ex, mx, and wx rectangles, with x ranging from 0 to 3, represent hart slots. The design shown has 4 harts, however, this can be varied from 2 to 8. The 6 stages are ordered from the left to the right on the upper part and from the right to the left on the lower part (the issue stage is followed by the execute stage). On Fig. 3, each stage has 4 slots (e.g. rectangles named i0 to i3 for the issue stage). Each slot may host 1 instruction. In the fetch stage, the slots are name pc0 to pc3. Each can host the code address of a running thread. Each stage includes a hart selection process in 2 steps. The first step is represented as a vertical line facing the stage 4 slots (e.g. the line joining the 4 dx slot outputs). It selects one of the hosted threads in the 4 hart slots. The second step is represented as a second vertical line after the first one (e.g. in the decode stage, the line joining the first line output and the decode stage bottom line input). It selects either the first step selection if any, or else the incoming instruction (or the 2 incoming pc for the fetch stage). Each selection vertical line represents a multiplexer to choose 1 of its inputs. A thread can be chosen if the instruction held in the stage or input is ready, i.e. fulfills some stage related condition. For example, an instruction in the issue stage is ready if its register sources and destination are not locked. The selection process follows a fixed priority order. The first step has priority over the second one. In the first step, harts are increasingly ordered (i.e. hart 0 has the highest priority and hart 3 has the lowest one). In the fetch stage, the pc incoming from the

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decode stage (jal instruction target) has priority over the one incoming from the execute stage (branch or jalr instruction target). Hence on the figure the highest input on the first line has the highest priority and the lowest input on the second line has the lowest priority. This deterministic selection policy ensures the determinism of the hart interleaving. To test our design, we have implemented a set of prototypes on a Xilinx XC7Z020 FPGA [16]. Our implementations were done with the Vitis HLS tool [17]. High-Level Synthesis is particularly suited to rapid prototyping. It takes less than a month to develop a multithreaded RISC-V processor from scratch. 4.2 A Multicore Processor We have implemented a single core and single hart version. The multihart version has 2 harts, 4 harts and 8 harts. The multicore version was limited to a total of 8 harts not to exceed the available resources on the FPGA, either as a 2 cores 4 harts IP (Intellectual Property) or as a 4 cores 2 harts IP. The different cores are interconnected through an AXI interconnect IP provided by the Vivado library, as shown on Fig. 4. (The axi_interconnect IP connects 1 or more AXI memory-mapped master devices to 1 or more memory-mapped slave devices. The Advanced eXtensible Interface (AXI), is an on-chip communication bus protocol developed by ARM.) On Fig. 4, from the left to the right, there is a Zynq IP (processor embedded in the FPGA, which is used as an interface between the host machine and the other IPs connected to the AXI interconnect). Under the Zynq is the reset and clock management IP. On the right of these 2 IPs is the AXI interconnect which routes the communications between the connected IPs. The third column is made of 2 multihart cores (multihart_ip_0 and multihart_ip_1) and 2 RAM controllers (axi_bram_ctrl_0 and axi_bram_ctrl_1). The 4th column contains 2 RAM blocks (blk_mem_gen_0 and blk_mem_gen_1).

Fig. 4. A 2-core 4-hart processor.

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The microcontroller itself is the combination of the 2 multihart IPs, the 2 RAM blocks and the AXI interconnect IP. To run a program on the 2 core processor IP, the code is sent from the Zynq to the code RAM embedded in each multihart IP through the AXI interconnect. Initial data may be written to the data RAM blocks through the RAM controllers. Once this initialization phase is done, the processor IP is started (a start signal is sent from the Zynq to the 2 multihart IPs). The code to be run on each hart is a main function. When the function returns, the hart halts. When all the harts on a core have halted, the IP sends a done signal back to the Zynq. Once all the core IPs have sent their done signal, the result of the run can be observed by reading the data RAM blocks through the AXI interconnect. The data memory is shared, i.e. each hart has access to the whole memory. The local access latency is 1 processor cycle (direct access from the core IP to its RAM IP) and the remote access latency through the AXI interconnect is 5 processor cycles. Thanks to the multicycle pipeline, no other unit is needed in the core to tolerate the variable latency. Thanks to multithreading, the CPI keeps close to 1.0 with other harts being run while a remote memory access is in progress. 4.3 Independent Codes The harts can be used to run independent programs (see Fig. 5). In this case, each hart in each core IP receives its individual main function (e.g. 4 main functions on a 4 hart core). Each program accesses the hart partition in the local RAM block (no remote access through the AXI interconnect; a hart data memory partition is made of a global space, labeled with a g, and a stack space, labeled with an s). The runs on the different cores are done in parallel. The harts on a core are interleaved on a cycle basis.

Fig. 5. The code and data memories of a 2-core 4-hart processor.

4.4 Parallelized and Distributed Code The harts can be used to run concurrent programs sharing some data (see Fig. 6). For example, a matrix multiplication can be divided into subtasks (1 per hart). Each subtask multiplies a subset of a first source matrix X with a second source matrix Y, producing a subset of the result matrix Z.

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On the figure, source matrix X is partitioned. Each partition is made of 1/8 of the lines of the X matrix. It is the same for matrix Y and for matrix Z. Hence, the code of hart h multiplies its local lines of matrix X by all the columns of matrix Y (1/8 of local accesses and 7/8 of remote accesses). The result is saved in the local partition of the Z matrix. With this algorithm, 30% of the accesses are local. Even though the number of remote accesses is high, i.e. the average memory access latency is 3.8 cycles, the measured CPI is 1.04, very close to the 1.0 peak. This shows that hiding latencies with multithreading is acceptable, compared to techniques to shorten latencies like caching or predicting.

Fig. 6. The shared data memory of a 2-core 4-hart processor to run a distributed matrix multiplication.

5 Experimental Evaluation of the Proposed Microcontroller 5.1 Evaluation of the Efficiency of the Multithreading Technique on the CPI Multithreading is a technique to hide latencies. It is in contrast with techniques to reduce the latencies like caches and predictors. With a memory hierarchy based on caches, the memory access latency is reduced from the memory access time to the first level cache access time. With a branch predictor, the branch latency is reduced from the time to compute the branch target to the time to predict it. The latency reduction is more effective when the success rate (of the cache or of the predictor) is closer to 100%. Multithreading does not reduce the latency in any way. However, when multiple threads are active, the pipeline can be filled by active threads while other threads are waiting for the termination of long latency operations. The efficiency of multithreading does not rely on the pattern of the thread run (e.g. the succession of cache accesses or of branches run, impacting the hit rates) but only on the number of active threads (e.g. to hide an n cycles latency, it is enough to have n active harts). To evaluate the capability of the multithreading mechanism to hide the latencies in our microcontroller design, we have run our processor on a set of benchmarks taken from the mibench suite [18] and from the riscv-tests provided by the RISC-V organization [19]. Not all the benchmarks of the 2 suites could be run, either because of the size of the code or data (the memory size on the FPGA is limited to 540KB), or because of the utilization of OS calls like malloc, not available on the FPGA environment.

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Table 1 shows the list of the benchmarks which were used to measure the CPI, the number of instructions of the run and the distribution of the three main categories of instructions: memory accesses (i.e. loads and stores), control (i.e. branches and jumps) and computations (i.e. Arithmetic and Logic Unit, ALU instructions). Table 1. The benchmarks. Name

Number of instructions

Memory accesses

Control

ALU

basicmath_mibecnh

30,897,739

5%

31%

65%

bitcounts_mibench

32,653,239

6%

24%

71%

6,683,571

6%

28%

66%

stringsearch_mibench

549,163

20%

17%

63%

rawcaudio_mibench

633,158

13%

28%

59%

rawdaudio_mibench

468,299

13%

26%

61%

qsort_mibench

crc32_mibench

300,014

20%

10%

70%

fft_mibench

31,365,408

3%

30%

67%

fft_inv_mibench

31,920,319

3%

30%

67%

median_riscv_tests

27,892

39%

42%

19%

157,561,374

3%

31%

67%

417,897

1%

32%

66%

qsort_riscv_tests

271,673

33%

32%

36%

spmv_riscv_tests

1,246,152

5%

26%

69%

403,808

62%

5%

33%

16,010

37%

12%

50%

mm_riscv_tests multiply_riscv_tests

towers_riscv_tests vvadd_risc_tests

The set of runs is varied enough in size and in instruction category distribution. Table 2 shows the number of processor cycles to run each benchmark and the CPI. Table 2. The benchmark CPI. Name

Number of cycles

CPI

basicmath_mibench

62,723,992

2.03

bitcounts_mibench

57,962,065

1.78

qsort_mibench

12,845,805

1.92

stringsearch_mibench

1,240,390

2.26

rawcaudio_mibench

1,363,673

2.15 (continued)

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B. Goossens et al. Table 2. (continued) Name rawdaudio_mibench crc32_mibench

Number of cycles

CPI

942,834

2.01

660,028

2.20

fft_mibench

64,979,537

2.07

fft_inv_mibench

66,054,232

2.07

median_riscv_tests mm_riscv_tests multiply_riscv_tests

53,141

1.91

328,860,252

2.09

745,904

1.78

qsort_riscv_tests

491,648

1.81

spmv_riscv_tests

2,426,687

1.95

510,511

1.26

24,016

1.50

towers_riscv_tests vvadd_risc_tests average

1.92

The CPI ranges from 1.26 to 2.26, with an average value of 1.92. What affects the CPI in the 6 stage multicycle pipeline is the control instructions delay (1 cycle for a jal instruction, 3 cycles for a branch or a jalr instruction) and the dependency delay (the delay of a Read-After-Write, or RAW dependency; for example, if an instruction writes to register r, the next instruction using r must wait in the issue stage until r has been written back; if the using instruction is back-to-back to the writing one, the delay is 3 cycles). As memory accesses are done in a single processor cycle, they have the same impact on the CPI as ALU instructions. The CPI is rather high compared to optimized state-of-the-art pipelines. For example, the PIC32MX microcontroller is built around a MIPS-like 5 stages pipeline and the CPI is 1.0. On this pipeline, control instructions imply a delay cycle, which the compiler can fill with a useful instruction to hide the delay or else with a NOP. In this case, the CPI is not impacted but the number of instructions run is. In codes having many control instructions run, the number of NOPs run may be high. Moreover on the PIC32MX, RAW dependencies are handled through a bypass mechanism which eliminates any delay between the producer and the consumer. In our design, we did not include a bypass (which simplifies the hardware) and instead, we rely on multithreading to hide the producer to consumer delay. Table 3 shows the CPI for a single core and multihart implementation. The experience is a run of the same benchmark on all the available harts (e.g. 4 runs of the basicmath benchmark in a 4 hart processor). These runs are done in parallel, with a cycle interleaving based on a selection of the highest priority active hart. The 4 harts average CPI is 1.09, showing that multithreading is efficient to reduce the CPI. The 8 harts average CPI is not improved, showing that 4 harts is a better choice for a multihart design.

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Table 3. The multihart CPI. Name

CPI 2 harts

CPI 4 harts

CPI 8 harts

basicmath_mibench

1.44

1.10

1.11

bitcounts_mibench

1.35

1.10

1.09

qsort_mibench

1.40

1.09

1.09

stringsearch_mibench

1.57

1.09

1.09

rawcaudio_mibench

1.56

1.13

1.12

rawdaudio_mibench

1.48

1.11

1.10

crc32_mibench

1.60

1.03

1.03

fft_mibench

1.46

1.10

1.11

fft_inv_mibench

1.46

1.10

1.11

median_riscv_tests

1.36

1.11

1.11

mm_riscv_tests

1.47

1.10

1.11

multiply_riscv_tests

1.32

1.13

1.07

qsort_riscv_tests

1.36

1.12

1.10

spmv_riscv_tests

1.41

1.09

1.09

towers_riscv_tests

1.12

1.00

1.01

vvadd_risc_tests

1.25

1.03

1.04

average

1.41

1.09

1.09

5.2 Evaluation of the Speed-Up on a Multicore Design To evaluate the efficiency of our multicore design, we have compared the run time of the matrix multiplication benchmark presented in section IV, D, applied to the multiplication of two 96x96 square matrices of integer numbers, on different implementations involving an increasing number of harts (the sizes of the matrices were chosen to fit in a 128KB RAM limit). The base RV32I ISA has been extended with the M extension (multiplication instruction) and the HLS implementation has been modified to integrate the new integer multiplier.

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Table 4 shows the speed-up when distributing the run on multiple cores (with a maximum of 8 harts). The speed-up is the ratio of the number of cycles of the run on a single hart microcontroller (the first line in Table 4) on the number of cycles of the run on a multihart microcontroller (the other lines). Table 4. The speed-up on a matrix multiplication (ratio single hart/multihart). Number of cores

Number of harts

Number of instructions

Number of cycles

Speed-up

1

1

6,257,962

12,488,275

-

2

2

6,509,120

4,171,582

2.99

2

4

6,792,968

3,459,050

3.61

4

2

6,792,968

2,181,072

5.73

The speed-up shows that the multithreading technique is efficient to hide the remote memory accesses latency and to provide more performance. Figure 7, shows the speed-up (lower line) and the size increasing factor (LUTs, upper line) for 2 cores and 2 harts, 2 cores and 4 harts and 4 cores and 2 harts, with the 1 core and 1 hart reference. Figure 8. is the histogram of the size (number of LUTs) for the designs varying the number of cores and harts (RV32I, no multiplier, Vivado 2022.1). Figure 9 is the histogram of the number of instructions run (left) and cycles of the run (right).

Fig. 7. The speed-up, the size increasing factor, the number of instructions and cycles.

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Fig. 8. The number of LUTs.

Fig. 9. The number of instructions and the number of cycles.

6 Conclusion This paper presents a multihart and multicore microcontroller design in which parallelism is used to increase performance. Multithreading is the main technique used to tolerate latencies. Our experiments based on an FPGA prototype show that multithreading is an efficient way to keep a pipeline filled and that multicore and multihart parallelism does give significant speed-up. Moreover, we propose in this paper to replace interrupt based parallelism on microcontrollers with more secure multithreading based parallelism. Multithreading parallelism can provide time determinism, which interrupt based concurrency cannot.

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References 1. Tullsen, D.M., Eggers, S.J., Levy, H.M.: Simultaneous multithreading: Maximizing on-chip parallelism. 22nd ISCA. IEEE. pp. 392–403. ISBN (1995) 2. https://www.factmr.com/report/4556/microcontroller-market?utm_source=adwords&utm_ medium=ppc&gclid=CjwKCAjwj42UBhAAEiwACIhADkHuBk3JSS9cIwzOGaNjEEJd NkNoEJply25EGmLSZ15XL0NpKieBzhoCji8QAvD_BwE 3. https://www.precedenceresearch.com/microprocessor-market 4. https://www.icinsights.com/news/bulletins/Microcontrollers-Get-A-Lift-From-AutomotiveAfter-2021-Rebound/#:~:text=IC%20Insights’%20forecast%20shows%20total,final%20y ear%20of%20the%20forecast 5. https://www.icinsights.com/news/bulletins/Microprocessor-Sales-Will-Continue-Double Digit-Growth-In-2021/#:~:text=With%20MPU%20shipments%20reaching%202.5,this% 20year%20(Figure%201) 6. https://www.eetasia.com/automotive-mcu-market-to-surge-23-in-2021-despite-shortages/ 7. https://www.xmos.ai/xcore-200/ 8. https://en.wikipedia.org/wiki/Parallax_Propeller 9. https://www.microchip.com/en-us/products/microcontrollers-and-microprocessors/32-bitmcus/pic32-32-bit-mcus/pic32mx#Parametric%20Chart 10. https://en.wikipedia.org/wiki/ARM_Cortex-M 11. https://www.nxp.com/products/processors-and-microcontrollers/arm-microcontrollers/mac 57d5xx-mcus/ultra-reliable-multi-core-arm-based-mcu-for-clusters-and-display-manage ment:MAC57D5xx 12. https://www.st.com/en/microcontrollers-microprocessors/stm32h7-series.html 13. https://www.i-micronews.com/products/microcontroller-quarterly-market-monitor/?cn-rel oaded=1 14. https://riscv.org/ 15. https://github.com/riscv/riscv-isa-manual/releases/download/Ratified-IMAFDQC/riscvspec-20191213.pdf 16. https://www.xilinx.com/products/silicon-devices/soc/zynq-7000.html 17. https://docs.xilinx.com/r/en-US/ug1399-vitis-hls 18. https://vhosts.eecs.umich.edu/mibench/ 19. https://github.com/riscv-software-src/riscv-tests

Brain Tumor Classification Using Convolutional Neural Networks and Transfer Learning Cherifi Dalila(B) , Cherifi Zakaria, and Belkadi Wassim Institute of Electrical and Electronic Engineering, University of Boumerdes, Boumerdes, Algeria [email protected], [email protected], [email protected]

Abstract. Brain tumors are one of the top causes of mortality in both children and adults across the world. Early detection of the tumor can give the patient a new chance in life to undergo effective treatment to save them. Despite the great medical and technological advances, the current test methods for diagnosing and classifying brain tumors are prone to human error, since human-assisted manual classification can result in incorrect prognosis and diagnosis. These drawbacks highlight the need of employing a completely automated system for the detection of brain tumors. The emergence of deep learning and its successes in classification of images warranted by its performance and ability to generalize on various data, led us naturally to use it to solve this problem. This work aims to be a concise exposition of deep learning architectures applied to medical imaging, with a focus on the analysis of MRI images for the automatic classification of brain tumors for the early diagnosis purposes. We consider classification as a supervised learning problem and we address it by means of Convolutional Neural Networks (CNN). Two different CNN models are proposed for two separate classifications, with changing and tuning various hyper-parameters. Two datasets were used, the first dataset of brain MRI Images provided by Navoneel Chakrabarty and the second dataset acquired from the Kaggle platform under the name BT-multiclass. The Using the first proposed model, brain tumor detection is accomplished with 91% percent accuracy. With an accuracy of 92% percent, the second proposed model can classify brain tumors into four types: non-tumor, glioma, meningioma, and pituitary. Using transfer learning, the proposed CNN models for both classifications are then compared to other popular pre-trained CNN models such as Inception-v3, ResNet-50, and VGG-16; and satisfactory findings are obtained. Thus, the inclusion of this type of methodologies favors both the patient and the physician, making it possible to carry out more precise quantitative diagnoses. Keywords: Brain tumors · IRM · CNN · Transfert learning

1 Introduction Tumors are blocks of cells characterized by uncontrollable division. They can be benign or malignant (cancer), depending on how fast they grow and whether they can be resected or cured by neurosurgical treatment. We can classify brain tumors into two types according to their nature, origin, growth rate and stage of progression. The first type, tumors © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 37–48, 2023. https://doi.org/10.1007/978-3-031-21216-1_4

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that originate from cells in the brain. Metastatic brain tumor occurs when cancer cells spread into the brain from a primary cancer located in another part of the body, this is the second type. Among all types of brain tumors, gliomas suppose most of them and are usually cancerous. Despite the great medical and technological advances, the diagnosis and prognosis of these tumors continues to be poor, especially gliomas, for which the life expectancy is less than two years and whose treatment, extremely aggressive, can cause severe problems to the patient. Many lives could be saved if cancer is detected early with rapid and cost-effective diagnostic techniques. Small tumors are much easier to remove, but small tumors become large tumors. Once symptoms appear, it is usually too late to treat the tumor. However, it is very difficult to treat cancer at higher stages where survival rates are low. The diagnosis of these pathologies has improved markedly thanks to the introduction into clinical practice of Magnetic Resonance Imaging (MRI) along with algorithms and diagnostic aid systems that are demonstrating great potential, not only to improve this, but also the treatment of this type of diseases. However, these tests can detect areas with high tumor suspicion, the diagnosis of which must be confirmed by performing a biopsy. These images are not easy to interpret, which means that the professional in charge of analyzing them, despite his experience, is not able to detect a significant percentage of tumors [1–6]. The objective of the proposed work is to minimize false alarms, increase the performance of early detection and to help reduce cancer mortality. The work relies on an algorithm and depends on imaging systems, feature extraction and classification using one type of artificial neural networks which are Convolutional Neural Networks (CNN). The algorithm analyzes the medical image and tries to detect areas suspected of containing an abnormality. The radiologist will then be able to interpret the information contained in the image with the information contained in the medical image with less difficulty. This work depicts two separate classifications, binary and multiclass, of brain tumors through different proposed and pre-trained models for classification of brain tumors. The second section gives an overview of the brain tumor. The third section introduces the convolutional neural network, its architectures and the different pre-trained models. Lastly, the obtained findings and the discussion are tackled in the fourth section.

2 Brain Tumors Tumors are one of the most dangerous and complicated diseases which can be on any part of the body with an irregular shape of lump on body’s part. The most dangerous tumor is a brain tumor and they are very difficult to cure it. A brain tumor occurs when abnormal cells form in the brain. Many distinct types of brain tumors exist. Some brain tumors are benign (noncancerous), whereas some brain tumors are malignant (cancerous). Cancerous tumors can be divided into primary tumors that start in the brain and secondary tumors also known as metastasis tumors that have spread from other parts of the body to the brain [1–4]. The influence of the brain tumor on the nervous system’s function is directly related to its rate of growth and location. The type of brain tumor along with the size and location influence the treatment options [5, 6]. Brain tumors that start in the brain are called primary brain tumors, they can be benign or malignant. However, we can find another type of brain tumors which are called secondary brain

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tumors which make up the majority of brain cancers and are always malignant. These tumors start in one part of the body and spread, or metastasize, to the brain [6]. For medical diagnosis, we need to take the medical image information of the brain. To obtain the internal structure of the brain, Brain Scan medical technique is used. The most used brain scan technique is MRI because of its high resolution. The MRI has a lot of information about the brain structure and it also displays any abnormalities within the brain cell. Neural networks have become very famous in the medical field due to their results in the detection and classification of different diseases. They have been implemented in the detection and classification of different types of diseases especially cancer diseases, skin diseases, and other different infections. The detection of the type of the tumor is a very tough process that requires a lot of experience in the cancer diseases.

3 Classification Using Convolutional Neural Networks For image classification, artificial neural networks are widely used and their architecture can represent complex relations. Convolution Neural Networks (CNN) is a method which used for patterns recognizing and image classification. CNN’s are a collection of neurons with learnable weights and biases, and it is used to achieve a good accuracy in image classification. It can able to learn complex features automatically from images. There are many advanced methods in Machine Learning and Deep Learning ready to be used for image processing. Convolutional Neural Networks (CNN) is an algorithm belonging to deep learning, a new branch of machine learning (one of the fundamental areas of artificial intelligence). CNNs are inspired by the findings of Hubel and Wiesel regarding two basic types of cells identified in the visual cortex of cats. In analogy to the visual cortex, CNNs use a deep architecture characterized by alternating layers of convolution and subsampling. CNNs are used for automatic two-dimensional pattern recognition problems such as the detection of objects, faces and logos in images or document analysis. The choice of this algorithm is due to the fact that the performances obtained in some problems related to computer vision are superior to those obtained with other methods in most of the previous researches [7–12]. This work presents the implementation of convolutional neural networks for the detection and classification of brain tumor infections. The interest in developing a robust and automatic approach to achieve this is fueled by the need to use it to provide an automatic system in the early diagnosis and prognosis of brain tumors which is important to help medical doctors in their decisions. We perform binary classification at first and then multi-classification [13–16], In addition, we have used three different pre-trained deep convolutional neural networks which are: ResNet-50, VGG-16 and Inception V3.

4 Experements and Results This section tackles the implementation of Brain Tumor Classification from MRI Images using Deep Neural Networks. At first, we will use the dataset that contains tumor and nontumor images to perform a binary classification. After that, we will use a different dataset that contains four different types of brain tumor to perform a multi-classification. We

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have used CNN to implement the algorithm and interpret it using Python with changing some parameters to track the performance. We have come up to different results for the two classifications which are discussed in details in this chapter. 4.1 Dataset Acquisition We have performed a set of experiments on two different brain MRI datasets which are publicly available for the tasks of brain tumor classification The first dataset of brain MRI Images provided by Navoneel Chakrabarty as shown in Fig. 1. The dataset was downloaded from the Kaggle platform under the name BT-binary and contains 253 Brain MRI images divided into 2 folders entitled: “yes” and “no” with each containing 155 and 98 Brain MRI Images respectively.

Fig. 1. Examples of brain MRI images in BT-binary dataset [17].

The second dataset consists of 3064 images. It was also acquired from the Kaggle platform under the name BT-multiclass. The examples of brain MR images in BT-binary, BT-multi datasets are shown in Fig. 1. The dataset is divided into three different folders according to the three distinct types of brain tumors known as Gliomas, Meningiomas, and Pituitary tumors as depicted in Fig. 2.

Fig. 2. Examples of brain MR images in BT-multiclass dataset [17].

4.2 Training The data set is divided into three sections: training, validation, and testing. To calculate the loss and train the network, the training set is used. After each training phase, the validation set is utilized to evaluate the network. It is used to figure out when the network has reached a point of convergence. Finally, the accuracy of the network is assessed using the test set.

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4.3 Evaluation Metrics An ideal test rarely overlooks the thing you are looking for (i.e., it is sensitive) and rarely mistakes it for something else (i.e. it is specific). Therefore, when evaluating diagnostic tests, it is important to calculate the sensitivity and specificity for that test to determine its effectiveness [18, 19]. A diagnostic test’s sensitivity is defined as the probability that a sample tests positive given that the patient has the disease whereas specificity is expressed as the probability that a test returns a negative result given that the patient does not have the disease, other metrics are used which are: Accuracy Average accuracy, Precision, Recall, F1-score, respectively of a given test. 4.4 Experiments 1) Binary Classification: CNN Model In the first classification, the dataset is split into three sets: training, validation and testing sets. The training set constitutes 70% of the dataset whereas the test and the validation sets represent each 15%. The output classes are ‘tumor’ and ‘non-tumor’. For each class, the accuracy, specificity, and sensitivity are calculated. After performing data augmentation, the number of samples in the train, validation, and test sets are 1444, 310 and 310 respectively. • Experiment 1 In this first experiment, we have designed a CNN of 4 convolutional layers. To prevent overfitting, four convolutional layers are followed by one max pooling layer and dropout layer with a dropout rate of 0.27. After that, the architecture end with two fully connected layers, with 256 units for the first layer and 2 units for the last layer; the latter represent the two output classes for classification. A dropout layer with a dropout rate of 0.2 exists between the two fully connected layers. The batch size is set to 32 with 25 epochs. The optimizer algorithm used here is Adam, with a learning rate of 0.0001. As revealed the training accuracy increases dramatically whereas the validation accuracy rises steadily for a short time before it reaches a plateau. As a result, we can deduce that the model is highly overfitting despite the utilization of numerous dropout layers. After plotting the confusion matrix, we have used its values to compute the three evaluative metrics; sensitivity, specificity and accuracy. The results are summarized in Table 1 below:

Table 1. Training results of the first proposed model for binary classification. Accuracy

Sensitivity

Specificity

77%

89%

73%

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• Experiment 2 In this second experiment, we have designed a CNN architecture of 5 convolutional layers with one max pooling layer followed by one dropout layer with a dropout rate of 0.3 both located after every two convolutional layers. Following that, there is one fully connected layer of 256 units, followed by a last fully connected layer of 2 units that represent the two outputs. Between the two fully connected layers, there is one dropout layer with a dropout rate of 0.5. The batch size and the number of epochs remain untouched as regard to the last experiment with values of 32 and 25 respectively. The optimizer algorithm used is Adam, with a learning rate of 0.0001. We plotted the accuracy after that the training was completed, and the results are as follows: The evaluation metrics are calculated based on the confusion matrix and the results are in the following table: (Table 2).

Table 2. Training results of the second proposed model for binary classification. Accuracy

Sensitivity

Specificity

87%

90%

82%

• Experiment 3 In this third experiment, we retain the same structure of the architecture used in the second model with adding batch normalization after each convolutional layer. The batch size is increased to 128 and the number of epochs is set to 27. The optimizer algorithm used is again Adam, however, the learning rate is increased to 0.001. The resulted evaluations metrics are given in the following table: (Table 3). Table 3. Training results of the third proposed model for binary classification. Accuracy

Sensitivity

Specificity

91%

91%

92%

2) Binary Classification: CNN Model with Transfer Learning After that we have worked on the proposed model and tried to change and tune different parameters to improve the accuracy of the model, we moved to use a different approach using some known pre-trained models on ImageNet. We have frozen the weights of bottleneck layers of the pre-trained CNN models on the ImageNet dataset. In addition to that, we have converted the images to the size 224 × 224 (or 299 × 299) pixels as the pre-trained networks used in our experiments require the input images to be 224 ×

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224 in size except for the Inception V3, which requires the input images with size 299 × 299. The Accuracy, Sensitivity and Specificity of the pre-trained models: VGG-16, ResNet-50, and Inception-v3 are shown in Table 4: Table 4. Training results of the three pre-trained models. Model

Accuracy

Sensitivity

VGG-16

92%

93%

Specificity 80%

ResNet-50

85%

96%

75%

Inception-v3

64%

63%

100%

3) Multi-classification: CNN Model In this second classification, as we have used another dataset, we have made some changes on the model comparing to the last two experiments by changing some parameters and algorithms that are more compatible with the new dataset. We have changed the input shape to match the images used for training and we have changed the output shape to meet the number of classes desired. • Experiment 1 In this first experiment, we have trained our 4 conv layers model already used in the first application of the binary classification. We have taken the new dataset and we made the proper changes to meet the compatibility. To prevent overfitting, the four convolutional layers are followed by one max pooling layer and one dropout layer with a dropout rate of 0.27. In addition to that, we have added two fully connected layers with 256 and 4 units respectively with the latter representing the four output classes. A dropout layer with a dropout rate of 0.2 exists between the two fully connected layers. The batch size and the number of epochs are set to 32 and 25 respectively. The optimizer algorithm used is Adam, with a learning rate of 0.0001. The resulted evaluations metrics are given in the following table: (Table 5). Table 5. Training results of the first proposed model for multi-classification. Accuracy

Precision

Recall

F1-Score

68%

54%

53.5%

54%

• Experiment 2 In this second experiment, we have added one convolutional layer to the last model so that we have 5 convolutional layers. Moreover, we have increased the dropout

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probability to avoid overfitting. The resulted evaluations metrics are given in the following table: (Table 6). Table 6. Training results of second proposed model for multi-classification. Accuracy

Precision

Recall

F1-Score

88%

76%

71%

73.4%

• Experiment 3 In this third experiment, we maintained the same algorithm used in the second model; however, we have added batch normalization after each convolutional layer. We have increased both the batch size and the number of epochs to 128 and 30 respectively. The optimizer algorithm used is still Adam, except that the learning rate has been increased to 0.001. The resulted evaluations metrics are given in the following table: (Table 7). Table 7. Training results of the third proposed model for multi-classification. Accuracy

Precision

Recall

F1-Score

91%

79%

71%

75%

4) Multi-classification: CNN Model & Transfer Learning After we had worked on our proposed model through changing different hyperparameters of the model, we have moved to using some pre-trained models whose weights are already pre-trained on ImageNet in the sake of improving the performance. We have evaluated the classification performance using the pre-trained architectures (VGG16, Inception-v3 and ResNet-50) for different values of epochs. The objective is to increase the classification accuracy and avoid the problem of overfitting. This can be achieved through using different transfer learning pre-trained models for different values of epochs. The accuracy, precision, recall and f1-score results are given in the following Table: (Table 8). 5) Discussion From the two first applications of each classification task, we can conclude that adding convolutional layer to the architecture enhances the performance in terms of accuracy. However, removing any of the middle layers results in the network’s accuracy degrading. Hence, the depth of the architecture really is critical to achieving our results. In terms

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Table 8. Training results of the three pre-trained models. Architectures Accuracy Precision Recall F1-Score VGG-16

91%

85%

76%

80%

ResNet-50

82%

61%

60%

61%

Inception-v3

76%

53%

50%

52%

of accuracy, the model built in the third application outperforms the previous models significantly. The combination of dropout and batch normalization significantly reduces overfitting and improves the performance of the model. Surprisingly, the increase in batch size (32 to 128) and learning rate (0.0001 to 0.001) from the second to third model does not degrade the model’s quality. With small datasets, satisfactory classification results are obtained. For example, brain tumor detection (first classification) is achieved with a highly satisfactory accuracy of 92% using the first designed CNN model. In addition, the brain tumor type classification (second classification) is performed with an accuracy of 92.66%. It is worthwhile to compare the results of the proposed CNN models with the outcomes of existing popular pre-trained CNN models. The same tests are carried out with the same dataset utilizing popular pre-trained CNN models: Inceptionv3, ResNet50, and VGG-16 using the transfer learning approach. In terms of accuracy, the proposed CNN models outperform the other pre-trained models in both classification task. In brain tumor detection task, the application of the VGG-16 architecture which achieved 97% accuracy on training set and 92% validation set is the closest model to the proposed model, while ResNet-50 obtained 96% on training set and 85% on validation set and lastly, Inception-v3 achieved 91% and 64% on training and validation sets respectively. On the other hand, in brain type classification, the pre-trained VGG-16 model achieved 99% classification accuracy on the training set and 91% on the validation set and is the closest model to the proposed CNN model, while ResNet-50 achieved 82% on both sets whereas Inception-v3 obtained 76% and 78% on training and validation sets respectively. The time taken by the model to train is another important attribute for performance. We compiled all the models for 30 epochs to be consistent and better model comparison, the three pre-trained models are slow compared to our proposed model. One possible explanation for the outperformance of the proposed CNN models comparing to the pre-trained models is that such pre-trained models are built and trained on general datasets for general image classification tasks. The proposed CNN models, on the other hand, are intended for more specialized tasks such as brain tumor detection and tumor-type classification. Furthermore, the proposed models are trained and tested using MRI images of brain tumors. Another reason why the proposed CNN models outperform the pre-trained models is because the proposed CNN architectures have been tuned for the specific purposes and have employed the hyper-parameters that produce the best results for the specific tasks in question. Looking at the literature, one can see that some researchers have classified the MRI images into tumor and non-tumor images, while other researchers have made brain tumor type classification. Because these two tasks are accomplished using the proposed model, they are compared to individual research in the literature. Different researchers have presented different systems to perform either of the

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two classifications and considerable results have been found. From the recent approaches obtained in the literature in terms of performance evaluation and classification type we found that in Abiwinanda et al. proposed 4-layered CNN and they got 84.19% [20], Hossam et al. proposed 16- layered CNN and they got 96.13% [21], Badža et al. proposed 22-layered CNN and they got 96.56% [22], Ayadi et al. proposed 18-layered CNN and they got 94.74% [23]. In this proposed work, we proposed 5-layered CNN and we got 92%. After thorough consideration of the literature [24–26], the key benefits and contributions of the proposed method in this work is that it has used a simple CNN with 5 convolutional layers and produced an accuracy that outperformed the other pre-trained models for both classifications tasks. In terms of complexity, the proposed model is by far less complex than the pre-trained models which will result in less execution time and less computational specifications. Moreover, the model being less complex means that it is systematically less prone to the problem of overfitting As a result, this model does not require high computing power and highly advanced hardware to be executed on.

5 Conclusion We have unveiled the results obtained by our approach of brain tumor classification from MRI images. Despite the very limited number of image available during the training of the model, our approach achieved classification that is judged according to the similarity measures employed and the observed qualitative results as successful and very encouraging in view of the increased difficulty of brain tumor deformations. At the same time, this has allowed us to confirm that the transfer learning proved to be an effective strategy to adapt a more generic model to the database of images of brain tumors. Interpreting CNNs is challenging due to many layers, millions of parameters, and complex, nonlinear data structures. We have used a number of different structures and parameters were trained and analysed in order to explore the effects of these parameters on the outcomes of the model, and to figure out the best methods in the building of the application. The dataset is extended using data augmentation to overcome the challenges faced during the experiment implementation. The accuracy achieved by the model is exceptional and reliable compared to other transfer learning methods. The strength of the proposed method is that the model learns of the instances rapidly, which leads to high accuracy at early epochs. One limitation that hindered our experiments is the unavailability of larger datasets. Another aspect that may hinder the performance is that MRI images are vulnerable to noise, so more complicated inhomogeneity correction should be applied. Moreover, improvement of the quality of the utilized visual feature is very essential to produce better classification results that can lead to augmenting the tumor region. Our results revealed that a large, deep convolutional neural network can achieve good results on a highly challenging dataset. It is worth noting that removing a single convolutional layer reduces the performance of our network in terms of accuracy, for example, models with five convolutional layers outperform models with four convolutional layers. However, removing any of the middle layers results in the network’s accuracy degrading. Also, Our proposed model needs less computational specifications as it takes less execution time and our accuracy rate is very fine as compared to VGG16, ResNet-50, and Inception-v3 model. As ideas for future improvements, is to use

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advanced deep learning algorithms. Finally, to overcome the crucial problem of noise in images, we propose to use different techniques of data preprocessing, which would allow to generate images that are noiseless and could be used more efficiently during training. The model existing today could be used as a point of comparison for future studies that aim to go beyond what we have provided and this is of paramount importance.

References 1. Brain Tumor: (27 April 2019). Retrieved from Mayo Clinic: https://www.mayoclinic.org/ 2. National Cancer Institute: (5 November 2018). Retrieved from www.cancer.gov 3. Anatomy of the brain: (April 2018). Retrieved from Mayfield Brain & Spine: https://mayfie ldclinic.com/ 4. Smiley, E.: The Four Lobes of the Brain (5 January 2015). Retrieved from Hubpages: https:// discover.hubpages.com/ 5. What Causes Brain Tumors? (5 May 2020). Retrieved from American Cancer Society: https:// www.cancer.org/ 6. Brain Cancer (Brain Tumor) (20 Feb 2020). Retrieved from Cleveland Clinic: https://my.cle velandclinic.org/ 7. Martelacci, M.: Why Deep Learning is important for Enerbrain (March 4 2021). Retrieved from enerbrain: https://www.enerbrain.com/ 8. Marais, F.: Machine learning algorithms in boiler plant root cause analysis (15 October 2019). Retrieved from ee publishers: https://www.ee.co.za/ 9. Everything you need to know about neural network: (10 May 2019). Retrieved from Hackernoon: https://hackernoon.com/ 10. Ashraf, M.T.H.: Brain Tumor Detection using Convolutional Neural. Dhaka, Bangladesh (June 2019) 11. What is Artificial Neural Network (ANN)? How ANN Works ? (13 Dec 2020). Retrieved from https://www.pythondotpy.com/ 12. Hidden Layer: (2019). Retrieved from DeepAI: https://deepai.org/ 13. Reynolds, M.: New computer vision challenge wants to teach robots to see in 3D (7 April 2017). Retrieved from New Scientist: https://www.newscientist.com/ 14. Xiangyu Zhang, J.Z.: Accelerating Very Deep Convolutional Networks for Classification and Detection (18 Nov 2015). Retrieved from Neurohive: https://neurohive.io/ 15. Christian Szegedy, V.V.: Rethinking the Inception Architecture for Computer Vision. University College London, United Kingdom (2016) 16. Mujtaba, H.: Introduction to Resnet or Residual Network (28 Sep 2020). Retrieved from https://www.mygreatlearning.com/ 17. Brain MRI Images for Brain Tumor Detection. (n.d.). Retrieved from Kaggle: https://www. kaggle.com/ 18. Steward, K.: Sensitivity vs Specificity (16 April 2019). Retrieved from Technology Networks: https://www.technologynetworks.com 19. Shalloway, B.: Weighting Confusion Matrices by Outcomes and Observations (7 Dec 2020). Retrieved from R Bloggers: https://www.r-bloggers.com/ 20. Abiwinanda, N., Hanif, M., Hesaputra, S.T., Handayani, A., Mengko, T.R.: Brain Tumor Classification Using Convolutional Neural Network. In: Lhotska, L., Sukupova, L., Lackovi´c, I., Ibbott, G.S. (eds.) World Congress on Medical Physics and Biomedical Engineering 2018. IP, vol. 68/1, pp. 183–189. Springer, Singapore (2019). https://doi.org/10.1007/978-981-109035-6_33

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21. Mohsen, H., El-Dahshan, E.S.A., El-Horbaty, E.S.M., Salem, A.B.M.: Classification using deep learning neural networks for brain tumors. Future Comput Informat J 3(1), 68–71 (2018). https://doi.org/10.1016/j.fcij.2017.12.001 22. Badža, M.M., Barjaktarovi´c, M.C.: Classification of brain tumors from MRI images using a convolutional neural network. Appl Sci 10(6), 1–13 (2020). https://doi.org/10.3390/app100 61999 23. Ayadi, W., Elhamzi, W., Charfi, I., Atri, M.: Deep CNN for brain tumor classification. Neural Process. Lett. 53(1), 671–700 (2021). https://doi.org/10.1007/s11063-020-10398-2 24. Çinar, A., Yildirim, M.: Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Med Hypotheses 139, 109684 (2020). https://doi.org/10. 1016/j.mehy.2020.109684 25. Rehman, A., Naz, S., Razzak, M.I., Akram, F., Imran, M.: A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Systems Signal Process. 39(2), 757–775 (2019). https://doi.org/10.1007/s00034-019-01246-3 26. Deepak, S., Ameer, P.: Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 111, 103345 (2019). https://doi.org/10.1016/j.compbiomed.2019. 103345

Design, Implementation, and Deployment of IoT/M2M Smart City Applications Based on MCNs Rania Djehaiche1(B) , Salih Aidel2 , Massinissa Belazzoug1 , and Nasir Saeed3 1 ETA Laboratory, Electronics Department, Faculty of Technology, University of Mohamed El

Bachir El Ibrahimi, 34030 Bordj Bou Arreridj, Algeria {rania.djehaiche,m.belazzoug}@univ-bba.dz 2 Electronics Department, Faculty of Technology, University of Mohamed El Bachir El Ibrahimi, 34030 Bordj Bou Arreridj, Algeria [email protected] 3 Remote Sensing Unit, Department of Electrical Engineering, Northern Border University, Arar, Saudi Arabia [email protected]

Abstract. Recently, the widespread adoption of the Internet of Things (IoT) and machine-to-machine (M2M) has led to a significant influx of smart services and applications. This research paper addresses the deployment and implementation of IoT/M2M technologies in the smart city, one of its most popular applications. The proposed solution presents a complete system for IoT/M2M smart cities based on different mobile cellular networks (MCNs) such as 2G, 4G, or 5G; to provide safety, convenience, energy saving, and urban quality of life improvement by using efficient and low-cost components such as Arduino microcontroller and NodeMCU board with several compatible sensors, actuators, and shields. The purpose of this paper is to present a practical example of an IoT/M2M smart city system; that contains several smart applications such as smart safety that consists of three services (fire and gas detection, air quality monitoring system, and automatic railway crossing system), smart agriculture and smart parking. All these applications are designed and implemented to control and monitor the city remotely via our mobile application called “Raniso,” which provides citizens with essential daily urban services in a simple and accessible way. The app also serves as a communication platform between citizens and city authorities, facilitating collaborative processes and digital participation within the smart city. Keywords: Smart city · IoT · M2M · MCNs · Raniso App

1 Introduction The rapid evolution of information technology (IT) has given rise to a hyperconnected society, in which objects are connected to mobile devices and the Internet and can communicate with each other. Internet of things (IoT) and machine-to-machine (M2M) communication are the core component of this hyperconnected society [1]. The term © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 49–57, 2023. https://doi.org/10.1007/978-3-031-21216-1_5

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“Internet of Things” or “Internet of Objects” refers to electrical or electronic devices of various sizes and capacities that are linked to the Internet [2]. The field of connections is expanding to include more than just M2M which allows mobile devices and machines to autonomously establish wireless communication links between them [3]. The paradigm of IoT/M2M has many applications in various fields such as smart buildings, smart healthcare, industry, intelligent energy management, smart grids, and more. The smart city is one of the emerging technologies most representative of this paradigm, which works in a challenging urban environment, including infrastructure, human behavior, technology, socio-political structures, and economics [4]. The smart city aims to make better use of public resources, improving the quality of services provided to inhabitants while lowering public administration operating expenses [5]. This chapter is the extension of our earlier published research papers [6–9]; where we’re keen in this work to deploy and implement the IoT/M2M smart city applications by using Arduino microcontroller, NodeMCU board, several compatible sensors, actuators, and modules, besides our mobile application named Raniso. By comparing the findings of this research work to other previously published papers [4, 10–12], we found several limited points in dealing with the topic of IoT/M2M smart city. While the objective of this chapter is to make a practical contribution to the rather large literature on smart city development by presenting an IoT/M2M smart city system model contains several important services under three main applications; which are smart safety that consists of three services (fire and gas detection, air quality monitoring system, and an automatic railway crossing system), smart agriculture, and smart parking. All these applications can be controlled and monitored remotely by the Raniso App containing a set of digital services needed in the daily life of the city. The Raniso app is used as a local server to control the city via different mobile cellular networks (MCNs) such as 2G, 4G, or 5G to make the proposed system more robust and flexible. This suggested IoT/M2M smart city system is in place to save lives, ensure city safety, provide comfort and reduce energy consumption by using available, cheap, simple, efficient, and more sustainable components.

2 The Proposed IOT/M2M Smart City Using Raniso App 2.1 Architecture Design The proposed architectural design for IoT / M2M smart city-based mobile cellular networks uses the Raniso app and a variety of well-known hardware such as the Arduino microcontroller which represents the brain and the NodeMCU board as the wireless communications. The main applications proposed are smart safety; which consists of (fire and gas detection, air quality monitoring system, and an automatic railway crossing system), smart agriculture, and smart parking; which citizens can remotely control and monitor the city through the Raniso App via different MCNs like 2G, 4G/5G. This IoT/M2M smart city infrastructure prevents the loss of resources and human lives, also improves operational efficiency, and provides a better quality of government service and citizen welfare. Figure 1 shows the proposed architectural design for the IoT/M2M smart city system.

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Fig. 1. System design architecture.

2.2 System Hardware The main components used are described in the following table (Table 1). Table. 1. The main specifications of hardware used. Components Type

Specifications

Arduino

Mega

It is a board with an ATmega2560-based microprocessor. On this board, there are 54 digital input/output pins, 16 analog inputs, 4 UARTs (hardware serial ports), a 16 MHz crystal oscillator, a USB connector, a power jack, an ICSP header, and a reset button [13]

UNO

It was created by "Arduino.cc" and is an open-source microcontroller board based on the ATmega328 processor. A 16 MHz ceramic resonator, a USB connector, a power jack, an ICSP header, and a reset button are all included on the board [14]

NodeMCU

V3

It is an open-source software and development board featuring an ESP8266 system-on-chip. It has an 8 MHz Tensilica Xtensa LX106 core with 32 bits [15]

Modules

SIM800L

It is a small GSM modem that can make calls and send messages, as well as connect to the Internet via GPRS and TCP/IP [16]

GPS

It uses satellite technology to continuously determine data such as longitude, latitude, speed, and distance traveled

SD Card

It allows to communicate with a memory card as well as write and read data from them (continued)

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Components Type

Specifications

Sensors

SW-420 Vibration

It detects vibration levels above a threshold using an LM393 comparator and outputs digital data, logic low or logic high, 0 or 1. During normal operation, the sensor outputs logic low, but when vibration is detected, it outputs a logic high

TCRT5000

It’s an infrared sensor that combines a photodiode and a phototransistor [8]

Ultrason HC-SR04 It’s an electronic device that emits ultrasonic sound waves and converts the reflected sound into an electrical signal to determine the distance of a target item [6]

Actuators

Rain

It’s a rain detection instrument that consists of two modules a rain board that detects rain and a control module that compares analog values and converts them to digital values

Soil moisture

It measures the content of water and humidity in the soil [17]

DHT11

It’s used to monitor temperature and humidity amounts

MQ-2

It detects different types of gasses like LPG, Alcohol, Propane, H2, CO, and even methane [17]

MQ-135

It’s a gas sensor used for air quality that detects NOx, Nh3, CO2, Benzene, Alcohol, and Smoke for air quality

Flame

It detects the presence of a flame and fire with a range is up to 100 cm and a wavelength ranging from 760 to 1100 nm [18]

Servo motor

It’s a static force-resistant engine whose position is constantly monitored and rectified by the measurement system [6]

Water pump

This submersible pump can be used as an amphibious pump and is ideal for reliable fountains

LCD 16*2

It’s a type of tool for displaying characters in processing information. It is used to display the response of different sensors [8]

Speaker

It emits a range of sounds and includes power amplification and voice outputs

2.3 System Software The software used is defined in Table 2.

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Table. 2. The main specifications of software used. Software

Specifications

Raniso App

It’s a mobile application that acts as a local server installed on the smartphone that allows controlling several electrical devices remotely through different wireless networks (WSNs) [9]. In this research work, we use three interfaces of the Raniso App (see Fig. 2) to control and monitor the proposed services

Arduino IDE It’s an application written in Java inspired by the processing language. It allows to write, modify a program and convert it into a series of understandable instructions for the Arduino card [7] Proteus

It’s a circuit simulation and virtual system modeling application. It’s used to simulate all circuits before they’re built into a real system [7]

Fig. 2. The different interfaces of the Raniso App used for the proposed IoT/M2M smart city.

3 Implementation of Smart City Applications 3.1 Smart Safety The suggested smart safety system is based on flame and MQ2 sensors to detect fire and gas detection; MQ135 sensor for air quality, ultrasonic sensor to sense when the train arrives; DHT11 sensor to monitor temperature and humidity amounts, GSM and GPS modules to send SMS/calls and the location of the incident to the police, fire station or civil protection. The audio system is utilized to alert citizens in case of any danger, and all information about the city is displayed on the LCD screen and our Raniso App’s interface. This proposed smart safety system contains three main smart services which are fire and gas detection, an air quality monitoring system, and an automatic railway crossing system that aims to shut the railway gates when the train approaches it, to block vehicles from going across the track. While, in case of fire and gas leakage, the gas and electricity meter will automatically turn OFF and the servo motor will open the water sprayer. Besides, in the case of air pollution, the air purifier will automatically turn ON. Thanks to this proposed IoT/M2M smart safety system, citizens can be alerted in realtime before a catastrophic event occurs. A global view can also be obtained that allows

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authorities to make data-driven policy and infrastructure planning decisions. Figure 3 shows the proposed system for the smart safety application.

Fig. 3. Smart safety system.

3.2 Smart Agriculture The proposed smart farming system is based on a soil moisture sensor used to measure the percentage of soil moisture; a DHT11 sensor used to monitor temperature and humidity amounts; an ultrasonic sensor to measure the water level inside the tank, a rain sensor to detect any rainfall falling; two pumps, one to water the plants and the second to supply water to the tank, an LCD screen to display the water level, and moisture content and the status of the pumps. The rainfall detection system is utilized to store rainwater in a water tank for use in the irrigation process. The proposed smart farming system works as follows if the soil is dry, the watering pump operates watering the plants and switches OFF when the soil is wet in part to save water. This system can also be remotely controlled and monitored anywhere via the Raniso App, making it easy to manage irrigation systems and make necessary adjustments in real-time as shown in Fig. 4. 3.3 Smart Parking System The proposed smart parking entails an IoT/M2M-based system that delivers data on the availability of all parking spaces in real-time. NodeMCU, GSM shield, vibration sensor, infrared sensors, servo motor, LCD screen, LEDs, and a 9-V battery were used to implement this system. IR sensors detect the presence of a vehicle, and the servo motor acts as a gate to allow cars to enter and exit. All information is displayed on the LCD screen and our Raniso App’s interface. A vibration sensor is used to prevent vehicle theft which is achieved by detecting vehicle status in theft mode and by sending an SMS which is generated automatically via a GSM shield. The following figure represents the proposed solution for a smart parking system (Fig. 5).

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Fig. 4. Smart agriculture system.

Fig. 5. Smart parking system.

3.4 Implementation of IoT/M2M Applications in the Final Model of the Smart City This is the final stage of realizing the proposed IoT/M2M smart city, in which we have used the Arduino Mega board along with the NodeMCU to perfectly meet the requirements of the system. In this last step, we implemented all the smart applications proposed in the model, and all of them work well, as shown in Fig. 6.

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Fig. 6. Smart city model.

4 Conclusion This research paper focuses on an IoT/M2M smart city system based on mobile cellular networks using open-source software and open hardware. Where a new approach that provides a reasonable and less expensive way to monitor and regulate a smart city system was proposed and realized utilizing the Raniso App. The suggested system has been deployed and designed to provide several smart city functionalities under three main applications; namely smart safety which consists of three services (fire and gas detection, air quality monitoring system, and an automatic railway crossing system), smart agriculture, and smart parking. Through the Raniso App connected to MCNs such as 2G, 4G, or 5G, citizens can continuously monitor any changes in their city and are alerted in real-time in case of anomalies. Besides, the app allows citizens to report any problems in the city directly to the local government. The aim of this research work is principally to design and implement an IoT/M2M smart city system providing a healthy and safe environment for citizens at a low cost and with optimal efficiency. Based on our findings in this chapter, we prove that the planned IoT/M2M smart city model was implemented and tested virtually and practically and it gave exactly the expected results. Future improvements include adding more intelligent applications in different sectors and adapting the proposed system to any city in the world. Efforts can also be made to design a methodology that allows the smart city to be easily adaptable based on citizen preferences.

References 1. Byun, J., Kim, S., Sa, J., Kim, S., Shin, Y.-T., Kim, J.-B.: Smart City Implementation Models Based on IoT Technology, pp. 209–212 (2016). https://doi.org/10.14257/astl.2016.129.41

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2. Miraz, M.H., Ali, M., Excell, P.S., Picking, R.: A Review on Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT). arXiv (2017) 3. Chen, K.C., Lien, S.Y.: Machine-to-machine communications: Technologies and challenges. Ad Hoc Netw. 18, 3–23 (2014). https://doi.org/10.1016/j.adhoc.2013.03.007 4. Jasim, N.A., Alrikabi, H.T.S.: Design and Implementation of Smart City Applications Based on the Internet of Things. Int. J. Interact. Mob. Technol. 15(13), 4–15 (2021). https://doi.org/ 10.3991/ijim.v15i13.22331 5. Zanella, A., Bui, N., Castellani, A., Vangelista, L., Zorzi, M.: Internet of things for smart cities. IEEE Internet Things J. 1(1), 22–32 (2014). https://doi.org/10.1109/JIOT.2014.2306328 6. Djehaiche, R., Aidel, S., Benziouche, N.: Design and Implementation of M2M-Smart Home Based on Arduino-UNO. In: Hatti, M. (ed.) Artificial Intelligence and Renewables Towards an Energy Transition. ICAIRES 2020. Lecture Notes in Networks and Systems, vol 174. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-63846-7_66 7. Djehaiche, R., Aidel, S.: Application of M2M Communication based on ZigBee to Control Smart home automation. figshare. Conference contribution (2021). https://doi.org/10.6084/ m9.figshare.14748486.v1 8. Djehaiche, R., Aidel, S., Saeed, N.: Implementation of M2M-IoT Smart Building System Using Blynk App. In: Hatti, M. (ed.) IC-AIRES 2021. LNNS, vol. 361, pp. 439–449. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-92038-8_44 9. Djehaiche, R., Aidel, S., Benhamimid, K.: A Smart Home Management based on M2M/IoT Technologies. figshare. Conference Contribution (2022). https://doi.org/10.6084/m9.figshare. 19103315.v1 10. Yadav, P., Vishwakarma, S.: Application of internet of things and big data towards a smart city. In: Proc. - 2018 3rd Int. Conf. Internet Things Smart Innov. Usages, IoT-SIU 2018, no. February, pp. 1–5 (2018). https://doi.org/10.1109/IoT-SIU.2018.8519920 11. Dagli, R.: Design and Implementation of Smart City using IoT. Int. Res. J. Eng. Technol., no. November, 460–465 (2018) 12. Babbar, H., Rani, S., Singh, A., Abd-Elnaby, M., Choi, B.J.: Cloud based smart city services for industrial internet of things in software-defined networking. Sustain. 13(16), 1–13 (2021). https://doi.org/10.3390/su13168910 13. Mega 2560 Rev3: https://docs.arduino.cc/hardware/mega-2560. Accessed 07 Apr. 2022 14. UNO R3: https://docs.arduino.cc/hardware/uno-rev3. Accessed 01 Apr. 2022 15. Jabbar, W.A., Member, S., Kian, T.K., Ramli, R.M., Shepelev, V., Alharbi, S.: Design and fabrication of smart home with internet of things enabled automation system. IEEE Access XX, 1–9 (2017) 16. Djehaiche, R., Benziouche, N.: Etude et Application d’un Système de Communication M2M. figshare. Thesis (2021). https://doi.org/10.6084/m9.figshare.14710710.v2 17. Marhoon, H.M., Mahdi, M.I., Hussein, E.D., Ibrahim, A.R.: Designing and implementing applications of smart home appliances. Mod. Appl. Sci. 12(12), 8 (2018). https://doi.org/10. 5539/mas.v12n12p8 18. Sisavath, C., Yu, L.: Design and implementation of a smart home system with two levels of security based on IoT technology. Procedia Comput. Sci. 183(October 2020), 4–13 (2021). https://doi.org/10.1016/j.procs.2021.02.023

Control of Three Phase Cascaded H Bridge Multilevel Inverter Supplied by a Photovoltaic System Fatima Zahra Khemili1(B) , Moussa Lefouilli1 , Omar Bouhali1 , and Lakhdar Chaib2 1 Mechatronics Laboratory (LMT), Mohamed Seddik Ben Yahia, Jijel University, Jijel, Algeria

[email protected] 2 Energy and Materials Laboratory, University of Tamanrasset, P.O. Box 10034, Sersouf, Algeria

Abstract. This work presents the control of a three phase cascaded H-Bridge Multi-Level Inverter supplied by the photovoltaic system. In order to obtain a nearly sinusoidal signal at the voltage level, we are interested in obtaining the smallest value of THD. Hence, a N-level space vector modulation (SVM) is used to control this inverter. The power source is produced from photovoltaic modules utilized as DC inputs for the cascaded H-Bridge Multilevel Inverter. The objective of this work aims to design a control strategy to supply the best output quality. The algorithm P&O is applied to extract from the panels the maximum energy. Each PV system employs with its own MPPT control. The PV system outputs are investigated as entries to the cascaded H-Bridge Multi-Level Inverter for achieving a staircase waveform output. A matlab/simulink is used to validate the system performance. The simulation results prove the efficiency of this work, which indicates the high effectiveness of the control and the superior performance of proposed scheme. Keywords: Photovoltaic system · Three phase cascaded H-Bridge Multi-Level Inverter · N-level SVM · THD

1 Introduction The Photovoltaic energy is an important renewable energy in industries. The installation of photovoltaic panels is easy and simple. Several studies have focused on developing algorithms to extract the maximum energy from the photovoltaic system. This system consists of a standard PV panel and a DC-DC converter with an MPPT controller that is connected to a so-called multi-level inverter. The most common multilevel topologies used in PV applications are the cascaded H-bridgeinverter. To control the cascaded H-bridge inverter, many modulation technique have been proposed among them the SVPWM which is widely used due to the advantages of low current ripple and good use of DC current [1, 2], as well as reduced switching losses and harmonic distortion in the multi-level inverter spectrum [3, 4]. Multilevel inverters are capable of generating output voltage waveforms consisting of a large number of steps. It is possible to manufacture higher voltages by using switching devices with © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 58–65, 2023. https://doi.org/10.1007/978-3-031-21216-1_6

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lower voltage values, and to reduce harmonic distortion with low f dv/dt in the output voltage [5, 6]. The multi-phase multi-level inverter has gained the attention of researchers in recent years [7, 8]. The first successful implementation of a multi-stage multi-level SVPWM algorithm based on the SVM approach is investigated in [9, 10], which have proposed three-phase SVM. We focus in this work on changing the voltage from 3 levels and 5 levels and comparing the results. N3 level vectors has been used, so if 2 level, 3 level or 4 level implies 8 vectors, 27 vectors, 125 vectors are used respectively to control the three-phase cascaded H-bridge inverter. The paper is organized in the following form: Sect. 2 formulates the main mathematical models for PV cells/modules based on single-diode model; Sect. 3; reviews the HHO algorithm; Sect. 4 analyses the efficiency of the suggested methodology. Finally, Sect. 5 concludes the study.

2 Mathematical Model A three-phase PV system with CHB based SVM control is prominently investigated in this work. The main block diagram of this system is shown in Fig. 1. Maximum power point tracking (MPPT) controller with a DC-DC converter are adopted to maintain the inverter input voltage at constant value. Despite of environmental change, DC-DC converter based the nearly fixed output voltage is fed to the inverter to link with grid system. Figure 1 shows the structure of system including N-level cascaded H-bridge controlled by multi-level SVM supplied by photovoltaic application.

Fig. 1. N level structure of cascaded H-bridge for photovoltaic application

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Photovoltaic energy is produced from the direct conversion of a part of the solar radiation into electrical energy. Through photovoltaic cells, which are optoelectronic components manufactured using semiconductor materials and are the basis for building the photovoltaic system chain. Figure 2 shows the equivalent diagram of the photovoltaic cell [12]. iph = ID + IP + I IP =

V + IRS RP

 V +IRs  ID=IS e nVT − 1

(1) (2) (3)

where iph is the photocurrent, ID is the junction diode current, RS is the series resistance, RP is the parallel resistance, and IS is the reverse saturation current. To produce more power, the solar cells are assembled to form a Solar panel. Series connection of several cells increases the voltage for the same current, while paralleling increases the current while conserving the voltage. All the solar panels are assembled to form a solar generator as shown in Fig. 3.

Fig. 2. Equivalent circuit model of PV cell

Fig. 3. Solar panel.

There are several types of DC-DC converters. In this work, we are interested in the boosted chopper converter, it has a parallel type transistor so it is a voltage booster. The photovoltaic system is affected by heat and solar radiation, which negatively affects the goal of reaching the maximum power point. It is therefore necessary to find a solution

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that makes it possible to find and continuously track the maximum power point, which is the Maximum Power Point Tracking (MPPT) system. The equipped system works to provide the maximum amount of energy continuously depending on the temperature and weather conditions. Various types of MPPT algorithm are employed in the literature such as hill climbing, perturbation and observation, fuzzy logic, and neural network [13]. In this work, we have utilized P&O technique.

3 Multilevel Three-Phase SVM The first step in the algorithm is to transform the vector of reference Vref in twodimensional plan. Figure 4 shows all the vectors of commutation of the N levels converter in the plan (g, h). − → − → V ref (g, h) = T . V ref (vab , vbc , vca )

(4)

with ⎡





sin wt



Va ⎢ ⎥ ⎢ sin wt − 2π ⎥ 2 −1 −1 N − 1 1 ⎢ ⎥ ⎥ ⎣ Vb ⎦ = r.⎢ ⎢ 3 ⎥, T = 3 2 . −1 2 − 1 ⎣ 2π ⎦ Vc sin wt + 3

(5)

N

N

Fig. 4. Commutation Vectors of the N levels converter in the (g, h) plan

The result of the transformation matrix change (d, q) → to (g, h) is as follows: − → → v ref (g, h) = T1 .− v ref (d , q)

(6)

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−1 ⎤ 1 √ 3 N −1 ⎢ 3⎥ ⎥ .⎢ T1 = ⎣ 2 ⎦ 2 2 0 √ 3

(7)

• Detection of the nearest three vectors (NTV) There is four vectors nearest to the reference vector can be simply identified; these vectors whose coordinates are combinations of the rounded values greater and lower than the number of the reference vector are calculated as follows: (8)

with: Vref : Indicates the upper rounded value of Vref ; and Vref : Indicates the lower rounded value of Vref. The final points of the four nearest vectors are divided into two equilateral triangles by the diagonal connecting the vectors V ul . These are always two of the NTV. The third nearest vector is one of the two remaining vectors existing on the same side of the diagonal; it is taken as a reference. For that reason, the closest third vector can be found by evaluating the sign of the expression: D = Vrefg + Vrefh − (Vu lg + Vulh )

(9)

If the variable Ð is positive, then the vector V uu is the third nearest vector. That is, the vector V ll is the nearest third vector. This concludes the identification of NTV for N-level inverters. Figure 8 explains how to obtain the closest third vector (Fig. 5).

Fig. 5. Localization of two different cases of the reference vector position of the same four nearest vectors.

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• Calculation of the switching times of the switches To aggregate the reference voltage vector, the three closest vectors must be used as; 







Vref = d1 V1 + d2 V2 + d3 V3

(10)

with the following additional constraint on the conduction times: d1 + d2 + d3 = 1

(11)

when TVP is specified, the switching times for the switches can be found by solving Eq. (11) and (12) using: ⎧− ⎧− → − → − →⎫ →⎫ ⎪ ⎪ V1 = Vul ⎪ V1 = Vul ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ⎨ ⎬ ⎬ → → − → − − → − (12) V2 = Vlu and V2 = Vlu ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩− ⎩− ⎭ ⎭ →⎪ → − → −→⎪ V3 = Vu V3 = Vuu The solutions are the partial parts of the coordinates: ⎧ ⎧ ⎫ ⎫ d = Vrefg − Vug ⎪ d = −(Vrefh − Vuuh ) ⎪ ⎪ ⎪ ⎨ ul ⎨ ul ⎬ ⎬ − → −→ − → − → and If V3 = Vuu then dlu = −(Vrefg − Vuug ) If V3 = Vu then dlu = Vrefh − Vuh ⎪ ⎪ ⎪ ⎪ ⎩ ⎩ ⎭ ⎭ du = 1 − dul − dlu du = 1 − dul − dlu

(13)

4 Simulation Results In this section, the simulation results of proposed scheme were presented (Fig. 6).

Fig. 6. Result of the simulation of the characteristic of the generator I (V) and P(V) at T = 25 C and E = 1000 W/m2

The proposed control scheme is developed in MATLAB/Simulink to demonstrate the design response and to evaluate the performance. Since, there are many different H-bridges, so N-level-shifted carriers are required for this SVM configuration. We have focused in this work on changing the three-phase based voltage from 3 levels and 5

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Fig. 7. The simulation result of the modulated voltages with parentage of the THD for three level H-bridge

Fig. 8. The simulation result of the modulated voltages with parentage of the THD for five level H-bridge

levels and comparing the results. N3 level vectors has been used, so if 2 level, 3 level or 4 level implies 8 vectors, 27 vectors, 125 vectors are used respectively to control the three-phase cascaded H-bridge inverter. The THD value of the suggested configuration is compared different level space vector modulation (SVM) to control the mentioned inverter. From the Figs. 7 and 8. It can be clearly observed that our proposed H-bridge based control has better THD profile. In addition to this, the 3-level output voltage has a THD of 27.27%. Also, we can see that with the reduction in voltage output level, the increasing of THD value is appeared, so with this topology the voltage has given a high THD value. Again, the configuration with 5-level output voltage has attained the THD value of 10.16% for modulation index ma as 0.80. It is obviously depicted that for 5-level configuration of this topology, the voltage output level is decreased while the THD is increased which prove the applicability of proposed scheme.

5 Conclusion In this paper, the control of a multi-level inverter for a cascading three-phase H-bridge equipped with a photovoltaic system is presented. The simulation results prove the efficiency and usefulness of the SVM algorithm with the photovoltaic system to make the system operate in its optimum conditions. This strategy is given better signal sinusoidal

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and little value of THD for the phase voltage and currents, especially when the level of inverter increased. From 3 level to 5 level, the simulation results show the efficiency and good performance of this system.

References 1. Lee, Y.H., Kim, R.Y., Hyun, D.S.: A novel SVPWM strategy considering DC-link balancing for a multi-level voltage source inverter. In: APEC 1999. Fourteenth Annual Applied Power Electronics Conference and Exposition. 1999 Conference Proceedings (Cat. No. 99CH36285), vol. 1, pp. 509–514. IEEE (1999) 2. Gui-Jie, Y., Li, S., Nai-Zheng, C.: Study on method of the space vector PWM. Proc. Chin. Soc. Electr. Eng. 21(5), 79–83 (2001) 3. Rodríguez, J., Morán, L., Correa, P., Silva, C.: A vector control technique for medium-voltage multilevel inverters. IEEE Trans. Ind. Electron. 49(4), 882–888 (2002) 4. Djeghloud, H., Benalla, H.: Space vector pulse width modulation applied to the threelevel voltage inverter. Electrotechnic’s Laboratory of Constantine, Mentouri-Constantine University, Constantine 25000, Algeria 5. Grandi, G., Tani, A., Sanjeevikumar, P., Ostojic, D.: Multi-phase multi-level AC motor drive based on four three-phase two-level inverters. In: SPEEDAM 2010, pp. 1768–1775. IEEE Pisa, Italy (2010) 6. Jones, M., Patkar, F., Levi, E.: Carrier-based pulse-width modulation techniques for asymmetrical six-phase open-end winding drives. IET Electr. Power Appl. 7(6), 441–452 (2013). https://doi.org/10.1049/iet-epa.2012.0372 7. Saad, K., Abdellah, K., Ahmed, H., Iqbal, A.: Investigation on SVM-Backstepping sensorless control of five-phase open-end winding induction motor based on model reference adaptive system and parameter estimation. Eng. Sci. Technol. Int. J. 22(4), 1013–1026 (2019) 8. Belkamel, H., Mekhilef, S., Masaoud, A., Abdel Naeim, M.: Novel three-phase asymmetrical cascaded multilevel voltage source inverter. IET Power Electr. 6(8), 1696–1706 (2013). https:// doi.org/10.1049/iet-pel.2012.0508 9. Kalaiselvi, J., Srinivas, S.: Bearing currents and shaft voltage reduction in dual-inverter-fed open-end winding induction motor with reduced CMV PWM methods. IEEE Trans. Ind. Electron. 62(1), 144–152 (2014). https://doi.org/10.1109/TIE.2014.2336614 10. Kong, W., Huang, J., Kang, M., Li, B., Zhao, L.: Fault-tolerant control of five-phase induction motor under single-phase open. J. Electr. Eng. Technol. 9(3), 899–907 (2014). https://doi.org/ 10.5370/JEET.2014.9.3.899 11. Sadouni, R., Meroufel, A., Djeriou, S., Khaldoune, A.: Field oriented control of dual star induction machine fed by photovoltaic solar panel with MPPT. In: Proceedings of Engineering and Technology – PET (2014) 12. Mahamudul, H., Saad, M., Ibrahim Henk, M.: Photovoltaic system modeling with fuzzy logic based maximum power point tracking algorithm. Int. J. Photoenergy 1–10 (2013) 13. Lyas, B., Bouhali, O., Khadar, S., Mohammed, Y.: A novel dual three-phase multilevel space vector modulation for six-phase multilevel inverters to drive induction machine. Model. Meas. Control A 92(2–4), 79–89 (2019). https://doi.org/10.18280/mmc_a.22-407

A Proposal of Blockchain and NFC-Based Electronic Voting System Hanane Echchaoui, Boudrali Roumaissa, and Rachid Boudour(B) Embedded Systems Laboratory, Badji Mokhtar-Annaba University, Annaba, Algeria

Abstract. One of the most significant ways for a community to make a decision and the most democratic event in today’s countries is voting. Therefore, conducting fair and trusted elections is a basic prerequires. To that end, a wide range of electronic voting systems has been proposed with great potential to minimize the cost of the process, ensure the participation of larger numbers and improve the conventional voting mechanisms. Security, reliability, secrecy and other challenges keep e-voting systems that have been implemented from being used on a large scale. Introducing new and trustworthy technologies such as blockchain and NFC to the voting process can make it faster, more efficient and less vulnerable to security breaches. In this paper, we introduce a new hybrid NFC and blockchainbased electronic voting system as a solution to address current e-voting concerns and issues. It ensures public and transparent voting process while maintaining voter anonymity. Keywords: Electronic voting · Blockchain · NFC

1 Introduction Voting is an essential mechanism for any democratic organization or government that seeks democratic decision-making among a community. It started in ancient Greece with counting raised hands and progressed into paper ballots, punishing cards and more [1]. However, it surprisingly hasn’t moved on much in most countries from the outdated traditional paper ballot system of voting that has a lot of different issues. First, this process is tedious in time and resource consumption. In addition, it is centralized and dependent on a third party to count and secure votes. Moreover, it is prone to tampering, failure and fraud such as Vote buying, ballot box stuffing, voter coercion, booth capturing, and votes cast by dead or unlawful voters [2]. They also offer no possibility for voters to check if their votes were counted or altered. To counter some of these problems, electronic voting mechanisms have been implemented through the years offering many advantages and improvements over conventional voting processes. Electronic voting is a sign of modern democratic society. Some countries have already adopted a variety of innovative solutions to make voting more efficient and cost-effective, as well as to boost public confidence at each stage of the process [3]. However, they also face some major vulnerabilities and are still a point of contention. Using new technology to bring revolutionary changes to many sectors is not unusual, and voting is no exception. Introduced in 2008, blockchain © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 66–75, 2023. https://doi.org/10.1007/978-3-031-21216-1_7

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technology can be curative to these major issues and bring voting to the 21st century by making the process transparent, trackable, verifiable and secure. Blockchain e-voting is an effective way to conduct a fair election it ensures the integrity, legitimacy and confidentiality of the process. It prevents it from being collected and controlled by a single central institution. An e-voting system based on blockchain is convenient, automated, transparent, secure and free from corruption [4]. Decentralization, transparency, and immutability are just a few of the properties that make blockchain appealing for creating decentralized electronic voting systems. Estonia, south Korea and Sierra Leone are three countries that successfully integrated or on the process of integrating blockchain into their voting systems [5]. NFC is another innovative technology that brings a new way of interacting with the environment and person-to-person communication. NFC provides a range of benefits through its inherent advantageous characteristics: it is intuitive, secure, open and standards-based. Which makes it an interesting candidate to be implemented into e-voting systems. This research aims to combine the advantages and properties of blockchain and NFC technologies to create a secure, traceable and verifiable e-voting system. The paper is structured as follows: first, we start with an overview of the two technologies that are used in the system. Then we discuss several related works that are proposed in the literature. After that, we present our proposed systems and finish with a conclusion and future works.

2 Related Work 2.1 Preliminaries • NFC NFC, short for Near Field Communication, is a short-range wireless RFID technology that makes use of interacting electrometric radio fields instead of typical direct radio transmissions used by other technologies; it enables the exchange of data between devices over less than 4 cm distance [6]. It is meant for applications where a physical touch, or closer to it, is required to maintain security. NFC is planned for use in mobile phones to share content between digital devices, pay bills wirelessly or even use cell phones as electronic travelling tickets. It brings the touch paradigm to mobile services. Near Field Communication, being a wireless communication technology retrieved from RFID operates on short-range radio frequency. It operates on a globally available and unlicensed radio frequency band of 13.56 MHz. The technology works when NFCenabled devices are brought within close proximity. The theoretical working distance with compact standard antennas is up to 10 cm, but practically it limits to 4 cm or less [7], which means, it forms a peer-to-peer network for data communication. NFC is completely effort-free, requiring nothing more than a tap. Bringing two devices containing NFC chips together activates magnetic induction, allowing NFC-enabled gadgets to both send and receive information. This gadget could be an NFC reader, an NFC-enabled smartphone or an NFC tag. It offers new opportunities for many real-life applications. • Blockchain Nakamoto described a peer-to-peer electronic cash system called Bitcoin in [8] as the first form of money that removes the need for a central authority, where everyone

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keeps the records of transactions and verifies the facts, so the ledger of transactions is unchangeable. Keeping money records is not the only place where decentralization can play a role. For example, Wikipedia has over 125.000 active editors to maintain its pages. Each edit is public and can be verified by anyone, keeping the information decentralized and reducing the risk of not noticing if one of them goes rogue. Blockchain technology is a solution to centralization problems. It is a decentralized system of keeping records by everybody with no need for centralized authority and maintaining a ledger that is impossible to falsify. A shared ledger with blocks of records where each block is linked to the data of the previous block. We have a chain of blocks, hence, the name blockchain. Each block comprises main data, previous hash, current hash, timestamp and other data as shown in Fig. 1 [9]. In addition to decentralization, blockchain technology offers many other benefits including transparency, immutability, verifiability, security and reliability. These properties attracted the attention of organizations and researchers and sparked the beginning of the implementation of a wide range of blockchain-based applications in different sectors to provide a strong and effective solution for securing networked ledgers.

Fig. 1. Blockchain technology structure.

• E-voting systems “Voting is a formal expression of opinion or choice, either positive or negative, made by an individual or a group of individuals.” [10]. The practice of voting aims to give legitimacy to a decision by showing that it does not come from an isolated individual. The history of the voting systems started from paper ballots to E-voting. The polling booth was the first voting system introduced by South Australia in 1856 [11] where counting votes and conducting results were done manually. It was a simple method but it was not scalable for a large-scale voting process. This led to the emergence of new voting techniques and digital voting systems. Electronic voting (e-voting) can be defined as an election system that uses electronic means and technologies to run elections, cast votes and count results. Generally speaking, electronic voting systems follow six steps: registration; authentication; authorization; vote casting; vote counting; vote verification [5]. They also should meet six criteria: anonymity, trust, verifiability, security, transparency and reliability. E-voting systems offer numerous advantages, including fraud prevention through reduced human involvement, increasing participation, reducing cost, gaining time and more [12]. EVM (Electronic Voting Machine)

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has been used in India and other Asian countries as one of the first forms of electronic voting since 1999[11]. The first use of the electronic voting system in the U.S.A was in the year 2000. France in 2001, the UK in 2002 and Spain in 2003. Other countries followed the suit the next year [13]. While e-voting systems offer plenty of benefits and improvements to traditional methods, they also face many challenges and introduce new issues: from security and privacy to integrity and transparency. Blockchain has the potential to remedy some of these issues and create a secure trustworthy e-voting system thanks to its numerous characteristics. Thus, some governments have already begun investigating and adopting blockchain-based e-voting systems. For example, South Korea conducted a successful local government election in 2017 and Estonia is now looking toward using blockchain in a voting process after introducing it to different sectors in 2012 [12, 14]. E-voting is a broad term that encompasses a wide range of systems, solutions, and implementations. Many scholars have been interested in implementing the benefits of blockchain, NFC and other technologies in voting. In this section, we discuss some of the literature work that has been implemented in the e-voting sector. Megalingam et al. proposed in [15] a dual-factor authenticationbased e-voting system where they combined the fingerprints and the ID card unique identification number of the voters to verify their identity aiming for better security and transparency. To save time, energy, and money and provide a secure and easy-touse e-voting system, Dyta et al. incorporated NFC technology into the authorization phase of the voting process where the voter’s identity and eligibility are checked using his RFID card that contains all his information [16]. Similarly, Nikam et al. utilized NFC technology to propose a secure and remote e-voting system in [6] where voters cast their votes using their android mobile phone and their NFC tag for authentication. Komatineni and Lingala proposed in [17] a two-factor biometric authentication-based e-voting system that couples face recognition using Eigen-face based algorithm and fingerprint recognition using the minutiae algorithm. The system was designed to minimize time and effort and avoid fraud in polling stations. In [12], the authors aimed to increase the security in e-voting systems by presenting the multi-agent concept where intelligent agents are distributed by nodes in the Auditable Blockchain Voting System (ABVS). The agents are responsible for processing and transmitting votes. BCvoteMDE is a two-layer blockchain architecture-based e-voting scheme that was proposed in [18]. It was designed to be suitable for multi-district elections and evaluated according to different criteria such as eligibility, correctness and scalability. A decentralized e-voting system based on blockchain technology was proposed in [19] where authors combined blockchain technology to offer votes verifiability and secret sharing and a homomorphic encryption scheme to provide anonymity and security. The system eliminates the need for third parties and relies on voters to count and verify the votes. Another e-voting system that employs blockchain technology was introduced in [20]. It uses the Hyperledger fabric platform and RAFT consensus algorithm and offers promising results in terms of latency, response time and throughput. For Adiputra et al., the idea was to combine double envelope encryption and blockchain technology in their proposed e-voting system to remedy availability and universal verifiability issues in similar systems [21]. BroncoVote is another e-voting system that makes use of Ethereum’s blockchain, smart contracts and homomorphic

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encryption to achieve security, transparency and accessibility at a university scale. It was presented in [22] and tested on the Ethereum test net network with different types and sizes of ballots. Combining the advantages and properties of blockchain and NFC technologies gives this system in comparison to the related work a new level of security, traceability and verifiably. it eliminates the need for the large quantity of equipment, infrastructure and personnel that traditional voting systems require. The voter or citizen, in general, will no longer need a specific electoral card, he will use its already NFC-enabled ID card. Table 1 summarizes the e-voting systems described above along with the technologies used in their development.

Table 1. E-voting systems in the literature Reference

Author

Title

Technologies used

[15]

Megalingam et al.

Voter ID card and fingerprint-based e-voting system

- Biometric - Fingerprint recognition

[16]

Dyta et al.

E-voting – secured NFC voting

- NFC - RFID

[6]

Nikam et al.

Secured E-voting using NFC technology

- NFC

[17]

Komatineni and Lingala

Secured E-voting system using two-factor biometric Authentication

- Biometric - Face recognition - Fingerprint recognition

[12]

Pawlaka et al.

Towards the intelligent agents for blockchain e-voting system

- Blockchain - Intelligent agents

[18]

Zhu et al.

BCvoteMDE: a blockchain-based e-voting scheme for multi-district elections

- Blockchain

[19]

Hsiao et al.

Decentralized e-voting systems based on the blockchain technology

- Blockchain - Secret sharing - Homomorphic encryption

[20]

AL-Maaitah et al.

Blockchain-based e-voting system for elections in Jordan

- Blockchain

(continued)

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Table 1. (continued) Reference

Author

Title

Technologies used

[21]

Adiputra et al.

A proposal of - Blockchain blockchain-based electronic - Double envelope voting system encryption technique

[22]

Dagher et al.

Broncovote: secure voting system using ethereum’s blockchain

- Blockchain - Homomorphic encryption

3 Proposed System The goal of our work is to develop a blockchain and NFC-based electronic system as a solution to some of the challenges and issues that face existing systems. The next sections will present the components, actors and modules of the system and describe the process of the election. 3.1 System Components and Actors Our system has two prime actors: the voter (citizens or community members that are eligible for voting in a certain election) and the admin (the entity that is responsible for starting a new election and verifying the registration and authentication phases described further below). The systems consist of different components: voter’s ID card (his biometric identification card that contains his information that is framed by a public key infrastructure as on-chip data), NFC card reader, and computer to run the registration, authentication, vote casting and counting applications. These components are illustrated in Fig. 2.

Fig. 2. System component and actors.

3.2 Block Diagram Our system contains two main modules. The first is the identification module where the registration and authentication steps of the voting process are conducted. It is devised into

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software (verification and key generation applications) and hardware (card reader, computer). The second module is the decentralization module that con-sist f the blockchain network where the casted votes are collected verified and counted. Both modules interact with the voters’ database that contains their information. The block diagram of our system is presented graphicly in Fig. 3.

Fig. 3. System’s block diagram.

3.3 Voting Process The first step is for the voter to register to the system prior to the start of the election. To do so, the voter presents their biometric ID card to the reader and the system will check their eligibility to vote and add them to the voters’ database. The system will also generate public-private key pairs for them. On the day of the election, the voter will reuse his ID card for authentication. The system will verify if he is indeed a registered voter by checking his existence in the voters’ database and also check in the blockchain if he did not already cast his vote in this specific election to prevent duplicated votes. After that, the system asks the voter to introduce their private key and redirect him to the voting page presenting him the list of the candidates to allow him to cast his vote. The vote will be signed with the voter’s private and collected. After collecting a certain number of votes from different voters they will be hashed using the SHA-256 cryptographic hash algorithm that prevents decryption back to the original data. Once votes are hashed, they will form a block after a certain elapsed time. The block will be then broadcasted to all the nodes in the network and the mining process will start. If the block is validated it will be added to the blockchain and the nodes will update their blockchain and the voter will not be able to vote again. At the end of the election, the blocks that contain the votes will form a blockchain where each block will contain the previous block hash. The structure of the block is represented in Fig. 4. After the election is over and the results are counted and displayed, the voter will have the ability to check that his vote was counted and was not tampered with using his public-private key pair. The voting process of our system is illustrated in Fig. 5.

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DATA

Block # - Vote 1 -Public key 1 - Vote 2 - Public key 2 - Vote N - Public key N Previous hash current hash

Fig. 4. Block structure.

Voter registration

NFC authenticati on

Verification

Casting the vote

Vote hashing

Results

Vote counting

Block creation

Block mining

Block broadcasting

Fig. 5. Voting process.

4 Conclusion and Future Works Collecting ballots from different polling stations and relying on a single central entity for counting votes makes the process of voting slow and the results unverifiable. This led to the emergence of electronic voting systems that offer different possibilities to fix these problems. Despite several publications, electronic voting remains a largely unexplored area. Plus, authentication and privacy lack of transparency fraud and manipulation from hackers or malicious inside parties that leads to reduced trust are challenges that face the existing e-voting systems. Attempting to propose a solution for some of these challenges, we proposed an e-voting system idea based on two leading technologies: blockchain and NFC. Blockchain is used to create a secure and reliable e-voting system. NFC adds a security layer to the authentication phase and provides the system with the voter’s ID in a secure manner. This paper is a work in progress. The implementation of the system is ongoing. For future work, a consensus algorithm will be added to the block mining. We also tend to complete the implementation of the system, make a usable prototype, test it and analyze the results by comparing it to existing systems in term f time, cost, latency and more.

References 1. Agate, V., De Paola, A., Ferraro, P., Re, G.L., Morana, M.: SecureBallot: a secure open source e-Voting system. J. Netw. Comput. Appl. 191, 103165 (2021) 2. Yadav, J.K., Jangirala, S., Verma, D.C., Srivastava, S.K., Chaudhry, S.A.: Blockchain for foolproof e-voting systems, In: Fong, S., Dey, N., Joshi, A. (eds.) ICT Analysis and Applications, pp. 455–466, Springer, Singapore (2022)

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3. Rajeshwari, M.: Role of technology in the development of smart and secure public voting systems–a review of literatures. Int. J. Manage. Technol. Soc. Sci. 5(1), 298–317 (2020) 4. Vivek, S.K., Yashank, R.S., Prashanth, Y., Yashas, N., Namratha, M.: E-voting systems using blockchain: an exploratory literature survey. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 890–895. IEEE, Coimbatore (2020) 5. Pawlak, M., Poniszewska-Mara´nda, A.: Blockchain e-voting system with the use of intelligent agent approach. In: Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia, pp. 145–154. New York (2019) 6. Nikam, R., Rankhambe, M., Raikwar, D., Kashyap, A.: Secured e-voting using NFC technology. Int. J. Comput. Sci. Inform. Technol. 5(6), 8325–8327 (2014) 7. Ok, K., Coskun, V., Aydin, M.N.: Usability of mobile voting with NFC technology. In: Proceedings of IASTED international conference on software engineering, pp. 16–18. Innsbruck, Austria (2010) 8. Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system. Decentralized Bus. Rev. 21260 (2008) 9. Lin, I.C., Liao, T.C.: A survey of blockchain security issues and challenges. Int. J. Netw. Secur. 19(5), 653–659 (2017) 10. Huang, J., He, D., Obaidat, M.S., Vijayakumar, P., Luo, M., Choo, K.K.R.: The application of the blockchain technology in voting systems: a review. ACM Compu. Surv. (CSUR) 54(3), 1–28 (2021) 11. Aswale, N.S., et al.: Privacy preserved e-voting system using blockchain. In: Proceedings of the International Conference on Smart Data Intelligence. https://ssrn.com/abstract=3852951 (2021) 12. Pawlak, M., Poniszewska-Mara´nda, A., Kryvinska, N.: Towards the intelligent agents for blockchain e-voting system. Procedia Comput. Sci. 141, 239–246 (2018) 13. Ta¸s, R., Tanrıöver, Ö.Ö.: A systematic review of challenges and opportunities of blockchain for E-voting. Symmetry 12(8), 1328 (2020) 14. Poniszewska-Mara´nda, A., Pawlak, M., Guziur, J.: Auditable blockchain voting system-the blockchain technology toward the electronic voting process. Int. J. Web Grid Serv. 16(1), 1–21 (2020) 15. Megalingam, R.K., et al.: Voter ID Card and Fingerprint-Based E-voting System. In: Smys, S., Balas, V.E., Palanisamy, R. (eds.) Inventive Computation and Information Technologies: Proceedings of ICICIT 2021, pp. 89–105. Springer Nature Singapore, Singapore (2022). https://doi.org/10.1007/978-981-16-6723-7_8 16. Dyta, P., Junjare, S., Pandita, A., Ingle, D.R.: E-voting–secured NFC voting. Int. J. Sci. Res. Dev.|3 (2015) 17. Komatineni, S., Lingala, G.: Secured E-voting system using two-factor biometric authentication. In: 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. 245–248. IEEE, Erode (2020) 18. Zhu, H., Feng, L., Luo, J., Sun, Y., Yu, B., Yao, S.: BCvoteMDE: a blockchain-based E-voting scheme for multi-district elections. In: 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 950–955. IEEE, Hangzhou (2022) 19. Hsiao, J.H., Tso, R., Chen, C.M., Wu, M.E.: Decentralized E-voting systems based on the blockchain technology. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds.) Advances in Computer Science and Ubiquitous Computing, pp. 305–309. Springer, Singapore (2017) 20. Al-Maaitah, S., Quzmar, A., Qatawneh, M.: Blockchain-based e-voting system for elections in Jordan. J. Theor. Appl. Inf. Technol. 100(5), 1584–1593 (2022)

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21. Adiputra, C.K., Hjort, R., Sato, H.: A proposal of blockchain-based electronic voting system. In: 2018 Second World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), pp. 22–27. IEEE, London (2018) 22. Dagher, G.G., Marella, P.B., Milojkovic, M., Mohler, J.: Broncovote: Secure voting system using ethereum’s blockchain. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy, pp. 96–107. Scitepress, Funchal, Madeira (2018)

Application of CRM Method for Reservoir Fluid Dynamic Characterization in Haoud Berkaoui Petroleum Field Mohamed Z. Doghmane1,3(B) , Sid-Ali Ouadfeul2,3 , Zakia Benaissa1,3 , and Said Eladj2,3 1 Department of Geophysics, FSTGAT, University of Science and Technology, Houari

Boumedienne, Bab Ezzouar, Algiers, Algeria [email protected] 2 Algeria Petroleum Institute, 35000 Boumerdes, Algeria 3 Department of Geophysics, FHC, University M’hamed Bougara, 35000 Boumerdes, Algeria

Abstract. Capacitance resistance modeling (CRM) approach is based on analogy between the dynamic of the current in RC circuits and the dynamic of the fluid in oil reservoirs. Wherein, the porosity of the rock can be seen as capacitance that charges the current, while the permeability of the fluid to pass through pores is seen as the electric conductivity, which is the ability of metal to permit the current pass through it. Moreover, the mathematical descriptions relating these parameters in the two different domains are identical. Therefore, it can be advantageous to use the advances in Electrical engineering in order to model the dynamic of the hydrocarbons fluid in heterogeneous reservoirs where the permeability can be very varying. Haoud Berkaoui field has been characterized by its highly changeable permeability, which do not permit obtaining a reliable model for water and gas injection in order to enhance its productivity. The main objective of this study is to apply CRM approach in order to provide much more reliable model in Haoud Berkaoui, which determines the connectivity between two injection and five production wells. The obtained results has demonstrated the superiority of CRM method over the nowadays methods used in Haoud Berkaoui field for reservoir fluid dynamic characterization. Keywords: CRM · Reservoir modeling · Haoud Berkaoui petroleum field · Fluid dynamic characterization · Wells connectivity

1 Introduction Haoud-Berkaoui region is part of the Oued-Mya basin, which is located in the central province of the Algerian Sahara, This region has the configuration of an elongated NorthEast/South-West trending depression acquired during Paleozoic [1]. It is limited, to the north by the upper zone of Djamâa–Touggourt, made up of Cambrian age; to the northwest by Talemzane pier of Hassi R’mel; and in the south-east by the Hassi Messaoud pier which extends to the north by the ridge of El Agreb-El Gassi, its width varies from © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 76–81, 2023. https://doi.org/10.1007/978-3-031-21216-1_8

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25 to 30 km to the south-west and 8 to 10 km to the north-east. Up to date, it has operated 139 wells spread across all fields including 104 oil producing wells (PPH) including 63 Gas lift wells and 41 eruptives; 16 water production wells; 19 wells water injections [2]. The cumulative production since the beginning is 79 million M (Fig. 1). The main activities of the region (HBK) essentially boil down to gross production, the recovery of flared gas (gas lift, LPG, condensate), and water injection [3].

Fig. 1. Haoud Berkaoui Field with all its potential reservoirs [4].

2 CRMT Method The aim of this work is to evaluate the efficiency of the CRMT method in modeling the reservoir based on injection and production data from wells in Benkahla reservoir of Haoud Berkaoui field. The CRM method is based on the idea of analogy between the dynamic of the fluid in petroleum reservoir and dynamic of the current in RC circuit, the method has been firstly proposed by W.A. Bruce in 1940s [5]. The first step is to identify models’ parameters using production and injection rates from the history of the field [6]. The resulting parameters will be then fixed and present a semi-physical relationship between injection rate and the production rate. In the second step, we find the optimal

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input (injection scheme) that maximizes the production. In both steps, we use gradient and heuristic based algorithms. We validate the described method by using data from an Algerian oil field [7]. The CRMT method is based on general CRM principle but the number of both the injection and production wells should be higher than one, i.e. multi-injectors multiproducers approach, it is used to find the dynamic behavior of the reservoir fluid in different directions [8].

3 Results and Discussion The model parameters have been identified by history matching by using a real field data and nonlinear optimization algorithms [9, 10]. Figure 2 shows the optimal configuration of the CRMT; where the injector I1 has its biggest influence on the producer P1, while the injector I2 is influencing directly on the producer P5. Moreover, it has been also noticed that these influences have the same direction, which indicates the type of the reservoir’s heterogeneity and anisotropy [5]. The other connectivity relations are summarized in Table 1, which shows clearly that all other connectivity values are negligible in comparison to the pairs (I1-P1) and (I2-P5) (Fig. 3 and Fig. 4).

Fig. 2. Different CRM methods with their mathematical relevant models

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Fig. 3. Reservoir model of one injector and one production well without and with geological default

Fig. 4. CRMP configuration of Haoud Berkaoui Field

Table 1. Correlation ration between injectors and producers in Haoud Bekraoui Field fij

I1

I2

I3

I4

P1

0.97

0.17

0.12

0.14

P2

0.11

0.13

0.03

0.05

P3

0.00

0.09

0.07

0.06

P4

0.13

0.23

0.16

0.27

P5

0.15

0.92

0.25

0.19

Table 1 shows the obtained connectivity rates between injector and producer wells, as can be deduced the closest values to 1 indicates strong connectivity, while those closer to 0 indicates weak connectivity [11, 12].

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4 Conclusion The main contribution of this study was based on the use to CRMT for dynamic fluid modeling in Haoud Berkaoui field by using production/injection history data. This method has allowed us to quantify the degree of connectivity between four injection wells and five production wells in the field [13–15]. The obtained results have confirmed that injector well I1 has big influence on Producer well P1, and I2 is much influencing P5, while the other connectivity rates were negligible. Therefore, in addition to providing a faster representation of the connectivity and the state of flow between the different wells, this CRMT model will provide the uncertainties analysis of the injection model and the robustness and usefulness of this solution in Haoud Berkaoui field. Moreover, it can even be used for reservoir monitoring and implementation of real time control of the reservoir, in the so-called smart digital oil and gas field. Thus, it is highly recommended to consider the proposed method (CRM) in that research topic.

References 1. Nam, T., Pardo, T.A.: Conceptualizing smart city with dimensions of technology, people, and institutions. In: Proceedings of the 12th annual international digital government research conference: digital government innovation in challenging times, pp. 282–291 (2011) 2. Eladj, S., Lounissi, T.K., Doghmane, M.Z., Djeddi, M.: Lithological characterization by simultaneous seismic inversion in Algerian south eastern field. Eng. Technol. Appl. Sci. Res. 10(1), 5251–5258 (2020) 3. Meghraoui, M., Bouraoui, S., Bougdal, R., Cakir, Z.: Monitoring of ground deformation in the Haoud Berkaoui oil field (Sahara, Algeria) using time series analysis of SAR images. In: American Geophysical Union, Fall Meeting 2012, abstract id. G51B-109. 2012AGUFM.G51B1099M (2012) 4. Bacetti, A., Doghmane, M.Z.: A practical workflow using seismic attributes to enhance sub seismic geological structures and natural fractures correlation. In: Conference Proceedings, First EAGE Digitalization Conference and Exhibition, vol. 2020, pp. 1–5 (2020) 5. Doghmane, M.Z., Belahcene, B., Kidouche, M.: Application of improved artificial neural network algorithm in hydrocarbons’ reservoir evaluation. In: Hatti, M. (eds.) Renewable Energy for Smart and Sustainable Cities. ICAIRES 2018, Tipasa, Algeria, Lecture Notes in Networks and Systems, vol. 62. Springer, Cham (2018) 6. Yousefi, S.H., Rashidi, F., Sharifi, M., Soroush, M., Ghahfarokhi, A.J.: Interwell connectivity identification in immiscible gas-oil systems using statistical method and modified capacitanceresistance model: a comparative study. J. Petrol. Sci. Eng. 198, 175–180 (2021) 7. Wei, L., Hui, Z., Guanglong, S., Huazhou, A.L., Lingfei, X., Yuhui, Z.: A rapid waterflooding optimization method based on INSIM-FPT data-driven model and its application to threedimensional reservoirs. Fuel 292, 120219 (2021) 8. Doghmane, M.Z., Belahcene, B.: Design of New Model (ANNSVM) Compensator for Saturation calculation Based on Logging Curves for Low Resistivity Phenomenon. In: Conference Proceedings, EAGE/ALNAFT Geoscience Workshop, vol. 2019, pp. 1–5 (2019) 9. Mamghaderi, A., Aminshahidy, B., Bazargan, H.: Prediction of waterflood performance using a modified capacitance-resistance model: a proxy with a time-correlated model error. J. Petrol. Sci. Eng. 198, 108152 (2021) 10. Zhou, Y., Sheng, L., Wei, L.: Injection-production optimization of carbonate reservoir based on an inter-well connectivity model. Energy Explor. Exploit. 39, 1666–1684 (2021). https:// doi.org/10.1177/0144598721994653

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11. Eladj, S., Lounissi, T.K., Doghmane, M.Z., Djeddi, M.: Wellbore stability analysis based on 3D Geo-mechanical model of an algerian southeastern field. In: Meghraoui, M., et al. (eds.) Advances in Geophysics, Tectonics and Petroleum Geosciences: Proceedings of the 2nd Springer Conference of the Arabian Journal of Geosciences (CAJG-2), Tunisia 2019, pp. 615–618. Springer International Publishing, Cham (2022). https://doi.org/10.1007/9783-030-73026-0_136 12. Eladj, S., Doghmane, M.Z., Belahcene, B.: Design of new model for water saturation based on neural network for low-resistivity phenomenon (Algeria). In: Meghraoui, M., et al. (eds.) Advances in Geophysics, Tectonics and Petroleum Geosciences: Proceedings of the 2nd Springer Conference of the Arabian Journal of Geosciences (CAJG-2), Tunisia 2019, pp. 325– 328. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-03073026-0_75 13. Eladj, S., Doghmane, M.Z., Aliouane, L., Ouadfeul, S.-A.: Porosity model construction based on ANN and seismic inversion: a case study of Saharan field (Algeria). In: Meghraoui, M., et al. (eds.) Advances in Geophysics, Tectonics and Petroleum Geosciences: Proceedings of the 2nd Springer Conference of the Arabian Journal of Geosciences (CAJG-2), Tunisia 2019, pp. 241–243. Springer International Publishing, Cham (2022). https://doi.org/10.1007/9783-030-73026-0_55 14. Cherana, A., Aliouane, L., Doghmane, M., Ouadfeul, S.-A.: Fuzzy Clustering algorithm for Lithofacies classification of ordovician unconventional tight sand reservoir from well-logs data (Algerian Sahara). In: Meghraoui, M., et al. (eds.) Advances in Geophysics, Tectonics and Petroleum Geosciences: Proceedings of the 2nd Springer Conference of the Arabian Journal of Geosciences (CAJG-2), Tunisia 2019, pp. 277–279. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-73026-0_64 15. Doghmane, M.Z., Ouadfeul, S.A., Benaissa, Z., Eladj, S.: Classification of ordovician tight reservoir facies in algeria by using neuro-fuzzy algorithm. In: Hatti, M. (ed.) IC-AIRES 2021. LNNS, vol. 361, pp. 889–895. Springer, Cham (2022). https://doi.org/10.1007/978-3030-92038-8_91

Embedded Machine Learning for Fault Detection and Diagnosis of Photovoltaic Arrays Using a Low-Cost Device M. Bouzerdoum, A. Mellit(B) , N. Djazari, and M. Laissaoui Renewable Energy Laboratory, University of Jijel, 18000 Jijel, Algeria [email protected]

Abstract. In this work, an embedded system (ES) for fault detection and diagnosis of photovoltaic (PV) arrays is presented. Two machine learning (ML) classifiers have been developed for PV fault detection and classification based on the I-V curves. The developed classifiers have been then integrated into a Raspberry Pi 4 to detect and classify faults occurred on a PV array. An open source IoT platform (ThingSpeak™) of MathWorks is used to remotely monitor the PV array parameters. Users could be alerted about the state of the PV array by phone message (SMS) using a GSM module and also by email. The whole system was designed and verified experimentally at the Renewable energy laboratory, the University of Jijel (Algeria). Simulation and experimental results demonstrated the feasibility of the developed ES for fault detection and identification of the inspected PV array. Furthermore, a dedicated guide user interface has also been developed. Keywords: Photovoltaic · Fault diagnosis · Embedded machine learning · Embedded system · IoT technique · Raspberry Pi 4 · GUI

1 Introduction With reference to the International Energy Agency (IEA) more than 940 GW [1] of photovoltaic (PV) capacity were installed at the end of 2021, which means a large number of PV plants were installed worldwide. To keep these plants safe and efficient, they should be equipped with monitoring and fault detection systems [2, 3]. Large-scale PV systems should have the capability to identify faulty areas with minimal human involvement. In most commercialized large-scale PV plants monitoring systems are always integrated, but without fault diagnosis system. PV fault diagnosis system, including fault detection, fault localization and fault identification play a vital role in the reliability of such PV plants [4]. Therefore, to address this issue smart remote-monitoring and automatic fault diagnosis system should be developed. In this regard, a good number of remote monitoring PV system based on the Internet of Things (IoT) have recently been published [5–9]. Currently, machine learning (ML) algorithms, including decision tree (DT), random forest (RF), K-nearest neighbor (K-NN), shallow neural networks (S-NN) and other have gained popularity in dealing with PV fault classification problems (binary and multiclass © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 82–90, 2023. https://doi.org/10.1007/978-3-031-21216-1_9

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classification) [10–15]. Actually, very limited attempts have been made to implement such methods into electronic devices for possible development of embedded systems (ES) [16]. Running ML algorithms on embedded devices is defined as embedded ML (EML). To overcome the discussed problem, here we propose an embedded system for remote monitoring and fault diagnosis of PV arrays using ML algorithms and the IoT technique. The idea is to integrate the fault diagnosis method-based ML into a Raspberry Pi 4 for real-time application. The ES will be able to detect faulty, classify fault and notify users regarding the state of their PV array using the IoT technique. Data (such as current, voltage, solar irradiance, cell temperature and the state of the system) related to the PV array are then automatically stored and can be visualized via an open source IoT platform (ThingSpeakTM ). A simple graphical user interface (GUI) is also developed.

2 Dataset and Features Selection 2.1 Dataset A dataset was collected at renewable energy laboratory (RELab) of the university of Jijel. It consists of measured I-V characteristics (626 curves) under various working conditions (126 samples belong into a normal class and 500 samples belong into a faulty class) of a PV array (three PV modules connected in parallel, the power capacity of the string is 180 Wp, as one PV module produce 60 Wp at STC). The I-V characteristics were taken using a Prova-210 I-V tracer (See Fig. 1a). Fig. 1.b shows an example of measured I-V characteristics (e.g. Normal condition).

Fig. 1. a) Test facility at the University of Jijel and b) example of measured I-V characteristic at normal working condition using Prova-210 I-V tracer.

2.2 Features Extraction From the I-V characteristics five main features have been extracted, which are: voltage at open circuit (Voc ), current at short circuit (Isc ), voltage at the maximum power (Vmp ), current at the maximum power (Imp ), maximum power (Pmp ) and Fill factor (FF). As example, Table 1 shows the calculated values of features.

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# of I-V curve

G (W/m2 )

T (°C)

Isc (A)

Voc (V)

Imp (A)

Vmp (V)

Pmp (W)

FF ()

1

215

19

4.13

19.11

3.67

13.66

50.54

0.64

2

250

20

4.85

18.81

4.35

13.36

58.16

0.65

3

300

21

6.60

18.61

5.44

13.16

71.61

0.58

4

310

25

6.54

18.41

5.87

12.96

76.13

0.63

5

315

22

7.27

18.21

6.39

13.03

83.34

0.63

6

320

25

7.75

18.01

6.82

12.83

87.53

0.62

















3 Machine Learning and IoT Technique 3.1 Machine Learning ML is a subset of artificial intelligence (AI) technique. It comprises three major kinds of learning: supervised learning, unsupervised learning and reinforcement learning. Numerous ML algorithms were developed to address various issues in real-world such as regression, classification, clustering, control, dimensionality and reduction. In this work, two easiest and common classification supervised algorithms (decision tree and random forest which belong in the ensemble learning category) are employed over various of ML and ensemble learning (EL) algorithms [17]. Both ML algorithms have been selected for the three main reasons: 1) simplicity implementation, 2) easy to understand (they mimic human thinking), and 3) good accuracy in solving classification problem with limited dataset. However, the performance is mainly related to the quality of the dataset. Here we are not intended to descript theoretical development of such algorithms, since most ML and EL algorithms are nowadays implemented in various libraries (e.g. Sklearn) and toolboxes (statistics and machine learning of MathWorks). The basic flowchart of a supervised ML algorithm for the classification topic is shown in Fig. 2.

Classification topic

Supervised ML algorithms

Raw data

Preprocessing and data preparation (scaling, normalization and features extraction)

Apply learning algorithm to data Training and evaluation

Candidate model?

Iterate to find the best model

Fig. 2. Basic structure of a ML algorithm for classification topic

Deploy chosen model

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3.2 Internet of Things IoT is simply the connection of things using sensors and networks. Then collected information or data could be analyzed via a platform in real-time [18] (See Fig. 3a). This can help users to access and visualize their data remotely without additional effect (e.g. reduce time and cost). While in solar energy field, this technique is in progress, particularly for monitoring of large scale plants. So it can make PV energy plants and facility more visible and cost-effective. IoT architecture is summarized in Fig. 3b, it regroups four stages. There are various free IoT platforms such as ThingSpleakTM , Blynk, Google cloud, OpenRemote, etc. In this work, an open source IoT ThingSpeak platform to upload and visualize data. Object # 1

(a)

Sensor # 1 wireless

Object # 2

wireless

Internet of things

Sensor # 2

Sensor # n

Object # n

wireless Cloud and platform

(b) Sensors/ actuators (wired, wireless)

Internet Gateways, DAS (A/D, data aggregation, measurement and control)

Edge IT (Analytics and preprocessing)

2nd stage

3rd stage

1st stage

Data center/Cloud (Analytics, managements, archive, etc)

4th stage

Fig. 3. a) Basic IoT configuration and b) Stages of the IoT architecture.

3.3 Embedded System The flowchart of the developed embedded system is shown in Fig. 4. The essential part of this flowchart is the embedded fault detection and classification method. It consists of two ML algorithms, the first one is a DT-based binary classifier aims to check if the PV array is healthy or not. The second one is a RF-based multiclass classifier used to identify the type of the occurred fault. This later should be immediately started in order to make a prompt decision. In this study four faults are considered, which are: partial shading (F1), disconnected PV modules (F2), dust accumulation (F3) and shunted PV module (F4). The dataset is used to train and test both classifiers. A part of 80% from the dataset is used to train the classifiers, while the rest part of 20% is used for testing the classifiers. The whole smart system contains: a) A Raspberry Pi 4 used to implement the developed fault detection and classification codes, b) A Data-Acquisition System (DAS) based on an ArduinoTM microcontroller, used to measure data (I-V characteristics), c) Posting the measured data on the cloud via the IoT ThingSpeakTM platform, and c) A SIM800 GSM module used to notify users about the state of the PV array by phone SMS. Codes are developed under C and Python languages. Figure 5 shows the electronic circuit of the proposed system.

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Measured I-V characteristics

PV array comprises three PV modules connected in parallel

Features extraction form the I-V characteristics

creating of an CSV file of features to feed the models

Embedded ML algorithms into the Raspberry Pi 4 for PV fault diagnosis and remote monitoring

Store and remotely display data into ThingSpeak TM App

Display the measured data locally on an LCD displayer

T sensor

Raspberry Pi 4, 4G

PV modules

GSM module SIM808

Fig. 4. The proposed embedded system for fault detection and classification of PV arrays.

Resistor

V and I sensors

Reference solar cell

ADC 16 bits

LCD-1604

Fig. 5. Electronic schematic of the proposed system.

4 Results and Discussion 4.1 Simulation Results To evaluate the performance of the developed classifiers (detection and identification), confusion matrix is calculated. The results are shown in Fig. 6, and the calculated precision, F1-score, recall and accuracy are reported in Table 2. With reference to Fig. 6a, only one sample is misclassified as normal class. From Fig. 6b it can be seen that two samples of class F1 are misclassified in F3, and one sample of class F3 is misclassified in F1.

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Fig. 6. The confusion matrices of a) detection classifier based on DT algorithm and b) identification classifier based on RF algorithm.

Table 2. Calculated error metrics: Precision, recall, F1-score and accuracy Faults’ classes

Precision (%)

Recall (%)

F1-score (%)

Accuracy (%)

Faut detection using DT algorithm Normal Fault

96

100

98

100

99

99

99

Fault classification using RF algorithm F1

96

93

94

F2

100

100

100

F3

93

96

94

F4

100

100

100

97

With reference to Table 2, the detection accuracy is 99%, while the classification accuracy is 97%. It can be concluded that the classifier can easily identify the disconnected PV module (F2) and shunted bypass diode (F4) with a very good precision (100%), while other two faults: partial shading and dust accumulation with low precision, which is mainly due to the good similarity between the I-V characteristics of both faults (F1 and F3). In such a situation more effective algorithms should be developed.

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4.2 Experimental Results Figure 7 shows a photo of the designed PV fault detection and classification system. It comprises of an I-V tracer circuit for measuring and saving the I-V characteristic into CSV format (Fig. 7a) [19], a Raspberry Pi 4, including codes: fault diagnosis, SMS, email, connecting with the ThingSpeakTM platform (Fig. 7b), and GSM module (Fig. 7c) for sending SMS. The developed codes are embedded into the Raspberry Pi 4 (See Fig. 6d). Figure 8a represents the posted data on the IoT ThingSpeakTM platform. In this example, a fault of class 2 (i.e., F2: disconnected PV module) is tested experimentally. Figure 8b shows the GUI developed for PV fault detection and identification. The results confirm the viability of the proposed embedded system for fault detection and classification of PV arrays in real-time.

Fig. 7. Designed PV fault detection and classification system: a) I-V tracer circuit for measuring and saving data into CSV format, b) Embedded codes into the Raspberry Pi 4, c) GSM module, and d) The performed codes into Raspberry Pi 4.

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Fig. 8. Experimental results: a) posted data on the IoT ThingSpeak® platform and b) the developed GUI for PV fault detection and identification

5 Conclusion and Future Work A smart embedded system for fault detection and classification of PV arrays was designed and verified experimentally. Simulation results showed a good accuracy for both ML algorithms (detection is 99%, and classification is 97%). Actually, the main objective was to check the feasibility of designing an embedded system, by integrating classifiers (ML-based algorithms) into a low-cost Raspberry Pi 4 (cost around 35$). It is worth noting that only single faults are considered in this article (i.e. one defect on the IV characteristic). Experiment tests demonstrate the capability of such an ES to detect and classify accurately the investigated PV array faults. We believe that the designed system could contribute to reduce effort and investment in this field and such embedded technologies will certainly contribute to expand this topic. Our future works will focus on techno-economic, optimization and performance analyses of the developed system, taking into account all costs and uncertainties.

References 1. Snapshot of Global PV Markets. Report IEA-PVPS T1-42:2022 (2022). https://iea-pvps.org/ snapshot-reports/snapshot-2022/. Accessed 25 Apr 2022 2. Hernández-Callejo, L., Gallardo-Saavedra, S., Alonso-Gómez, V.: A review of photovoltaic systems: design, operation and maintenance. Solar Energ. 188, 426–440 (2019) 3. Hong, Y.Y., Pula, R.A.: Methods of photovoltaic fault detection and classification: a review. Energy Rep. 8, 5898–5929 (2022) 4. Mellit, A., Kalogirou, S.: Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions. Renew. Sustain. Energy Rev. 143, 110889 (2021) 5. Kumar, N.M., Atluri, K., Palaparthi, S.: Internet of Things (IoT) in photovoltaic systems. In: IEEE 2018 National Power Engineering Conference (NPEC), pp. 1–4 (2018)

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6. Sharma, M., Singla, M.K., Nijhawan, P., Ganguli, S., Rajest, S.S.: An application of IoT to develop concept of smart remote monitoring system. In: Haldorai, A., Ramu, A., Khan, S.A.R. (eds.) Business Intelligence for Enterprise Internet of Things. EICC, pp. 233–239. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44407-5_15 7. Pereira, R.I., Dupont, I.M., Carvalho, P.C., Jucá, S.C.: IoT embedded linux system based on Raspberry Pi applied to real-time cloud monitoring of a decentralized photovoltaic plant. Measurement 114, 286–297 (2018) 8. Deshmukh, N.S., Bhuyar, D.L.: A smart solar photovoltaic remote monitoring and controlling. In: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 67–71 (2018) 9. Li, Y.F., et al.: November. On-line monitoring system of PV array based on internet of things technology. In: IOP Conference Series: Earth and Environmental Science, vol. 93, p. 012078 (2017) 10. Eskandari, A., Milimonfared, J., Aghaei, M.: Fault detection and classification for photovoltaic systems based on hierarchical classification and machine learning technique. IEEE Trans. Ind. Electron. 68(12), 12750–12759 (2020) 11. Mellit, A., Kalogirou, S.: Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems. Renew. Energy 184, 1074–1090 (2022) 12. Kapucu, C., Cubukcu, M.: A supervised ensemble learning method for fault diagnosis in photovoltaic strings. Energy 227, 120463 (2021) 13. Li, B., Delpha, C., Migan-Dubois, A., Diallo, D.: Fault diagnosis of photovoltaic panels using full I-V characteristics and machine learning techniques. Energy Convers. Manage. 248, 114785 (2021) 14. da Costa, C.H., et al.: A comparison of machine learning-based methods for fault classification in photovoltaic systems. In: 2019 IEEE PES Innovative Smart Grid Technologies ConferenceLatin America (ISGT Latin America), pp. 1–6 (2019) 15. Lu, S., Sahoo, A., Ma, R., Phung, B.T.: DC series arc fault detection using machine learning in photovoltaic systems: recent developments and challenges. In: IEEE 2020 8th International Conference on Condition Monitoring and Diagnosis (CMD), pp. 416–421 (2020) 16. Mellit, A., Herrak, O., Rus Casas, C., Massi Pavan, A.: A machine learning and internet of things-based online fault diagnosis method for photovoltaic arrays. Sustainability 13, 13203 (2021) 17. Panesar, A.: Machine learning algorithms. In: Machine Learning and AI for Healthcare, pp. 119–188. Apress, Berkeley, CA (2019). https://doi.org/10.1007/978-1-4842-3799-1_4 18. Holler, J., Tsiatsis, V., Mulligan, C., Karnouskos, S., Avesand, S., Boyle, D.: Internet of things. Academic Press (2014) 19. https://www.instructables.com/IV-Swinger-2-a-50-IV-Curve-Tracer/

Planar Micro-thermoelectric Generators Based on Cu55 Ni45 and Ni90 Cr10 Thermocouples for IoT Applications I. Bel-Hadj(B) , Z. Bougrioua, and K. Ziouche UMR 8520 - IEMN-Institut d’Electronique de Microélectronique et de Nanotechnologie, Univ. Lille, CNRS, Centrale Lille, Polytechnique Hauts-de-France, 59000 Lille, France {ibrahim.bel-hadj,katir.ziouche}@univ-lille.fr, [email protected]

Abstract. In this work, we present our novel planar micro-thermoelectric generators (μTEGs) integrating an original “folded” thermopile topology, periodically distributed on suspended membranes. The thermopile integrates eco-friendly and low-cost materials as Constantan (Cu55 Ni45 ) and Chromel (Ni90 Cr10 ) that build up thermocouples associated both in series and in parallel. This dual association allows to drastically reduce the thermopile electrical resistance (down to a few tens to a few hundred ohms) and so to electrically better adapt them. To optimize the structural dimensions of the μTEG, numerical simulations have been performed by 3D-finite element modeling using COMSOL Multiphysics© software. Several μTEG modules integrating this folded thermopile have been manufactured using Silicon micro-technologies that differ from our former expertise. In 3-membranebased modules the harvested output power can reach 108 μW/cm2 (for 1 W heat injected into the μTEG) with an output voltage up to few hundred of millivolts, which is enough to supply micro devices for IoT applications. Keywords: Energy harvesting · Thermoelectrics · Micro-generator · Planar · Silicon technology · Thermal modeling

1 Introduction Numerous technologies of the future such as smart-energy management (˙Ican and Çelik 2021) but also smart-city development (Sampathkumar et al. 2020) will undoubtedly rely on the Internet of Things (IoT). Even though, applications of IoT are growing at a rate never seen before (IoT Analytics 2022), these ones are delayed by the lack of energy solutions to supply them with no maintenance requirement as for instance the need of battery replacements. Thus, all innovative solutions contributing to improve the renewable production of energy are playing a strategic role and many of them consist to harvest energy from the direct environment to produce electricity and supply the IoT nodes (Sun et al. 2018). Due to the abundance of heat, micro ThermoElectric Generators

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 91–98, 2023. https://doi.org/10.1007/978-3-031-21216-1_10

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(μTEGs) have become an actual promising solution. The active part of these devices is mainly made up of the association of a set of thermocouples (TCs) generally connected in series electrically and in parallel thermally (Rowe 2006). Their working principle is based on the Seebeck effect (Seebeck 1821) corresponding to the conversion of temperature gradients into electrical voltage. The performances of μTEGs are generally related to the nature of TE materials but also to their geometrical structures that can be classified in two main categories: vertical or planar configurations (Fig. 1). Vertical configurations provide more power and often use better performing TEs such as Bismuth and Antimony alloys (Snyder and Toberer 2008). However, these materials are expensive to develop, incompatible with Silicon technologies and are highly toxic. Planar structures, generally using thin TE films, present the advantage of allowing the thermal adaptation of μTEGs to their environment due to their high and adjustable thermal resistance (Yuan et al. 2015). This paper presents first results of such μTEGs that use a new thermopile topology made of Ni90 Cr10 (Chromel) and Cu55 Ni45 (Constantan) based TEs integrated in an original topology periodically folded and distributed on a multi-membrane template. Two configurations of the μTEG, with 2 and 3 membranes, are designed and modeled using COMSOL Multiphysics® software, and are afterward fabricated using CMOScompatible Silicon technology. The fabricated modules were tested under calibrated heat flux to measure the Seebeck voltage and the output power for various heat input.

Fig. 1. The two typical μTEG structures: (a) vertical (π), (b) planar.

2 µTEG Design The structure of the planar μTEG proposed in this work (total area AμTEG ~ 6 mm × 5.7 mm) is mainly composed of two parts (Fig. 2) made up of two etched Silicon (Si) wafers: The first part (bottom part) is made of a set of membranes hollowed out and released in a Si substrate (380 μm thick (100) oriented Si). These periodically etched membranes are build up with a low-stress SiO2 /Six Ny bilayer, obtained by stress compensation of the two layers of 800 and 600 nm (Haffar 2007) which limits bowing events and contribute to the mechanical strength of the whole structure. An innovative thermopile

Planar Micro-thermoelectric Generators 3D exploded view (not to scale) of a 2-membranes µTEG

2-pillars concentrator

93

Thermopile on suspended membranes

µTEG after assembly of the concentrator on the thermopile

Fig. 2. Design of the novel μTEG.

based on Cu55 Ni45 and Ni90 Cr10 TCs is designed with an original topology,”periodically folded”, and distributes perpendicularly on the multi-membrane template. The details of the design and the fabrication steps of the thermopile were described in Bel-Hadj et al. (2022). Finally, a thick Polyimide layer (12 μm thick) is used for the passivation, electrical insulation of the thermopile and also contributes to the robustness of the membranes. For a given strip width of 200 μm (typical example), we count a quantity of TCs proportional to the number of membranes: N TC = 50 × N m (taking into account the area used by the thermopile). The measured Seebeck coefficient of an elementary TC is about 48 μV/K. • The second part of the module (top part) corresponds to a Silicon heat concentrator (top surface ~ 5.7 mm × 5.2 mm, 380 μm thick) made of a set of N m pillars (230 μm thick, length ~ 5.1 mm, and width optimized according to number of pillars (Bel-Hadj et al. 2019). These allow to canalize the harvested heat flux, from the surface of the concentrator down towards the heat sink, through the membranes (the pillar contacts with the membranes are located at the hot junction places of the thermopile). Four mechanical supports (bosses) located at the 4 corners of the concentrator are used to ensure a rigid support of the concentrator on the substrate and avoid the breaking of the membranes. So one part of the incident heat flux that is lost through the 4 bosses (i.e., without passing through the membranes): this part is directly proportional to the total contact surface between these bosses and the heat sink wafer. This contact surface must be as small as possible to minimize these lateral losses. But a compromise must be made in order not to weaken the μTEG too much (surface too small), experimentally, this surface was fixed at 1.2 mm2 . The heat concentrator will be aligned on the suspended membranes such a way that the pillars are centered over the middle of the membranes. This is performed under optical microscopy using alignment wedges. Thermal grease is used to ensure a good wet contact between concentrator pillars and membranes and minimize thermal contact resistance.

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3 Results and Descussion 3.1 3D Thermal Modeling To predict the temperature distributions across the μTEGs structures and the temperature gradients between the TC’s junctions, the 3D structure of the μTEG is modeled using COMSOL Multiphysics® software, based on the finite element method. The description of the model and the simplifying computational assumptions as well as the boundary conditions on which the μTEG is submitted are detailed in Bel-Hadj et al. (2022). The symmetry of the structure allows to model only half of the μTEG, in order to reduce the calculation time and memory requirements. The Fig. 3 shows an illustration of the temperature distribution simulated for the half structures of μTEGs with 2 and 3 membranes. A heat flux density of 3.3 W/cm2 is applied on the surface of the concentrator (surface ~ 0.3 cm2 ), corresponding to a net input heat of 1 W. A part of the input heat is lost by natural convection and radiation between the surface of the concentrator and the ambient environment. Another part is lost through the bosses. The temperature of the bottom surface of the heat sink is set at 25 °C (equal to the ambient temperature). The heat flow through the N m pillars of the concentrator, towards the membranes, creates periodic temperature gradients between the junctions of each TC constituting the thermopile, as shown in Fig. 3. The simulation results corresponding to the applied boundary conditions, allow to calculate that the temperature differences generated between the hot and cold junctions T hc , are 137 K and 120 K and those between the surface of the concentrator and the heat sink T ext , are 142 K, 126 K, respectively for the 2 and 3-membranes μTEGs. The percentages of the fluxes exchanged by convection, radiation and that lost through the bosses are respectively of 11%, 5% and 37% for 2-membranes μTEG and of 9%, 4% and 33% for 3-membranes μTEG.

Fig. 3. Temperature distribution calculated for 2- and 3-membranes μTEGs. An input power of 1 W is injected into the concentrator and the temperature of the bottom side of the substrate is kept at 25 °C.

To optimize the structural dimensions of the μTEG, simulations have been carried out for modules integrating up to 10 membranes. These studies will be presented in an upcoming paper.

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4 Characterization of the µTEGs The two fabricated devices (2 and 3-membranes μTEGs) were tested using a fourprobe set-up over a heat input range of 0–1.2 W. The measurement principle consists in injecting a calibrated heat input by Joule effect in the heater (resistance in gold realized directly upon the surface of the concentrator as seen in Fig. 2) with two probes, and measuring with the two other probes the Seebeck voltage generated from the device. The characteristics of the μTEG configurations realized and characterized in this work are presented in Table 1. All these modules have an identical footprint (AμTEG ); the only difference is the number of thermocouples and membranes (and the number of concentrator pillars) depending of the length of the thermoelements. Concerning the thicknesses of TE materials, the both modules have 150 nm of Ni90 Cr10 and 450 nm of Cu55 Ni45 . Table 1. Main parameters of the 2 fabricated and characterized μTEG configurations. μTEG

Number of Thermocouple Strip widths (μm) Number of Internal membranes lengths (μm) Ni Cr thermocouples electrical 90 10 Cu55 Ni45 resistance ()

2 m-μTEG 2

1060

200

150

100

305

3 m-μTEG 3

670

200

150

150

140

Each μTEG is characterized several times by adjusting the alignment between the concentrator and the bottom part of the module. The Fig. 4 shows the best measured Seebeck (V S ) voltage generated from the both devices as a function of the heat injected into the concentrator. V S increases linearly with increasing heat input and reach a maximum of about 201.6 mV obtained by the μTEG with 2 membranes for a maximum injected heat input of 1.2 W. The linear characteristics of the Seebeck voltage as a function of the input power are naturally explained by Seebeck effect: V S = N s α TC T hc = N s α TC Rth i φi where N s is the number of TCs connected in series fixed by the stripe width (N s = 25 for a stripe width of 200 μm), α TC = (α NiCr − α CuNi ) is the equivalent Seebeck coefficient of a TC, T hc = Rth i φi is the effective temperature difference between the hot and cold junctions, Rth is the internal thermal resistance of the μTEG, and φi is the i input power injected into the concentrator. The dependence of the Seebeck voltage on the thermal resistance of the μTEG, which depends on the number of membranes (fixed by the length of the TCs, see Table 1) explains the differences between the Seebeck voltages delivered by the both μTEG configurations.

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Fig. 4. Seebeck voltage generated by 2 and 3-membranes μTEGs versus input power injected into the concentrator.

The maximum output power density generated by the μTEG (corresponding to the power generated to a matched load resistance) can be calculated using the measured Seebeck voltage by: P max =

V 2S 4Rint AμTEG

where Rint is the internal electrical resistance of the μTEG (305  and 140  respectively for 2 and 3 membranes μTEG). We note that the internal resistances of this new family of μTEGs with an all-metal thermopile, are clearly lower than those of the first generation modules, based on a periodically plated poly-Silicon thermopile, for whom Rint were of the order of a few hundred k to a few M for modules with 2 membranes (Yuan et al. 2015). Figure 5 shows the maximum output power density (P max ) generated by the μTEGs as a function of the input heat injected into the concentrator. The maximum generated output power density is about 125 μW/cm2 for an injected input power of ~1.07 W obtained with 3-membranes μTEG. Normalized to 1 W of injected input power, this is equivalent to the output power of 108.3 μW/cm2 . This corresponds to an efficiency factor (Strasser et al. 2004) defined by P max /T 2ext (where T ext is the external temperature difference between the two heat sources in which the device is placed) of about 6.82 × 10–3 μW·cm−2 ·K−2 when the T ext is about 126 K which correspond to 1 W of heat injected into the concentrator. These performances are much better than of stateof-the-art modules using metallic thermoelectrics (for instance see Shimizu et al. 2018 and Iezzi et al. 2017).

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Fig. 5. Maximum output power density generated versus input power injected into the concentrator.

5 Conclusion In this study, we have presented a novel planar μTEG for thermal energy harvesting. The technology implemented is based on an original folded thermopile topology periodically distributed onto suspended membranes. The thermopile integrates metallic TE materials based on Cu55 Ni45 and Ni90 Cr10 that build up thermocouples associated both in series and in parallel. The use of metallic TE materials allowing to reduce the internal electrical resistance of the μTEGs compared to our first generation based on polysilicon thermopile. The fabrication of these devices is implemented using CMOS-compatible process of microfabrication. For 1 W heat injected into the heat concentrator, the maximum power is 108 μW/cm2 with an efficiency factor of 6.82 × 10–3 μW·cm−2 ·K−2 for μTEG configurations with 3 membranes. These first results will be followed by other studies to improve the performances of these μTEGs.

References ˙Ican, Ö., Çelik, T.B.: A review on smart energy management systems in microgrids based on power generating and environmental costs. In: Dorsman, A.B., Atici, K.B., Ulucan, A., Karan, M.B. (eds.) Applied Operations Research and Financial Modelling in Energy, pp. 51–67. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-84981-8_4 Sampathkumar, A., Murugan, S., Sivaram, M., Sharma, V., Venkatachalam, K., Kalimuthu, M.: Advanced energy management system for smart city application using the IoT. In: Kanagachidambaresan, G.R., Maheswar, R., Manikandan, V., Ramakrishnan, K. (eds.) Internet of Things in Smart Technologies for Sustainable Urban Development. EICC, pp. 185–194. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34328-6_12 IoT Analytics: State of IoT 2022: Number of connected IoT devices growing 18% to 14.4 billion globally (2022). https://iot-analytics.com/number-connected-iot-devices. Accessed 14 Jun 2022 Sun, H., Yin, M., Wei, W., Li, J., Wang, H., Jin, X.: MEMS based energy harvesting for the Internet of Things: a survey. Microsyst. Technol. 24(7), 2853–2869 (2018). https://doi.org/10.1007/s00 542-018-3763-z

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Rowe, D.M. (ed.): Thermoelectrics handbook: macro to nano. CRC/Taylor & Francis, Boca Raton (2006) Seebeck, T.J.: Ueber den magnetismus der galvanischen kette. In: Abh. K. Akad, Wiss Berlin, pp. 290–346 (1821) Snyder, G.J., Toberer, E.S.: Complex thermoelectric materials. Nature Mater. 7(2), 105–114 (2008) Yuan, Z., Ziouche, K., Bougrioua, Z., Lejeune, P., Lasri, T., Leclercq, D.: A planar micro thermoelectric generator with high thermal resistance. Sens. Actuators, A 221, 67–76 (2015) Haffar, M.: Étude et réalisation de matrices de microcapteurs infrarouges en technologie silicium pour imagerie basse résolution. These de doctorat, Université de Lille (2007). https://www.the ses.fr/2007LIL10053 Bel-Hadj, I., Bougrioua, Z., Ziouche, K.: Metal-based folded-thermopile for 2.5D microthermoelectric generators. Submitted to Sens. Actuators A-Phys. August 2022 Bel-Hadj, I., Bougrioua, Z., Ziouche, K.: Modélisation et optimisation de la structure géométrique d’un microgénérateur thermoélectrique planaire. In: TELECOM’2019 & 11èmes JFMMA, Saidia, Morocco, 12–14 June 2019. https://hal.archives-ouvertes.fr/hal-02414487 Strasser, M., Aigner, R., Lauterbach, C., Sturm, T.F., Franosch, M., Wachutka, G.: Micromachined CMOS thermoelectric generators as on-chip power supply. Sens. Actuators, A 114(2–3), 362– 370 (2004) Shimizu, Y., Mizoshiri, M., Mikami, M., Sakurai, J., Hata, S.: Fabrication of Copper/CopperNickel thin-film thermoelectric generators with energy storage devices. J. Phys. Conf. Ser. 1052, 012032 (2018) Iezzi, B., Ankireddy, K., Twiddy, J., Losego, M.D., Jur, J.S.: Printed, metallic thermoelectric generators integrated with pipe insulation for powering wireless sensors. Appl. Energy 208, 758–765 (2017)

Edge Detection of MRI Brain Images Based on Segmentation and Classification Using Support Vector Machines and Neural Networks Pattern Recognition Zouhir Iourzikene(B) , Djamel Benazzouz, and Fawzi Gougam Laboratoire Mécanique des Solides et Systèmes (LMSS), Université M’Hamed BOUGARA de Boumerdes, Boumerdes, Algeria {z.iourzikene,d.benazzouz,f.gougam}@univ-boumerdes.dz

Abstract. Brain tumor (brain cancer) is a mass of abnormal cells that grow in the brain in an uncontrolled way. Brain CT and brain MRI are the most frequently performed examinations. The objective of this paper is to develop a method for the classification of brain MRI images of healthy cases and tumor cases. MRI brain database is obtained by preprocessing, segmentation, feature extraction. Feature extraction based on support vector machines (clustering) is used in this research. The objective of this method is to create several vectors and each vector contains a number of features of each image, so that we can make the classification by these features. Keywords: Neural networks pattern recognition · Support vector machine · Segmentation · Brain image

1 Introduction In the brain, there are nerve cells, called neurons, and around the neurons, glial cells. Brain tumors can develop from different types of glial cells [1]. There are different surveillance techniques for detecting brain tumors. X-ray, magnetic resonance imaging (MRI), computed tomography (CT) and radiology. MRI, on the other hand, is the most common technique for the diagnosis of brain tumors. Image processing is a branch of computer science and applied mathematics that studies digital images and their transformations [2], in order to improve their quality or to extract information from them (feature extraction), is also important for the identification and classification of disease types [3]. The goal of medical image processing is to develop a system to help doctors solve medical diagnostic problems with the help of computers [4].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 99–105, 2023. https://doi.org/10.1007/978-3-031-21216-1_11

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Many works have been done by different researchers for the detection and classification of brain tumors. Among them Support Vector Machine and neural networks pattern recognition, which we have used in this article. In order to make the detection and classification between healthy and tumor images.

2 Overview Support Vector Machine (SVM) et Neural Pattern Recognition App 2.1 Support Vector Machine (SVM) The application of classification is an important responsibility suitable for different applications such as, information retrieval, computer vision, data classification, image recognition problems. Support vector machine, is a supervised machine learning algorithm that can be used for classification purposes. In this paper, the training data includes MRI brain images with tumors and without tumors. The training samples have data structured as vectors such that the number of rows in each vector indicates different medical images and the number of columns represents the set of features. Based on training samples, the classifier was able to classify images with and without tumors, and was also able to predict from our model which class the remaining images belonged to [4, 7]. 2.2 Neural Networks Pattern Recognition In pattern recognition problems, a neural network classifies inputs into a set of target categories. For example, classifying a tumor as benign or malignant. The Neural Pattern Recognition application will allow us to select data, train a network and evaluate its performance using cross-entropy and confusion matrices [5, 6].

3 Methodology The figure below (Fig. 1) summarizes the procedure that was followed to make the classification. 3.1 Image Database Binary Image All brain images obtained by magnetic resonance in this are taken from the kaggle database [11], 390 images without tumor and 390 with tumor JPG format, (Fig. 2(a)) shows two normal brain images, and (Fig. 2(b)) shows two abnormal brain MRI images. 3.2 Pre-treatment First, the input MRI image is converted to a grayscale one (Fig. 5(a)), followed by adjustment (Fig. 5(b)), and I added the size padding [3 3], and the images were resized [200 200].

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Input IRM image

Pre-processing

Segmentation

Feature extraction

Neural networks pattern recognition and SVM

Output prediction Fig. 1. Diagram of the brain image classification based on the neural networks pattern recognition [2].

Fig. 2. Examples of IRM images of the brain.

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3.3 Segmentation and Region Boundaries in a Binary Image OTSU is a nonparametric and unsupervised automatic threshold selection method for image segmentation is presented an optimal threshold (Fig. 3) [8, 9].

Fig. 3. Bimodal histogram with selected threshold T.

We used a segmentation according to Otsu’s algorithm thanks to Matlab (Fig. 4(c)). And finally we draw the boundaries of a region in a binary image The BW must be a binary image with non-zero pixels belonging to the object and zero pixels representing the background. The color map converts the label matrix of the objects into an RGB image (Fig. 4(d)), by specifying optional parameters. We obtained the following results:

Fig. 4. Pre-processing results, segmentation and contours.

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3.4 Extraction of Characteristics In this step, we used the bag of visual words object function (bagOfFeatures) [10]. We used 70% of our database as input samples (two sets, the first contains 273 images without tumor and the second contains 273 images with tumor), for feature extraction and create a bag of features object [3]. 3.5 Classification Using SVM We train the dataset we use a support vector machine (SVM), 70% of our feature bag, we have two subsets, the first set contains 390 brain images without tumor, the second contains 390 brain images with tumor, we take for training then 70% of first set (273 images), and 70% of second set (273 images). And for our test set we used 30% of our database which is 30% of first set (117 images without tumor), and 30% of second set (117 images with tumor). 3.6 Classification Using Neural Networks Pattern Recognition We used the same training data of SVM, but for this time we built a matrix of size 500x546 as the input of our neural networks, such that the number of line represents the characteristics of our images extracted by the bag function, and the number of colony represents the number of images that we are going to classify by this neural network, and we made two output for our networks such as 0 for the brain images without tumor and 1 for the brain images with tumor. We used 70% for the training of our network, these are presented to the network during the training, and the network is adjusted according to its error, and for the validation we used 15% (82 images), they are used to measure the generalization of the network and to stop the training when the generalization doesn’t improve anymore, finally we tested our network with 15% of our data (82 images), these have no effect on the training and therefore provide an independent measure of the performance of the network during and after the training, such as for the division of the data they have randomly.

4 Results From the result, 167 of 234 images were correctly evaluated by SVM classification. As 94 non-tumor images were correctly classified and 73 tumor images, the test accuracy is 71% (Table 1). Table 1. The confusion matrix for the SVM test. Test

TP

TN

FP

FN

Accuracy

234

94

73

44

23

71%

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TP (True Positive), TN (True Negative), FP (False Positive), FN (False Negative). Accuracy =

TP + TN TP + TN + FP + FN

(1)

The following Fig. 5 shows the confusion matrices for training, testing, and validation, and the three types of data combined. The network outputs are very accurate, as you can see by the high number of correct responses in the green squares and the low number of incorrect responses in the red squares. The blue squares in the lower right illustrate the overall accuracies. Notice that the classification accuracy for training is 96.9%, validation is 85.8%, testing is 81%, the three data combined 93%.

Fig. 5. Confusion matrices for training, testing and validation, and the three types of data combined.

Table 2 shows a comparison results.

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Table 2. Comparison results. Methods

Accuracy

SVM

71%

Neural networks pattern recognition

81,7%

5 Conclusion The proposed method can help medical personnel such as surgeons and radiologists to diagnose brain cancer from MRI images. The brain images were classified using Neural networks pattern recognition, with the help of mathematical morphology and OTSU’s threshold. The database that was used in this study, obtained by algorithm developed can detect the contours of our images and objects with precision in all the places of the brain where it can be present. The algorithm gives a rate of precision of the classification 81,7% .

References 1. Holland, E.: Glioblastoma multiforme, the terminator. Proc. Natl. Acad. Sci. 97(12), 6242– 6244 2. Van den Boomgard, R., van Balen, R.: Methods for fast morphological image transforms using bitmapped images. Computer Vision, Graphics, and Image Processing: Graphical Models and Image Processing 54(3), 254–258 (May 1992) 3. Gonzalez, R., Eddins, S.L.: Digital Image Processing Using MATLAB. Gatesmark Publishing (2009) 4. Christianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge, UK (2000) 5. Abiodun, O.I., Comprehensive review of artificial neural network applications to pattern recognition. Rev. IEEE 7, 158820–158846 (Oct 2019) 6. Burges, C.J.C.: Un didacticiel sur la machine à vecteurs de support pour la reconnaissance de formes. Data Min. Knowl. Discov. 2(2), 121–167 (1998) 7. Bankman, I.N.: Handbook of Image Processing and Analysis. 2nd edn. Elsevier (2009) 8. Gonzalez, C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesely (2004) 9. Cheriet, M., Saïd, J.N., Suen, C.Y.: Une technique de seuillage récursive pour la segmentation d’image. IEEE Trans. Process. Image 7, 918–921 (1998) 10. Csurka, G., Dance, C.R.: Visual Categorization with Bags of Keypoints. Workshop on Statistical Learning in Computer Vision. ECCV 1(1–22), 1–2 11. https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri

Determination of Intrinsic Parameters of PV Module Using Pattern Search Mohamed Rezki1(B) , Ghania Ouadfel2 , Hamza Houassine1 , and Samir Bensaid1 1 Electrical Engineering Department, Bouira University, Bouira, Algeria

[email protected] 2 Faculty of Technology, University Yahia Fares, 26000 Médéa, Algeria

Abstract. It is a paper that deals with the modelling of photovoltaic cells/modules based on the use of techniques from Artificial Intelligence. The model sought to be determined is the classical model widely commented on in the literature, which is that of a single diode and five parameters (Saturation current, ideality factor, series and shunt resistances). These parameters are intrinsically related to the photovoltaic cell model. As far as our work is concerned, it is to apply an intelligent technique in order to extract these parameters and then calculate the statistical error compared to the data given by the manufacturer. The technique in question is pattern search. To give a plus to this study, simulations on the effect of the variation of these parameters on the characteristics of the PV cell/modules were carried out. Finally, the application of different techniques offered by artificial intelligence can only be beneficial as long as we have not had a final and definitive model PV cell/module. Keywords: PV modelling · Artificial intelligence · Intrinsic parameters · Pattern search

1 Introduction The energy challenge remains a crucial issue and with the current geopolitical context, the interest of renewable energies and especially that of photovoltaic has increased [1, 2]. The exploit of solar energy and the increase in its effectiveness has attracted the interest of many scientists via various studies [3]. The modeling of photovoltaic cells or their grouping as modules is still relevant and until now we still hesitate between the model of a single diode or the model of two diodes or even that of three diodes [4, 5]. Indeed, each one of them has advantages and disadvantages but the model of a single diode still finds its interest given its simplicity [6]. The problem of this modeling comes from the fact of the presence of nonlinearity’s term in the governing equation which explains the operation of the photovoltaic cell. And to solve this equation, the researchers used either traditional techniques based on iterative methods or techniques derived from artificial intelligence, especially those inspired by nature, such as Genetic Algorithm (GA) [7], PSO algorithm [8], Differential evolution © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 106–112, 2023. https://doi.org/10.1007/978-3-031-21216-1_12

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(DE) algorithm [9], Cuckoo search (CS) [10], Flower pollination algorithm (FPA) [11], and so on. To see the effectiveness of such a technique or another the calculation of errors such as the Root Mean Square Error RMSE is required [12]. If we take the widely used model, that of a single diode, we will have the representative diagram of the following photovoltaic cell (Fig. 1). This model gives as a governing equation, the following:   q(v+R ·I)     S q(v + RS I) AkT (1) −1 − I = Iph − Irs e Rsh For the module which is a grouping of PV cells, the equation will be the next:   q(v+R ·I)     S q(v + RS I) I = Np Iph − Np Irs e A.k.T.NS − 1 − Np NS .Rsh

(2)

where that ‘Iph’ is the photocurrent, series resistor noted “Rs”, saturation current “I0 ”, parallel resistor “Rsh” and the ideality factor “A”. And Np with Ns are the number of solar cells connected in parallel and in series respectively.

Fig. 1. Single diode model of a PV cell.

From the equations above, we see the problem of non-linearity due to the presence of the diode openly. And that’s a good reflection of the problem. To discuss this topic, this paper will be organized as follows: Sect. 2, which presents effect of variation of the mainly PV cell/module parameters. In Sect. 3, we present the pattern search technique, followed by the different results and discussion that goes with it in Sect. 4. Finally, we end this paper with a short explanatory conclusion.

2 Effect of Variation of the Mainly PV Cell/Module Parameters 2.1 Effect of Series Resistance (Rs) Due to Changing Materials on the Performance of PV One of the objectives is to see the effect of changing the series resistance from changing the material, whether it is the rear and top metal contacts or changing the materials used in the metal-semiconductor junction. Indeed, its impact is seen through the reduction of the fill factor or simply the maximum power point. As a first result, the Fig. 2 clearly shows this effect, which is harmful

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because the greater this resistance, the more the power of the module decreases. To increase the performance of the photovoltaic module, the value of the series resistance must be reduced as much as possible, ideally being equal to zero.

Fig. 2. I-V & PV curves of BP SX 150S PV module across different values of Rs.

2.2 Effect of Changing Number of Serial Cells on the Performance of PV We have simulated different numbers of cells to be associated in series for the module BP SX 150S to see the curve visualizing the output of this module (I (V)), see Fig. 3. As a remark, we can deduce that the more we increase the number of cells connected in series, the more we will have a high voltage at the output but the output current remains unchanged.

Fig. 3. I-V curve of BP SX 150S PV module across association of different numbers of cells in series

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2.3 Effect of Changing of Material Type on the Performance of PV As we know, the majority of photovoltaic modules are built from semiconductors and according to Eq. (2), the ideality factor which characterizes this semiconductor influences the output current of the PV module, knowing that ideally this factor is equal to 1. We changed this factor from 1 to 2 (most used upper limit), the result of this application can be seen in the following curve (Fig. 4):

Fig. 4. I-V curve of BP SX 150s PV module according to the change of the type of the material

Figure 4 shows the variation of the I-V curve according to changing type of material and as it can be seen the more the ideality factor more the maximum point of the power decreases and therefore the efficiency of the PV module decreases too. 2.4 Effect of Changing Number of Cells in Parallel on the Performance of PV This time, we changed the number of cells with parallel mounting (Fig. 5), this type of assembly gave us a stabilization of the voltage but the output current has clearly increased.

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Fig. 5. I-V curve of BP SX 150S PV module across association of different numbers of cells in parallel.

3 Extarction of Intrinsec Parameters of PV Module – Results and Discussion The PV module that we have chosen is a polycristalline module (DT050P-12) from DENGTAL Solar manufactorer wth the following characteristics (Table1): Table 1. Manufacturing datasheet of DT050P-12 PV module— at standard test conditions (STC) (AM 1.5G, 1000 W/m2 and 25 °C) Type of electrical characteristics

Value

Max. Power (Pm)

50 W

Max. Power Voltage (Vmp)

17.4 V

Max. Power Current (Imp)

2.87 A

Open Circuit Voltage (Voc)

22.0 V

Short Circuit Current (Isc)

3.16 A

Cell efficiency

14.3%

No. of Cells (pcs)

36

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For resolving the non linear equation (Eq. (2)) in order to get the intrinsec parameters that characterise the single diode model, we have used as a method the pattern search. Let’s recall that this latter also named direct search is an optimisation technique based on the convergence. The pattern moves at each itteration to the point which best minimizes its objective function. Tne inventors of this technique were robert Hooke and T.A.Jeeves in 1961 [13]. By applying the program with taking into account the data collected from the manufactorer of the DT050P-12 PVmodule, we get the following curve (see Fig. 6):

Fig. 6. Pttern search model of the single diode model the DT050P-12 PV module.

The applied model search method gives us as optimal solution the following parameters (Table 2): Table 2. Determined parameters from application of Pattern search Ideality factor

Series resistance []

Shunt resistance []

0.362

1.042

635016

To validate our results, we calculated the Root Mean Square Error (RMSE) which is widely used in this kind of cases.$ We get the following: RMSE = 0.0911. It is clearly seen that the calculated error is minimal which is in favor of our work.

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4 Conclusion A determination of intrinsic parameters of a photovoltaic module through a computational technique has been made. The technique used was the Pattern Search. The technique used has proven its effectiveness because we had minimal errors when we calculated RMSE. As future perspective, we plan to diversify our techniques by using different algorithms and doing by the way a comparison between them.

References 1. Sinke, W.C.: Development of photovoltaic technologies for global impact. Renew. Energy 138, 911–914 (2019) 2. Sharma, V.K., et al.: Imperative role of photovoltaic and concentrating solar power technologies towards renewable energy generation. Int. J. Photoenergy 2022, 13 (2022) 3. Rezki, M., et al.: Opportunities of the silicon technology in algeria. Int. J. Res. Eng. Sci. (IJRES) 1, .01–04 (2013) 4. Harrag, A., Daili, Y.: Three-diodes PV model parameters extraction using PSO algorithm. Revue des Energies Renouvelables 22(1), 85–91 (2019) 5. Prakash, S.B., et al.: Modeling and performance analysis of simplified two-diode model of photovoltaic cells. Front. Phys. 9, 1–9 (2021) 6. Senthilkumar, S., et al., Analysis of single-diode PV model and optimized MPPT model for different environmental conditions. Int. Trans. Electr. Energy Syst. 2022, 17 (2021) 7. Zhang, L., Bai, Y.F.: Genetic algorithm-trained radial basis function neural networks for modelling photovoltaic panels. Eng. Appl. Artif. Intell. 18, 833–44 (2005) 8. Rezki, M., Bensaid, S., Griche, I., Houassine, H.: Five PV model parameters determination through PSO and genetic algorithm, a comparative study. In: ICAIRES 2019, LNNS 102, pp. 14–21 (2020) 9. Shanka, N., Saravanakumar, N., Indu Rani, B.: Solar photovoltaic module parameter estimation with an enhanced differential evolutionary algorithm using the manufacturer’s datasheet information 224, 16 (2020) 10. Maand, J., et al.: Parameter estimation of photovoltaic models via cuckoo search. J. Appl. Math. 2013, 8 (2013) 11. Alam, D.F., Yousri, D.A., Eteiba, M.B.: Flower pollination algorithm based solar PV parameter estimation. Energy Convers. Manag. 101, 410–422 (2015) 12. Askarzadeh, A., Rezazadeh, A.: Parameter identification for solar cell models using harmony search-based algorithms. Sol. Energy 86, 3241–3249 (2012) 13. Hooke, R., Jeeves, T.A.: “Direct search” solution of numerical and statistical problems. J. ACM 8(2), 212–229 (1961)

Sensing and Communication in Renewable Energy

Development of a Supervision/Control Interface for an Experimental Wind-Storage-Grid-Diesel Microgrid System Djohra Saheb-Koussa(B) , Mustapha Koussa, Saida Makhloufi, Naserdine Belhaouas, Farid Hadjrioua, Azzedine Aissaoui, and Khaled Bakria Centre de Développement des Energies Renouvelables, BP. 62 Route de l’Observatoire Bouzareah, 16340 Algiers, Algeria

Abstract. Wind energy source with storage system coupled to diesel or grid offers a promising prospect for covering the fundamental needs of power without interruption. In this context, this study proposes and demonstrates an energy management, operation and control concepts of the micro-grid system (wind-storagediesel-grid) in both stand-alone and interconnected mode in Bouzareah (Algeria). The micro-grid system is composed by two wind turbines, storage system, diesel generator and grid. In order to ensure an optimum exploitation of the produced energy without interruption, a novel power management algorithm is developed to control the different energy flows exchanged among the system components. This algorithm supervises the batteries state of charge and determines which source ensure the continuous supply of the installation by favoring wind energy. The instantaneous simulation of the micro-grid system with the proposed algorithm of the energy supervision algorithm are carried out by new graphical interfaces developed by using the MATLAB package. These interfaces facilitate the simulation and display the different curves describing the micro-grid system behavior. Therefore, the developed control software is open source in order to implement different intelligent techniques.The obtained results indicate that the developed control strategies provide good installation autonomy. Keywords: Controller · Supervisor · Interface · Experimental · Microgrid system

1 Introduction The development and the use of renewable energies has grown significantly in recent years. Naturally decentralized, it is interesting to exploit them locally, by transforming them directly either into heat or into electricity according to needs. Decentralized production of electricity by renewable energy sources offers greater secure energy supply to the consumers all with good respect for the environment. So, this study will focus on wind in the microgrid System as a power generation source because it seems that wind energy is the best shared resources of energies and therefore those that lend themselves best to the decentralized production of electricity [1–9]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 115–125, 2023. https://doi.org/10.1007/978-3-031-21216-1_13

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In the literature, there are a lot of studies related to energy management of microgrid System [10–14]. Therefore, previous studies have shown the usefulness of the experimental part to lead to concrete conclusions [15–21]. It was in this context that is registered the present work realized in the CDER (Renewable Energies Development Center), which will encourage and raise awareness of the use of wind energy. Therefore, the present study proposes and demonstrates an energy management, Operation and control concepts of the micro-grid system (wind-storage-diesel-grid) in both standalone and interconnected mode in Bouzareah (Algeria). Therefore, an algorithm was developed and implemented in ARDUINO to ensure proper and intelligent supervision, which guarantees the optimal use of energy without interruption by favoring wind energy and lead to the obtaining of an intelligent network allowing for the security of the complete system components, with the integration of preventive maintenance [15, 22, 23]. Therefore, for the real time supervision of the system behavior and by using the registered data, the computing code is implemented in MATLAB software and an open source graphical interface is designed in order to simplify the program manipulation and testing.

2 The Proposed Algorithm 2.1 The Experimental Micro Grid Description The following modules [15, 24, 25] compose the prototype that is currently in service at Bouzareah, Algiers, (see Fig. 1). Two wind turbines Whisper 200 and Whisper 100, the batteries, a diesel engine, the electrical Grid, a novel control strategy and two inverters: the first one operates in standalone mode and the second one: provides the interconnection to the grid. The overall architecture adopted for the experimental platform is shown below:

Fig. 1. The experimental micro grid system

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2.2 Energy Management Algorithm • Functional details [26] The proposed microgrid is operated as a smart system by an energy management and control system. Therefore, as described in Sect. 2, the considered system contains a storage system. Thus, during its use, the battery can be in the following cases: 1. Overload when its state of charge (SOC) is greater than 80%; 2. Deep discharge when its state of charge (SOC) decreases below 30%. Based on these two cases and in the view of protecting the battery, an algorithm for managing the micro-grid system was developed. The role of the developed and proposed algorithm for the management is as follows: In this study, the type of load demands is AC to be satisfied by system. The difference between wind power generation and load demand at any time is P. Case 1: At time t if P(t) < 0, then discharge the battery with prevention of deep discharges where SOC (t) must be superior to 30%. SOC(t) = SOC(t − 1) −

P(t) PnB

(1)

With PnB is the nominal battery capacity. Case 2: At time t if P(t) > 0, then charge the battery with prevention of battery overcharging where SOC (t) must be inferior or equal to 80%. SOC(t) = SOC(t − 1) +

P(t) PnB

(2)

Case 3: At time t if P(t) = 0, then there is no power exchange and the total demand is met by the wind generation. In this case, the wind source is promoted as much as possible. Case 4: The manager directs the surplus energy to the grid when the procedure mentioned in case 2 indicated that SOC (t) is superior then 80%. Case 5: Engaging the electrical network if the procedure mentioned in case 1 indicated that SOC (t) is inferior then 30%. Therefore, if the grid is unavailable, a diesel generator will be hunted. Case 6: Completion of four attempts to start up the diesel generator, respecting its response time and the battery state of charge if the procedure mentioned in case 5 indicated that a diesel generator is engaging. Case 7: In the case of a power supply failure, an audible alarm will sound proclaiming a flawed condition. The suggested control strategy approach flowchart and the Energy management system are illustrated respectively in Fig. 2 and Fig. 3.

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Fig. 2. The flowchart of the proposed control strategy

Fig. 3. The energy management system

2.3 The Supervisor/Controller Interface Development The program interface is designed using the ToolBox ‘Guide’ of MATLAB. It has been chosen for the easy use of the analysis and the flexibility to enter the parameters by the user; so, the developed program is based on the real time recorded data by the experimental micro-grid production system, Wind / Grid/ Diesel, including an energy storage system operating in standalone mode or connected to the grid. Figure 4 shows the program main windows, which are used in the developed interface. The main menu consists of the following sub menu: – The principal technical program; Economical study; Wind resource; Temperature; Statistical study.

3 Results and Discussion More information and results can be obtained by holding on pointer over an element in diagram given in Fig. 5 below.

Fig. 4. Program main windows

3.1 The Principal Technical Program As shown in Fig. 5 the principal program is composed by different sub-system component. Furthermore, the detailed information concerning the measured parameters and

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the characteristics of each component can be obtained by holding on pointer over an element. Therefore, from Fig. 6, it can observed the detailed wind turbine characteristics where the user can choose the wind turbine type and enter capital, maintenance and O&M costs including the cost of tower, wiring, controller, labor and installation. From this sub interface, the user can also see the detailed information relative to the chosen wind turbine with the possibility to introduce a new one.

Fig. 5. The principal technical program

Fig. 6. Program windgenerator main windows (1), (2) windgenerator details, and (3) new windgenerator

The graphical program permitting to visualize the recorded three-phase current at the input of the regulator as well as the direct current at its output is given in Fig. 7 below.

Fig. 7. Program of the regulator system main windows

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Figure 8 (1 and 2) shows storage system characteristic and the corresponding measured voltage obtained by holding on pointer over the red box in Fig. 8 (1). Concerning the Program of the autonomous inverter main windows shown in Fig. 9 (1) and the inverter voltages details in Fig. 9 (2). Figure 10 shows the Algerian grid properties, the kWh price with and without TTC and the load voltage ensured by the grid. Figure 11 (1) shows the Program of the diesel generator main windows. The display of the cost details, fuels type and emissions was obtained by clicking on desired one as shown in Fig. 11(2, 3 and 4). Furthermore, the Diesel Generator voltages details shown in Fig. 11 (2.1) were obtained by clicking on the Diesel voltage button of Fig. 11 (2) and the details of the chosen fuel as shown in Fig. 11 (3.1) were obtained by clicking on the details button of Fig. 11(3).

Fig. 8. Program storage system main windows (1), (2) Batteries voltage details

Fig. 9. Program autonomous inverter main windows (1), (2) inverter voltages details

Fig. 10. The Algerian grid properties

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Figure 12 shows the load characteristics window where the user has two possibilities to choose a data: • Introducing the data manually • Download the data from a specific file Concerning, the developed Program of the managing system main windows based on the algorithm cited on the Sect. 2.2 below is showed in Fig. 13. Furthermore, the input and the output voltages details in Fig. 13 (2 and 3) were displayed by clicking respectively on the input and the output button of Fig. 13 (1). From these results, it can be concluded that the behavior of the developed control strategy system is able to meet the system requirements and conditions imposed by flowchart illustrated in Sect. 2.2. Finally and in the context of Sect. 3.1 relative to the principal technical program, we present in Fig. 14 the Program of the monitoring and diagnosis system main windows in which all information concerning the system is displayed in real time. 3.2 Economic Study Regarding the economic balance sheet, the considered factors taken into account are as follows: The choice of the site; Investment; Total price installed; Annual depreciation relative to the system without batteries and that of batteries alone; Total annual cost; Total cost discounted; The cost of kWh. Thus, results for three lifetimes and three interest rates are displayed as a window as indicated in Fig. 15. 3.3 Wind Resource Information To get the data potential of wind power, the data acquisition sky instruments of wind speed, and wind direction was used. The aim of this section is to generate a device for wind speed and wind direction with the real time condition as shown in Fig. 16 (1). With this device and by clicking on the plot button, we obtain an analysis about the potential of wind power electrical generation around the studied site as indicated in Fig. 16 (2). 3.4 Temperature Information The temperatures are also generated with the real time condition as shown in Fig. 17. Three temperatures type are presented: Ambiant temperature, the first battery temperature and the last battery temperature.

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Fig. 11. Program of the diesel generator main windows

Fig. 12. The load characteristics window

Fig. 13. Program of the managing system main windows

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3.5 The Statistical Study The objective of the statistical analysis in the context of this work is to study from recorded observations a set of events, phenomena, analyze and put into perspective. The obtained results are presented in Fig. 18. In this context, the considered factors taken into account are as follows: Statistical analysis; Daily average values obtained from hourly ones; Electrical production; Daily load information; Daily and global produced and consumed energy; Correlation coefficient between different recorded parameters.

Fig. 14. The Program of the monitoring and diagnosis system main windows

Fig. 15. Program of the economic study main windows

Fig. 16. Program of wind resource information main windows

Fig. 17. Program of the temperatures information main windows

Fig. 18. Program of the statistical study main windows

4 Conclusion In this manuscript, a supervisor/controller interface is proposed and developed relative to an experimental Wind-Storage-Grid-Diesel Micro-grid system based on an intelligent

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load management algorithm. Monitoring the functioning of the experimental platform was achieved through a graphical interface developed MATLAB software for local supervision and devices production units and storage. It also oversees the actual management of all these production and storage facilities in order to optimize the services provided to the micro-grids. A developed graphical interface not only involves the development of MATLAB software for monitoring purposes but also includes the development of the sensor system on the one hand. On the other hand, it is designed such as to provide ease of use, be practical and interactive. Therefore, the developed control software is open source in order to implement different intelligent techniques. Finally, the simulation and the experimentation results visualized by the developed interface validate the expected conception of the micro grid supervision and this tool contributes to the enhancement of education and research the field of renewable energy and distributed energy systems.

References 1. Komiyama, R., Zhidong, L., Ito, K.: World energy outlook in 2020 focusing on China’s energy impacts on the world and Northeast Asia. Int. J. Global Energy Issues 24(3–4), 183–210 (2005) 2. Global Wind Energy Council: Global wind report 2016–annual market update. Global Wind Energy Council, Brussels, Belgium (2016) 3. Stambouli, A.B., et al.: A review on the renewable energy development in Algeria: current perspective, energy scenario and sustainability issues. Renew. Sustain. Energy Rev. 16(7), 4445–4460 (2012) 4. Khiat, S.: Modeling and real time digital simulation of microgrids for campuses Malta and Jordan based on multiple distributed energy resources. Indonesian J. Electr. Eng. Comput. Sci. 21(2) (2021) 5. Dweekat, A.A., Shaaban, M., Ngu, S.S.: On the dispatch of minigrids with large penetration levels of variable renewable energy. In: IAES, vol. 21, no. 2 (2021) 6. Sami, S.A., Mahmood, A.L.: Design and simulation of stand-alone photovoltaic system supplying BTS in Iraq. Int. J. Power Electr. Drive Syst. 12(1) (2020) 7. Amin, M.N., Soliman, M.A., Hasanien, H.M., Abdelaziz, A.Y.: Hybrid PSO-GSA algorithmbased optimal control strategy for performance enhancement of a grid-connected wind generator. Int. J. Appl. Power Eng. 1(10) (2021) 8. Wang, X.L., et al.: A two-stage optimal dispatching model for provincial and regional power grids connected with wind farms to promote accommodation of wind power. Power Syst. Technol. 39(7), 1833–1828 (2015) 9. Huang, C., Li, F., Jin, Z.: Maximum power point tracking strategy for large-scale wind generation systems considering wind turbine dynamics. IEEE Trans. Industr. Electron. 62(4), 2530–2539 (2015) 10. Shafiullah, G.M., et al.: Potential challenges of integrating large-scale wind energy into the power grid – a review. Renew. Sustain. Energy Rev. 20, 306–321 (2013) 11. Vautard, R., et al.: Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms. Nat. Commun. 5, ncomms4196 (2014) 12. Xiang, H., et al. Generating units maintenance scheduling considering peak regulation pressure with large-scale wind farms. In: 2016 China International Conference on Electricity Distribution (CICED), IEEE (2016) 13. Badger, J., Volker, P.J.H.: Efficient large-scale wind turbine deployment can meet global electricity generation needs. Proc. Natl. Acad. Sci. USA 114, E8945 (2017)

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14. Dursun, E., Kilic, O.: Comparative evaluation of different power management strategies of a stand-alone PV/Wind/PEMFC hybrid power system. Electr. Power Energy Syst. 34(1), 81–89 (2012). https://doi.org/10.1016/j.ijepes.2011.08.025 15. Aissou, S., et al.: Modeling and control of hybrid photovoltaic wind power system with battery storage. Energy Convers. Manage. 89, 615–625 (2015) 16. Tankari, M.A., et al.: Use of ultra capacitors and batteries for efficient energy management in wind–diesel hybrid system. IEEE Trans. Sustain. Energy 4(2), 414–424 (2013) 17. Tani, A., Camara, M.B., Dakyo, B.: Energy management in the decentralized generation systems based on renewable energy ultracapacitors and battery to compensate the wind/load power fluctuations. IEEE Trans. Industry Appl. 51(2), 1817–1827 (2015) 18. Carta, J.A., et al.: Preliminary experimental analysis of a small-scale prototype SWRO desalination plant, designed for continuous adjustment of its energy consumption to the widely varying power generated by a stand-alone wind turbine. Appl. Energy 137, 222–239 (2015) 19. Gan, L.K., Shek, J.K.H., Mueller, M.A.: Analysis of tower shadow effects on battery lifetime in standalone hybrid wind-diesel-battery systems. In: IEEE Transactions on Industrial Electronics (2017) 20. Olatomiwa, L., et al.: Energy management strategies in hybrid renewable energy systems: a review. Renewable Sustainable Energy Rev. 62, 821–835 (2016) 21. https://www.connaissancedesenergies.org/qu-est-ce-que-le-petit-eolien 22. Saharia, B.J., Manas, M.: Viability analysis of photovoltaic/wind hybrid distributed generation in an isolated community of Northeastern India. Distrib. Gener. Altern. Energy J. 32(1), 49–80 (2017) 23. Saheb-Koussa, D., et al.: Fuzzy logic management supervisor for wind-diesel-battery hybrid energy system. In: Renewable Energy Congress (IREC), 2016 7th International, IEEE (2016) 24. Koussa, D.S., et al.: Simulation of a wind generator coupled to a diesel generator. In: Renewable Energy Congress (IREC), 2016 7th International, IEEE (2016) 25. Pan, H., Hou, E., Ansari, N.: M-NOTE: a multi-part ballot based-voting system with clash attack protection. In: 2015 IEEE International Conference on Communications (ICC), London, pp. 7433–7437 (2015) 26. Pan, H., Hou, E., Ansari, N.: Re-note: an e-voting scheme based on ring signature and clash attack protection. In: 2013 IEEE Global CommunicationsConference (Globecom), IEEE, pp. 867–871 (2013)

Design of Smart Irrigation System in the Greenhouse Using WSN and Renewable Energies Achouak Touhami1,3,4(B) , Sana Touhami3,5 , Nawal Touhami2,6 , Khelifa Benahmed3 , and Fateh Bounaama2,4 1 Department of Mathematics and Computer Science, Ali Kafi University Center, Tindouf,

Algeria [email protected] 2 Department of Electrical Engineering, Tahri Mohamed University, Bechar, Algeria 3 Department of Mathematics and Computer Science, Tahri Mohamed University, Bechar, Algeria 4 Energetic in Arid Zones Laboratory, Tahri Mohamed University, Bechar, Algeria 5 Information and Telecommunication Laboratory, Tahri Mohamed University, Bechar, Algeria 6 Smart Grids and Renewable Energies Laboratory, Tahri Mohamed University, Bechar, Algeria

Abstract. Water is becoming a high demand resource in most of the countries due to the increasing growth of population and industries. To ensure that water does not cause problems to the crops, sensors can be employed to guarantee the quality of the water used for irrigation. In this paper, we propose a design of irrigation system in the greenhouse using wireless sensor networks and renewable energy. In addition, the system is composed of algorithms that are responsible for controlling soil moisture and detection of water tank level. Keywords: Water · Greenhouse · Irrigation system · Wireless sensor networks · Renewable energy · Soil moisture

1 Introduction Over recent years, the demand of fruits and vegetables is increasing [1]. From where several countries have directed to agriculture under the greenhouses [2]. A greenhouse is an enclosure in which plants are grown [3] with good health anyplace and before the developing season [2, 4]. Agriculture uses 85% of available freshwater resources worldwide, and this percentage will continue to be dominant in water consumption because of population growth and increased food demand [5]. There is an urgent need to create strategies based on science and technology for sustainable use of water, including technical, agronomic, managerial, and institutional improvements [6]. Irrigation is the method of watering the soil. The soil water requirement depends on soil properties like moisture of soil and the crop which is grown in the soil [7]. To © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 126–131, 2023. https://doi.org/10.1007/978-3-031-21216-1_14

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ensure that the water does not cause any problems for the plants, sensors can be deployed to ensure the quality of the water used for irrigation. Also, to reduce water usage for irrigation, we have to use smart sensor-based systems. Wireless sensor networks (WSN) can be used to determine water quality and irrigation needs in irrigation [8]. In this paper, we propose a design of irrigation system in the greenhouse using wireless sensor networks, actuators, microcontroller and renewable energy. In addition, the system is composed of algorithms that are responsible for controlling soil moisture and detection of water tank level. The rest of the paper is structured as follows. The related work in this axis of research is presented in the second section. In the third section, we propose a design of smart irrigation system in the greenhouse, we also propose the algorithms that control soil moisture of our greenhouse and detect the water tank level. The last section is dedicated to the conclusion of our work with some perspectives that we hope to realize in the future.

2 Related Works There are many works and researches that have been done in the field of smart irrigation system, to mention but a few. An automated irrigation system was developed [6]. The system uses a distributed wireless network of soil-moisture and temperature sensors, a gateway unit handles sensor information, triggers actuators, and transmits data to a web application. An algorithm was developed with threshold values. It was programmed into a microcontroller. The system was powered by photovoltaic panels. The authors of [9] proposed a new water-saving intelligent irrigation system. The system is designed on the basis of information technology and communication for environmental monitoring using ZigBee wireless sensor network (WSN) and renewable energy technologies. This paper presents an intelligent algorithm for irrigation automation and management. In [7] had analyzed the profitability of photovoltaic (PV) systems for irrigating Mediterranean greenhouse crops. A standalone direct pumping PV system is proposed. A simulation model of the system was developed. The model is composed of several submodels: the photovoltaic power generation capacity submodel, the direct pumping management submodel and the submodel that evaluates the irrigation water requirements. The authors of [10] used a three-step approach to design, implement, and validate a greenhouse smart strawberry irrigation solution. They developed a small smart irrigation prototype solution using off-the-shelf hardware and software equipment. They introduced a reference network architecture specifically for smart irrigation and edge data distribution in strawberry greenhouses. They took the proposed reference architecture and implemented a complete system in a real strawberry greenhouse environment in Greece. An algorithm based on wireless sensor network technologies and linear equations model were proposed in [2]. The proposed algorithm monitors the microclimate inside a greenhouse. In addition, they also proposed a novel smart greenhouse design. They

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validated their algorithm by running simulations on benchmarks based on experimental data produced at lNRA by Montfavet, France.

3 Our Suggested Approach 3.1 The System Design Our system design (Fig. 1) is composed of: – Wireless sensor network system. – Irrigation system. – Renewable energy power system. Wireless sensor network system contains soil moisture sensors (EC-5), a clusterhead (CH), a microcontroller, an actuator in electro-valve and in water pump, a sensor in water tank. – – – –

Soil moisture sensors: Measure soil moisture of the greenhouse. CH: Compares the data with the threshold. Microcontroller: Sends commands to the actuators. Sensor of the water tank: Detects the level water in the tank.

Irrigation system contains water pump, well, water tank, electro-valve and sprinklers in the greenhouse. – Water pump: Moves water from the well to the tank, if the tank is empty of water. – Electro-valve: Sprays the plants via the sprinklers. Renewable energy power system contains a solar panel, a charger controller, a battery and a converter. – Solar panel: Converts the solar radiation into electricity. The solar panel should be oriented towards the sun to maximize solar energy output. – Charger controller: Regulates the voltage coming from the solar panel to the battery, hence protecting the battery from overcharging. – Battery: Stores and accumulates the electrical energy to restore it. – Inverter: Changes the direct current from the solar panel into alternating current for the pump. Our system design is usefulness in reality exceptionally in the south of Algeria. To use it, we need: – A greenhouse. – The three proposed systems with their materials.

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Fig. 1. Our proposed irrigation system design.

3.2 Control System Firstly, system checks the status of our solar power (SP) and compares it with a set point. If our SP is less than the set point, we turn on another energy resource (like: wind energy). Once our system is on, the deployed soil moisture (SM) sensors (Fig. 2) collect the information and send it to the cluster-head (CH) where it will be compared with threshold values. Then, the CH sends only the information below threshold to the microcontroller.

Fig. 2. Flowchart of soil moisture control.

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The microcontroller obtains the information, and sends a command to the actuator of the electro-valve. The electro-valve is going to be on, otherwise the valve stays off until the comparison gives the other result. Firstly, system checks the status of our solar power (SP) and compares it with a set point. If our SP is less than the set point, we turn on another energy resource (like: wind energy). Else, we define the level water in the tank. Then (Fig. 3), the sensor of the water tank detects the level water in the tank, and transmits the collected data to the CH. The microcontroller sends a command to the actuator of the pump to activate (if the tank is empty of water) and to deactivate (if the tank is full of water).

Fig. 3. Flowchart of detection of water tank level.

To determinate the threshold value, you should know the climatic conditions and plant types. 3.3 Advantages of the Proposed System The main benefits of this system are: – – – – – –

Clear and easy system to use it. High quality of crops. Optimize water use for agricultural crops Automation of the irrigation process. The system is based on sustainable and renewable energy. Our system helps farmers to irrigate plants exceptionally in the summer season and in the south of Algeria.

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4 Conclusion Life in the south of Algeria is very hard exceptionally in the summer season. Farmers need an automatic system for irrigation to help them. For this, in this paper, we have proposed a design of irrigation system in the greenhouse using wireless sensor networks and renewable energy. Also, we proposed algorithms that are responsible for controlling soil moisture and detection of water tank level. We treated, in this system, two parts. The first part is detecting the level of water in the tank and moving water from the well to the tank by the water pump. The second part is to irrigate by sprinkles the plants according to the needs. As a perspective, we plan to validate our algorithms with real data. Finally, this paper is the continuity of our published papers [2–4].

References 1. Ghosh, A., Chakraborty, S., Ghosh, A., Mondal, P., Mondal, A., Guha, M.: A smart irrigation system. In: Proceedings of 2018 IEEE Applied Signal Processing Conference, pp. 110–113 (2018) 2. Touhami, A., Benahmed, K., Parra, L., Bounaama, F., Lloret, J.: An intelligent monitoring of greenhouse using wireless sensor networks. Smart Struct. Syst. 26(1), 117–134 (2020). https://doi.org/10.12989/sss.2020.26.1.117 3. Touhami, A., Benahmed, K., Bounaama, F.: Monitoring of greenhouse based on internet of things and wireless sensor network. In: Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), vol. 2, pp. 1–9 (2020) 4. Achouak, T., Khelifa, B., Garcia, L., Parra, L., Lloret, J., Fateh, B.: Sensor network proposal for greenhouse automation placed at the south of Algeria. Network Protocols and Algorithms 10(4), 53–69 (2018). https://doi.org/10.5296/npa.v10i4.14155 5. Jury, W.A., Vaux, H.J.: The emerging global water crisis: managing scarcity and conflict between water users. Adv. Agron. 95, 1–76 (2007) 6. Gutiérrez, J., Villa-Medina, J.F., Nieto-Garibay, A., Porta-Gándara, M.Á.: Automated irrigation system using a wireless sensor network and GPRS module. IEEE Trans. Instrum. Meas. 63(1), 166–176 (2014) 7. Sanglikar, T., Puranik, V.G.: Design and implementation of automated irrigation control system using WSN: an overview. Int. J. Recent Innov. Trends Comput. Commun. 4(4), 157–161 (2016) 8. Parra, L.: Design of a WSN for smart irrigation in citrus plots with fault-tolerance and energysaving algorithms. In Network Protocols and Algorithms 10(2), 95–115 (2018). https://doi. org/10.5296/npa.v10i2.13205 9. Khelifa, B., Amel, D.: Design of a new smart irrigation system in the south of Algeria. In: International Conference on Information Technology for Organization Development (2014) 10. Angelopoulos, C.M., Filios, G., Nikoletseas, S., Raptis, T.P.: Keeping data at the edge of smart irrigation networks: a case study in strawberry greenhouses. Comput. Netw. 167 (2019)

Application of Metamaterials Based on Resonators -e- for the Design of Miniature Planar Antennas Becharef Kada1(B) , Nouri Keltouma2 , Bouazza Nadjet Nadia2 , Daoudi Wafaa2 , Abes Turkiya2 , and Saidi Amaria2 1 Division of Research in Education Technology, National Institute for Research in Education,

Al Achour, Algeria [email protected] 2 LTC Laboratory, Department of Electronic, Faculty of Technology, University of Saida-Dr. Moulay Tahar, Saida, Algeria

Abstract. The main objective of this work was to contribute to the design and simulation of patch antennas based on metamaterials. Then we associate a new resonator -e- miniature with a rectangular patch antenna. Several antenna topologies based on different resonators -e- to improve different antenna parameters. The results obtained showed an interesting variation of the antenna parameters in terms of matching, bandwidth and gain. The antenna is fed by microstrip operating in the X band, using an FR4 type substrate of thickness h = 1.6 mm, of relative permittivity εr = 4.4. Keywords: Metamaterials · Miniaturization · Negative permeability · SRR · Patch antenna · Design · HFSS · Matlab

1 Introduction The rapid development of telecommunications systems has enabled the creation and innovation of several technologies. Metamaterials are one of the new discoveries of the last decade and are an exciting area of research, emerging and promising to bring important technological and scientific advancements in many important fields such as telecommunications, radar, defense, l medical imaging, etc. [1]. Metamaterials are artificial media with unusual electromagnetic properties. Their concept was first theorized by Russian physicist Victor Veselago [2, 3]. These are periodic, dielectric or metallic structures, which behave like homogeneous materials that do not exist in nature. There are several types of metamaterials in electromagnetism, the best known of which are those liable to present both negative permittivity and permeability [4]. Since their advent in the 2000, they have enabled many advances in electromagnetism and have opened up interesting prospects for microwave frequencies, whether for circuit applications (filters, phase shifters, etc.) or for radiation applications (antennas, diffraction, stealth). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 132–147, 2023. https://doi.org/10.1007/978-3-031-21216-1_15

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Among the potential benefits of these structures, let us quote for example the miniaturization of the antennas, the widening of their bandwidth, the reduction of the interelement coupling within a network or the increase of the efficiency (yield) of the antennas miniature antennas. The patch antenna made from metamaterials is designed to meet these needs, it is a metallic conductor of a particular shape placed on a substrate terminated by a ground plane; its miniature character offers the possibility of easily integrating it into transmission-reception systems. The aim of this article is to study and design printed “patch” antennas made from metamaterials. The design of the antenna will be demonstrated by determining its microwave parameters (resonant frequency, bandwidth, radiation pattern and gain) using software for electromagnetic simulation HFSS (High frequency structure simulator [5].

2 Extraction of Effective Parameters The principle of the Nicolson-Ross-Weir (NRW) method makes it possible to extract the index and the impedance of a composite medium from a simulation or an experiment [6, 7, 8]. It is valid only in normal incidence. This method was first applied in the context of metamaterials by Smith et al. [9]. Its validity is subject to the following conditions: to be able to assign an index to a material, only one propagative mode must exist in it at the frequency in question. The extraction of effective parameters is possible only in the case where the incident wavelength is much greater than the sizes and distances between the elementary constituents of the composite medium. The NRW method is simply based on the classical interference calculation giving the transmission and reflection of a layer of material according to its index (effective), its impedance (effective) and its thickness. By inverting these formulas, we deduce the values neff and Zeff depending on the thickness of the simulated layer at the transmission coefficient t = S21 and reflection r = S11 [9, 10]:      (1) Re(neff ) = ±Re arccos 1 − r 2 + t 2 /2t  /kd + 2π m/kd      Im(neff ) = ±ImRe(neff ) arccos 1 − r 2 + t 2 /2t  /kd

(2)

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neff Zeff

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3 Design of the Resonators Constituting the Metamaterials In this work, we proposed the design of a new e-shaped resonator which allows to have a negative permeability. The different dimensions of the resonator for operation in X band [8.2 GHz; 12.4 GHz] are given in Fig. 1. The shape of resonator ‘e’ is deposited on a RO4003C type substrate Rogers 3.38 relative permittivity and loss tangent of 0.00197.

Fig. 1. Topology and dimensions of an “e” shaped cell unit

The reflection S11 and transmission S21 coefficients of the resonator “e” obtained by means of HFSS software are presented in Fig. 2. We notice that the resonator has a transmission of –10.44 dB. For the resonant frequency 10.32 GHz. XY Plot 1

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Figure 3 illustrates the variations of the real and imaginary parts of the calculated effective permeability. It can be noted that at resonance (frés = 10.37 GHz), the real part of the permeability is negative in a frequency band around the resonance and takes values varying from 0 to –9.13dB. Outside this band, Re (μeff ) is positive.

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Fig. 3. Resonator -e- simulations, real and imaginary parts of the effective permeability.

4 Patch Antenna Design In the beginning of this work and to see the performance of the micro-ribbon line, we designed a rectangular patch antenna of dimensions (W p × Lp) mm2 , operating in the X band around the frequency 9.40 GHz, using a substrate of type FR4 of thickness h = 1.6 mm, of relative permittivity εr = 4.4 and of width ws of length Ls. The patch antenna is powered by a coaxial probe with a characteristic 50  impedance. The distance of the feed point from the edge of the antenna is (wf , lf ). The geometry of the antenna and their dimensions are shown in Fig. 4 and in Table 1.

Fig. 4. Geometry of the patch antenna

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This antenna was simulated with the HFSS software. Figure 5 shows the plots of parameter S11 as a function of frequency. This coefficient reaches its first peak of – 37.33 dB obtained at the frequency of 9.40 GHz and another peak S11 = –11.37 dB at the frequency 10.99 GHz. The bandwidth can be deduced from S11 by considering a certain threshold, generally –10 dB. From the following figure, it can be concluded that the antenna bandwidth is BP = 410 MHz. XY Plot 1

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The gain of the antenna as a function of Theta is also obtained using HFSS software in the E and H planes (Fig. 6.a). Figures 6.b and 6.c show the two-dimensional radiation patterns in the E and H planes and the total three-dimensional gain, respectively. We notice that this antenna has a low bandwidth. To improve the electrical performance of this antenna (gain, bandwidth and matching). To see the influence of the latter on the performance of this type of antennas, we then draw up a state of the art on some types of antennas based on metamaterials.

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5 Design of Patch Antennas Based on Metamaterials In order to assess the contribution of metamaterials to the antenna, a study on the antenna itself is carried out. As a first approach, the dimensions of the patch will be calculated

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according to the characteristics of the FR4 type substrate of relative permittivity εr = 4.4, tanδ = 0.02 and the thickness h = 1.6 mm. 5.1 Antenna Based on a Single Resonator -eIn the rest of this work, we will add an -e- type resonator to the patch antenna to design a new antenna based on metamaterials. This antenna was etched on the same substrate and fed by a coaxial cable. Figure 7 shows the geometry of the proposed antenna. The width of the substrate is 30 mm and its length is 35 mm. The size of the patch is 15 × 14.5 mm2 , the dimensions of resonator e are shown in Fig. 7.

Fig. 7. The structure of the patch antenna loaded by e-resonator (a) three-dimensional view, (b) two-dimensional view, (c) geometry of the e-resonator with r3 = 1.5 mm, g2 = 0.1 mm, g4 = 0.8 mm.

Figure 8 shows the plots of parameter S11 as a function of frequency. This coefficient reaches its first peak of –33.77 dB obtained at the frequency of 9.33 GHz. Showing good matching to this frequency. The bandwidth of this –10 dB antenna is around 0.37 GHz. Figure 9 (a) shows the variation of the total gain of this antenna as a function of the angle θ in the H and E planes (ϕ = 0° and ϕ = 90°). The maximum gain is 4.63 dB. XY Plot 1

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The curves in Fig. 9 (b) and (c) respectively show the radiation patterns representing the total gain for ϕ = 0° and 90° of the two- and three-dimensional antenna. XY Plot 2

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The total gain reaches a maximum value of around 4.63 dB at the 9.33 GHz resonant frequency. We see from the results from Fig. 9, that the resonator-based antenna -e- still needs optimizations in terms of gain and bandwidth. To achieve the best performance, several optimizations using HFSS of this resonatorbased antenna -e- are essential. 5.2 Sensitivity of the Response of the Resonator-Based Patch antenna -eto a Variation in the Geometric Parameters In our work, we estimated the sensitivity of the frequency response of the antenna to variations in the physical dimensions of the structure (radius r3 and width g2 ). Several simulations of the patch antenna have been obtained by changing the dimensions of the structure arbitrarily. From these simulations, we can know the most critical dimensions for a patch antenna associated with an e resonator. First, we changed the width of resonator g2 with the following steps: 0.2 mm, 0.4 mm, 0.6 mm, 0.8 mm keeping the radius r3 = 1.5 mm constant. We choose the width g2 which gives the best results then we set this width to a value of 0.2 mm, 0.4 mm, 0.6 mm, 0.8 mm and we change the radius r3 with a step of 1.5 mm, 2 mm, 2.5 mm, 3 mm. We fix the two previous dimensions which give the best results by changing the gap g4 . The different curves are shown in the following figure (Fig. 10).

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Fig. 10. Sensitivity of the response of the antenna to a variation in the geometric parameters: g2 , r3 and g4 . (a) Influence of the width of resonator g2 on the frequency response by setting the radius r3 = 1.5 mm, (b) influence of radius r3 on the frequency response by fixing the width of resonator g2 = 0.8 mm, influence of gap g4 on the frequency response by fixing g2 = 0.8 mm and r3 = 3 mm

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From the results of patch antenna simulations that were obtained by changing the dimensions of the structure, we find that the best dimensions for this patch antenna are: g2 = 0.8 mm and r3 = 3 mm. Using these latter values again brings us back to optimizing our results. Figure 11, illustrates the new frequency response of the optimized resonator-based patch antenna -e-, obtained using HFSS software. XY Plot 1

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After the simulation, we notice that our antenna shows an adaptation around the resonant frequency 10.20 GHz with a bandwidth of 1.12 GHz: BP = fmax − fmin with fmax = 10.94 GHz and fmin = 9.82 GHz ⇒ BP = 1.12 GHz Figure 12 shows the radiation patterns of this antenna at the resonant frequency fr = 10.20 GHz. Radiation Pattern 2

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The three-dimensional radiation pattern is shown in Fig. 13 at the frequency 10.20 GHz. The maximum gain is of the order of 5.24 dB.

Fig. 13. Variation of the total three-dimensional gain at the frequency 10.20 GHz

5.3 Antenna Based on Two Resonators -e- of Different Dimensions In order to further improve the antenna matching and bandwidth, we added a second resonator -e- to the previous antenna by reducing the size of the latter (Fig. 14).

Fig. 14. The structure of the patch antenna loaded by resonators -e- (a) two-dimensional view, (b) geometry of the resonator with R0 = 1.7 mm, g1 = 0.4 mm, g0 = 0.1 mm.

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The result of the simulation for the adaptation of the proposed antenna is illustrated in Fig. 15. The reflection coefficient S11 indicates an adaptation of about –25.16 dB at the frequency of 10.58 GHz and matching of –21.73 dB at the frequency 10.20 GHz. We notice that the bandwidth of this antenna is BP = 1.28 GHz at 10 dB. XY Plot 7

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The gain of the antenna as a function of Theta is also obtained using HFSS software in the E and H planes (Fig. 16.a). Figures (16.b) and (16.c) show respectively the two and three-dimensional radiation patterns in the E and H planes. All these results are obtained respectively at two frequencies: f = 10.20 GHz and f = 10.58 GHz. We observe that this antenna shows an improvement in total gain, this gain is of the order of 5.32 dB for the frequency 10.58 GHz, and of the order of 5.11 dB for the 10.20 GHz. 5.4 Antenna Based on Three Resonators -e- of Different Dimensions The third step, we added the third resonator -e-, while reducing their size (Fig. 17). The result of the reflection coefficient S11 of the antenna loaded by resonators -e- is presented in Fig. 18.

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We obtained an matching of –49.39 dB at the 9.97 GHz resonant frequency. We note an improvement in the reflection coefficient S11 with a widening of the band compared to previously studied antennas of the order of BP = 2.03 GHz.

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Fig. 17. The structure of the patch antenna loaded by three e-resonators (a) three-dimensional view, (b) two-dimensional view, (c) geometry of the e resonator with R1 = 1.5 mm, W1 = 0.4 mm, g1 = 0.6 mm. XY Plot 1

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Figure 19 shows the radiation patterns of this antenna at the resonant frequency 9.97 GHz. By comparing the simulation results of the different antennas based on the resonators -e- that we studied previously. Table 2 shows the different values of matching, gain and bandwidth for the different configurations.

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6 Conclusion In this article we have presented the Design of patch antennas based on metamaterials. Then we designed a patch antenna fed by a coaxial line to show their performance and limitations. This study allowed us to make a state of the art on other topologies of antennas based on metamaterials to improve the different parameters of patch antennas. we have associated with a basic antenna supplied by a coaxial line of the proposed -eform resonators: one resonator, two resonators, three resonators,….. We also presented the results of the simulations of these antennas obtained by HFSS, these are the various antenna parameters including the S11 parameter, the bandwidth and the gain.

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The objective of these additions is to improve the characteristic parameters of our antenna such as: S11 matching, bandwidth and gain. The application of these metamaterials has helped increase the performance of the antenna in terms of reflection coefficient, gain and bandwidth. Acknowledgments. This work was financially supported by the Directorate General of Scientific Research and Technological Development (DGRSDT) under the authority of the Ministry of higher education and scientific research MESRS.

References 1. Kada, B., Keltoum, N., Turkiya, A.: Design of bandpass filters based on metamaterials. In: 2019 International Conference on Advanced Electrical Engineering (ICAEE), Algeria, 19 Nov 2019, IEEE. https://doi.org/10.1109/ICAEE47123.2019.9014678. https://ieeexplore. ieee.org/abstract/document/9014678 2. Veselago, V.G.: The electrodynamics of substances with simultaneously negative values of ε and μ. Sov. Phys. Usp. 10, 509–514 (1968) 3. Pendry, J.B., Holden, A.J., Robbins, D.J., Stewart, W.J.: ’Magnetism from conductors and enhanced nonlinear phenomena’. IEEE Trans. Microwave Theory Tech. 47, 2075–2084 (1999) 4. Kada, B., Keltoum, N., Seddik, B.B., Mahdi, D., Chawki, B.T.H.: Design of array CSRRs band-stop filter. In: International Conference in Artificial Intelligence in Renewable Energetic Systems, pp. 87–98, 22 Oct 2017. https://doi.org/10.1007/978-3-319-73192 6_10 5. Naoui, S., Latrach, L., Gharsallah, A.: Metamaterials microstrip patch antenna for wireless communication RFID technology. Microw. Opt. Technol. Lett. 57(5), 1060–1066 (2015) 6. Kada, B., Keltoum, N., Turkiya, A.: Application of metamaterials for the design of antenna. In: The IEEE Sixth International Conference on the Image and Signal Processing and their Applications, Mostaganem, Algeria, 24 Nov 2019, IEEE. https://doi.org/10.1109/ISPA48 434.2019.8966859, https://ieeexplore.ieee.org/document/8966859 7. Nicolson, A.M., Ross, G.F.: Measurement of the intrinsic properties of materials by timedomain techniques. IEEE Trans. Instrum. Meas. 19(4), 377–382 (1970) 8. Kada, B., Keltoum, N., Seddik, B.B., Mahdi, D., Chawki, B.T.H.: Design of band-stop filter composed of array rectangular split ring resonators. J. Nano Electr. Phys. 10(2), 1–3 (2018) 9. Smith, D.R., Schultz, S., Markoš, P., Soukoulis, C.M.: Determination of effective permittivity and permeability of metamaterials from reflection and transmission coefficients. Phys. Rev. B 65, 195104 (2002) 10. Kada, B., Keltoum, N., Seddik, B.B., Mahdi, D., Chawki, B.T.H.: Design and simulation of a broadband bandpass filter based on complementary split ring resonator circular. In: CSRRs, Wireless Personal Communications, 16 Nov 2019 11. Kada, B., Keltoum, N., Turkiya, A.: Enhanced performance of substrate integrated waveguide bandstop filter based on metamaterials SCSRRs. In: IEEE Sixth International Conference on the Image and Signal Processing and their Applications, Mostaganem, Algeria, 24 Nov 2019, IEEE. https://doi.org/10.1109/ISPA48434.2019.8966811, https://ieeexplore.ieee.org/ document/8966811 12. Kada, B., Keltouma, N., Turkiya, A.: Design of patch antennas based on metamaterials CSRRs. In: 2019 International Conference on Advanced Electrical Engineering (ICAEE), Algeria, 19 Nov 2019, IEEE. https://doi.org/10.1109/ICAEE47123.2019.9014741, https:// ieeexplore.ieee.org/abstract/document/9014741

Aspect Oriented Web Service Composition Based Petri Net Model F. Khalifa(B) and B. Guelta Université des sciences et de la technologie d’ Oran Mohamed BOUDIAF USTO’MB, Oran, Algérie [email protected]

Abstract. The concept of aspect-oriented programming is an emerging programming paradigm that stretches across different development phases in different domains. Many researchers have focused on the use of this paradigm in web service composition in different research axes. However, none of them combine aspect-oriented programming and the design by contract to deal with the adaptation of the parameters in the web service composition process based on the Petri net graph technique as a formal method. This paper proposes a web service composition algorithm based on the Petri net graph that incorporates both Aspect-oriented programming and the design by contract concept. Aspect-oriented programming provides explicit support for the separation of cross-cutting concerns in web service composition, and the design by contract approach allows parameters to be executed in pre-condition and post-condition mode by using contracts in order to ensure correct service execution and adaptation to external parameters without affecting properties that can be dealt with through re-construction of web services composition. Keywords: Aspect oriented programming · Design by contract · Web service composition · Petri net · Parameters adaptation

1 Introduction Aspect-Oriented Programming (AOP), is a new programming paradigm introduced in information systems. It presents a novel element called aspect, in order to encapsulate the crosscutting concerns of the program. As opposed to reiterating the same concern multiple times in multiple code blocks, the aspect can represent all of these concerns in a single code block completely separate from the source code. The aspect contains three main elements, a joinpoint, a pointcut, and advice. AOP also introduces the notion of a weaver. Weaving behavior is the process that allows weaving the program with these different aspects [2, 3]. Researchers have studied the application of AOP to the Web service composition domain. Their research centered around increasing the adaptability of web services [12, 13] or modularizing crosscutting concerns in web service composition [6]. However, none of them has addressed the problem of parameter adaptation and conflict between input and output parameters in the composition phase using AOP techniques © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 148–159, 2023. https://doi.org/10.1007/978-3-031-21216-1_16

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and design by contract. Design by Contract (DbC) is an approach that uses a contract to specify and define the mutual obligations and expected parameters of the communication between services composite process, and uses assertions to check whether an application complies with a contract [4, 5]. The failure of an assertion is typically a symptom of a bug in the software. There are three different kinds of assertions [11]: 1) Pre-conditions: is a case in which some parameters must be satisfied before an operation execute. 2) Post-condition: specifies parameters conditions that must be satisfied after an operation completes, hence, post-condition is evaluated after a method completes. 3) Invariant: specifies a parameters condition that must be satisfied before and after a call of an object’s method. This paper presents a semantic formal web service composition technique that uses both AOP and DbC solutions to address the problem of parameter adaptation between input and output parameters as part of the web service composition phase, even using separation of cross-cutting concerns. To the effect that web services are applications available on the internet, each of them performs a special task [1]. Except that, the requirements of the client always exceed the demand of a single request or a single task. As an example, if a client wants to afford a vacation, he wishes to find a web service that offers him simultaneously the option of purchasing a plane ticket, making a hotel reservation, and reserving a car. As no specific web service can meet all of these requirements at the same time, it should be possible to combine several existing services to fulfill one’s needs. This is the composition of web services. However, one of the most critical issues to be addressed in the composition of web services is that some services impose certain input or output parameters that are defined by their suppliers and/or imposed by their clients. These constraints must be satisfied to ensure correct execution and appropriate interaction with the different services contained in the composition. In this context, the main contributions of our research work are focused on: Applying the AOP paradigm to web services composition to increase the adaptability of services and to modularize crosscutting concerns. When crosscutting concerns are separated from the code of each service, it becomes easy to modularize the crosscutting concerns of the composite service and then monitoring these parameters as discussed by Sk. Riazu Rahemana and al. in [17]. Additionally, we applied the DbC paradigm to avoid conflicts and exceptions in the interaction between input parameters of the new services added to the composition and the output parameters of the composite services within the composition of web services. The remainder of this paper is structured as follows: Section 2 reviews related work, Sect. 3 presents a conceptual architecture as formal foundation. In Sect. 4 an Implementation of Petri Net graph techniques is given. Finally, Section 5 concludes the paper.

2 Related Work In recent years, many types of research have been published regarding the use of aspectoriented programming in the composition of web services. They revolve around different

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areas of research, like those in [7, 8, 14, and 15]. Among them various approaches have been investigated for building a Petri net model for web service composition based on the aspect-oriented paradigm. However, none of them considered the concept of design by contract. In [9], the author presents a Petri net-based approach to support aspect-oriented modeling for web service composition. Author in this paper have the composition operation including a crosscutting operation for modeling aspects is based on Petri nets. The authors present a Petri Net model for service composition in [18]. The author does not introduce the aspect paradigm; therefore he does not discuss the crosscutting concerns comprised in web service compositions. In [19], the author presents aspect-oriented web service composition based on a Petri net-based approach. As part of this study, AOP is utilized to consider web service development as a set of aspects, which may be selected at run-time based on the service request without considering the crosscutting concerns of the service participants. Also the concepts of design by contract are not used. In [20] the author proposes an aspectoriented web service composition model based on Petri-Net where web services are developed as aspects and the services are composed through a weaving mechanism. The research does not use design by contract and does not address the problem of parameter adaptation in composing web services. In [10] the author is introducing the design by contract concept to support the software developer, although it has not yet been applied to the composition of the web service. However, none of these approaches have been applying both AOP and DbC in the same context of the Petri Net based model for web service composition. Thereby this paper is the first attempt at using both the AOP approach and DbC benefit in web service composition focused in crosscutting concers and parameters adaptation.

3 Conceptual Architecture as Formel Fondation 3.1 Concepts and Definitions Within this section, we describe how Web service composition algorithms employ the Petri net graph that combines both AOP programming and DbC concepts. A Petri net graph technique is a powerful search tool for studies in the Artificial Intelligence planning domain in order to help refine the programming paradigm. In order to illustrate some concepts necessary background on petri net graph techniques, this section contains some definitions Definition 1: A web service is a 7 tuple S = (P, T , A, W , I , O, E), where, P = {P1, P2, P3, . . . Pn} is a finite set of places, T = {T 1, T 2, T 3, . . . Tn} is a finite set of transitions representing the operations of the service, A = {A1, A2, A3, . . . An} is a set of aspects that represents crosscutting concerns of services W ⊆ (P × T ) ∪ (T × P) is a set of directed arcs I : represents the input parameters which belongs to the specifications required by the service S.

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O: represents the output parameters generated by a service E is a finite set of control transitions which compose atomic web services or other composite services into a composite service. 3.2 Web Services Composition as Petri Nets A composite service can be regarded as a partially ordered set of several places and transitions where we have added a several aspect and contract relation according to the flow relation. In this section, we present a construct of the basic web services composition according to several methods: Sequence, alternative, iterative and unordered specified in the control flow of the Petri net graph. Definition 2: Given two web services nets S1 = (P1 , T1 , A1 , W1 , I1 , O1 , E1 ) and S2 = (P2 , T2 , A2 , W2 , I2 , O2 , E2 ) their compositions can happen in either of the following approaches: Sequential Composition: Sc = S1  S2 represents a composite service Sc that performs the service S1 followed by the service S2 , where: Sc = (Pc , Tc , Ac , Wc , Ic , Oc , Ec ) Pc = P1 ∪ P2 ∪ {i, o} Tc = T1 ∪ T2 ∪ {t1, t2} Ac = A1 ∪ A2 Wc = W1 ∪ W2 ∪ {(i, t1), (t1, i1), {(o1, t2), (t2, i2), {(o2, t3), (t3, o)} i = i1, o = o2ando1 = i2 Ec = E1 ∪ E2 ∪ {t1, t2, t3}

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Alternative Composition: Sc = S1 ⊕ S2 represents a composite service Sc that performs as either service S1 or service S2 . That is, only one of them can be executed, where (Figs. 1, 2 and 3)

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Sc = (Pc , Tc , Ac , Wc , Ic , Oc , Ec ) Pc = P1 ∪ P2 ∪ {i, o} Tc = T1 ∪ T2 Ac = A1 ∪ A2 Wc = W1 ∪ W2 ∪ {(i, t1), (t1, i1), {(o1, t2), (t2, i2), {(o2, t3), (t3, o)} i = i1, o = o2ando1 = i2 Ec = E1 ∪ E2 ∪ {t1, t2, t3}

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Iterative Composition S = μS, represents a web service composite S that performs a certain number of times by itself.S = (P, T , A, W , i, o, E), where P = P ∪ {i, o} T = T1 A = A1 W = W 1 ∪ {(i, t1), (t1, i1), {(o1, t2), (t2, i2), {(o2, t3), (t3, o)}

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E = E1 ∪ E2 ∪ {t1, t2, t3}

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Unordered Composition S = S1 ♦ S2, represents a composite service S that performs either the service S1 followed by the service S2, or S2 followed by S1 as shown in Fig. 4.S = (P, T , A, W , i, o, E) , where P = P1 ∪ P2 ∪ {i, o, p1, p2, p3, p4, p5} T = T1 ∪ T2 A = A1 ∪ A2

W = W 1 ∪ {(i, ti), (ti, p1), {(ti, p2), (ti, p3), (p1, t1), (p2, t3), (p3, t1), (p3, t2), (p3, to), (ti, i1), (t2, i1), (t2, i2), (o1, t3), (o2, t4), (t3, p3), (t4, p3), (t3, p4),

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(t4, p5), (p4, to), (p5, to), (to, o), } E = E1 ∪ E2 ∪ {t1, t2, t3, t4, t5}

Fig. 4. Service composite created of an unordered composition

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4 Recognized Petri Nets Overview of Aspect Oriented Model for Petri Extention First all crosscutting concerns are separated from each web service of the composition which will be weaved later in the composition. a final service composition can be seen as a composite web service weaved with aspects. All aspects are modularizing individually. In this section, we first give the definition of aspect. Then, we give the formal semantics for Aspect Oriented Web Service Composition based Petri net model. 4.1 Aspect oriented model Aspect-oriented paradigm is based on the concept of separation of the cross-cutting concern. Crosscutting is a symmetric relationship between a web service and its concerns. To build the model for aspect, we define certain crosscutting operation. Definition 3. Weaving Operation A is the set of different Aspect, A = {A1, A2, A3 . . . An}. where: Ai is an Aspect defined by Ai = Cc, Joinpoint, Pointcut, Advice – – – –

Cc: is crosscutting concern functionality. Advice: is a workflow code that encapsulates Cc. Joinpoint some points in the program of the service related to pointcuts of the aspect. Pointcut is a function that relates a joinpoint to a set of advice.

A weaving operation WOP represent an explicit link between a service Sa and the crosscutting concerns functionality Cc that touches this service representing by the set of Aspect {A1, A2, A3.., An} Sa = Sa {A1, A2, A3 . . . , An} The weaving operation WOP can take three forms of execution: A before weaving operation: Sa . Cc → A.advice represent that advice is executed before the execution of the web service Sa . An after weaving operation: Sa . Cc → A.advice represent that advice executed after the execution of the web service Sa . An around weaving operation: Sa . Cc → A.advice represent that advice executed around execution of web the service Sa . If an aspect A advice crosscuts a crosscutting concern of a service Sa , it gives us: S a = Sa A S a represents that the service Sa is weaved with aspect A. We describe the formal semantics of the weaving operation used in our petri net graph for the four interception operations quote above:

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1) Sa .Cc → A.advice S a = Sa A, where, Pa = P ∪ Pa Ta = T ∪ Ta Aa = Aa wa = wa ∪ {(ia , t), (t, oa )} =

ia {ia ∪ ib = {A1, A2, . . . A}|ib ∩ ia = ∅} = oa oa = Ea Ea ∪ {t} 2) Sa . Cc → A.advice S a = Sa A, where, Pa = P ∪ Pa =

Ta T ∪ Ta =

Aa Aa wa = wa ∪ {(ia , t), (t, oa )} =

ia {ia ∪ ib = {A1, A2, . . . A}|ib ∩ ia = ∅} = oa oa = Ea Ea ∪ {t} 3) Sa .Cc → A.advice S a = Sa A, where, =

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Ta T ∪ Ta =

Aa Aa wa = wa ∪ {(ia , t), (t, oa )} =

ia {ia ∪ ib = {A1, A2, . . . A}|ib ∩ ia = ∅} = oa oa = Ea Ea ∪ {t}

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Definition 4 Contract Relationship A contract relationship βPCr (Sa , Sb ) represents an explicit link between two Petri nets Sa = (Pa , Ta , Aa , Wa , Ia , Oa , Ea ) and Sa = (Pb , Tb , Ab , Wb , Ib , Ob , Ea ) which implies that the parameters of a service composite Sa is adapted by an contract PCr. A contract relationship has a contract method β with either one or two contract point Sc = Sa  Sb A Contract method β(PCr) can take three formats: 1) @Pré: contract method β (a precondition of β) specify a contract that must hold before the execution of the input parameters of the service Sa . 2) @post: contract method β (a postcondition of β) specify a contract that must hold before the execution of the input parameters of the service Sa . 3) @Inv: contract method β (invariant) specifies a contract that must behold any time when service features are invoked.

5 Conclusion and Perspectives In this paper, a novel technique for web service composition algorithms based on the petri net graph using two new programming paradigms AOP and DbC is proposed to solve the problem of code redundant of the crosscutting concerns and parameters conflict in web service composition. Our contribution consists of two points important in the web services composition: First, using the AOP programming model all the crosscutting concerns which affect all participating services in the composition have been separated and programmed independently as entities called aspects. Second, DbC proposes a Boolean relation which imposes a contract test during the execution of the input and output parameters, in the form of pre-condition, postcondition, and invariant. Our investigation has demonstrated that the proposed technique is the best way to solve static detection of resolving conflict situation in web service composition. There are no attempts have been made in the same field to compare the estimates results. This work was the first challenge which dealt with this problem and which will open up other opportunities for researchers in this field to do further studies. Future development of the model will include the introduction of the dynamic way and add more comparison results.

References 1. Nam, T., Pardo, T.A.: Conceptualizing smart city with dimensions of technology, people, and institutions. In: Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, pp. 282–291 (2011)

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2. Rao, J., Su, X.: A survey of automated web service composition methods. In: Cardoso, J., Sheth, A. (eds.) SWSWPC 2004. LNCS, vol. 3387, pp. 43–54. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30581-1_5 3. Kiczales, G., et al.: Aspect-oriented programming. In: Ak¸sit, M., Matsuoka, S. (eds.) ECOOP 1997. LNCS, vol. 1241, pp. 220–242. Springer, Heidelberg (1997). https://doi.org/10.1007/ BFb0053381 4. Elrad, T., Filman, R., Bader, A.: Aspect-oriented programming: introduction, Commun. ACM 29–32 (2001) 5. Meyer, B.: Applying design by contract. IEEE Comput. 40–51 (1992) 6. Thüm, T., Schaefer, I., Kuhlemann, M., Apel, S., Saake, G.: Applying design by contract to feature-oriented programming. In: de Lara, J., Zisman, A. (eds.) FASE 2012. LNCS, vol. 7212, pp. 255–269. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-288722_18 7. Charfi, A., Schmeling, B., Heizenreder, A., Mezini, M.: Secure and transacted web service compositions with AO4BPEL. In: Proceedings of the 2nd International Conference on Service Oriented Computing ICSOC, pp. 23–34 (2004) 8. Shanmuga Priya, R., Rajaram, K.: AOP based QoS monitoring of dynamic web service compositions. In: IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 1913–1917 (2014) 9. Zaimer, F., Yutao, M., Keping, H., Gong, P.: A requirements-driven and aspect-oriented approach for evolution of web services composition. In: Conference: Web Mining and Web-based Application (WMWA), pp. 201–204 (2009) 10. Yang, X., Hung, H.: A petri net based model for aspect oriented web service composition. In: International Conference on Management and Service Science, pp. 1–4 (2009) 11. Klaeren, H., Pulvermüller, E., Rashid, A., Speck, A.: Aspect composition applying the design by contract principle. In: Butler, G., Jarzabek, S. (eds.) GCSE 2000. LNCS, vol. 2177, pp. 57– 69. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44815-2_5 12. Minh Le, N.: Contracts for Java: A practical framework for contract programming. http:// code.google.com/p/cofoja/. Accessed 08 Aug 2019 13. Charfi, A., Mezini, M.: AO4BPEL: An Aspect-Oriented Extension to BPEL. Springer Netherlands, pp. 309–344 (2007). https://doi.org/10.1007/s11280-006-0016-3 14. Hmida, M.M.B., Tomaz, R.F., Monfort, V.: Applying AOP concepts to increase web services flexibility. In: Proceeding of International Conference on Next Generation Web Services Practices, p. 6 (2005) 15. Braem, M., Joncheere, N.: Requirements for applying aspect-oriented techniques in web service composition languages. In: Lumpe, M., Vanderperren, W. (eds.) SC 2007. LNCS, vol. 4829, pp. 152–159. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-773511_12 16. Xu, Y., Tang, S., Xu, Y., Tang, Z.: Towards aspect oriented web services composition with UML. In: Proceedings of 6th IEEE International Conference on Computer and Information Science (ICIS2007), IEEE Computer Society Press, July 11–13, pp. 279–284 (2007) 17. Havinga, W., Nagy, I., Bergmans, L., Aksit, M.: A graph based approach to modeling and detecting composition conflicts related to introductions. In: Proceedings of 6th International Conference on Aspect-Oriented Software Development, pp. 85–95 (2007) 18. Raheman, S.R., Maringanti, H.B., Rath, A.K.: Aspect oriented programs: issues and perspective. J. Electr. Syst. Inf. Technol. 5(2), 562–575 (2018) 19. Muschevici, R., Clarke, D., Proenca, J.: Feature petri nets. In: Proceedings of the 14th International Software Product Line Conference (SPLC 2010), vol. 2 (2010) 20. Hamadi, R., Benatallah, B.: A petri net-based model for web service composition. In: proceedings of the 14th Australasian database conference. Australian Computer Society, pp. 191–200 (2003)

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21. Mallayya, D., Ramachandran, B.: Aspect-oriented web service composition: a petri net based approche. In: 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, pp. 88–95 (2011) 22. Xu, Y., Tang, D.: An aspect-based web service composition model based on petri-net. Adv. Serv. Lett. 10, 388–390 (2012)

High-Efficiency 60-GHz Printed Antenna Using a Triple-Layer Metasurface Tarek Messatfa(B) and Fouad Chebbara Department of Electronic and Telecommunications, Electrical Engineering Laboratory (LAGE), Université Kasdi Merbah Ouargla, 30000 Ouargla, Algeria [email protected]

Abstract. In this research article, a microstrip printed antenna operating at 60 GHz with an ultrathin triple-layer FSS metasurface is presented. Two different FSS metasurface structures without a dielectric substrate are proposed in order to improve antenna performance. The first structure is a cross-slot, while the second is a double circular split-ring resonator (DCSRR). The effect of their size and shape on the gain and bandwidth of the antenna has been investigated. The simulation results show that the antenna performance can be significantly improved by using a triple-layer metasurface structure with a cross-slot. This design achieves a maximum gain of 10.7 dB, a wide bandwidth of 4.9%, and an efficiency of 97%. This proposed antenna has outstanding performance in broadband and can be used in millimeter-wave wireless communication. Keywords: Microstrip printed antenna · FSS · Double circular split-ring resonator (DCSRR) · Metasurface · Millimeter-wave

1 Introduction Millimeter-wave (MMW) communications have recently attracted much attention due to the wide frequency available. Antennas with a high gain and efficiency in communication systems operating in the MMW bands are extremely attractive. The design of wireless communication antennas at 60 GHz has generated enormous interest, and many of these antennas do not conform to some 60-GHz standards, such as IEEE 802.15.3c [1, 2]. Therefore, the design of the 60-GHz antenna will remain to be a prominent research topic in millimeter-wave wireless communication in the coming years. Many antenna researchers have recently become interested in the metasurfaces concept. Metasurfaces with a converging electromagnetic wave function can be classified into two types: transmission and reflection type. This paper mainly deals with the transmission type of metasurface. Transmitarray metasurfaces are a type of artificial structured lens that is frequently used in beamforming to collimate light from a source by altering cells with independently controlled phases [3]. Additionally, they can be used in typical phased array components that have significant intricate feed networks or need many transceiver modules [4]. To

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 160–169, 2023. https://doi.org/10.1007/978-3-031-21216-1_17

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prevent the grating lobe, the period of the element is approximately half wavelength [4, 5]. The mutual connection between transmitarray components frequently needs to be as low as possible for reducing scan blindness and thus enable wide-angle scanning [6, 7]. Typically, the transmitarray structure is powered by a single antenna [8], which can be a horn antenna, an open-ended rectangular waveguide probe, a patch antenna, or a substrate integrated waveguide (SIW) slot antenna [9]. They have the potential to give high gain, great aperture efficiency over a wide bandwidth, and eliminate feeding obstruction [10]. Further, they can be employed as amplifiers and phase shifters to increase the power of spatial improvement or to produce reconfigurable antennas [11]. There are several techniques for designing metasurface transmitarray to control the transmission phase of each unit cell within the array. One of these techniques is the multilayer of frequency-selective surfaces (FSSs) [12–16]. By manipulating the magnitude and phase of the element transmission, the frequency-selective surfaces with multilayers increase antenna bandwidth. The phase compensation cannot be achieved with a single layer of the frequency-selective surfaces. At the same time, an air gap or a thicker substrate is required to increase the transmission phase range of multilayer FSS. Recent research has concentrated on low-cost frequency-selective surfaces based on metasurface transmitarray with no dielectric substrates. The entire unit cell structure is made of pure metal sheets, which offers two significant advantages. The first advantage is its applicability for aerospace applications, as the conductive layers are more resistant to temperature changes in outer space than the dielectric substrates. The second advantage is the low cost, where high-performance microwave substrates are not required [17]. In this work, a 60-GHz printed antenna with a triple-layer frequency-selective surface based on metasurface is proposed to improve antenna performance such as bandwidth and gain. We have adapted the structures described in [17] and [18] to operate at 60 GHz. We first proceeded by optimizing the phase and transmission coefficient of the FSS unit cell. Then we combined them on the antenna with distance to boost the output parameters of the proposed antenna. Separate performance comparisons were conducted for the antenna with the cross-slot and DCSRR metasurface structure, as well as for the antenna without any metasurface structure. The results indicated that improvements occurred following the application of the first structure, which had a gain of up to 10.7 dB, 4.9% bandwidth, and 97% efficiency, in addition to the small structure size compared to designs published in [19–23].

2 Antenna and Metasurface Unit Cell Design 2.1 60-GHz Antenna Geometry Figure 1 shows the geometry of the proposed antenna with the λ/4 transmission-lines transformer for impedance matching. The dimensions of the 60-GHz antenna were obtained through the empirical Equations in [23].

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Fig. 1. The 60 GHz antenna design geometry

The radiating element is a rectangular patch with size 1.97 mm × 1.51 mm placed on the Rogers RT5880 dielectric substrate of size 6 mm × 9 mm with relative permittivity of 2.2, loss tangent (tan δ = 0.0009), thickness h = 0.254 mm, and is fed by a 50 . The 50  microstrip feed line and λ/4 transformer lines widths are Wf , Wt 1 , and Wt 2 , respectively. Table 1 lists the main antenna parameters values. The antenna resonates at 60 GHz and provides bandwidth from 57 GHz to 64 GHz. According to IEEE 802.15.3c standards, there are four channels in the 60 GHz spectrum [24]. Thus, the 60-GHz antenna should have a minimum channel bandwidth of 2.16 GHz. Table 1. The 60-GH antenna dimensions Parameters

Value (mm)

Ws

6

Ls

9

W

1.97

L

1.51

Wt 1

0.20

Wt 2

0.36

Wf

0.75

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2.2 Metasurface Unit Cell Design In order to optimize the performance of the proposed antenna, we have implemented two different slot-type metasurfaces elements without dielectric substrate due to several positive factors, such as suitability for space applications; this is because the conductive layers withstand temperature changes in outer space compared to the dielectric substrate. Besides, the cost is reduced as a result of the unnecessary use of high-performance substrates. The first unit cell is a cross-rectangular slot element, as shown in Fig. 2(a), used in [17] to enhance the gain of an antenna at 11.2 GHz. The second cell is a double circular split-ring resonator (DCSRR, Fig. 2(b)) used in [18] to increase antenna gain at 13.58 GHz with a high efficiency and no reliance on polarization angle. Initially, we reduced the size of the two metasurface unit cells to operate at 60 GHz and adjusted their dimensions in order to optimize their transmission coefficient. The three FSS metasurface layers are stacked vertically, and the air gap between the metasurface layers is H = λ0 /4 =1.25 mm, as shown in Fig. 2(c). The geometries of the cross-slot and the double circular-split ring resonator (DCSRR) structures are shown in Figs. 2(a) and 2(b), respectively. The dimensions of the two metasurface unit cells are listed in Table 2.

Fig. 2. (a) Cross-unit cell (b) DCSRR-unit cell (c) Side view of metasurface FSS layers

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T. Messatfa and F. Chebbara Table 2. Dimensions values of the metasurface unit cells Parameters

Value (mm)

P1

3.1

W1

0.4

L1

2.5

P2

2.5

G

0.37

W2

0.2

Figures 3 and 4 show the transmission magnitude (dB) and phase (degrees) versus Frequency (GHz) of the cross-slot unit cell and double circular split-ring resonator, respectively. The Transmission coefficient |S21 | result indicates that transmission is maximum at a resonant frequency range. The phase of S21 shows a 180° phase variation near-resonant frequency (60 GHz) where the incident radiation transmission is changed with the phase of 180° via the unit cell structure. Furthermore, it indicates the presence of metamaterial properties.

Fig. 3. Transmission coefficient (magnitude and phase) versus frequency of the cross-unit cell

Fig. 4. Transmission coefficient (magnitude and phase) versus frequency of the DCSRR-unit cell

These unit cells exhibit a band-pass FSS behavior that is less susceptible to the oblique incidence angle of the electric field [25]. Each metasurface element is simulated at 60 GHz with a normal incidence plane wave to illuminate these elements. The antenna and unit cells are simulated in the CST Microwave Studio (CST MWS) simulator with Floquet ports and perfect electric and magnetic boundary conditions using the frequency domain solver [26].

3 Periodic Structures Application and Discussion In this phase, three identical conductor layers of the periodical metasurface unit cells discussed above have been implemented and separated by air gaps for quarter-wavelengths.

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This method was chosen based on the study of multilayer metasurfaces presented in [12] and [13], which can achieve good performance for an antenna. Equation (1) [27] and (2) [28] of EBG (electromagnetic band-gap) can be used to define the separation distance of air gap between the proposed antenna and the metasurface layers: Hp =

D 2 × tan α 0

(1)

λ0 2

(2)

Hp =

where D is the width of the metasurface periodic structure and α 0 is the antenna angular width (3 dB opening angle), in our case (α 0 = 71.4°) obtained by simulation in CST Studio Suite. λ0 is the wavelength in a vacuum at the operating frequency (60 GHz). 3.1 Cross-slot Periodic Structure The initial periodic structure consists of 3 × 3 metasurface cross unit cells with crossslots (Fig. 5). The parameters of the cross-slot metasurface array structure are simulated and optimized using CST. Table 3 reports the simulation results of the periodic structure of the cross-slot metasurface after it was applied to the antenna.

Fig. 5. Antenna with triple-layer of cross-slot metasurface

We can easily conclude from Table 3 that Eq. (1) gives the maximum efficiency (96.85%) and the widest bandwidth (4.9%) with a full opening angle. In contrast, the highest gain (10.7 dB) is achieved with the half-opening angle. Figs. 6 and 7 depict the reflection coefficient (S11 ) and realized gain diagram results of the antenna with triplelayer of the cross-slot metasurface, respectively, for the Hp values noted in Table 3. As a point of comparison, the response of the antenna alone (without the cross metasurface layers applied) is also plotted in Figs. 6 and 7. We can notice some improved performance, especially in terms of a significant gain obtained in the case of the half-opening angle (Hp = 6.5 mm) with a value of 10.7 dB.

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Table 3. Performance results of antenna before and after using the 3 × 3 cross metasurface layers Hp (mm)

Bandwidth (GHz)

FBW (%)

Antenna without layers

2.28

3.8

8.11

95.07

1.6 (α 0 )

2.93

4.9

8.28

96.85

6.5 (α 0 /2)

2.2

3.7

2.5 (λ0 /2)

2.05

3.3

Fig. 6. S11 of the antenna with triple-layer cross-slot metasurface vs. frequency

Realized gain (dB)

10.7 9.35

Efficiency (%)

94.72 93.97

Fig. 7. Realized gain of the antenna with triple-layer cross-slot metasurface

3.2 DCSRR periodic structure In this case, the antenna performance is affected by a triple-layer of 3 × 3 double circular split-ring resonators (Fig. 8) compared to cross-slot unit cells above. As in the previous case, the DCSRR array structure parameters are also simulated and optimized in the CST simulator. The performance comparison results in Table 4 show that the most significant bandwidth (4.34%) is achieved with Eq. (1) at a full-opening angle and the maximum gain (8.95 dB) at the half-wavelength resonant frequency, in addition to the best efficiency (97.30%) performance at the half-opening angle. Compared with the first case and the antenna without metasurface layers, this structure has improved the antenna efficiency with the half-opening angle. Figures 9 and 10 illustrate the comparison of reflection loss (S11 ) and the achieved gain (from 57 GHz to 64 GHz) for various values of Hp, respectively. Table 5 compares the size and efficiency of the proposed work (antenna with DCSRR) with some recently published works. It can be concluded that the second metasurface structure (DCSRR) of this work is more efficient and has a smaller array size than the designs reported in [18–22].

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Fig. 8. Antenna with triple-layer of DCSRR metasurface Table 4. Performance results of antenna before and after using the 3 × 3 DCSRR metasurface layers Hp (mm)

Bandwidth (GHz)

FBW (%)

Realized gain (dB)

Efficiency (%)

Antenna without layers

2.28

3.8

8.11

95.07

1.3 (α 0 )

2.6

4.34

7.41

92.54

5.2 (α 0 /2)

2.22

3.7

8.3

97.30

2.5 (λ0 /2)

2.41

4.01

8.95

94.45

Fig. 9. S11 of the antenna with triple-layer DCSRR metasurface vs frequency

Fig. 10. Realized gain of the antenna with triple-layer DCSRR metasurface

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T. Messatfa and F. Chebbara Table 5. Comparison of current work with recently publications

Ref

Freq (GHz)

No. of layers

Layer size (mm2 )

Efficiency (%)

This work (DCSRR)

60

3

7.5 × 7.5

97.30

[18]

13.58

4

132.2 × 132.2

77.6

[19]

9.8

4

300 × 300

52.9

[20]

11.3

3

381 × 381

74

[21]

30

4

126 × 126

47

[22]

60

3

50 × 50

53.6

4 Conclusion In this work, we have implemented two distinct ultrathin metasurface structures without a dielectric substrate. Triple layers of these periodic metasurface structures were used to optimize the performance of a 60-GHz rectangular patch antenna with a quarter-wave transformer fed by a microstrip line. According to the comparison results, it was found that the first structure (cross-slot) outperformed the second structure (double split-ring slot) in terms of gain and bandwidth. In contrast, the best efficiency was achieved with the second structure (DCSRR). This proposed antenna is compliant with IEEE 802.15.3c and suitable for usage in 5G millimeter-wave wireless communication systems.

References 1. Sheng, H., Orlik, P., Haimovich, A.M., Cimini, L.J., Zhang, J.: On the spectral and power requirements for ultra-wideband transmission. In: IEEE International Conference on Communications, 2003. ICC’03, vol. 1, pp. 738–742. IEEE (2003) 2. Liang, J., Chiau, C.C., Chen, X., Parini, C.G.: Study of a printed circular disc monopole antenna for UWB systems. IEEE Trans. Antennas Propag. 53(11), 3500–3504 (2005) 3. Hum, S.V., Perruisseau-Carrier, J.: Reconfigurable reflectarrays and array lenses for dynamic antenna beam control: a review. IEEE Trans. Antennas Propag. 62(1), 183–198 (2013) 4. Mailloux, R.J.: Phased Array Antenna Handbook. Artech house (2017) 5. Valavan, S., Tran, D., Yarovoy, A., Roederer, A.: Planar dual-band wide-scan phased array in X-band. IEEE Trans. Antennas Propag. 62(10), 5370–5375 (2014) 6. Pozar, D., Schaubert, D.: Scan blindness in infinite phased arrays of printed dipoles. IEEE Trans. Antennas Propag. 32(6), 602–610 (1984) 7. Pozar, D., Schaubert, D.: Analysis of an infinite array of rectangular microstrip patches with idealized probe feeds. IEEE Trans. Antennas Propag. 32(10), 1101–1107 (1984) 8. Qu, S.-W., et al.: Terahertz reflectarray and transmitarray. In: 2016 International Symposium on Antennas and Propagation (ISAP). IEEE, pp. 548–549 (2016) 9. Jiang, M., Chen, Z.N., Zhang, Y., Hong, W., Xuan, X.: Metamaterial-based thin planar lens antenna for spatial beamforming and multibeam massive MIMO. IEEE Trans. Antennas Propag. 65(2), 464–472 (2016) 10. Liu, G., Kodnoeih, M.R.D., Pham, K.T., Cruz, E.M., Gonz´alez-Ovejero, D., Sauleau, R.: A millimeter-wave multibeam transparent transmitarray antenna at ka-band. IEEE Antenn. Wirel. Propag. Lett. 18(4), 631–635 (2019)

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Mobile User Profile in the Context of Mobile Crowd Sensing S. Ichou1 , S. Hammoudi2(B) , A. Benna3 , and A. Meziane3 1 ESI, National School of Computer Science, Algiers, Algeria

[email protected]

2 ESEO-TECH – ERIS TEAM, Angers, France

[email protected]

3 CERIST, Scientific and Technical Information Research Center, Algiers, Algeria

{abenna,ameziane}@cerist.dz

Abstract. Mobile user profiling refers to efforts to extract user characteristics from mobile activities. The purpose of generating effective user profiling is to recommend personalized services in sustainable and smart cities as well as to think deeply about the way of acquisition and management of resources, transportation, methods of waste disposal, air conditioning of buildings, and especially the energy use models. This paper aims to create and enrich the profile of the mobile user who lives in sustainable and smart cities in order to recommend personalized services while respecting the user’s privacy. First, we present the main requirements that mobile user profiling and user privacy on mobile user profiling and privacy protection. Next, we propose a methodology for mobile user profiling and protecting privacy. Then, we create a mobile user profile model that specifies relevant data on mobile users in order to recommend personalized services. After, we propose architecture and justify an organization of data. Finally, we implement a Framework that creates the mobile user profile, and then we will recommend cultural events according to this profile. Keywords: Mobile user · Privacy · User profile · Mobile crowd sensing · Recommendation service · Smart cities

1 Introduction In recent years, smart and sustainable cities have emerged as one of the most important technological developments, with various means (sensor networks, open data platforms, expert systems, and so forth). These means allow collecting and analyzing a huge quantity of data which may enrich user profiling and service recommendation. Several approaches and paradigms are explored in the literature in order to use smart city technology to recommend services, like mobile crowd sensing (MCS) and Sustainable Urban Mobility (SUM). MCS is a new form of data collection using the Internet of thing and the multitude terminals of smart cities already deployed around the world to massively collect environmental data or mobile user’s data in smart cities. This data collection aroused the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 170–182, 2023. https://doi.org/10.1007/978-3-031-21216-1_18

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interest of a large number of industrial and academic in many fields such as the study of urban mobility, environmental monitoring, health or the study of socio cultural behaviors by Gou and Wang (2015). Enriching the user profiling, can has a great impact on many aspects of the user’s life and behavioral patterns. SUM sustainable urban mobility is the Sustainable Urban Mobility Plan (SUMP) a document that sets the framework for urban mobility planning and management and is the result of a participative Sustainable Urban Mobility Planning process (SUMP process). The objective of the process should be to improve the urban quality of life by ensuring a safe, reliable, integrated, multi-modal, and effective and environment- friendly transport system by Okraszewska and Romanowska (2018). Mobile user profiling is the process of extracting, and integrating data about a user’s area of interest, with the objective of generating a profile structure. The goals of mobile user profiling are (1) better understanding the mobile user, and (2) improve retrieval needs. For many years, there have been different researches in the literature by Khanthaapha and Pipanmaekaporn (2018), Zhao and Li (2019), Wang and Fu (2019) related to users profiling for referral system such as collecting the data and classifying information according to an individual interest. However, these researches does not take into consideration all types of a user profile (Geo-Social, demographic, life style, and annotation), since each type gives more information to the user which then allows to recommend good quality services, used methods for analyzing, organizing the data and they do not take into account user privacy, because it contains sensitive information about the user, user privacy is an important criterion in user profiling. We aim in this work to discuss the issue of achieving mobile user profiling and user privacy by focusing on two points: How to define and enrich the profile of a mobile user based on their trips and/or activities, and how to ensure the privacy of a mobile user while using his profile to recommend services? More precisely, we aim to make the following contributions: 1) Identify a methodology for profiling mobile users and protecting privacy composed by a set of steps each one dedicated to a specific task; and 2) Define a mobility profile model for recommending services in smart city. In this model, we organize the obtained data and identify relationship between data. This model defines how to represented user mobility, and how to save the mobility history, and the recommended services history since these criteria are very important in the recommendation of personalized services. 3) Describe the architecture. This architecture is organizes each level in the methodology according to their role. 4) Illustrates our prototype of the mobile user profile and privacy protection for recommendation personalization services. The rest of this paper is organized as follows: Section 2 summarizes the main requirements of mobile user profiling and user privacy. Section 3, presents our Mobile User Profiling & Privacy protecting and security approach (MUP&PPS). Section 4, shows the implementation of the framework and results. Section 5, is a conclusion and future works.

2 Background In this section, we introduce the main concepts related to mobile user profiling: namely, user profiling taxonomy, creation methods.

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2.1 Mobile User Profiling Mobile user profiling refers to the efforts of extracting user interests and behavioral patterns from mobile activities. Consider the existence of many mobile users in a city, each user is equipped with mobile sensing equipment moving from one location to another location and generates a mobility event stream in real time. Classical mobile user profiling collects large-scale spatial-temporal event data, and then, learns profile representations to characterize user patterns and preferences using the collected data by Wang and Wang (2022). Mobile user profiling is to learn users’ profiles from historical mobility records. Mobile user profiling has drawn significant attentions from various disciplines, such as mobile advertising, recommended system, and urban surveillance by Wang and Wang (2021a). 2.2 Mobile Crowd Sensing of Internet of Thing Mobile Crowd Sensing (MCS) refers to the wide variety of detection models by which individuals collectively share data and extracts information to measure and map phenomena of common interest by Gou and Wang (2015). The architecture of Mobile Crowd Sensing for Internet of Thing provides a large amount of data from the sensing devices, which consumes many resources. We adopt architecture of Mobile Crowd Sensing for Internet of Thing to enrich the mobile user profiling. 2.3 Privacy Protecting and Security Methods Privacy protection and security methods aim to secure users can make the spread of influence maximization and privacy disclosure minimization. Privacy protection methods are summarized as follows by Zhang and Shi (2022): • Individual Privacy Risk Evaluation Model (IPREM) since the actual multidimensional attribute data may not be completed; it is difficult to deal with the complex non- linear relationship between the individual privacy risk and the multidimensional attribute evaluation index by using the regression analysis method. However, Bayesian Network has the function of reverse reasoning. Under the premise of some serious privacy risk, the trained Bayesian Network can be used to carry out reverse operation and analyze the objective factors causing risk. • Cascade Influence Capability Evaluation Model (CICEM) is designed to evaluate the influence capability based on the cascade influence model. According to the users’ cascade influence capability, the benefits and threats for the friends’ influence capability can be measured. 2.4 Hardware Security ModuleS (HSMS) Hardware Security Modules (HSMs) are trusted machines that perform sensitive operations in critical ecosystems. They are usually required by law in financial and government digital services. The most important feature of an HSM is its ability to store sensitive credentials and cryptographic keys inside a tamper-resistant hardware, so that every operation is done internally through a suitable API, and such sensitive data are never exposed outside the device by Focardi and Luccio (2021).

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2.5 Communication Profile Handler (CPH) Communication Profile Handler (CPH) is enables applications to abstract the diversity of communications. With this component, applications can transparently use multiple communication protocols and access technologies. Only applications need to do is to register their communication requirements, the type of communication, destination, quality, priority, etc. This communication requirement is mapped to each application, and then Communication by Silva and Noguchi (2014).

3 Mobile User Profiling & Privacy Protecting and Security (MUP & PPS) Given that a user needs a certain service anywhere and anytime, our goal is to enrich the mobile user profile using the last technologies in the smart city or in smartphones and to provide personal services that meet the needs . Our work focuses on: How to define and enrich the profile of a mobile user based on their trips and/or activities, (2) How to ensure the privacy of a mobile user while using his profile to recommend services.This section, to achieve this objective and answer these questions, we have proposed an approach that consists of three steps: 1. The methodology specifies the approach, the techniques used to retrieve the profile data while ensuring the privacy of mobile users; 2. The mobile user profile model specifies the relevant data on mobile users in order to make a recommendation; 3. The architecture and framework for the implementation of the methodology. 3.1 Mobile User Profiling and Privacy Protecting Methodology Figure 1 shows a multi levels structure representing our methodology for Mobile User Profiling and Privacy Protection. We adopt Mobile Crowd Sensing (MCS) for Internet of Thing, to enrich the mobile users profiling. We have focused on information and services quality that enhance the performance of user profiling and the recommendation of services while taking into account user’s privacy. To accomplish this goal, we propose the process based on five hierarchical levels. 1. Data sources level Mobile phones are also a valuable source of data for research in human behavior, environmental analysis, commuting, social networks, and industry. The use of data provided by cell phones is gaining popularity at a rapid pace, thanks to the growth and widespread availability of phones with advanced capabilities. We aim in this work to use GPS, Wi-Fi, temperature sensor, and light sensor. 2. Data collection level collecting data from data sources. There are a variety of data collection methodologies available that offered the required openness, we will adopt Location based services (LBS), and Near-field communication (NFC).

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Fig. 1. Mobile user profiling and protecting privacy methodology.

• Location based services (LBS) we used LBS for get Point of interest, and the region of interest, because the scheme has become an essential part of people’s daily life, and it is widely used in various industries and cloud server applications. In LBS, users send the query regarding their current location to the cloud server by Wu and Li ( 2021). The cloud server collects the data related to the nearest hospital, restaurant, etc., corresponding to the received users’ query and sends them. • Near-field communication (NFC) we used NFC in order to obtain the location of the user from each use of the NFC service. NFC is based on a simple idea. Two coils of conductors in close proximity can exchange electrical power over short distances ( 3000 nm. The large optical losses are mainly involved by the intermediate size particles [9]. The intermediate size particles in vacuum form a composite material with an effective dielectric permittivity εeff , which can be expressed with the Maxwell-Garnett mixing rule as [10, 11]     βp 2i π d1 3 βp εeff = 1 + 3f 1+ (1) 1 − βp f 3 λ 1 − βp f where the constant βp is given by βp =

εp − 1 εp + 2

(2)

εp , f and λ are the dielectric permittivity of the particle, the volume fraction of the particles and the incidence wavelength, respectively. In this study, we evaluate the impact of dust intermediate size particles deposition on the reflectivity by using the Transfer Matrix Method [12–15]. The incident and reflected waves at the input layer are related to the incident and reflected waves at the output layer by a matrix M obtained by the multiplication of the individual transfer matrices Mj . The transfer matrix M for a stack of N layers and the individual transfer matrices Mj are given by ⎡ ⎤ i sin δj     cos δj eff N M M 11 12 nj ⎦ , Mj = ⎣ Mj Mou = (3) M = M−1 in eff 1 M21 M22 in sin δ cos δ j

j

j

Influence of Dust Particles Deposition on the Reflection Loss

with M−1 in

⎤ ⎡ 1   1 ⎣ 1 neff 1 1 in ⎦ and M = = ou eff neff 2 1 − n1eff ou −nou

309

(4)

in

eff

eff

eff

where nin , nou and nj are the effective refractive indices of the input layer, the output layer and the j layer, respectively. eff The effective refractive indices nj are expressed for the transverse electric polarization (TE) and the transverse magnetic polarization (TM) by

nj cos θj TE polarization eff nj = (5) nj TM polarization cos θj The phase thickness δj is defined by δj =

2π nj dj cos θj λ

(6)

where θj , λ, nj and dj are the angle of refraction, the incidence wavelength, the refractive index of the j layer and the thickness of the j layer, respectively. The angle of refraction θj is related to the incidence angle θin by the Snell’s law nin sin θin = nj sin θj

(7)

The reflection amplitude r is written in terms of the transfer matrix coefficients as r=

M21 M11

(8)

The reflection coefficient R is the square of the reflection amplitude R = |r|2 , the total reflection coefficient RTot is the average of reflection coefficients for TE polarization RTE and TM polarization RTM RTM RTot =

RTE + RTM 2

(9)

3 Numerical Simulation Results In this study, the composite material is modeled as a thin layer, with thickness equal to d_1, deposited on top of a glass substrate layer coated with a 60 nm antireflective layer of silica sol-gel. The dust particles, glass and silica sol-gel are with refractive indices of 1.53-i0.0007, 1.53 and 1.23, respectively [16]. The Transfer Matrix Method is applied to the thin-film stack air/composite material/silica sol-gel/glass. Figure 1 combines the behavior of the reflectivity under normal incidence as a function of the incidence wavelength for several particles’ diameters and different values of the volume fraction of the particles f. It can be seen from the figure that the reflectivity

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increases with the increasing diameter and volume fraction of the particles. Fig. 1(a) clearly shows no influence of the particles of diameter 300 nm on the reflectivity in the wavelength range 500–1000 nm. A weak dependence of the reflectivity on the incidence wavelength is observed in Fig. 1(b), Fig. 1(c) and Fig. 1(d) for particles of diameters 400 nm, 500 nm and 600 nm. This dependence vanishes for particles of diameter 900 nm, as seen in Fig. 1(e).

Fig. 1. Reflectivity under normal incidence versus wavelength at at air/dusty coated glass interface. (a) d1 = 300 nm, (b) d1 = 400 nm, (c) d1 = 500 nm, (d) d1 = 600 nm and (e) d1 = 900 nm.

Figure 2 displays the effect of the dust particles of diameters 300 nm and 600 nm with a volume fraction of 0.45 (f = 0.45) on the reflectivity under various incident angles. As ◦ it is clear from the figure, the small angles of incidence lower than 45 influence weakly the reflectivity and in contrast to the case of normal incidence, a strong influence of the particles of diameter 300 nm on the reflectivity is remarked under an incident angle of ◦ 60 .

Influence of Dust Particles Deposition on the Reflection Loss

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Fig. 2. Reflectivity versus wavelength at air/dusty coated glass interface under various incident angles for f = 0.45. (a) d1 = 300 nm, (b) d1 = 600 nm

A linear dependence on the small incident angles at a fixed wavelength of 600 nm, is shown in Fig. 3 for several particles’ diameters.

Fig. 3. Reflectivity versus incident angles for several particles’ diameters at λ = 600 nm and for f = 0.45

4 Conclusion To study the effect of the deposition of intermediate-size dust particles on a coated glass surface of a photovoltaic module on the reflection losses, we have modeled the dust particles in a vacuum as a thin layer with an effective permittivity expressed by MaxwellGarnett mixing approach. The stack air/composite material/silica sol-gel/glass has been considered a thin-film structure.The reflectivity has been derived by the Transfer Matrix Method for both normal and oblique incident polarized light. It has been found that the reflectivity is significantly affected by dust particles with big diameters and increasing values of the volume fraction. A weak dependence on the small angles of incidence ◦ lower than 45 is also observed.

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References 1. El-Shobokshy, M.S., Hussein, F.M.: Effect of dust with different physical properties on the performance of photovoltaic cells. Sol. Energy 51(6), 505–511 (1993) 2. Qasem, H.: Effect of accumulated dust on the performance of photovoltaic modules. Loughborough University, Institutional Repository, Loughborough (2013) 3. Adel, A.H.: Effect of dust accumulation on solar transmittance through glass covers of platetype collectors. Renew. Energy 22(4), 525–540 (2001). https://doi.org/10.1016/S0960-148 1(00)00093-8 4. Appels, R., et al.: Effect of soiling on photovoltaic modules. Sol. Energy 96, 283–291 (2013) 5. Micheli, L., Caballero, J.A., Fernandez, E.F., Smestad, G.P., Nofuentes, G., Mallick, T.K., et al.: Correlating photovoltaic soiling losses to waveband and single-value transmittance measurements. Energy 180, 376–386 (2019). https://doi.org/10.1016/j.energy.2019.05.097 6. Xingcai, L., Kun, N.: Effectively predict the solar radiation transmittance of dusty photovoltaic panels through Lambert-Beer law. Renew. Energy 123, 634–638 (2018) 7. Al-Hasan, A.Y.: A new correlation for direct beam solar radiation received by photovoltaic panel with sand dust accumulated on its surface. Sol. Energy 63(5), 323–333 (1998) 8. Sarver, T., Al-Qaraghuli, A., Kazmerski, L.: A comprehensive review of the impact of dust on the use of solar energy: history, investigations, results, literature and mitigation approaches. Renew. Sustain. Energy Rev. 22, 698–733 (2013) 9. Mazumder, M., et al.: Optical and adhesive properties of dust deposits on solar mirrors and their effects on specular reflectivity and electrodynamic cleaning for mitigating energy-yield loss. Presented at the SPIE Solar Energy + Technology, San Diego, California, United States, p. 91750K (2014) 10. Mallet, P., Guerin, C.A., Sentenac, A.: Maxwell-Garnett mixing rule in the presence of multiple scattering: derivation and accuracy. Phys. Rev. B 72, 014205 (2005) 11. Shabat, M.M., El-Amassi, D.M., Schaadt, D.M.: Design and analysis of multilayer waveguides with different substrate media and nanoparticles for solar cells. Sol. Energy 137, 409–412 (2016) 12. Hass, G.: Physics of Thin Films Advances in Research and Development, pp. 69–81. Academic Press, New York and London (1963) 13. Hamouche, H., Shabat, M.M.: Enhanced absorption in silicon metamaterials waveguide structure. Appl. Phys. A 122(7) (2016). https://doi.org/10.1007/s00339-016-0206-5 14. Hamouche, H., Shabat, M.M., Schaadt, D.M.: Multilayer solar cell waveguide structures containing metamaterials. Superlattices Microstruct. 101 (2017). https://doi.org/10.1016/j. spmi.2016.08.047 15. Hamouche, H., Shabat, M.M.: Artificial metamaterials for high efficiency silicon solar cells. In: Abdelbaki, B., Safi, B., Saidi, M. (eds.) SMSD 2017, pp. 105–115. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-89707-3_13 16. Wagner, R., et al.: Complex refractive indices of Saharan dust samples at visible and near UV wavelengths: a laboratory study. Atmos. Chem. Phys 12, 2491–2512 (2012). https://doi.org/ 10.5194/acp-12-2491-2012

An Improved Fuzzy OTC MPPT of Decoupled Control Brushless Doubly-Fed Induction Generator M. Hamidat(B) and K. Kouzi Laboratoire Matériaux, Systèmes Énergétiques, Energies Renouvelables et Gestion de l’Énergie (LMSEERGE), Laghouat, Algeria {mohamed.hamidat,k.kouzi}@lagh-univ.dz

Abstract. This work presents an improved fuzzy Optimal Torque Control (OTC) MPPT using metaheuristic Bat algorithm for brushless doubly fed generator (BDFG) introduced in wind power generation. The main advantage of OTC MPPT scheme that there is no need to use the wind speed sensors. The indicated control algorithm aims to extract a maximum of power under fluctuating wind speed. The control algorithm employs PI and fuzzy logic controllers (FLC) to perform this target. To select the right parameters of any control method the best way is using optimization methods. In this study, the Bat algorithm optimization is proposed. The two controllers were used one for generator side and one for grid side converters. The function of the generator side controller is to track the maximum power through controlling the wind turbine speed using optimized PI and FLC regulators. As for the grid side converter, active and reactive stator power controllers had been achieve by setting d-axis and q-axis current components respectively. Simulation results show that the wind turbine can operate at its optimum power point for a wide range of wind speed where power quality was greatly improved. Keywords: Brushless Dual-Fed Induction Machine (BDFM) · Vector control · MPPT · Optimal Torque Control (OTC) · Fuzzy logic controller · BAT optimization

List of symbols V ρ s pT Tm mec ωp and ωc ωr Pp Qp

Wind speed (m/s). Air density, kg/m3 . Surface area swept by the blade, m2 . Mechanical power of the turbine Watts. Mechanical torque of the turbine. Mechanical speed of the rotor, rad/s. Power winding angular frequency and control winding angular frequency. Synchronous rotor speed. Active power of the power winding Watts. Reactive power of the power winding Var.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 313–321, 2023. https://doi.org/10.1007/978-3-031-21216-1_34

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Vdp , Vqp , Vdc and Vqc Vdr and Vqr ψdp , ψqp , ψdc and ψqc ψdr and ψqr idp , iqp , idc and iqc idr and iqr Rp , Rc and Rr Mp Mc Lp , Lc and Lr αβ

vc

Components of the power winding voltage and the control winding voltage respectively. Components of the rotor winding voltage. Components of the power winding flux and control winding flux respectively. Components of rotor winding flux. Components of the power winding current and control winding current respectively. Components of rotor winding current. Power winding resistance, control winding Resistance and rotor winding Resistance respectively. Mutual inductance between Power winding and rotor. Mutual inductance Control winding and rotor. Power winding inductance, Control winding inductance and rotor inductance respectively. The tension in the clarke Transformation.

1 Introduction Generation of power out of renewable energy sources is more promising due for its clean features and availability. In the last two decades, research is consistently carried on wind power generation systems to capture more power at fluctuating wind speeds. With rapid development of wind turbine and power electronic technology [1], Many types of generators are existed, In recent years the research has spotlight to (BDFG) due to such characteristics 1) vector control is applied to realize the decoupling control of both active and reactive power, 2) simple and robust construction so its reliability is predicted to improve and to be more suitable to use in severe weather conditions, 3) the feeding converter only has to handle a partially rated power of BDFG (the slip power), which means significant cost savings, compared with conventional systems with fully rated converter [2]. However, variable speed WECS with BDFG need robust control under dynamic conditions, Thus fuzzy control system can be improved with the proposed BAT optimization. This work is set up as follow: Sect. 1 as an introduction in, Sect. 2 the mathematical model of the wind energy conversion system is presented. Section 3 deals with the vector control algorithm of BDFG. In Sect. 4 MPPT with Optimal Torque Control was implemented. Section 5 The fuzzy control with BAT optimization is proposed. In Sect. 6 the system performance are illustrated by simulation results.

2 Modeling of Wecs-Based BDFIG 2.1 Modeling of the Turbine and Gearbox The turbine mechanical power and torque under Betz limit is [4]: Pt =

1 cp ρsv3 , 2

Tm =

cp ρs v 3 2ωm

(1)

An Improved Fuzzy OTC MPPT

with cp is the e power coefficient expressed as:   21 Pt 116 , Cp = 0.5176 − 0.4β − 5 e λi , Cp = Pw λi

315

(2)

where the turbine and the generator are coupled shaft via a gearbox G, the torque and speed the torque and speed are expressed as: Tg =

Tm , G

ωm =

 G

(3)

2.2 Mathematical Model of the BDFIG The electrical equations of BDFIG model in (d-q) given as: [5, 7] Vdp = Rp .Idp +

d ψdp − ωp .ψqp dt

(4)

Vqp = Rp .Iqp +

d ψqp + ωp .ψdp dt

(5)

Vdc = Rc .Idc +

d ψdc − ωc .ψqc dt

(6)

Vqc = Rc .Iqc +

d ψqc + ωc .ψdc dt

(7)

0 = Rr .Idr +

d ψdr − ωr .ψqr dt

(8)

0 = Rr .Iqr +

d ψqr + ωr .ψdr dt

(9)

The reactive powers and the electromagnetic torque of the PW are given as [10]: Tem =

 3   3  Pp ψdp . iqp − ψqp .idp + Pp ψdc .iqc − ψqc .idc 2 2  3 Qp = Vqp .idp − Vdp .iqp 2

(10) (11)

3 Field Oriented Control of a BDFIG The PW flux orientation, is ψdp = ψp and ψqp = 0. So, the relation between the PW voltage and p is [3]. Vdp = 0,

Vqp = Vp = ωp .ψp

ψdp = Lp .idp + Mp .idr ,

ψdp = Lp .idp + Mp .idr

(12) (13)

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3.1 Control of PW Current To find of the ratio between the currents of the PW and the CW we consider the flux of the PW as a variable fixed by the supply voltage (independent variable). The relation between ip and ic gives as [10]:   Lr .Lp didp σp .Lr .Lp Rr .Lp didc Rr i + σp ψp − ωp iqp + ωp .iqc iqc , iqp , ψp = .i + dt Mc .Mp dp Mc .Mp dt dp Mc .Mp Mc .Mp

(14)

  diqc Lr .Lp diqp σp .Lr .Lp Rr .Lp Lr iqp + σp idp − ωp .idc − ωp ψp idc , idp , ψp = .iqp + ωp dt Mc .Mp Mc .Mp dt Mc .Mp Mc .Mp

(15) where σp = 1 −

Mp2

Lp .Lr

3.2 Control of CW Current We obtained the voltage equation Vc in terms of ic. From the electrical equations of the BDFIG.   (16) Vdc = Vxdc .idc + Vydc iqc , idp , iqp , ψp   Vdc = Vxqc .iqc + Vyqc idc , idp , iqp , ψp

(17)

3.3 PW Power and Torque Control Since The PW is connected to the grid with constant voltage, so the ψp is maintained constant [6, 7]. ⎫ 3 ⎪ Qp = ωp .ψp idp ⎬ 2 (18)  3 ⎪ Tem = Pp + Pc .ψp .iqp ⎭ 2

4 MPPT with Optimal Torque Control The aim of the MPPT-OTC is controlling the Generator torque to obtain the reference torque curve according to maximum power of the turbine with a given wind speed. The electromagnetic torque of the turbine can be determined by λ and mec . If the turbine is running with speed accompany speed ratio λ = λopt , That is mean the Cp = Cp−max [9]. The MPPT-OTC can be obtained according to Eq. (19) [8]. ∗ = Kopt .2mec Tem

1 ρ.Cp−max .R5 . 2 G 3 .λ3 The block diagram in (Fig. 1) illustrate the principle of MPPT-OTC. Kopt =

(19) (20)

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317

To Load Power Converter

Controller

+ MPPT Controller

Fig. 1. MPPT with optimal torque control of wind turbines.

5 Fuzzy Logic Control of BDFG The main purpose of using fuzzy is improving the dynamic performance of BDFG, and to realize the independent control of the electromagnetic torque and the reactive power, Thus the fuzzy system consists with two paths: The control loop of reactive power uses FC1 and FC3, the other control loop of the electromagnetic torque path uses FC2 and FC4. Each fuzzy controller has two inputs linguistic variables: the error e and its variation e, and output is the reference’s consign U. As showing on (Fig. 2).

Fig. 2. Block diagram of fuzzy control of BDFM

5.1 Design of Fuzzy PI Controller for the BDFIG The structure of Fuzzy- PI controller is illustrated in (Fig. 3). error K1



PI K2

controller

K3

Fig. 3. Structure of fuzzy PI controller

Control signal

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Where K1,K2 and K3 are respectively the normalization and denormalization gain for fuzzy controller. The ISE criterion is employed as a cost function and is presented in Eq. (21). J = ISE =

n

(e)2 dt

(21)

i=0

The optimization problem will be formulated as follows: Minimize J {K1, K2, K3} ⎧ min max ⎪ ⎨ K1,i ≤ K1,i ≤ K1,i min ≤ K max i = 1, 2, 3 . . . n Subjected to K2,i 2,i ≤ K2,i ⎪ ⎩ K min ≤ K max 3,i ≤ K3,i 3,i

(22)

5.2 BAT Algorithm The Bat algorithm presented in details in [12], it is a metaheuristic algorithm for global optimization. It was developed by Xin-She Yang in 2010. BAT algorithm has a bad at exploration and exploitation. In order to tackle this problem, there is a proposed structure for the original algorithm. Bat algorithm for optimization of tuning of the adjustable parameter in fuzzy-PI controller as follows [11] (Fig. 4). start

A No

Initialize the random Bat

Is the fitness of the new Temporary bat better than Fitness of the old best bat ?

Initialize the maximum and minimum pulse frequency (fmin and fmax)

Yes Initialize the pulse rate Pi loudness factor Ri and maximum number of iteration

Is the loudness of this bat bigger than a Random number (0 to 1)

∑ No

Evaluate fitness for the initial bat population and determine the best bat

Current Interation < Maximum Number of Interation

C

Yes Select the temporary bat as new bat and increase The pulse rate and decrease the loudness of this bat

D No

yes

Replace the fitness of the old bat with Fitness the temporary bat

Generate the new bat population by updating the Velocity and pulse frequency

B

Consider Next bat

No

Keep the old bat as its new bat

Consider first Bat



All the bats are Considered ?

random number 0 to 1> Pi

No

B

yes Generate a local bat around the Best bat

Save the best bat solution, pulse rate And loudness of its bat

Replace the temporary local bat with the bat Of the local search

No C Evaluate fitness of the new temporary

A

Is the stopping criterion reached ?

yes

D End

Fig. 4. Flowchart of the BAT

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6 Simulation and Discussion The model of the BDFG is developed in Matlab/simulink to show the performance of it, where the system is studied with two types of controllers (PI controller and Fuzzy-PI with BAT optimization). The BDFG parameters are in (Table 1). Table 1. Simulation’s parameters BDFG

WIND TURBINE

P = 2.5 Kw

R = 5 m, G = 20

Pp = 3, Rp = 1.732 , Lp = 714.8 mH, Mp = 242.1 mH

J = 61 kg. m2

Pc = 1, Rc = 1.079, Lc = 121.7 mH, Mc = 59.8 mH

f = 0.01 kg.m/rd

Rr = 0.473 , Lr = 132.6 mH

Cpopt =0.5, λopt=9.14

J = 0.053 kg. m2 . f = 0.003 N.ms/rd

Fig. 5. Wind speed and power coefficient of rotor blades

Fig. 6. The reactive powers (a) and The electromagnetic torque (b)

Figure 5. The result shows that Cp with BAT is almost constant Cp-max = 0.5. Which mean that whether the changing of wind profile. The system yields small error and will work with the optimal power. Figure 6. Shows that, the electromagnetic torque and its reference we get from MPPTOTC with BAT represents a good track of its reference and the reactive power which is kept zero. Which indicates that the system works with optimal power and the decoupling between the electromagnetic torque and reactive power is achieved.

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Fig. 7. D-q PW current

Fig. 8. D-q CW current

From Fig. 7 and Fig. 8 it can be seen that correspond to the currents idp , iqp of the PW and idc , iqc of the CW. These currents can increase or decrease depending on the wind conditions. Consequently. The simulation results shows that the control performances the electromagnetic torque, generated currents and stator reactive power. It is obvious that the traditional PI controller presents a satisfactory result with appear small ripple in t = 1 s, Therefore the response of fuzzy with BAT is faster and more improved contrasted to PI controller and the effect of the cross coupling is reduced with FLC (BAT).

7 Conclusion In order to increase the robustness and reliability of wind power generation based on BDFG, and maximize wind energy extraction an intelligent optimal torque control MPPT-OTC was proposed. The main advantage of OTC MPPT scheme that there is no need to use the wind speed sensors. Besides, to select an optimal parameters suggested controller, and overcome the problem of two regulators PI and PI Fuzzy scaling factors determination, it has suggested the Bat algorithm optimization algorithm which give the optimal parameters hence improve the performance of control scheme. The performance of completely proposed algorithm scheme has been tested under different changes in wind. From simulation results, one can conclude that the dynamic and static behavior of the proposed control scheme is very satisfactory. Actually, in order to enhance the performance of wind power generation based on BDFG in terms to maintain a constant power transit and to contribute in wind energy system services, (voltage regulation, frequency regulation…), our work is to develop the present work scheme with intelligent Flywheel Energy Storage System.

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References 1. Tria, F., Ben Attous, D.: simulation for strategy of maximal wind energy capture of doubly fed induction generators. Int. J. Chem. Pet. Sci. (IJCPS) 1(1), 17–26 (2012) 2. Liu, G., Wang, S., Zhang, R.: Modeling and control of BDFG-based wind power generation systems under grid voltage sag. In: 2009 Asia-Pacific Power and Energy Engineering Conference, pp. 1–5. IEEE (2009) 3. Serhoud, H., Benattous, D.: Simulation of grid connection and maximum power point tracking control of brushless doubly-fed generator in wind power system. Front. Energy 7(3), 380–387 (2013) 4. Trejos-Grisales, L., Guarnizo-Lemus, C., Serna, S.: Overall description of wind power systems. Ingeniería y Ciencia 10(19), 99–126 (2014) 5. Chen, J., Zhang, W., Chen, B., Ma, Y.: Improved vector control of brushless doubly fed induction generator under unbalanced grid conditions for offshore wind power generation. IEEE Trans. Energy Convers. 31(1), 293–302 (2015) 6. Tir, Z., Abdessemed, R.: Control of a wind energy conversion system based on brushless doubly fed induction generator. Revue des Energies Renouvelables 17(1), 55–69 (2014) 7. Rahab, A., Senani, F., Benalla, H.: Direct power control of brushless doubly-fed induction generator used in wind energy conversion system. Int. J. Power Electron. Drive Syst. (IJPEDS) 8(1), 417–433 (2017) 8. Asri, A.: Intelligent maximum power tracking control of a PMSG wind energy conversion system. Asian J. Control 21(4), 1980–1990 (2019) 9. Kumar, D., Chatterjee, K.: A review of conventional and advanced MPPT algorithms for wind energy systems. Renew. Sustain. Energy Rev. 55, 957–970 (2016) 10. Madbouly, S.O., Soliman, H.F., Hasanien, H.M., Badr, M.A.: Fuzzy logic control of brushless doubly fed induction generator. German University in Cairo, Egypt, p. 14 (2010) 11. Premkumar, K., Manikandan, B.V.: Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor. Eng. Sci. Technol. Int. J. 19(2), 818–840 (2016) 12. Yang, X.S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. (2012)

Choosing the Adapted Artificial Intelligence Method (ANN and ANFIS) Based MPPT Controller for Thin Layer PV Array Elaid Bouchetob(B) and Bouchra Nadji Laboratoire d’électrification des Entreprise Industrielles, LREEI, Faculté des Hydrocarbures et de la chimie, Université M’hamed Bougara de Boumerdes, Avenue de l’Indépendance, 35000 Boumerdès, Algerie {e.bouchetob,b.nadji}@univ-boumerdes.dz

Abstract. Because of the many advantages that artificial intelligence technologies provide in comparison to more conventional methods, a rising number of solar power plants are beginning to use them in their monitoring of the MPP. When there is a sudden change in solar temperature and irradiance, it is possible that the MPP will not be tracked as accurately. As a consequence of this, these methods could make up for the deficiencies of those that are more well-established (P&O, IC, etc.). Aside from that, there is a wide range of methods to AI, each of which has a particular advantage. By making some minor adjustments to the architecture, an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) were used to monitor the MPP of Thin Layer panel technology at the Oued Nechou installation in Ghardaia. Each connection channel now has six panels rather than the previous maximum of 12 panels, and the junction box has 210 channels rather than the prior maximum of 105 channels. In the last step, a DCDC boost converter is used to increase the power output voltages produced by the module. Keywords: PV system · Artificial intelligence · ANN · ANFIS · MPPT · DC-DC converter

1 Introduction The SKTM central in Ghardaia is just one of several that may be found in various locations around Algeria. Algeria’s newly appointed minister of “Energy Transaction and Renewable Energies” is working hard to move the country away from its reliance on fossil fuels and toward the use of more sustainable forms of energy. There are 8 modules located in that center, and there are 4 different technologies (monocrystalline, Polycrystalline, Amorphous, and Cadmium Telluride). It is capable of producing 1.1 MW [1]. Nonlinear PV curves are the consequence of the fact that the performance of photovoltaic (PV) modules is not linear in nature with regard to the effect of external factors. When the nonlinear PV curve reaches a given point at some point, the power reaches © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 322–331, 2023. https://doi.org/10.1007/978-3-031-21216-1_35

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its maximum achievable level. Because of this, the solar module needs to be operated at the maximum power point regardless of the irradiance or temperature conditions that are present. To extract the most power out of a solar array, a photovoltaic power system would often need a maximum power point tracking controller [2]. This is because of the nature of how photovoltaics work. It is too difficult to follow the MPP using traditional techniques such as perturb and observe P&O [3, 4], incremental conductance IC [5, 6], and hill climbing HC [7, 8]; because the MPP has a delayed reaction to the fast-changing temperature and irradiance [9]. As a direct consequence of this, the approaches of artificial intelligence have sought to fill common gaps in functionality [10, 11].

2 Material and Method 2.1 PV Cells Modeling: P-N semiconductor junctions are the fundamental building blocks of photovoltaic (PV) cells. These junctions are necessary to convert light (photons) into electric current via photoelectric effects. To create a PV module, numerous PV cells are linked in series and parallel with one another [12]. The primary equation for determining the output current is as follows:     q Vn −1 I = Np Iph - Np Irs exp KTA Ns where I and V represent the PV array’s output current and voltage, Ns represents the number of series-connected cells, Np represents the number of parallel-connected cells, q represents the elementary charge in Coulombs (1,6.10−19 C), is the Boltzmann constant in Joules (1,38.10–23 J), and Irs represents the cell’s reverse saturation current. The following equation explains how Irs adapts to temperature.    3  T q Eg 1 1 exp − Irs = Tr k A Tr T Tr is the cell’s reference temperature, Irs is its reverse saturation current, and Eg is its bandgap energy. The cell photocurrent equation is:   G Iph = Iscr + Ki (T - Tr ) 100 where Iscr is the short circuit at reference temperature and radiation, G is solar radiation in W/m2 , Ki is the short circuit current temperature coefficient. We characterize the Panel (Thin Layer) in Matlab/Simulink as the below table shows: 2.2 Proposed Design for Module 3 To connect the panels of module 3, we suggested a new design in which the number of panels connected in series was reduced from 12 to 6, and the number of panels connected in parallel was increased from 105 to 210. After that, the junction box of the collected panel was linked to a DC-DC converter, which increased the output voltage until it was equal to the initial voltage.

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Parameter

Symbol

Thin layers

Maximum power

PMPP

80 W

Voltage @ Pm

VMPP

48.5 V

Current @ Pm

IMPP

1.65 A

Short circuit current

Isc

1.88 A

Open voltage current

Voc

60.8 V

Temperature coefficient of open circuit voltage

β

−0.27%/°K

Temperature coefficient of short circuit current

α

0.04%/°K

Surface

S

0.72 m2

Number of series cells

Ns

154

2.3 Boost Converter Design The DC-DC converters are becoming more suggested for use in power conversion, and the majority of the systems that use this form of converters are involved in the conversion of renewable energy to electricity for the MPP [13]. During the course of this study, we made use of the boost converter, also known as the step-up converter, in order to bring the input voltage to a higher level (Fig. 1). Vin = Vo ∗ (1 − D) Vin = input voltage; Vo = output voltage. And the other component (Capacitor, Inductor) value we follow the equation [14, 15]. L=

Vin ∗ D IL ∗ fs

While: I L =

Vin−min ∗ D fs ∗ L

IL = estimate ripple current, Vin-min = minimum input voltage, fs = switch frequency, D = duty cycle, L = inductor. C=

Io ∗ D fs ∗ Vo

C = capacitor, Io = output current, Vo = estimate ripple voltage.

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Fig. 1. Boost converter

2.4 The ANN Based MPPT Controller One of the techniques used by learning machines is called an artificial neural network (ANN) [16]. This methodology has several benefits over the traditional approaches, including efficiency, MPP oscillation, convergence speed, precise monitoring of irradiation and/or temperature changes, and the ability to attach hardware [17]. These benefits may be found in this approach’s installation. Other than that, ANN is made up of an input layer known as Nin, a hidden layer known as Nh, and an output layer known as Nout. The following is an equation describing the relationship between three layers: [18, 19] Nh =

Nin + Nout  + Ne 2

Our research relied on the irradiance and temperature readings taken during the winter of 2016 to determine both the input and the output Voltage at MPP. The samples are taken once every four minutes (Fig. 2).

Fig. 2. Matlab/Simulink hidden layers configuration used to determine reference voltage at MPP

2.5 Adaptive Neuro-Fuzzy Inference System In ANFIS, the FLC and ANN techniques are merged to produce a result that is both more accurate and better appropriate to the investigation’s circumstances. Fuzzy logic computing requires a lot of time and effort to determine the correct fuzzy rules based on prior mistakes and changes in those mistakes. Despite the fact that it is a controller capable of mapping nonlinearity, the ANN’s workings remain obscure. Consequently,

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controllers based on the ANFIS architecture are presently being developed. This is being done to make up for the shortcomings that have been found [20]. This structure has a total of five separate levels or tiers. Layer 1 receives the E and CE crisp inputs. Crisp inputs are subsequently passed on to Layer 2, which is in charge of fuzzifying crisp inputs by fuzzing their values. Layer 2 This layer is responsible for receiving and transmitting the clear inputs. Layer 3 is responsible for converting inputs to outputs based on the degree to which two separate data sets are congruent. Layer 3 is the rule basis. The normalization layer, which can be found on layer 4, is in charge of calculating the normalized firing strength of each rule. The fourth layer may contain this layer. Layer 5’s defuzzification layer is responsible for transforming fuzzy data into data that is easier to understand. As a result of this, MPPT controllers can recognize MPPs with better precision thanks to the needed output duty cycle [21] (Fig. 3).

Fig. 3. Matlab/Simulink ANFIS configuration used to determine reference voltage at MPP

3 Result and Discussion 3.1 The Simulated Model The developed model of the PV system on the Matlab/Simulink is shown on Fig. 4, the Fig. 5 presents the ANN based MPPT controller, and Fig. 6 shows the developed ANFIS controller.

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Fig. 4. Matlab/Simulink step of model simulation

Fig. 5. The developed ANN controller used to Fig. 6. The developed ANFIS controller used determine reference voltage at MPP to determine reference voltage at MPP

The sun radiation distribution (Fig. 7) and the temperature of 25°C are utilized to examine the efficiency of the MPPT approaches. The chosen weather conditions are based on a sunny day.

Fig. 7 . Solar radiation measurement.

3.2 The Results Discussion The simulation results of boost output voltage and power are displayed in Figs. 9 and 11, respectively, when either the ANN or ANFIS MPPT techniques were used to manage the duty cycle of the boost converter. When compared to a waveform that does not use any MPPT approaches, this voltage and power waveform is more stable and efficient (Fig. 8).

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Fig. 8. PV panel voltage output.

Fig. 9. DC-DC boost converter voltage output.

Fig. 10. DC-DC boost converter power output (At of increase the solar irradiance).

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Fig. 11. DC-DC boost converter power output.

Fig. 12. DC-DC boost converter power output (At of decrease the solar irradiance).

Fig. 13. The output MPP voltage for two strategies.

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As shown in Figs. 10 and 12, ANFIS and ANN controllers affect the output voltage and power of the boost converter. After a brief settling period, the ANFIS waveform stabilizes at its maximum value. Figure 13 shows the output of ANFIS and ANN algorithms, the output voltage on ANFIS algorithm of MPP is varies with small wave than the ANN.

4 Conclusion In this research, a comparison study of artificial neural networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) MPPT controller approaches in Matlab/Simulink is presented. The primary issues with solar photovoltaic systems are their poor efficiency and expensive cost, as well as the variable output caused by unfavorable weather conductions. Therefore, we require a reliable MPPT controller. In the last stage of our comparison research, we discovered that the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is more effective than the artificial neural networks (ANN) controller. The Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is responsible for increasing output power while reducing fluctuations and ensuring a prompt response to shifting atmospheric conductions. In comparison to artificial neural networks, the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller showed better performance than artificial neural networks (ANN).

References 1. Abdel-Salam, M., El-Mohandes, M.T., El-Ghazaly, M.: An efficient tracking of MPP in PV systems using a newly-formulated P&O-MPPT method under varying irradiation levels. J. Electr. Eng. Technol. 15(1), 501–513 (2020). https://doi.org/10.1007/s42835-019-00283-x 2. Toumi, M., Bakir, A.: Energies Renouvelables (2015) 3. Esram, T., Chapman, P.L.: Comparison of photovoltaic array maximum power pointtracking techniques. IEEE Trans. Energy Convers. 22(2), 439–449 (2007). https://doi.org/10.1109/ TEC.2006.874230 4. Femia, N., Petrone, G., Spagnuolo, G., Vitelli, M.: A technique for improving P&O MPPT performances of double-stage grid-connected photovoltaic systems. IEEE Trans. Ind. Electron. 56(11), 4473–4482 (2009). https://doi.org/10.1109/TIE.2009.2029589 5. Li, J., Wang, H.: A novel stand-alone PV generation system based on variable step size INC MPPT and SVPWM control. In: 2009 IEEE 6th International Power Electron. Motion Control Conference IPEMC 2009, vol. 3, pp. 2155–2160 (2009). https://doi.org/10.1109/ IPEMC.2009.5157758 6. Reisi, A.R., Moradi, M.H., Jamasb, S.: Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review. Renew. Sustain. Energy Rev. 19, 433–443 (2013). https://doi.org/10.1016/j.rser.2012.11.052 7. Kamarzaman, N.A., Tan, C.W.: A comprehensive review of maximum power point tracking algorithms for photovoltaic systems. Renew. Sustain. Energy Rev. 37, 585–598 (2014). https:// doi.org/10.1016/j.rser.2014.05.045 8. Liu, F., Kang, Y., Yu, Z., Duan, S.: “Comparison of P&O and hill climbing MPPT methods for grid-connected PV converter”, 2008 3rd IEEE Conf. Ind. Electron. Appl. ICIEA 2008, 804–807 (2008). https://doi.org/10.1109/ICIEA.2008.4582626

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9. Salas, V., Olías, E., Barrado, A., Lázaro, A.: Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems. Solar Energy Mater. Solar Cells 90(11), 1555–1578 (2006). https://doi.org/10.1016/j.solmat.2005.10.023 10. Al-gizi, A.G., Craciunescu, A., Al-chlaihawi, S.J.: The use of ANN to supervise the PV MPPT based on FLC. In: 2017 10th International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 703–708 (2017). 11. Bouakkaz, M.S., Boudebbouz, O., Bouraiou, A.: ANN based MPPT algorithm design using real operating climatic condition. In: 2020 2nd International Conference Mathematics and Information Technology, pp. 159–163 (2020). https://doi.org/10.1109/ICMIT47780.2020. 9046972. 12. Rezk, H., Aly, M., Al-dhaifallah, M.: Design and hardware implementation of new adaptive fuzzy logic-based MPPT control method for photovoltaic applications, pp. 106427–106438 (2019) 13. Nayak, B., Mohapatra, A., Mohanty, K.B.: Selection criteria of dc-dc converter and control variable for MPPT of PV system utilized in heating and cooking applications. Cogent. Eng. 26, 1–16 (2017). https://doi.org/10.1080/23311916.2017.1363357 14. Noman, A.M, Addoweesh, K.E., Mashaly, H.M.: A fuzzy logic control method for MPPT of PV systems. In: IECON 2012–38th Annual Conference on IEEE Industrial Electronics Society, pp. 874–880. IEEE (2012) 15. Li, W., He, X.: Review of nonisolated high-step-up DC / DC converters in photovoltaic grid-connected applications. IEEE Trans. Ind. Electron. 58(4), 1239-1250 (2011) 16. Kulaksız, A.A., Akkaya, R.: A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive. Sol. Energy 86(9), 2366–2375 (2012). https://doi.org/10.1016/j.solener.2012.05.006 17. Elsheikh, A.H., Sharshir, S.W., Elaziz, M., Kabeel, A.E., Guilan, W., Haiou, Z.: Modeling of solar energy systems using artificial neural network: a comprehensive review. Sol. Energy 180, 622–639 (2019). https://doi.org/10.1016/j.solener.2019.01.037 18. Teo, K.T.K., Lim, P.Y., Chua, B.L., Goh, H.H., Tan, M.K.: Maximum power point tracking of partially shaded photovoltaic arrays using particle swarm optimization. In: Proceedings - 2014 4th International Conference Artificial Intelligence with Applied Engineering Technology ICAIET 2014, pp. 247–252 (2014). https://doi.org/10.1109/ICAIET.2014.48 19. Brano, V.L., Ciulla, G., Di Falco, M.: Artificial neural networks to predict the power output of a PV panel. Int. J. Photoenergy 2014, 1–12 (2014). https://doi.org/10.1155/2014/193083 20. Arora, A., Gaur, P.: Comparison of ANN and ANFIS based MPPT controller for grid connected PV systems. In: 12th IEEE International Conference Electron. Energy, Environmental Communication Computing Control (E3-C3), INDICON 2015, pp. 1–6 (2016). https://doi. org/10.1109/INDICON.2015.7443568 21. Padmanaban, S., Priyadarshi, N., Bhaskar, M.S., Holm-Nielsen, J.B., Ramachandaramurthy, V.K., Hossain, E.: A hybrid ANFIS-ABC based MPPT controller for PV system with antiislanding grid protection: experimental realization. IEEE Access 7, 103377–103389 (2019). https://doi.org/10.1109/ACCESS.2019.2931547

Methods Improving Solar Power System Efficiency Based on Geographical Coordinates and Sun Position Calculators K. Dahli(B) and N. Cheggaga Department of Electronics, Faculty of Technology, University of Blida 1, Blida, Algeria [email protected], [email protected], [email protected]

Abstract. Many extensive research efforts has been conducted on solar energy over the past decades. Granted, scientists believe that the sun can actually provide more than enough energy to satisfy the world’s energy needs. However solar power is limited in terms of producing power unfailingly in all conditions. This paper presents new methods of increasing solar system power efficiency without using sensors, but rather using sun position calculators and algorithms based on the astronomical equation. A solar tracking system was implemented and compared using three different control methods; a tracking code that uses Algerian postal codes, a GPS based tracker and a graphical user interface. The system was designed and simulated using Arduino microcontroller with MATLAB and Proteus environments. Keywords: Aastronomical equation · Graphical user interface · GPS based tracker · Sun position calculators · Solar tracking system · Tracking code

1 Introduction The Sun is considered to be one of the prominent source of clean and predominant energy that emits more than enough power onto earth to satisfy all future energy needs, it can be leveraged in place of conventional power generation systems on the condition of increasing its efficiency as well as reducing the cost of production, as a solution for this quest solar tracking system could be used by dint of their ability to maximize solar radiation and increase power generation efficiency up to 40–50% more than conventional solar systems [1]. Many research institutions in Algeria where drawn about solar trackers, where the government aims to install an additional 5,000 MW to the already deployed 450 MW of solar capacity by the end of 2028.the country has a goal of installing 22,000 MW total of renewable capacity as one of the official 2030 targets [2]. The solar tracker can be defined as a system that directs the panels toward the sun to maximize the efficiency of converting the solar energy into electricity. Since the efficiency of a solar tracker depends on the amount of solar radiation absorbed by the solar panel,the active surface should be always faced directly to the sun [3]. For this purpose, it is essential to carefully determine the exact location of the sun in the sky © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 332–341, 2023. https://doi.org/10.1007/978-3-031-21216-1_36

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through the day, at the observation point on earth [4]. The position of the sun is mainly determined by two angles the altitude angle and the azimuth angle [5]. According to the tracking mechanisms solar trackers can be classified into two types: a single-axis tracker and a dual-axis tracker. The difference between the two appears in the fact that single-axis tracker moves the panel on one axis of movement which is considered to be parallel to the earth’s axis of rotation.while dual-axis tracker moves the panel on two axis, aligned both north-south and east-west [6]. One way of controlling a solar tracker in an effective and simple way is the open-loop control. This method is based on determining the location of the sun by calculating the latitude and longitude angles of the sun using the location’s geographic coordinates [7], then controlling the solar panel to move accordingly to the sun.In this work, an open-loop solar tracking system is designed with three different control methods; a tracking code that uses postal codes, a GPS based tracker and a graphical user interface using pre-calculated sun position based on the astronomical equation. This work aims to present a simplified and effective solar tracking methods without using sensors nor feedback loops. By simulation results, it has been shown that the solar tracking system using one of the three methods can has a huge impact on solar power generation in Algeria. This paper is divided into three main sections. In the first section, the three methods are explained and modeled using Arduino microcontroller with MATLAB and Proteus software.In the following section, the comparison of the dynamic behavior of the system in each method was analyzed with some obtained results. In the last section, it has been concluded that the tracking code (postal code) method could be the best option for solar tracking implementation in Algeria.

2 Methods and Materials In this study, a solar tracker with different control methods has been designed using MATLAB and Proteus 8 software, the developed algorithm can accurately determine the tracker’s angle depending on the calculated coordinate database of the position of the sun in the sky throughout the year. In this project we have chosen a dual axis solar tracker due to its high efficiency and good performance. The position of the sun was determined then the calculated tilt and pitch angles of the solar panel were converted into motors motion such that the panel is faced accordingly to the sun radiation. For height movement control, the first motor will drive the panel up from sunrise to midday, and lowers the panel to its original position at sunset. Simultaneously, the second motor will rotate the panel clockwise or counterclockwise.in order to control the two motors we connected Arduino directly to MATLAB via a USB port and used MATLAB algorithm to

Fig. 1. Dual axis solar tracking control system.

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generate a certain commands that has been eventually used to help the tracker follow the sun vertically and horizontally in in aim of obtaining maximum solar energy generation at all time. The proposed method is shown in Fig. 1.

3 Control Methods This work was designed using three different control methods: a tracking code method (postal codes), a GPS based tracker and a graphical user interface, these methods will be represented to see the performance of the system with each case and choose the most convenient option. 3.1 Postal Code Method This method consists of building a solar tracking system without using sensors but rather determining the sun’s position from a specific set point based on a pre-calculated geographic coordinates. The location of the solar tracker is determined by converting a tracking code that uses postal codes into geographic location. We developed a MATLAB code that helps the user to get latitude and longitude in all of the provinces and municipalities across Algeria including 2324 different regions [8] (shown in Table 1). The main program chart is shown in Fig. 2.

Fig. 2. Flowchart of dual solar tracker based on tracking code

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Table 1. Example of the data used in the MATLAB code Postal code

Latitude

Longitude

Provinces

Municipalities

9000

36.473571

2.832315

Blida

Blida

9002

36.458546

2.849169

Blida

Sidi Kebir

16000

36.779443

3.061738

Alger

Alger Gare

16081

36.694293

2.973317

Alger

Baba Hassen

14191

35.373814

1.315758

Tiaret

Ouarsenis el Beida

14200

35.185742

1.493502

Tiaret

Sougueur

13016

34.878963

−1.348694

Tlemcen

Mansourah (Tlemcen)

13111

34.931076

−1.324027

Tlemcen

Ain el Houtz

3.2 GPS Method The main system is designed to complete the calculation and generate the database of the sun’s path trajectory throughout the year for a particular site. Hence, the location of the solar tracker is determined by using a global positioning system receiver module (GPS), which uses multiple orbiting satellites to calculate its position [9]. The GPS module is connected to the Arduino and continuously sends position information that includes longitude, latitude, altitude, date and time for current location to be used for the optimum tilt and yaw angles calculation of the tracker. The main program chart is shown in Fig. 3.

Fig. 3. Flowchart of dual solar tracker based on GPS sensor

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3.3 User Interface Method This interface is designed to detect the position of the sun for a given location at a given time and moves the panel accordingly. In order to firmly follow the movement of the sun the tracker involves no feedback loop nor sensors, but rather uses predefined algorithms based on mathematical calculations about sun’s trajectory particularly azimuth and altitude angles. The designed user interface (shown in Fig. 4.) will be connected directly to the Arduino through the USB port to control the two motors of the tracker using the command app.a = Arduino() [10]. The main parts of the interface are described as follows: • Geographic coordinates part where the user must insert location coordinates (latitude and longitude or province and municipality) and select the date, time and GMT offset. • Monitor part in which the user can choose between visualizing the solar panel power output, the irradiance, the azimuth angle or altitude angle. • Solar tracker type part where the user can select between three deferent types: dual axes, vertical single axes and horizontal single axes solar tracker. • Solar panel part, in this part the user will have the privilege to (a) monitor the power, voltage and current of the solar panel (b) visualize the solar panel moving (c) get the optimum yaw and pitch angles (d) turn on or off the solar tracker and (e) reset the solar tracker to its original position.

Fig. 4. Solar tracking user interface

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4 Simulation 4.1 MATLAB Software Both of the methods were simulated using MATLAB Simscape Multibody to simplify the design of the control system and to test its level of performance. This example illustrates the use of the Worm and Gear Constraint block to model a solar tracker. The yaw and pitch rotation are specified as a motion input to the gear revolute joint. The simulation block is shown in Fig. 5.

Fig. 5. Solar tracker system simulation block

The Simscape Multibody provided the visualization of the solar tracker’s dynamics as shown in Fig. 6 below:

Fig. 6. An automatically generated 3D animation of the solar tracker

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4.2 Proteus Software The circuit was also simulated on Proteus software to test its feasibility and its proper work. The system contains a solar panel connected to the booster and two stepper motors connected to Arduino. The simulation of the solar tracker circuit is shown in Fig. 7.

Fig. 7. Dual axes solar tracker simulation using Proteus software

5 Results and Comparisons Solar orientations based on Location and Time method gives high precision results and allows the system to perform with high efficiency, the set point studied in this paper is located in Algeria with the latitude of 36.4667 N and longitude of 2.8167 W (Blida, Blida). Using the GPS based solar tracker the maximum solar energy production was about 189.69 W with an optimum tilt angle equal to 77.77° and a yaw angle equal to 89.9°, this results an 1028,6 kWh/m2 overall solar irradiance (the results are shown in Fig. 8).

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Fig. 8. GPS solar tracker: Altitude angle; Azimuth angle; Output power; Solar irradiance

For the Postal Code solar tracker the solar energy production reached a maximum value of 189.67 W and the optimum tracker angles were equal to 77.75° for the tilt angle and 89.82° for the yaw angle, with an overall solar irradiance equal to 1028,6 kWh/m2 (the results are shown in Fig. 9).

Fig. 9. Postal code solar tracker: Altitude angle; Azimuth angle; Output power; Solar irradiance

Both of the methods gave a really good results, by comparing the two we can notice a very small deference which is about 1% (as shown in Fig. 10). Hence it is clear that the tracking code method is the best to be used in the field of solar tracking system in renewable energy’s power plants, because it costs less than the GPS one since it doesn’t require any sensors and it is less complicated than the user interface method.

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Fig. 10. Comparison of Postal Code and GPS solar trackers

6 Conclusion This paper aims to encourage the use of solar energy in a wider range of modern power plant and increase the efficiency of the solar power production using a low cost and efficient solar tracker that uses geographic coordinates and sun position calculators rather than sensors. The maximum efficiency can be obtained by controlling the angles of the solar panel to be faced directly to the sun light, three methods were applied in this research to solve this quest, and we got different results for each method. When taking into consideration the stability, response time, performance, costs and error probability. It can be concluded that the tracking code system using postal codes is the best option for a better performance and higher productivity of photovoltaic systems in Algeria.

References 1. Alexandru, C.: A novel open-loop tracking strategy for photovoltaic systems. Sci. World J. 2013, 1–12 (2013). https://doi.org/10.1155/2013/205396 2. Algeria charts a path for renewable energy sector development. Middle East Institute (2022). https://www.mei.edu/publications/algeria-charts-path-renewable-energy-sectordevelopment. Accessed Jan 7 3. Chang, C.: 5 - Tracking solar collection technologies for solar heating and cooling systems. In: Wang, R.Z., Ge, T.S. (eds.) Advances in Solar Heating and Cooling, pp. 81–93. Woodhead Publishing (2016). https://doi.org/10.1016/B978-0-08-100301-5.00005-9 4. Habib, M.K. (ed.) Handbook of Research on Advancements in Robotics and Mechatronics: Advances in Computational Intelligence and Robotics. IGI Global (2015). https://doi.org/10. 4018/978-1-4666-7387-8 5. Jagoo, Z.: Tracking Solar Concentrators. SpringerBriefs in Energy. Springer Netherlands, Dordrecht (2013). https://doi.org/10.1007/978-94-007-6104-9 6. Kalogirou, S.A.: Solar Energy Engineering: Processes and Systems, 2nd edn. AP, Academic Press/Elsevier, Amsterdam (2014) 7. Liste des codes postaux d’Algérie. Wikipédia (2022)

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8. Prinsloo, G., Dobson, R.: Solar Tracking, Sun Tracking, Sun Tracker, Solar Tracker, Follow Sun, Sun Position (2015). https://doi.org/10.13140/2.1.2748.3201 9. Sidek, M.H.M, et al.: GPS based portable dual-axis solar tracking system using astronomical equation. In: 2014 IEEE International Conference on Power and Energy (PECon), pp. 245–249 (2014). https://doi.org/10.1109/PECON.2014.7062450 10. Wagieh, A.: Using MATLAB App Designer With Arduino. Instructables (2022). https://www. instructables.com/Using-MATLAB-App-Designer-With-Arduino/. Accessed June 7

Aerial Forest Smoke’s Fire Detection Using Enhanced YOLOv5 Dalila Cherifi(B) , Belkacem Bekkour(B) , Assala Benmalek, Meroua Bayou, Ines Mechti, Abdelghani Bekkouche, Chaima Amine, and Ahmed Halak Institute of Electrical and Electronic Engineering, University of Boumerdes, Boumerdes, Algeria [email protected], [email protected]

Abstract. Forest fires around the world are the main cause of devastating millions of forest hectares, destroying several infrastructures and unfortunately causing many human casualties among both fire fighting crews and civilians that might be accidentally surrounded by the fire. The early detection of more than 58,950 forest fires and the real-time fire perception are two key factors that allow the firefighting crews to act accordingly in order to prevent the fire from achieving unmanageable proportions [1]. Forest fire detection is such a challenging problem for the current world. Traditional methodologies depend on a set of expensive hardware and sensors that might be not accurate due to some environment parameters and weather fluctuations. This paper proposes an accurate intelligent deep learning-based YOLOv5 model to detect forest fires from a given aerial images. Keywords: Aerial fire detection algorithm · Deep learning · YOLOv5

1 Introduction The current fire detection methods consist of applying image processing techniques to onboard visual and infrared sensors data [2, 3]. These techniques use characteristic features such as color, motion, and geometry to detect the flame or smoke generated by the fire [4–7]. Object detection is one of the classical problems in computer vision. It not only classifies the object in image but also localizes that object. In previous decades, the methods used to address this problem consisted of two stages: first extract different areas in the image using sliding windows of different sizes and then apply the classification problem to determine what class the objects belong to. These approaches have the disadvantage of demanding a large amount of computation and being broken down into multiple stages. That makes the system difficult to be optimized in terms of speed. One of the most popular purposes of using deep learning models for computer vision is object detection. Convolutional neural networks (CNNs) represent the core of state-of-the-art object detection methods, where they are used as features extractors. There are several CNNs available, for instance, AlexNet [8], VGGNet [9] and ResNet [10]. These networks are mainly used for object classification tasks and evaluated on some widely used benchmarks and datasets such as ImageNet [11]. You Only Look Once © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 342–349, 2023. https://doi.org/10.1007/978-3-031-21216-1_37

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(YOLO), is an algorithm that detects and recognizes various objects in a picture (in realtime). Object detection in YOLO is done as a regression problem and provides the class probabilities of the detected images. People glancing at an image, can instantly recognize what the objects are and where they are located within the image. The ability to detect objects fast combined with the knowledge of a person helps to make an accurate judgment about the nature of the object. A system that simulates the ability of the human visual system to detect objects is something that scientists are researching. Fast and accurate are the two prerequisites for which an object detection algorithm is examined [12]. In this work we propose an intelligent, fast and efficient Deep learning-based model YOLOv5s for Aerial Forest smoke’s fire detection. This article consists of four sections, the second section presents an overview about the Convolutional Neural Networks and YOLOv5 used for fire detection. The third section includes the experimental parts and the obtained results, followed by a discussion and a conclusion.

2 Methodology This paper describes the implementation of a fire segmentation algorithm to perform aerial forest fire detection using Deep learning. Which will be described in the next subsections. 2.1 Convolutional Neural Networks A Convolutional Neural Network (CNN) is a type of Artificial Neural Network used in image recognition and processing and specifically designed to process pixel data. Convolutional Neural Networks are powerful image processing, artificial intelligence (AI) systems that use deep learning to perform both generative and descriptive tasks, using Machine Vision which includes image and video recognition, as well as recommendation systems and natural language processing (NPL). The main idea behind CNN is feature extraction because the second part is a simple artificial neural network, by doing the convolution we are extracting the features and reducing the dimension. So, after extracting feature values from images, pooling layers are used to reduce the number of feature values while retaining the key differentiating features that have been extracted. One of the most common kinds of pooling is max pooling in which a filter is applied to the image, and only the maximum pixel value within the filter area is retained. There are many different pooling methods. One of the most difficult challenges in a CNN is to avoid over-fitting, where the resulting model performs well with the training data but doesn’t generalize well to new data which wasn’t trained. One technique you can use to minimize the over fitting is to include layers in which the training process randomly eliminates feature maps. Thus, the model doesn’t learn to be over-dependent on the training images. After using convolutional and pooling layers to extract the salient features in the images, the resulting feature maps are multidimensional arrays of pixel values. Flattening layers is used to flatten the feature maps into a vector of multiple values which can be used as input to a fully connected layer later. Usually, a CNN ends with a fully connected network in which the feature values are passed into an input layer, through one or more hidden layers, and generate predicted values in an output layer [13, 14].

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2.2 YOLOv5 Model YOLOv5, is an object detector deep learning model based CNNs, it comes with different models namely, YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x. YOLOv5s (see Fig. 1) consists of three CNNs: Model Backbone, Model Neck and Model Head. In the Model Backbone is mainly used to extract the important features from the given input image. There are several features’ extractors, we mention ResNet, CSPNet, VGGNet. The model Neck is Usually used to generate feature pyramids, it helps to generalize well on object scaling, thus the model will be able to identify the same object with different sizes and scales. There are many architectures used as model neck, for instance PANet. The Model Head used to perform the final detection part, it applies anchor boxes on features and generates a final output vector with class probability [12]. It’s used in the work for aerial forest smoke’s fire detection.

Fig. 1. Accuracy and performance of YOLOv5 [12].

3 Experiments and Results In this section we will explain each step passed for the implementation of the custom YOLOv5 object detector for forest fire detection. The objective is to prepare, clean, balance and enough the dataset, then create an accurate deep learning model for fire’s smoke detection. 3.1 Data Preprocessing As any study of deep learning, YOLOv5 requires 3 sets namely training, validation, and testing. The pre-processing of our dataset consists of collecting data from Robflow open-source datasets base and Google image; then resizing the dimensions of the images, such that all images have the same size. Draw the bounding boxes around the object of interest “ fire’s smoke”. Our total dataset contains 700 images of fire’s smoke, which is enough to train a YOLOv5 object detector model. Before starting creating bounding

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boxes around the object of interest. We divided the hole dataset into 2 separated sets with python, the code we made can go through each image on the given folder path, resize it and then save it in a splitted way such that the training set takes 80% of the hole dataset while the validation set takes the remaining 20% training: 3.2 Evaluation Metrics We have used four main evaluation metrics which are: sensitivity, specificity, accuracy and mean average precision which are given as follows: • Accuracy: Accuracy is one metric for evaluating classification models is given as the fraction of the number of correct predictions by Total number of predictions. Accuracy can also be calculated in terms of positives and negatives as follows [8]: Accuracy =

TP + TN TP + TN + FP + FN

(1)

where TP = True Positives, TN = True Negatives, FP = False Positives, and FN = False Negatives. • Specificity and sensitivity: Sensitivity and specificity are evaluation metrics defined as follows [8]: TP FP + FN TP Specif icity = FP + TN Sensitivity =

(2) (3)

where TP = True Positives, TN = True Negatives, FP = False Positives, and FN = False Negatives. • Mean Average Precision: To assess protest discovery models like R-CNN and YOLO, mean average precision (mAP) is utilized. The mAP compares the ground-truth bounding box to the recognized box and returns a score. The higher score, the more exact the show is in its detections. 1 K=n (4) mAP = (AP k ) k=1 n where: AP k : is the AP of class k and n:is the number of classes. 3.3 YOLOv5 Model Implementation The basic YOLOv5 model comes with a pre-trained data set “COCO” dataset which consists of 80 classes. Since we are going to train custom data for smoke’s forest detection, we have created our own.yaml configuration file for YOLOv5 where we can define the class, train, validation, test datasets. As YOLOv5 architecture consists of multiple CNN networks, the training process needs a powerful hardware such as GPU, which is not available in all nowadays computers. For this reason, we will use Google Colab notebook which is an open-source online notebook that provides a free GPU to train complex networks via cloud. We have trained 700 images of 640 x 480,.in each experiment we changed some parameters to get better results (see Fig. 2).

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Fig. 2. YOLOv5 model architecture.

3.4 Training • Experiment 1: Effect of Batches variation Batches: The first training was using the small YOLOv5 model using different batches. Batches are considered as a hyper parameter value. Since we can’t pass the overall dataset to the neural network, we divide it into a few batches or sets. • Experiment 2: Effect of Epochs variation Epochs: is when the entire dataset passes both forward and backward through the neural network. Thus, it will be able to update its parameters due to the obtaining result only once. One epoch consists of one or more batches. • Experiment 3: Effect of Batches and Epochs variation For the third training experiment, and due to the obtaining results in both the first and the second experiment, we have changed both the batches and epochs to get a better result. We have also change the optimizer from ADAM to SGD. The obtained results are presented in the following table and in the Fig. 3 (Table 1):

Table 1. Results of the experiments Model

Batches

Epochs

Optimizer

Accuracy

Sensitivity

YOLOv5S

50

50

YOLOv5S

20

100

YOLOv5S

16

100

ADAM

Specificity

ADAM

73.77

68.89

73.88

ADAM

94.21

93.74

81.00

98.42

98.18

98.45

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Fig. 3. Evaluation graphs of the experiments.

3.5 Testing For the testing process, we have used a distinct image of smoke’s fire with different positions and angles, so that we can perfectly test the efficiency of our model (see Fig. 4 and 5).

Fig. 4. Testing labels results.

3.6 Discussion In this section build a custom YOLOv5 object detector model which can detect the smoke’s fire, through different experiments. Batches, batch sizes and training epochs are the main hyper parameters of our deep learning model which can give generate better results for the right value. Optimizers are considered as algorithms or methods used to change the attributes of the neural network such as weights and learning rate in order to reduce the losses. Yolov5 object detector uses either Adam or SGD algorithms for optimization while SGD is a much more generalized algorithm which makes it generalizes better than Adam and thus results in improved final performance.

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Fig. 5. Testing with accuracy results.

4 Conclusion At the conclusion, we have proved the efficiency of YOLOv5 deep learning object detector models comparing the other architectures, where YOLO provides a faster multidetection of objects using a set of CNN networks. Batches, training epochs are considered as hyperparameters that might affect the accuracy of our deep learning model. Clean and well preprocessed dataset is the most important note for this work, where a corrupted dataset might cause either overfitting or less accuracy. Optimizers classes or methods used to change the attributes of the deep learning model, where stochastic gradient descent is much more generalized than adam optimizer. As a further work we are aiming to deploy this deep learning model on a raspberry pi4 nano-computer.

References 1. Akhloufi, M.A., Castro, N.A., Couturier, A.: UAVs for wildland fires. In: Autonomous systems: sensors, vehicles, security, and the internet of everything, SPIE, pp. 134–147 (2018) 2. Yuan, C., Liu, Z., Zhang, Y.: Fire detection using infrared images for UAV-based forest fire surveillance. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 567– 572. IEEE (2017) 3. Yuan, C., Zhang, Y., Liu, Z.: A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Can. J. For. Res. 45(7), 783–792 (2015) 4. Martinez-de Dios, J.R., Arrue, B.C., Ollero, A., Merino, L., Gómez-Rodríguez, F.: Computer vision techniques for forest fire perception. Image Vis. Comput. 26(4), 550–562 (2008) 5. Chamoso, P., González-Briones, A., De La Prieta, F., Corchado, J.M.: Computer vision system for fire detection and report using UAVs. In: Robust Solutions for Fire Fighting (RSFF), pp. 40–49 (2018)

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6. Cruz, H., Eckert, M., Meneses, J., Martínez, J.F.: Efficient forest fire detection index for application in unmanned aerial systems (UASs). Sensors 16(6), 893 (2016) 7. De Sousa, J.V.R., Gamboa, P.V.: Aerial forest fire detection and monitoring using a small uav. KnE Engineering, 242–256 (2020) 8. Kim, P.: Convolutional neural network. In: MATLAB Deep Learning, pp. 121–147. Apress, Berkeley, CA (2017) 9. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016). http://arxiv.org/abs/1512.03385 10. Krizhevsky, A.: One weird trick for parallelizing convolutional neural networks (2014). arXiv: 1404.5997. http://arxiv.org/abs/1404.5997 11. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556. http://arxiv.org/abs/1409.1556 12. Jocher, G., Stoken, A., Borovec, J., et al.: ultralytics/yolov5: v3. 1-bug fixes and performance improvements. Version v3, vol. 1 (2020) 13. Convolutional neural network architecture Understanding of Convolutional Neural Network (CNN) Deep Learning. https://www.educba.com/. Accessed June 2022 14. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural network. Commun. ACM 60(6), 84–90 (2017)

Sizing, Modeling and Energy Flow Management of PV-Diesel-Batteries Microgrid for Agricultural Application Salma Nait Bachir1(B) , Mustapha Hatti2 , and Saliha Arezki3 1 Department of Electrotechnics, University of Science and Technology Houari Boumediene,

Algiers, Algeria [email protected] 2 Solar Equipment Development Unit, Renewable Energy Development Center, Tipasa, Algeria [email protected] 3 Laboratory of Electrical and Industrial Systems, University of Sciences and Technology Houari Boumediene, Algiers, Algeria [email protected]

Abstract. Through the years, agriculture has continued to evolve. Today, agricultural machinery provides a guarantee of efficiency and performance but it leads to an increase in electrical energy demand which creates new challenges especially in rural areas where the acces to medium voltage networks is not always possible, that’s why multi-source electrical systems are the best suited. The aim of the present work is to size a microgrid composed of photovoltaic array, diesel generator and energy storage batteries in order to meet the electrical energy needs of an agricultural farm and also to manage the sized microgrid energy flow. The mathematical models of microgrid components were built on Matlab Simulink before using Load Following strategy. According to this study, we note that a good electrical energy needs estimation is the first step to succeed in microgrid sizing and the power produced by photovoltaic generator varies from site to site, depending on the geographical location and mainly on two factors that are: solar irradiance, temperature. One-diode solar cell model was validated on Matlab Simulink for a chosen solar panel from PVsyst library under standard test conditions before building the PV array and introducing real measurements of solar irradiance and temperature as parameters. Simulations results showed that the proposed energy flow management Load Following strategy is functional based on the power generated by different sources in order to power the entire load during the day under various meteorological conditions. Keywords: Microgrid · Energy · Sizing · Modeling · Management

1 Introduction Agricultural technology evolves rapidly. Agricultural machines, agricultural buildings and production installations are constantly improved. Especially since the agricultural © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 350–367, 2023. https://doi.org/10.1007/978-3-031-21216-1_38

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sector is booming in our country, which is leading to an increase in energy demand, although it is associated with greater prosperity, this growth creates new challenges. Electricity production in Algeria comes mainly from thermal power plants using gas as fuel, the latter are responsible of polluting air emissions harmful to health; Moreover, fossil fuels (coal, oil, natural gas) are limited. Currently, the most widespread view of the future availability of fossil fuels is based on the idea that access to their reserves will certainly cost more and more. They are the main responsible of greenhouse gas emissions and therefore of the earth’s surface temperature increase that we have observed since the end of 19th century. Although their contribution to the world’s energy supply can only decrease slowly, it is important to make progress in order to relay them in a meaningful way because they are not energies on which we can rely indefinitely. The extension of electricity networks for the electrification of rural areas is not always possible, it is technically impossible to satisfy the energy needs by extending medium voltage networks, due to the huge distances involved. Sometimes it’s not profitable. To face the different electrical energy production problems due to the use of fossil fuels, we focus on renewable energies, these cheaper and improved sources are necessary for efficient and optimal functioning of installations. As they are safe and don’t represent a danger to humans and environment, they are more essential than ever. In view of the present energy challenges, multi-source electrical systems are the best suited and present a particularly growing interest. These resources must be mobilized and combined to meet energy requirements. In this work, we will do the sizing, modeling and simulation of a microgrid PV-DieselBatteries by proposing an energy management solution to meet the energy demand of a farm in isolated site.

2 Material and Method 2.1 Case Study A 6 hectares agricultural farm of market gardening “tomato, pepper, zucchini, etc.” located in Tipasa, Algeria. The geographical position is shown in Fig. 1 (Geographic coordinates: 36° 39 43.4” North, 2° 43 10.5” East).

Fig. 1. Geographical position of Tipasa on the map of Algeria (A), exact location of the agricultural farm in Tipasa.

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2.2 Microgrid Sizing Before any studies of a system, the sizing of its components is a decisive step in order to ensure a good functioning even in unstable situations that’s why the choice of these components must be carried out according to a precise methodology. The photovoltaic system as a whole consists of a photovoltaic source, a direct currentdirect current (DC-DC) converter supposed to be perfect, it’s role is to power the directcurrent bus (DC Bus) [1] and also of an electrochemical storage device (lead acid batteries pack). In our case, we will use a diesel generator as a backup power source.

Fig. 2. Synoptic schema of the microgrid composed of photovoltaic source with energy storage device using diesel generator as a backup power source.

Figure 2 represents a synoptic schema of the microgrid to size. A micro-grid is defined as a local set of consumers and small producers of electrical energy, which can operate in on-grid mode or off-grid mode; For agricultural application in rural areas, off-grid mode is the best suited because the acces to medium voltage networks is not always possible. There are many ways allowing to generate electrical energy, they can be renewable (cheap and non-polluting) or non-renewable (using gas as fuel); In our study a photovoltaic source has been chosen because Tipasa is one of the regions with a very large solar field, hence it is important to exploit this energy resource. A lead acid battery is a kind of rechargeable battery that stores electrical energy by using chemical reactions between lead, water, and sulfuric acid [2]; It’s better to use it as an energy storage device because it is robust, reliable, and cheap to make and use. Diesel generator is used as a backup power source. The whole of microgrid components is connected to a DC bus that allows fewer transmission losses, several power generation sources are connected to the DC bus via appropriate electronic converters. In order to power the entire DC load, PV source is connected directly to the DC bus via a DC/DC converter and diesel generator is connected to this DC bus via AC/DC converter. 2.2.1 Photovoltaic Generator Sizing Irrigation is an operation that involves bringing complementary water to crops in case of lack or insufficiency of water, in arid regions or in a very dry climate, for crops that demand more water than they can find on site [3], that’s why the sizing of solar pumping system must be done before sizing the photovoltaic array.

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2.2.1.1 Solar Pumping System Sizing Hypotheses: – Total Manometric Head (HMT) is equal to 50 m. – Reference solar irradiation is 7.8 kWh/m2 /day. – Motor pump group yield is equal to 70%. Solar pumping system sizing is done with the following steps: Step 1: Determination of water requirements The water requirements of crops are measured in height of water expressed in mm. A rain of 1 mm corresponds to a height of water accumulated on a surface of 1 m2 (1 L of water): 1 mm = 1 l/m2 . The daily water needs of the market gardening are estimated at 7 mm per day the equivalent of 7 l/m2 . Daily water needs = water needs of one square meter. a

(1)

where: daily water needs are expressed in (m3 /day). water needs of 1 m2 are expressed in (mm or l/m2 ). a: area to irrigate (m2 ). Step 2: Calculation of hydraulic energy Hydraulic energy is calculated by the following equation: Eh = Ch . Q . HMT

(2)

where: E h : hydraulic energy (Wh/day). Q: water debit (m3 /day). HMT: Total Manometric Head (m). C h : hydraulic constant is given by the following equation: Ch = g .ϑ. k

(3)

where: g: gravitational acceleration constant (9,81 m/s2 ). ϑ: water density (1000 kg/m3 ). k: constant equals to (1/3600). Step 3: Calculation of electrical energy Calculation of electrical energy is given by Eq. 4: Eelec= Eh / ȵpump

where: E elec : electrical energy (Wh/day). őpump : motor pump group yield is equal to 70%. Step 4: Calculation of the power that must be generated by photovoltaic array

(4)

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It is determined by electrical energy demand [4] using the following equation:   Pmax = Eelec / Nsi .Cp (5) where: Pmax : maximum power generated by photovoltaic array (W). C p : losses coefficient, we take it equal to 1. N si : number of solar irradiation hours (h/day) is calculated as follows: Nsi = Gref /1000

(6)

Gref : reference solar irradiation expressed in (kWh/m2 /day). 2.2.1.2 Photovoltaic Array Sizing For this study, we consider the characteristics of solar panel Poly 250 Wp 60 cells – 30 V manufactured by GENERIC that are given in Table 1. At the maximum power point, the generated power value is 250 Wp. The electrical energy needs of the agricultural farm for each hour of the day are given in Table 2. Table 1. Characteristics of solar panel Poly 250 Wp 60 cells – 30 V. Characteristics

Values

Maximum power (Wp)

250

Open circuit voltage (V)

Table 2. Electrical energy needs of the agricultural farm for each hour of the day. Hour

Energy (kWh)

Hour

Energy (kWh)

37.4

0 am

2.8

12 pm

14

Short circuit current (A)

8.63

1 am

2

1 pm

15

Optimal voltage (V)

30

2 am

1.9

2 pm

15

Optimal current (A)

8.33

3 am

1.8

3 pm

14.2

Ideality factor

0.943

4 am

2.1

4 pm

7

Series resistance ()

0.265

5 am

3

5 pm

6.5

Shunt resistance ()

500

6 am

3.1

6 pm

4

1.627

7 am

3.8

7 pm

3.5

8 am

4.1

8 pm

3

Solar panel surface (m2 )

9 am

6

9 pm

3

10 am

13.7

10 pm

3

11 am

13.7

11 pm

2.9

Photovoltaic array sizing is done with the following steps: Step 1: Calculation of the daily electrical energy consumed Ec =  Ei where:

0≤ i ≤ n

(7)

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E c : daily electrical energy consumed (Wh/day). E i : electrical energy needs of the agricultural farm for each hour of the day (Wh). n: hours of the day. Step 2: Calculation of the daily electrical energy produced Ep = Ec / k

(8)

where: E p : daily electrical energy produced (Wh/day). k: coefficient depending on different factors that are weather uncertainty, uncorrected source tilt following the season, loss of source yield over time (aging and dust), losses in cables and connections. Step 3: Calculation of the power that must be generated by photovoltaic array Pmax = Ep / Nsi

(9)

where: Pmax : maximum power generated by photovoltaic array (W). N si : number of solar irradiation hours (h/day). Step 4: Calculation of the solar panels number needed Ntot = Pmax / Ppanel

(10)

where: N tot : total number of solar panels needed. Ppanel : maximum power generated by the solar panel (W). Step 5: Calculation of the PV array surface Stot = Spanel . Ntot

(11)

where: S tot : total surface of the PV array (m2 ). S panel : surface of the solar panel (m2 ). 2.2.2 DC/DC Converter Sizing The calculation of the components inductance L and capacities (C1 = C2) is as follows: Ts = 1 / f

(12)

D = V0 / (Vin . ȵ )

(13)

I0 = (Vin . Iin ) /V0

(14)

IL = 10% I0

(15)

L = (Vin . DTs ) / 2 IL

(16)

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V0 = 10% V

(17)

C = I0 . D Ts /V0

(18)

where: D: duty cycle. 2.2.3 Batteries Pack Sizing Hypotheses: – 1 day of autonomy. – Nominal voltage batteries pack equals to the optimal voltage of solar panels connected in series. – Depth of discharge is 90%. For this study, lead acid battery S12-290 manufactured by ROLLS is selected, its characteristics are given in the table below (Table 3): Table 3. Characteristics of lead acid battery S12-290. Characteristics

Values

Nominal capacity (Ah)

234

Nominal voltage (V)

12

Mass energy density (Wh/Kg)

8.63

Weight (Kg)

78.2

The sizing of batteries pack requires special attention in order to increase its lifetime and thereby to reduce the system overall cost. The nominal capacity of batteries pack is given as follows: CBP = EBP . AutBP /(VBP . DD )

(19)

where: C BP : nominal capacity of batteries pack (Ah). E BP : daily energy to store in the pack (Wh/j). Aut BP : days of autonomy. V BP : nominal voltage batteries pack (V). DD : depth of discharge (%). The number of batteries connected in series is given by the following equation: NBS = VBP . VB

(20)

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Table 4. Manufacturer specifications of solar panel Poly 250 Wp 60 cells - 30 V. Specifications

Values

Iscref (A)

8.63

Rs ()

0.265

Rsh ()

500

µsc (A/°K)

15.69e−6

n

0.943

K (J/°K)

1.38e−23

Ns

60

Eg (eV)

1.1

Vocs (V)

37.4

Voc (V/°C)

−0.137

Fig. 3. Equivalent electrical schema of one-diode solar cell.

where: N BS : number of batteries connected in series. V B : nominal voltage of the battery (V). The number of batteries connected in parallel is calculated as follows: NBP = CBP . CB

(21)

where: N BP : number of batteries connected in parallel. C B : nominal capacity of the battery (V). This equation gives the total number of necessary batteries in the pack: NBtot = NBS . NBP

(22)

where: N Btot : total number of the batteries in the pack. 2.3 Microgrid Modeling 2.3.1 Photovoltaic Generator Modeling In this part, we will model the solar panel Poly-250 Wc-60 cells manufactured by GENERIC in order to validate the model of one-diode solar cell built on Matlab Simulink, manufacturer specifications are given in Table 4: The model of one-diode solar cell model is characterized by its equivalent electrical schema which is shown in Fig. 3: The current source models the conversion of solar radiation into electrical energy, the resistance shunt Rsh represents the surface state at the cell periphery, Rs series resistance corresponds to the various contact and connection of a solar cell and parallel

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diode models the PN junction. These two resistances represent the different losses in a solar cell. The mathematical model for solar panel using one-diode solar cell model [5] is given by the following equation: I = Iph − Ish − Id

(23)

where I: Current generated by the solar panel (A). I ph : represents the photocurrent. It depends on solar irradiation and the temperature, it is represented by the following equation: Iph =

  G  Iphref + μsc T − Tref Gref

(24)

G: Solar irradiation (W/m2 ). Gref : Solar irradiation under standard test conditions STC, we take it equals to 1000 W/m2 . μsc : Temperature coefficient of short circuit current (A/°K). T: Operating cell temperature (°K). T ref : Operating cell temperature under standard test conditions STC, it is equal to 298°K. Iphref is the photocurrent in standard test conditions, it is given by the following equation:   Rs (25) Iphref = Iscref 1 + Rsh Iscref : Short circuit current in standard test conditions STC (A). The current I sh est is given by the following equation: Ish = (V + Rs .I )/Rsh

(26)

Rs : Series resistance (). Rsh : Parallel resistance (). The current Id is given as follows:   q.(Rs.I +V )  Id = Isat e n.K.T .Ns − 1

(27)

q: Electron charge equals 1,6.10–19 C. n: Ideality factor of the solar cell. K: Boltzmann constant is equal to 1,38.10–23 J/°K. N s : Number of solar cells connected in series in the photovoltaic panel. I sat represents the diode saturation current [6], it is given by the following equation:  ⎤  ⎡ T 3 q.Eg. Tref −1

n.K.T .Ns ⎥ ⎢ T (28) .e Isat = Isatref ⎣ ⎦ Tref

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E g : Gap energy of the semiconductor (eV). Isatref : Inverse current (saturation) of diode in standard test conditions STC (A) [7], it is expressed as follows: Iphref −

Isatref =



e

q.Voc n.K.Tref .Ns

Voc Rsh

.

(29)

−1

c is the open circuit voltage (V), it is expressed as follows:   Voc = Vocs + T − Tref .Voc

(30)

ΔVoc: Temperature coefficient of open circuit voltage (V/°C). 2.3.2 DC/DC Converter Modeling Buck-Boost converter combines the properties of two configurations series und parallel, it can be used to transform any input voltage into any desired output voltage, its equivalent electrical schema is shown in Fig. 4.

Fig. 4. Equivalent electrical schema of series-parallel DC/DC converter.

The dynamic modeling of DC/DC converter is given by the following equations: iL =

1 dV (i − C1 . ) α dt

is = −(1 − α).iL − C2 . V =

dVs dt

1 diL .(−(1 − α)Vs + L. ) α dt

(31) (32) (33)

2.3.3 Battery Modeling Figure 5 represents the equivalent model of lead acid battery, it includes the equivalent components to the main operating characteristics of this battery [8]. Electrochemical capacity of the battery is represented by Cbp capacitor. The battery internal resistance is represented by the two resistors Rbs and Rb1 . Electrolyte resistance is Rbs while the resistance Rb1 represents the electrolyte diffusion.

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Fig. 5. Equivalent model of lead acid battery.

Fig. 6. Microgrid structure.

The impedance equivalent to Fig. 5 is: Z(s) =

a2 s2 + a1 s + a0 b2 s2 + b1 s + b0

(34)

Coefficients ai et bj are used to represent the different components: a2 = Rbs .Rb1 .Rbp .Cb1 .Cbp = 4, 29.105 a1 = Rbs .Rb1 .Cb1 + Rbs .Rbp .Cbp + Rb1 .Rbp .Cbp + Rbp .Rb1 .Cb1 = 1, 318.108 a0 = Rbs + Rb1 + Rbp = 1, 0003.104 b2 = Rb1 .Rbp .Cb1 .Cbp = 330157100 b1 = Rb1 .Cb1 + Rbp .Cbp = 4, 6501.107 b0 = 1 where: Rbs = 0, 0013 , Rb1 = 2, 84 , Rbp = 10−3 , Cb1 = 2, 5 mF, Cbp = 4, 6515 KF it’s a model that describes well all components of an acid lead battery. The state of charge of the battery is given by the following expression: SOC = 1 −

Ibatt .t .100 C

(35)

2.4 Microgrid Energy Flow Management [9] The microgrid structure described in 2.2 is shown in Fig. 6: Figure 7 represents the organigram of energy flow management Load Following (LF) strategy for PV-Diesel-Batteries microgrid. Functioning of this system model may be classified as follows:

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• The first case is when the power generated by PV array is equal to the load. Here, the PV generator meets the load, batteries don’t charge, and the diesel generator remains off. In this case, no excess power exists. • The second case occurs when the power generated by PV array is greater than the load. The PV generator supplies the load with excess power. In this case, the excess power will be absorbed if the battery is fully charged. If the battery is not fully charged, the excess power generated by PV array is used to charge the battery. In this case, the diesel generator remains off. • The last case is when the power generated by PV array is less than the load. The two possible cases are as follows: • If the state of charge SOC = SOCmin , the diesel generator runs to supply the load. It provides only enough power to satisfy the net load without charging the battery. • If the SOC > SOCmin , the power generated by the two sources battery and PV generator is compared to the load; If this power is higher than the load, the battery works and provides only enough power to satisfy the net load power. If this power is less than the load, the battery works at its maximum; Moreover, the diesel generator starts to assist in meeting the net load.

Fig. 7. Organigram of energy flow management Load Following (LF) strategy for PV-DieselBatteries microgrid.

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3 Result and Discussion 3.1 Microgrid Sizing Table 5 represents sizing calculations of the solar pumping system, the aim of this solar pumping system sizing is the determination of maximum electrical power that must be generated by photovoltaic array for irrigating 6 hectares market gardening in order to estimate electrical energy needs of the agricultural farm that are irrigating and supplying domestic electrical equipments of the farmer’s house for each hour of the day, they are given in Table 2 and represented in Fig. 8. Table 6 represents sizing calculations of photovoltaic generator using the method described in 2.2.1.2. To obtain 27 kW, the 108 solar panels will be connected as follows: 4 branches consisted of 27 solar panels Poly 250 Wp 60 cells – 30 V manufactured by GENERIC in series configration. Table 5. Results of solar pumping system sizing. Results

Values

Water requirements (m3 )

420

Hydraulic energy (kWh/day)

57.225

Electrical energy (kWh/day)

81.750

Maximum PV power generated (kW)

10.5

Table 6. Results of photovoltaic generator sizing. Results

Values

Daily electrical energy consumed (kWh/day)

149.1

Daily electrical energy produced (kWh/day)

210

Maximum PV power generated (kW)

27

Number of solar panels

108

PV array surface (m2 )

176

To meet energy requirements, the photovoltaic generator must provide a voltage of 1009.8 V (27 panels in series). In this case, the use of a DC/DC converter of type Buck allows to diminish voltage of PV source to the value of DC Bus voltage which is 810 V. Using the method described in 2.2.2 and the following values obtained from PV generator sizing that are: Vin = 1009, 8 kV

Iin = 34, 52 A

Vo = 810 V

f = 20 kHz

η = 90%

We obtain the following results for the DC/DC converter sizing: L = 0, 0052 H and C = C1 = C2 = 2, 3 825e − 5F For the other parts of this study, a 15 kW diesel generator is chosen as a backup source. Table 7 represents sizing calculations of batteries pack. To obtain 205 Ah, the 68 batteries will be connected as follows: one branche consisted of 68 batteries lead acid battery S12-90 manufactured by ROLLS in series configration.

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Table 7. Results of batteries pack sizing. Results

Values

Nominal capacity of batteries pack (Ah)

205

Number of batteries connected in series

68

Number of batteries connected in parallel

1

Total number of batteries

68

Voltage of batteries pack

816

Batteries pack weight (Kg)

5181.6

Fig. 8. Daily load profile.

Figure 8 represents the daily load profile, the site chosen as a case study is located in Tipasa and has no access to the electric grid; In this situation, PV-Diesel-Batteries microgrid is the best suited. The energy requirements are the irrigation of an agricultural farm using pumps as well as the domestic electrical equipment of the farmer’s house supply. 3.2 Microgrid Modeling In this part, only one solar panel Poly-250 Wc-60 cells manufactured by GENERIC is modelled under various solar irradiation values in order to validate the model of one-diode solar cell built on Matlab Simulink. At the maximum power point under STC conditions the results were: Pm = 250 W; Vm = 30,76 V; Im = 8,13 A. This model is validated because the results obtained from this modeling are the same as the manufacturer’s characteristics. Figure 9 shows the model of PV-Diesel-Batteries microgrid model built on Matlab Simulink under real measurements of solar irradiance and temperature in the agricultural farm according to the equations given in 2.3.

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Fig. 9. PV-Diesel-Batteries microgrid model built on Matlab Simulink under real measurements of solar irradiance and temperature in the agricultural farm.

3.3 Microgrid Energy Flow Management Scenario 1 Figure 10 represents solar irradiation and temperature of a defavorable day in the agricultural farm situated in Tipasa, measurements were taken every minute during the 24 h of the day, solar irradiation reaches its maximum value that is equal to 685 W/m2 at 1 pm and the maximum value of temperature reched is 41 °C.

Fig. 10. Solar irradiation and temperature of a defavorable day.

Figure 11 shows the contribution of each source under solar irradiation and temperature of a defavorable day to supply the load using Load Following strategy for energy management, when the power of PV generator is equal to the load. Here, the power of

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Fig. 11. Generated power by different sources in order to supply the load in a defavorable day.

Fig. 12. Solar irradiation and temperature of a favorable day.

the PV generator meets the demand. When the power of the PV generator is greater than the power of the charge, the PV generator supplies the load with the presence of an excess power if the battery is not fully charged, the excess power of the PV generator is used to charge the battery. When the battery reaches the SOCmin the diesel generator works to power the charge and if the latter is higher than the minimum value, the power generated by the two sources PV generator and battery iscompared to the charge; In case this power is higher than the power of the charge, the battery works and provides only enough power to satisfy the net load. In case this power is less than the power of the

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load, the battery works at its maximum; moreover, the diesel generator starts to satisfy the net charge. Scenario 2 Figure 12 represents solar irradiation and temperature of a favorable day in the agricultural farm situated in Tipasa, measurements were taken every minute during the 24 h of the day, solar irradiation reaches its maximum value that is equal to 997 W/m2 at 1 pm and the maximum value of temperature reched is 36 °C.

Fig. 13. Generated power by different sources in order to supply the load in a favorable day.

Figure 13 shows the contribution of each source under solar irradiation and temperature of a favorable day to supply the load using Load Following strategy for energy management, the result of this scenario differs from that of scenario 1 from 10 pm to 00 am when the batteries deliver enough power to supply the load; This is due to the power generated by the PV array, which allowed the batteries to be charged enough to minimize the use of the diesel generator.

4 Conclusion This work allowed sizing, modeling and simulating a PV-Diesel-Batteries microgrid by proposing an energy management solution to meet the energy demand of a farm in isolated site. The agricultural farm of market gardening is located in Tipasa, the energy demand is mainly for irrigating 6 hectares market gardening and also for supplying the domestic electrical equipments of the farmer’s House. Microgrid sizing requires a clear vision of the microgrid environment: on one hand, it depends on the geographical location site and mainly on two factors that are: solar irradiance, temperature. On the other hand, it is necessary to have good electrical energy needs estimation. Calculation of daily electrical energy consumed was made by the addition of electrical energy needs

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for each hour of the day, which made it possible to determine the power that must be generated by photovoltaic array compared to a daily solar energy received. Hence the sizing of photovoltaic generator, namely: the number of solar panels and their configuration, as well as the sizing of DC/DC converter and batteries pack. In general, this study provided the necessary elements for a photovoltaic system sizing. Mathematical models of the various microgrid essential components were modeled using Matlab Simulink, the model of one-diode solar cell was validated for a chosen solar panel from PVsyst library under standard test conditions before building the PV array and introducing real measurements of solar irradiance and temperature as parameters. The energy management Load Following strategy proposed aimed to minimize the use of diesel generator by firstly promoting the power produced by photovoltaic generator then the one by the batteries and using diesel generator only when these two sources are insufficient to meet the electrical energy needs, this strategy was functional based on the power generated by different sources in order to power the entire load during the day under various meteorological conditions. The microgrid studied with Load Following strategy for its energy management is easy to implement, it may be the basis for further studies and particularly introducing artificial intelligence in renewable energetic systems.

References 1. Bouarroudj, N., Boukhetala, D., Benlahbib, B., Batoun, B.: Sliding mode control based on fractional order calculus for DC-DC converters. Int. J. Math. Mod. Computations 05(04(FALL)), 319–333 (2015) 2. Mtshali, T.R., Coppez, G., Chowdhury, S., Chowdhury, S.P.: Simulation and Modelling of PV-Wind-Battery Hybrid Power System (2011) 3. Abu-Aligah, M.: Design of photovoltaic water pumping system and compare it with diesel powered pump. Jordan. J. Mech. Indus. Eng 5(3), 273–280 (2011) 4. Tasghat, F., Bensenouci, A., Fathi, M., Belkhiri, Y.: PVsyst sizing of a PV system for a water supply of an agricultural farm in an isolated area using pivot technique. In: Hatti, M. (ed.) IC-AIRES 2021. LNNS, vol. 361, pp. 193–200. Springer, Cham (2022). https://doi.org/10. 1007/978-3-030-92038-8_19 5. King, D.L.: Photovoltaic module and array performance characterization methods for all system operating conditions. In: Proceeding of NREL/SNL Photovoltaic Program Review Meeting, Lakewood, Colorado, November 18–22 (2000) 6. Khezzar, R., Zereget, M., Khezzar, A.: Comparaison between the different electrical models and determination of I-V characteristics parameters of a photovoltaic panel. J. Renew. Energies 13(3), 379–388 (2010) 7. Nguyen, X.H., Nguyen, M.P.: Mathematical modeling of photovoltaic cell/module/arrays with tags in matlab/simulink. Environ. Syst. Res. 4, 1–13 (2015) 8. Manwell, J., McGowan, J.: Lead acid battery storage model for hybrid energy systems. Sol Energy 50(399), 399–405 (1993) 9. Zereg, H., Bouzgou, H.: Multi-objective optimization of stand-alone hybrid renewable energy system for rural electrification in Algeria. In: Hatti, M. (ed.) IC-AIRES 2021. LNNS, vol. 361, pp. 21–33. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-92038-8_3

Machine Learning-Based Techniques for False Data Injection Attacks Detection in Smart Grid: A Review Benras Mohamed Tahar1(B) , Sid Mohamed Amine2 , and Oussama Hachana3 1 Electric Engineering Laboratory, University of Kasdi Merbah, Ouargla, Algeria

[email protected]

2 Mechatronics Laboratory (LMETR), Institute Ferhat Abbas Setif 1, Setif, Algeria

[email protected] 3 Drilling and Oil Mechanics, Kasdi Merbah University of Ouargla, Ouargla, Algeria

Abstract. Smart grids (SG) provide new technological solutions for optimal energy utilization and management. Along with its significance and status as a cyber-physical system (CPS), it is vulnerable to a variety of cyber-security risks. The greatest threat to smart grid security is a False Data Injection (FDI) Attack. To efficiently detect this threat, numerous machine learning-based algorithms have been proposed in the literature. This article presents the most up-to-date machine learning-based techniques and methods for bogus data injection detection. The article begins with an overview of the smart grid and a brief history of cyberattacks on smart grids, followed by a discussion of security needs and a taxonomy of false data injection depending on delivery mode. Finally, we discuss the research that has been performed in the detection of false data injection attacks, which have been categorized according to the used learning approach. Keywords: Smart grid · Cyber security · Machine learning · False data injection attack · Deep learning

1 Introduction According to the connectivity nature of electrical systems with telecommunication networks in one system, the smart grid is considered one of the biggest technologies for cyber-physical systems [1]. In fact, SCADA systems, smart meters, advanced metering infrastructure (AMI), advanced distribution management (ADM), and other smart systems are included in smart grids to regulate power management. The Smart meters are in charge of collecting consumption data and transmitting it to service provider data centers via IP-based systems [2]. The sensitivity of the transmitted information, such as the quantity of power consumed, customer information, and so on, provides the opportunity for cyber-criminals to disrupt systems and benefit from any grid vulnerabilities. Because of the vulnerability of smart grids, cyber-security professionals and researchers are investigating this field in order to increase cyber security and develop bad data detection systems. Several projects are underway in this sector seeking the enhancement of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 368–376, 2023. https://doi.org/10.1007/978-3-031-21216-1_39

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manipulated or false data detection systems [3]. Artificial intelligence, or more precisely, machine learning-based approaches are getting particular interest in the last decade [4]. In this paper, we present a summary of the most recent approaches and techniques for detecting FDI attacks using machine learning. The rest of the paper, paper is organized as follows: The second section introduces current attacks in the energy sector around the world. The third section introduces a basic explanation of the smart grid and its design, as well as the security of a smart grid and its requirements. Then, based on the mode of delivery, we categorize cyber-attacks. We explain the many consequences of a cyberattack on a smart grid. We demonstrate False data injection attacks in smart grids and categorize them based on learning methods in the fourth part, and we highlight new work in the field.

2 History of Cyber-Attacks in the Energy Sector To better assess the dangers posed by cyber-attacks on vital infrastructure, we’ll look at some of the most high-profile cyber-attacks that have occurred in recent years around the world. In 2003, a well-known Slammer worm infiltrated the David-Besse nuclear power station in Ohio, USA. The worm gained access to the plant network, Due to this intrusion, the safety parameters, and indicator systems were disabled for 5 h [5]. In 2007, the US Department of Homeland Security conducted an “Aurora” cyberattack. During this attack, a hacker obtained access to a test generator’s control system, causing desynchronization in the power network and the explosion of $1 million generators [5]. In 2008, a tram system hack in Lodz, Poland, resulted in the injuries of a dozen passengers, making it the first cyber-kinetic attack to cause human injury [5]. Texas Power Company has been hacked. In 2009, a Texas Power Company employee who had recently been fired hacked the company’s network to disable power forecasting systems. He used logins that had not yet been disabled. The Stuxnet computer virus was used to target an Iranian nuclear power plant. A worm thought to have been produced by the US and Israeli governments to target Iranian uranium enrichment devices caused significant damage to Iran’s nuclear program in 2009 by destroying uranium enrichment centrifuges at an Iranian nuclear plant [5]. In December 2015, hackers used the BlackEnergy malware to compromise Ukraine’s electrical grid’s supervisory control and data acquisition (SCADA) system. This caused a massive blackout that impacted over 700,000 people for several hours [5, 6]. Attack on a smart building in Lappeenranta, Finland in the heart of the Finnish winter in 2016, a targeted DDoS assault knocked out heat and hot water in two residential buildings in Finland. Cyberattack on a petrochemical factory in Saudi Arabia in August 2017 [5, 6], a failed cyber attack on a Saudi Arabian petrochemical factory was meant to impair the plant’s operations and produce an explosion that could have killed people. Fortunately, a glitch in the attackers’ computer programming stopped the explosion from taking place.

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Disrupted power control and many small electrical plants in the United States in September 2019 [7]. Hackers’ abilities have increased with time, yet the majority of the world’s vital infrastructure systems continue to rely on antiquated technology, leaving them vulnerable to even the most basic kinds of cyber-attack. The growing interconnectedness of infrastructure, as well as the recent spike in ransomware assaults throughout the world, are both reasons for alarm. Because there are already over 6.4 billion IoT devices in operation, with the number expected to climb to about 50 billion in the future years, the situation can only become worse. These attacks on infrastructure are expected to grow more widespread in the future.

3 Cyber Security in Smart Grids Whereas smart grid technologies are providing several benefits, the usage of networked connections within these devices poses security threats. The smart grid’s integration of digital and information technologies, as well as the system’s increased complexity, raises the risk of cyber assaults and malfunctions spreading from one system to another. As a result, cyber-security in the smart grid poses several issues. The complexity of simulating the numerous forms of cyber assaults that might infect the system is one example [8] in this subsection We’ll go, through the smart grid design and security needs. 3.1 Smart Grid Architecture The concept of upgrading a power infrastructure by integrating electrical and information technology between any point of generation and any point of consumption is known as a smart grid. The convergence of two interdependent layers (i.e., cyber and physical systems) that are connected and create a cyber-physical ecosystem is what Smart Grid is all about. There are 4 stages to smart grid applications. Generation, transmission, distribution, and consumption. Geothermal heat, flowing water, solar radiation, wind, hydro plants, chemical combustion, and nuclear fission are all examples of energy. The process of creating electricity from various forms of energy is known as electricity generation. The transmission system connects the bulk generating system to the distribution system, which carries power across long distances [6, 9]. A smart grid is made up of a vast number of devices that are all linked together. Information data and operational data are the two forms of data that are transferred across the smart grid. The electricity bill, trending, logging, tagging, historical reporting of geographical locations, customers’ information, and emails are all examples of information data. Real-time current and voltage values, transformer tap changers, capacitor banks, transformer feeder current loads, fault locations, relay status, and circuit breaker status are examples of operational data. To safeguard smart grid systems from any vulnerability or threat that might result in a blackout, operational data requires a high level of security [10]. The key cyber-physical features of the Smart Grid are covered in this section.

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The following are three major architectures. SCADA (supervisory control and data acquisition networks), AMI (advanced metering infrastructure), and WAMS (wide-area measurement systems) [4]. • Supervisory Control and Data Acquisition (SCADA) The power distribution network is controlled and monitored in real-time using supervisory control and data acquisition (SCADA). It can assist guarantee the reliability of the power supply while also lowering the network’s maintenance and operating expenses through decentralized automated management and remote control in medium voltage substations. SCADA subsystems include the distribution management system (DMS) and the energy management system (EMS) [4, 10]. • Advanced Metering Infrastructure (AMI) The Advanced Metering Infrastructure (AMI) collects, analyzes, stores, and transmits metering data from smart meters to utility company computers for invoicing, outage management, and demand forecasting. Smart metering is another name for AMI. The primary components of AMI are the Home Area Network (HAN), smart meter, operational gateway, and meter data management system [4, 10]. • Wide-Area Measuring System (WAMS) To run energy systems over a larger geographical region, a wide-area measuring system (WAMS) is designed. WAMS can view all of the system’s data in one location at the same time. WAMS synchronizes from the phasor measurement units (PMUs) and provides real-time data from the essential nodes to the central authority via a GPS-based satellite signal [4, 10]. The following figure (Fig. 1) illustrate a smart grid exposed to a False Data Injection Attack.

Fig. 1. False data injection attack in smart grid

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3.2 Impact of False Data Injection Attacks False Data Injection Attacks on the smart grid have a significant impact on many parts of the electricity system, including the economic side. For example, if an attacker successfully executes an FDIA in a power system, it will cause a protracted power outage, causing major damage to other businesses that rely on the supply of electricity [5]. Electricity theft is another example of the financial damage that FDI attacks may cause by manipulating the data that smart meters provide to power providers’ stations and presenting misleading information about actual electricity use levels. Even though service providers are attempting to strengthen security assessment techniques, one of the worst consequences of FDI attacks is a detrimental impact on the power system’s secure operation and stability. Static and dynamic security evaluations are commonly used to assure the secure operation, system design, and stability of the power grid [6]. Customer data security and privacy are other areas of the smart grid that are subject to FDI attacks. This might occur if attackers overcome smart meters’ cryptographic features, obtain access to user data, and modify it, resulting in data falsification or even the utility center’s integrity being compromised [6]. Researchers in the field are interested in the many ways of cyber-attacks, where there is a significant number of studies regarding potential solutions to reduce the impacts of FDI assaults, by creating and developing algorithms that work on detecting FDI attacks.

4 False Data Attacks Detection Using Machine Learning Machine learning (ML) is a broad discipline within artificial intelligence. We can use ML to train machines to do complex tasks such as cyber security in smart grids. However, being a data-driven approach implies a high reliance on historical data from the system under test to allow machine learning. The detection techniques based on machine learning include supervised, unsupervised, and reinforcement learning methods. ML has contributed to improved cyber-security in recent years by implementing numerous methods. Researchers have developed many methods for detecting FDIA by using the characteristics of ML algorithms. ML algorithms categorization is usually dependent on the learning methods. We will discuss ML types and recent research findings in the following subsections. 4.1 Supervised Machine Learning-Based Algorithms The supervised machine learning algorithms require outside intervention. The input dataset is separated into train and test datasets. The output variable in the training dataset has to be predicted or categorized. All algorithms discover patterns from training data and apply them to test data for prediction or classification. The work of [11] describes a machine learning-based data-driven strategy for detecting stealthy fake data injection assaults on state estimation. Ensemble learning is employed in this approach, in which several classifiers are used and individual classifier decisions are further categorized. This technique employs two ensembles, one using supervised classifiers and the other using unsupervised classifiers. The performance of both supervised individual

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and ensemble models is equivalent, according to the results. Ensembles, on the other hand, outperformed individual classifiers for unsupervised models. In [12], the authors suggest using adversarial machine learning to create targeted and covert fake data injection assaults. a parallel optimization approach was suggested for efficiently designing additional assaults with lower attack costs. The success rate of two-state sparse assaults with small-scale targets is as high as 80%, according to experimental data. Furthermore, as the number of attack states grows, the attack success rate may continue to rise. 4.2 Unsupervisedsed Machine Learning Algorithms Unsupervised learning is the process of presenting unlabeled data to a machine for it to discover hidden categorization schemes and patterns. Thus, the machine’s task is to partition the data points into classes based on the hidden properties of the data points. Several unsupervised learning methods have been used in identifying FDIA in smart grids. Several unsupervised learning algorithms have been applied in smart grids to identify FDIA. Many research in the area have been proposed in [13] suggested an unsupervised technique for detecting CDIAs in SG communications networks based on a realistic situation of a non-labeled, historical SE MF dataset in a power network’s PCC. The suggested technique is based on the iForest algorithm, which is currently the most advanced algorithm available. To deal with the rising complexity of power systems, we use a PCA-based FE mechanism to convert a high-dimensional space into a low-dimensional space where data points can be separated easily without sacrificing accuracy. The simulation findings demonstrate that the suggested approach is capable of managing non-labeled. The simulation results suggest that the proposed approach is capable of dealing with non-labeled historical measurement datasets and improves attack detection accuracy significantly. 4.3 Reinforcement Learning-Based Algorithms In this sort of learning, the computer attempts to learn the best course of action to pursue based on prior actions. Unlike supervised learning, which uses data from samples to train, reinforcement learning learns via trial and error. As a result, a series of beneficial decisions will result in the process being “reinforced” since it solves the problem well. Some works were performed for this kind of detecting algorithm, A POMDP problem is presented as an online cyber-attack detection problem in [14], and a solution based on model-free RL for POMDPs is provided. The numerical simulations demonstrate the benefits of the proposed detection technique in detecting cyber-attacks on the smart grid quickly and reliably. The findings also show that RL algorithms have a lot of potential for tackling complicated cyber-security concerns. 4.4 Deep Learning-Based Algorithms Deep learning (DL) is a subset of machine learning that utilizes multilayer transformations to conduct representation learning, providing higher accuracy for detection and prediction problems. In cybersecurity, DL-based defensive systems, in particular, are

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being used to simplify the detection of cyber-attacks while improving and strengthening their capabilities over time [15] proposes a deep learning-based approach for identifying inserted data measurement. In time-series anomaly detection, a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network are utilized. Experiments show that deep learning can find anomalies that traditional state estimate and poor data and detection cannot. Authors in [16] introduced a Deep Learning (DL)based approach for identifying stealthy FDI assaults on the SE of the power system. The DL approach’s performance is compared to three frequently used machine learning algorithms: gradient boosting machines (GBM), generalized linear modeling (GLM), and distributed random forests (DRF). Each program investigates a dataset simulating the IEEE 14-bus system. The results demonstrate that these algorithms are capable of detecting covert FDI attacks on the smart grid with high accuracy and precision, with the DL-based solution outperforming the others. The authors of [7] present a comprehensive review of deep learning’s potential applications across a wide spectrum of security challenges. The fundamentals of common deep learning architectures used in cybersecurity applications were covered. Emerging deep learning issues, as well as an overview of crucial resources such as the generic framework and pertinent datasets, were emphasized. Traditional bad data detection (BDD) techniques are unable to recognize the fake data injection (FDI) assault, thus authors of [17] propose an extremely randomized trees algorithm to handle the problem. Because it outperforms other methods like support vector machine (SVM), random forest, and k-nearest neighbor in terms of accuracy and speed (KNN). It is self-evident that as the system size grows, so does the computational complexity. To deal with the dimensionality problem, a stacked autoencoder is used with a very randomized trees classifier. The authors of [1] present an intelligent attack detection and identification model based on an ensemble of machine learning approaches that can categorize the assault-type in the physical layer. Furthermore, the suggested approach pinpoints the attack or defect to certain system characteristics or measurements, assisting cyber-security experts in reducing the assault’s impact on communication networks. The suggested model is compared against classic machine learning classifiers using a smart grid dataset simulated. By separating the data and assessing the correlation of the localization metrics obtained by the proposed model, the localization of assaults and defects is tested. When compared to peer techniques, the findings show that the suggested method is more effective at identifying and localizing attacks. The authors of [2] suggest a semi-supervised AAE-based approach for recognizing FDIAs in smart distribution systems. The proposed method utilizes a cutting-edge state-of-the-art GAN architecture to successfully identify unobservable FDIAs, bypassing the traditional BDD method in the event of only a small amount of labeled measurement data. When compared to existing semi-supervised learning approaches, the suggested strategy delivers a high and robust detection accuracy due to the powerful coupling of autoencoders and GAN. The proposed approach is entirely data-driven, with no dependence on specialist estimation techniques or system knowledge. Numerical simulations have proven the detection performance of this approach. The smart grid study provided in [18] illustrates the necessity for false data detection to assess the potential and physical harm caused by FDCA. The suggested agent-based model’s physical features improved the dependability of smart grids, and the developed framework was validated using a cyber-attack

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replay. The agent-based method reinvents the FDDCA problem in smart grids and offers a superior solution to those now available. According to this study, the AFN’s assault boosted dependability more than the FDDCA machine learning. The following table summarizes the papers that have been studied (Table 1). Table 1. Summary of false data injection detection algorithms Learning method

Algorithm

Ref

Year

Supervised learning

Adversarial machine learning Ensemble-based ML

[12] [11]

2022 2020

Unsupervised learning

iForest Ensemble-based ML

[13] [11]

2019 2020

Reinforcement learning

RL for POMDPs

[14]

2018

Deep learning

CNN and LSTM DL DL DL, SVM & KNN Ensemble DL GAN DL

[15] [16] [7] [17] [1] [2] [18]

2019 2018 2022 2022 2021 2021 2021

5 Conclusion The most crucial aspect of the smart grid’s growth is cybersecurity. The smart grid is most vulnerable to false data injection attacks. The most recent experiments on detecting bogus data injection attacks using machine learning-based techniques are presented in this publication. We discussed the most recent machine learning-based approaches and strategies for detecting fraudulent data injection in this paper. The results of examined research show that the deep learning-based detection algorithms have more accuracy and ability to detect false data injection attacks compared with supervised and unsupervised learning-based algorithms. According to these features, deep learning-based technics have received more attention in the detection of false data injections last few years. There are more future directions that should be followed: • Increasing detection time to ensure that the FDIA causes the least amount of damage. • Robustness to help ensure the least amount near misses possible. • High sensitivity to detect even the smallest FDIA.

References 1. Sakhnini, J., Karimipour, H., Dehghantanha, A., Parizi, R.M.: Physical layer attack identification and localization in the cyber-physical grid: an ensemble deep learning-based approach. Phys. Commun. 47, 101394 (2021). https://doi.org/10.1016/j.phycom.2021.101394

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2. Zhang, Y., Wang, J., Chen, B.: Detecting false data injection attacks in smart grids: a semisupervised deep learning approach. IEEE Trans. Smart Grid 12(1), 623–634 (2021). https:// doi.org/10.1109/TSG.2020.3010510 3. Kimani, K., Oduol, V., Langat, K.: Cyber security challenges for IoT-based smart grid networks. Int. J. Crit. Infrastruct. Prot. 25, 36–49 (2019). https://doi.org/10.1016/j.ijcip.2019. 01.001 4. Cui, L., Qu, Y., Gao, L., Xie, G., Yu, S.: Detecting false data attacks using machine learning techniques in smart grid: a survey. J. Netw. Comput. Appl. 170, 102808 (2020). https://doi. org/10.1016/j.jnca.2020.102808 5. Musleh, A.S., Chen, G., Dong, Z.Y.: A survey on the detection algorithms for false data injection attacks in smart grids. IEEE Trans. Smart Grid 11(3), 2218–2234 (2020). https:// doi.org/10.1109/TSG.2019.2949998 6. Reda, H.T., Anwar, A., Mahmood, A.: Comprehensive survey and taxonomies of false injection attacks in smart grid: attack models, targets, and impacts. Renew. Sustain. Energy Rev. 163, 112423 (2021). https://doi.org/10.1016/j.rser.2022.112423 7. Macas, M., Wu, C., Fuertes, W.: Survey paper a survey on deep learning for cybersecurity: progress, challenges, and opportunities. Comput. Netw. 212, 109032 (2022). https://doi.org/ 10.1016/j.comnet.2022.109032 8. Sakhnini, J.: Security of Smart Cyber-Physical Grids: A Deep Learning Approach by (2020) 9. Kotut, L., Wahsheh, L.A.: Survey of cyber security challenges and solutions in smart grids. In: Proceedings of the 2016 Cybersecurity Symposium CYBERSEC 2016, pp. 32–37 (2016). https://doi.org/10.1109/CYBERSEC.2016.013 10. Gunduz, M.Z., Das, R.: Cyber-security on smart grid: threats and potential solutions. Comput. Netw. 169, 107094 (2020). https://doi.org/10.1016/j.comnet.2019.107094 11. Ashrafuzzaman, M., Das, S., Chakhchoukh, Y., Shiva, S., Sheldon, F.T.: Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning. Comput. Secur. 97, 101994 (2020). https://doi.org/10.1016/j.cose.2020.101994 12. Tian, J., Wang, B., Li, J., Wang, Z., Ma, B., Ozay, M.: Exploring targeted and stealthy false data injection attacks via adversarial machine learning. IEEE Internet Things J. 4662, 1–10 (2022). https://doi.org/10.1109/JIOT.2022.3147040 13. Ahmed, S., Lee, Y., Hyun, S.H., Koo, I.: Unsupervised machine learning-based detection of covert data integrity assault in smart grid networks utilizing isolation forest. IEEE Trans. Inf. Forensics Secur. 14(10), 2765–2777 (2019). https://doi.org/10.1109/TIFS.2019.2902822 14. Kurt, M.N., Ogundijo, O., Li, C., Wang, X.: Online cyber-attack detection in smart grid: a reinforcement learning approach. IEEE Trans. Smart Grid 10(5), 5174–5185 (2018). https:// doi.org/10.1109/TSG.2018.2878570 15. Niu, X., Li, J., Sun, J., Tomsovic, K.: Dynamic detection of false data injection attack in smart grid using deep learning. In: 2019 IEEE Power Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019, pp. 8–13 (2019). https://doi.org/10.1109/ISGT.2019. 8791598 16. Ashrafuzzaman, M., Chakhchoukh, Y., Jillepalli, A.A.: Detecting stealthy false data injection attacks in power grids using deep learning, pp. 219–225 (2018) 17. Majidi, S.H., Hadayeghparast, S., Karimipour, H.: FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid. Int. J. Crit. Infrastruct. Prot. 37, 100508 (2022). https://doi.org/10.1016/j.ijcip.2022.100508 18. Sengan, S., Subramaniyaswamy, V., Indragandhi, V., Velayutham, P., Ravi, L.: Detection of false data cyber-attacks for the assessment of security in smart grid using deep learning. Comput. Electr. Eng. (2021). https://linkinghub.elsevier.com/retrieve/pii/S00457906210 02068. Accessed 23 June 2021

Artificial Intelligence in Renewable Energy

Pervasive System in Smart Houses Mokhtaria Derkaoui(B) , Mansour Abou Chemala, and Hadj Meridja LARATIC Laboratory, National Upper School of Telecommunications and ICT (ENSTTIC-Oran), Sénia, Algeria [email protected]

Abstract. The presented study provides an overview on the Internet of Things and how it can be implemented in a futuristic smart house via pervasive system, using various sensors to collect data, and a number of actuators to perform the required actions. All the components are controlled via android mobile application programmed with Kotlin language, used by the client while connected to the internet, specifically to the Firebase data store, and for the node MCU to be able to figure out the decisions needed to be made in case of connectivity loss. Keywords: Android · Firebase · IoT · Node MCU · Pervasive system

1 Introduction Internet of Things “IoT” is a recent technology that creates a global network of machines and devices able of communicating and exchanging data with each other through the internet [1–3]. The smart house is one of the main applications that use the Internet of Things infrastructure to connect several sensors. Smart house provides security, energy efficiency, low operating costs and convenience. Pervasive computing is an emerging trend associated with embedding microprocessors in day-to-day objects, allowing them to exchange information [4, 5]. The term pervasive signify "existing everywhere" which refers to a new type of computing in which the computer completely permeates the life of the user. The idea behind pervasive system is for the microprocessor to supply services or information to a user without the user having to think about it [6, 7]. In this way, the user is not even obliged to be connected the entire time.

2 General Description of a Proposed Package A proposed hybrid edutainment kit in this paper, works towards to the enhancement of interactive learning methods, the figure below shows synoptic of our IoT device (Fig. 1).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 379–383, 2023. https://doi.org/10.1007/978-3-031-21216-1_40

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Fig. 1. IoT device architecture

2.1 Hardware Implementation We use an IoT application development platform called Fritzing to describe how the simulation mechanism can be built into this IoT platform. We first elaborate on how to implement the simulator for an input IoT device (a sensor), then we describe how an output IoT device (an actuator) can be simulated by an animated simulator (Fig. 2).

Fig. 2. Schematic of full circuit.

• Node Mcu V3 (Micro Controller ESP8266): is a self-contained Wi-Fi networking solution offering as a bridge from existing micro controller to Wi-Fi and is also capable of running self-contained applications; • Sensors: infrared motion sensor PIR (SR-501), gas sensor (MQ-2), humidity and temperature sensor (DHT11), LDR (Light Dependent Resistor) • Actuators: Buzzer, heater, fan, electric door, light

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All the components are used for the development of an efficient system based on collecting, processing, and transmitting data. The source codes is created using Arduino interface (Fig. 3).

Fig. 3. Source code on Arduino

2.2 Software Implementation To configure the application, we use Android studio that is an integrated development environment (IDE). We configure firebase plugin and wire it to the database. Firebase is a mobile and web application development platform, it replaces a whole server. Then, we create the main interface of the application with the visual editor (Fig. 4).

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Fig. 4. (a) Main interface XML, (b) Creation object of the main interface of the application in Android studio

3 Project Implementation Following figures show the schematic prototype (Fig. 5) and the screen pic of application from the phone (Fig. 6).

Fig. 5. Schematic of a prototype

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Fig. 6. (a) Main page in the application, (b) Settings page in the application

4 Conclusion Our project can be classified as a modern solution in order to create an efficient and a time saving environment with endless possibilities, from adding a variety amount of sensors and different actuators such as magnetic field calculators, irrigation systems, to develop more features within the application itself.

References 1. Nam, T., Pardo, T.A.: Conceptualizing smart city with dimensions of technology, people, and institutions. In: Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, pp. 282–291 (2011) 2. Bano, M., Zowghia, D., Kearney, M., Schuck, S.: Mobile learning for science and mathematics school education: A systematic review of empirical evidence. Comput. Educ. 121, 30–58 (2018) 3. Sundmaeker, H., Guillemin, P., Friess, P.: Vision and Challenges for Realising the Internet of Things. European Commission (2010) 4. International Telecommunications Union: ITU Internet Reports 2005: The Internet of Things. Executive Summary, ITU, Geneva (2005) 5. Alam, F., Mehmood, R., Katib, I., Albogami, N.N., Albeshri, A.: Data fusion and IoT for smart ubiquitous environments: a survey. IEEE Access 5(2017), 9533–9554 (2017) 6. Amershi, S., Cakmak, M., Knox, W.B., Kulesza, T.: Power to the people: the role of humans in interactive machine learning. AI Mag. 35(4), 105–120 (2014) 7. Candanedo, L.M., Feldheim, V.: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Energy and Buildings 112(2016), 28–39 (2016) 8. Sezer, O.B., Dogdu, E., Ozbayoglu, A.M.: Context-aware computing, learning, and big data in internet of things: a survey. IEEE IoT J. 5(1), 1–27 (2017)

Control and Power Management of Microgrid Supplied a Domestic and Industrial Loads H. Guentri1(B) , F. Lakdja2 , M. Belhamidi2 , and A. Dahbi3 1 Department of Mechanic and Electromechanics, Institute of Science and Technologies,

Abdelhafid Boussouf University Centre, GE Laboratory Saida University, Mila, Algeria [email protected] 2 Department of Electrical Engineering, GE Laboratory Saida University, Saida, Algeria 3 Unité de Recherche en Energies Renouvelables en Milieu Saharien (URERMS), Centre de Développement des Energies Renouvelables (CDER), Laboratoire de Developpement Durable et d’Informatique (LDDI), Université Africaine Ahmed Draia, Adrar, Algeria

Abstract. Renewable energies such as photovoltaic, fuel cell, and wind power have become an important role in the microgrid. Control and power management have become the center of recent research. The present system in this paper is composed of a photovoltaic (PV), small wind turbine generator (SWTG), fuel cell (FC), and a big wind turbine generator (BWTG), with the associated DC/DC, DC/AC, and AC/DC converters, to assure proposed system stability. This paper develops a comprehensive control and power management system to regulate DC bus, achieve an effective balance between supply and demand, and control the MPPT to extract the maximum power from the PV system. When the control and power management systems are integrated, and the loads change suddenly, the DC bus voltage remains stable, and power remains balanced. The simulation was used to prove the performance of the proposed control and power management system. Keywords: Wind turbine generator · Solar PV · Fuel cell · Integration · Control · Power management

1 Introduction The objective Global demand for electric energy has increased rapidly over the past decades due to population growth and social and economic development, especially in emerging economies. For example, China and the United States, the world’s two largest power markets, accounted for 70% of global demand growth. In China, electricity demand increased by 8.5%, a notable uptick compared with recent years [25]. Also, global electricity demand increase to 57% by 2050 [26]. As an example, a typical small house can now have several household appliances such as an oven, washing machine, several TVs, lights… Etc. Because of technological progress, there has been a trend toward new types of domestic loads. Therefore, increasing the overall energy consumption compared to the previous times, during the recent © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 384–397, 2023. https://doi.org/10.1007/978-3-031-21216-1_41

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decades, the demand for power consumption of one house has augmented from 2 kW to 5 kW. The same case on the industrial level, where the increasing trend in the adoption of electric machines, devices, and related technologies in large sectors of the economy has a significant contribution to the global increase in electricity consumption. To cope with this increase, we must produce enough energy, adding new power plants to the electricity networks. Unfortunately, this solution increases production costs and has an environmental impact. Renewable energy sources such as solar and wind, either in standalone mode [20] or integrated into power grids combined with energy storage, have an excellent potential to respond to the growing demand for energy. The interconnected mode is most preferred to have the effect of increasing global efficiency. The installation requires a controlled inverter to synchronize this integration without disturbing the network. Several research works have been done in this field in [12] Guentri et al. propose various robust design methods for the control of the power electronics converters and enhance the performance of the power management of the hybrid electrical energy storage system. Also, Junchao et al. [14] focus on the energy management strategy of a single-phase grid-connected PV generation system. Furthermore, Eghtedarpour et al. [15] propose a decentralized power-sharing method to eliminate the need for any communication between distribution generations or microgrids. Also, Nejabatkhah et al. [17] present an overview of power management strategies for a hybrid ac/dc microgrid system. Finally, Macko et al. in [18] present a Power-Management High-Level Synthesis. To enhance renewable energy integration efficiency and promote renewable energy sources compared to fossil sources, and subsequently reduce production costs, it is necessary to implement a power management system. Also, an extensive literature has been published in recent years on power management in electricity networks with renewable energies integration, like in [13, 16], and [19]. The analysis of these proposed methodologies shows that the integration of renewable energy into the grid is not relatively easy and requires strict compliance with some conditions such as: The control of all devices that facilitate this integration, namely static converters, power sources, and protective devices; Optimal power management in electrical networks promotes renewable energy exploitation and reduces production costs. This paper proposes the control and power management of a system composed of a Microgrid connected to two sides, the residential side with residential load, photovoltaic (PV), small wind turbine generator (SWTG), and fuel cell. Second, the industrial load side with the big wind turbine generator (BWTG). This system not only performs as a DC bus voltage regulation and power balance, but it also harvests renewable energy required for residential and industrial loads.

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2 Presentation of the System 2.1 The Configuration of the System The proposed system illustrated in Fig. 1 consists of two sides. The first side is residential, while the second is industrial. The residential side consists of the PV array, a DC/DC boost converter that controls the operating point of the photovoltaic array, a small wind turbine with a non-controlled AC/DC converter, and a storage device, which is the fuel cell. The assembly is connected to a DC bus, which is connected to an inverter that can help link this side with the utility network [3]. The industrial part is composed of a wind farm of a large size (8 MW) connected to an industrial load. Both sides (residential and industrial) are connected to the same utility network.

Fig. 1. Configuration of the model power system.

2.2 The Residential Load The residential part is composed of twenty (20) houses, each one characterized by low energy consumption in a short time, as indicated in [1, 2]. As cited in the table, we adopt a system corresponding to a real-life case, including all the devices needed for a home. These appliances and devices operate according to their usage priorities during the day. The use of these appliances is done according to the utility and the characteristics of each device. Considering the peak and off-peak hours of electrical energy consumption, the possibility that such a device can be used several times or that its use can be interrupted during the day is also considered. The use of these devices (loads) is deferred between one home and another. Nevertheless, use it rationally over 24 h in the spring period, and each time we add the load, we get Table 2. For example, between 7 a.m. and 8 a.m., it is time to get ready for work or school. For this, we can use in a house the following charges: lights in the living room (35 W) x2, lights in the toilet, refrigerator, freezer, light in the kitchen, light in the room 2 × 24, water pump, microwave, and hair dryer). By summarizing these different

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charges, we will have a total load for one home during this period of about 3494 kW. By multiplying this load by 20 houses, we will have 69880 kW. On this basis of calculation for each period, we obtained Table 2. The residential charge has an average power demand of 41052.5 W and a maximum peak of 125600 W (Table 1). Table 1. The list of the loads of one home. Devices

Consumption (W)

Lights on the living room (35 W) x2

70

Light in the toilet

11

Refrigerator

90

Freezer

225

Light on kitchen

24

Light on room 2*24

48

Washing machine

750

Vacuum cleaner

1200

Air conditioning

516

Water pump

100

Computer

125

Microwave

1050

Iron

1200

Printer

44

Television

65

Dishwasher

800

Hair dryer

1000

DVD or demo

12

2.3 The Industrial Load The industrial load is a factory at a distance of 19 km from the residential neighborhood. This factory has a charge that is distributed during the 24 h. It works from 08 h to 16 h. The production starts at 8 a.m. with a peak of 20 MW of power required to start the machines. The factory stops work between 12 h and 13 h and repeats until 16 h. Other times it is the consumption of interior and exterior lighting (see Table 2).

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Consumption (W) Residential load

Industrial load

1

6520

1000000

2

6300

1000000

3

6520

1000000

4

6300

1000000

5

6300

1000000

6

70000

1000000

7

50360

1000000

8

69880

1000000

9

21800

20000000

10

41300

15000000

11

65300

15000000

12

27300

15000000

13

52620

6000000

14

18160

20000000

15

21300

15000000

16

45540

10000000

17

66540

8000000

18

85660

7000000

19

62540

6000000

20

64420

3000000

21

125600

1000000

22

81600

1000000

23

23640

1000000

24

22760

1000000

2.4 The Sizing System The model suggested in this case study has a residential load with an average power of 41052.5 W with a maximum value (peak) of 126700 W. For this purpose, we installed a PV array of 0 to 40.5 kW and a wind farm that provides power from 0 to 24 kW. Both cover the average power demand for the entire residential load. Due to the insufficiency of the two sources, a utility network is obligatory. Also, a fuel cell is used due to the inefficiency of the two sources during peak hours. The fuel cell–electrolyzer combination provides backup for the system. The fuel cell needs to supply the peak load demand during the

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peak hours, which is close to 125600 W. Therefore, the fuel cell stack size has to support the utility network to avoid blackouts [6]. It is known that electrolyzers are expensive equipment, and there is a high probability of obtaining high solar irradiance and wind speed values. Therefore, the fuel cell is used to supply only 50% of this peak demand to define the electrolyzer size. For the reasons mentioned above, the electrolyzer will be 62.5 kW, which means the system can produce hydrogen until it reaches this level. Concerning the dimensioning of the industrial side, we use a large wind farm that provides 0–8 MW to cover the average power demand, which is about 7 MW. The utility network supplies the power shortage when the demand exceeds normal or during peak hours.

3 The Control of the System In this system we must control the DC bus, the MPPT and also the synchronization of inverter output with the utility grid. 3.1 MPPT Control There are many MPPT (Maximum power point tracking) techniques available in the literature, such as incremental conductance (INC), constant voltage (CV), and perturbation and observation (P&O). The incremental conductance (INC) method utilizes the incremental conductance (dI/dV) of the photovoltaic array to compute the sign of the change in power concerning voltage (dP/dV). The INC method provides rapid MPPT (Maximum power point tracking) tracking even in rapidly changing irradiation conditions with higher accuracy than the Perturb and Observe method [11]. Considering the complexity of this system, we modified the algorithm by adding an integral regulator. 3.2 Synchronization and DC Bus Control A three-phase inverter provides the DC/AC conversion. We have a DC voltage delivered by a DC bus with a capacitor. If we connect the two terminals of this capacitor to the inverter input, thanks to its semiconductors, it supplies to the network equipment a three-phase sinusoidal voltage of 240 V/60 Hz. In this order, the PV system, the wind farm, and the fuel cell are considered conventional generators. A unified nonlinear control device is designed to keep the terminal voltage close to its nominal value [10]. The system utilizes an internal loop regulating the current Iabc and continuously improving it. The main goals of this system are to control the DC bus voltage Vdc, to follow the reference voltage Vdcref all the time [21, 22], to maintain a power factor close to unity [23], and the output current should have a sinusoidal form [9]. Also, it is necessary to synchronize the whole system with the utility grid to have the same frequency, phase, and amplitude. This system controlled the device composed of an inverter (DC/AC) connected to the utility grid with a resistor R and an inductance L [7, 8].

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The synchronization of the controller concerning the evolution of the voltages of the network Uabc is an indispensable part of the control system realization. The purpose of synchronization is to estimate the voltage angle ø of the network at any time. This estimated angle is used in the generator control to generate the sinusoidal quantities. As several disturbances can occur in the electrical grid (voltage dips, harmonics, imbalances), the established synchronization system aims to reconstruct information on the direct component of the actual voltage. The chosen method produces a signal generator whose role is to extract the voltage or the direct voltage system with a minimum distortion from the measured voltage. The system allows control of the frequency, as shown in Fig. 2, and the phase, thus synchronising. It is the Phase Locked Loop (PLL) [24].

Fig. 2. Control of the system.

4 The Operating System The power management shown in Fig. 3 that was built into this system operates as follows: for the residential side, power generated by the PV source is used as a priority to supply the residential load demand. When the PV power is insufficient, we have recourse to the wind farm. Moreover, we add the utility grid when both the PV and the wind farm are inefficient. The fuel cell is used only to support the supplied residential load during peak hours. When PV power is higher than the domestic load demand, the surplus of PV power is injected into the grid. When PV and wind farm power are higher than the residential load demand, the excess of PV and wind farm power is injected into the grid. The hybrid system allows purchasing electricity from the grid and selling PV and/or wind farm power to the grid [4]. The industrial side is the same; the wind farm’s industrial source power is used as a priority to supply the industrial load demand. We will introduce the utility grid if the wind farm industrial power is insufficient. As soon as the wind farm power is higher than the industrial load demand, the surplus of wind farm power will be injected into the utility grid.

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Considering the sign convention on Fig. 1, the power balance in the side of the residential load system is obtained as follow: [5]. Balance power residential side P_gridom (t) = P_PV (t) + P_windom (t) + P_fc (t) + P_loadom (t)

(1)

Balance power industrial side P_gridind (t) = P_windind (t) + P_loadind (t)

(2)

Fig. 3. Power management system.

5 Results and Discussion In this section, we present the simulation results of our approach. The system shown in Fig. 1 was modelled and simulated by dynamic simulation in MATLAB/Simulink using a comprehensive nonlinear model of the different hybrid energy system components. We start with the simulation of the DC bus voltage shown in the figure below, and we can say that the voltage is stable at around 500 V with some fluctuation in order to ± 2% (Fig. 4 and 5).

Fig. 4. The DC bus voltage.

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Fig. 5. The output frequency of the inverter.

In order to verify the performance of the proposed power management algorithm, which is discussed in the above section, valid load demand data (domestic and industrial) and accurate weather data, such as wind speed and solar irradiance, are used, as shown in Figs. 6 and 7.

Fig. 6. The solar irradiation in a typical day.

Fig. 7. The wind speed in a typical day.

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5.1 The Residential Side To facilitate the understanding of these results, see Fig. 8. These were treated during the twenty-four hours (a full day). • From 00:00 until 02:00: the night period, the residential load is minimal. It varies from 7.2 kW to 7.4 kW, the wind farm provides power, and the utility network consumes the surplus of the power. • From 02 h to 03h00: also the night period, the residential load is 7.4 kW. The utility network fully supports this load. • From 03:00 until 05:00: the residential load is in the order of 7.4 kW, the wind farm supports it, and the utility network ensures the lack. • From 05:00 until 06:00: the residential load is always in the order of 7.4 kW. At this time, we observe introducing a new PV array source, which begins at the start of the day. Therefore, the load is supported by two sources, the wind farm and the PV array. The public network consumes the surplus of the power produced. • From 06:00 until 07:00: at the beginning of the day, an increase in the residential load is observed at 50 kW. The latter is supported by the two photovoltaic and wind power sources. The public network compensates the lack of power.

Fig. 8. Residential power management.

• From 07:00 until 08:00: the preparation of the beginning of the day, the household record consumption reached 71 kW; this is the first peak hour of the day. The homes are powered by three sources, the wind farm, the PV array and the fuel cell. The public network compensates for the lack of power as usual.

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• From 08:00 until 09:00: after the passage of the first peak time, the residential load decreases to 20 kW. During this period, the load is supported by two sources, and the public network absorbs wind farm and photovoltaic power surplus. • From 09:00 until 16:00: during midday, the load varies between 20 kW and 60 kW. In this period, the latter is supported essentially by the PV array, and the rest of the power is delivered by the utility network. This period includes the period of daily maintenance of the wind farm. • From 16:00 until 17:00: the residential load is still increasing. In this period, we note the reintroduction of the wind farm. During this phase, the load is supported by the PV array and the wind farm sources, and the public network compensates for the lack of power. • From 17:00 until 22:00: the phase of the peak hours of the day, i.e., the residential load, continues to increase to a maximum value of 127 kW. During this period, we again notice the engagement of the fuel cell. The load is supported by the wind farm, the PV array and the fuel cell. As usual, the insufficiency is filled by the utility network. • From 22:00 until 23:00 (night period): the residential load is at the minimum possible. During this period, the load is assisted by the wind farm source, and the public network compensates for the slight power loss. • From 23:00 until 24:00 (night period): the residential load is at its minimum. During this period, the wind farm source feeds the load, and the public network absorbs the surplus power. 5.2 The Industrial Side Same reasoning followed; we will treat the simulation throughout the day shown in Fig. 9. • During the night, the factory (industrial load) is inactive. For this purpose, the load is only external and internal lighting, which is 1.47 MW. During this period, the load is fed by a significant wind source, and the public network consumes the surplus. • During the working hours, which start at 08:00 in the morning, the phase of starting up machines requires more power, and this phase is the first peak hour with a maximum consumption of up to 20 MW. The wind farm supports the load with a power of 8 MW. The rest of the power is delivered by the public network. After this challenging period, the consumption of machines returns to normal, with a value of 15 MW. The wind farm provides half of this value, and the public network provides the rest. At noon, the factory’s production is halted, i.e., the pause phase. During this period, the consumption drops to 7 MW. During this time, the load is supported only by the wind farm. • With the restart of production, a new peak hour of 20 MW is recorded, the wind farm supports this consumption, and the public network compensates for the rest. After this period, the machines return to nominal consumption. During this period, the load is assured by the wind farm, and the rest of the power is compensated by the public network until the factory closes at 16:00.

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Fig. 9. Industrial power management.

5.3 Results Interpretations The results show that on both the industrial and residential side, installing a power flow management and control tool has enabled us to achieve a considerable gain in electricity production, up to 50% using renewable energy. For the residential side, in the first peak hour of the morning, the charge reaches up to 70 kW, the utility network covers less than 20 kW, and the rest of the consumed power is provided by renewable energy, i.e., more than 50 kW is produced by renewable energy. At the second peak hour of the evening, consumption reaches 130 kW, the public network covers less than 40 kW, and the rest of the power is produced by sources of renewable energy, which is more than 90 kW. For the industrial side, the power delivered by the significant model wind power source at any time of the day represents 50% of the power consumed by the industrial load.

6 Conclusion This study presents a new energy management algorithm designed to preserve the energy sustainability of renewable energy systems integrated into residential and industrial areas. A renewable energy system is used to test the proposed algorithm. This system consists of two parts. The first is the residential portion, consisting of an agglomeration of twenty (20) households, a variable charge throughout the day, a photovoltaic source, a small wind source, and a storage unit, the fuel cell, and the whole are connected to a DC bus. The second part consists of the industrial side, consisting of a production plant and a significant source of wind power. Both parts are connected by a DC/AC converter and are connected to the same utility network. A management system is needed to ensure load-side sustainability since wind, and photovoltaic sources are unreliable regarding durability and electricity quality. The proposed algorithm collects electricity from public and industrial grids and feeds the loads, avoiding any power interruption. In addition,

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this algorithm manages the effects of changes in wind speed, solar irradiation, and load quantity by exploiting wind, PV, and grid networks accordingly, using intelligent decision-making capabilities. The proposed system can play a vital role in promoting renewable energy use and reducing the use of the public grid during peak hours.

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16. Paraveen, T.K., Subrahmanyam, N., Sydulu, M.: Fuzzy controlled power management strategies for a grid connected hybrid energy system. In: Presented at the IEEE PES T&D Conference and Exposition IL, USA, Chicago (2014) 17. Nejabatkhah, F., Li, Y.W.: Overview of power management strategies of hybrid AC/DC microgrid. IEEE Trans. Power Electr. 30(12), 7072–7089 (2015) 18. Macko, D., Jelemenska, K., Cicak, P.: Power-management high-level synthesis. In: Presented at the IEEE International Conference on Very Large Scale Integration, Daejeon, South Africa (2015) 19. Lamdica, R., Santini, E., Teodori, S.: Electrical loads management in a smart building by PV sources in power scenario. Int. Rev. Electr. Eng. 9(5), 966–975 (2017) 20. Kaabache, A., Ibtiouen, R.: Techno-economic optimization of hybrid photovoltaic/wind/diesel/battery generation in a stand-alone power system. Sol. Energy 103, 171–182 (2014) 21. Kwasinski, A., Onwuchekwa, C.N.: Dynamic behavior and stabilization of DC microgrids with instantaneous constant-power loads. IEEE Trans. Power Electr. 26(3), 822–833 (2011) 22. Anand, S., Fernandes, B.G., Guerrero, J.: Distributed control to ensure proportional load sharing and improve voltage regulation in low-voltage DC microgrids. IEEE Trans. Power Electr. 28(4), 1900–1913 (2013) 23. Wang, C., Li, X., Guo, L., Li, Y.W.: A nonlinear disturbance observer based DC bus voltage control for a hybrid AC/DC microgrid. IEEE Trans. Power Electr. 29(11), 6162–6177 (2014) 24. Krishna, L.M., Chandra Sekhar, G.N., Naresh, M., Samuel, P.: Performance analysis of grid integrated photovoltaic systems using marx multilevel onverter in different environmental conditions. U.P.B. Sci. Bull. 80(2), 217–230 (2018) 25. Global Energy and CO2 Status Report 2018. International Energy Agency (2019) 26. Project Partner, Paul Scherrer Institute (PSI), Switzerland: World Energy Scenarios, Composing energy futures to 2050. World Energy Council, For sustainable energy (2013) 27. Owusu, P.A., Asumadu-Sarkodie, S.: A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Eng. J. 3, 1–14 (2016)

Survey on Artificial Intelligence Algorithms Application for Alzheimer’s and Elderly People Safety in Smart Homes Wissam Benlala1(B) , Siham Bouchelaghem2 , and Mohand Yazid1 1 Research Unit LaMOS (Modeling and Optimization of Systems), Faculty of Exact Sciences,

University of Bejaia, 06000 Bejaia, Algeria {wissam.benlala,mohand.yazid}@univ-bejaia.dz 2 Laboratory of Medical Informatics (LIMED), Faculty of Exact Sciences, University of Bejaia, 06000 Bejaia, Algeria [email protected]

Abstract. Alzheimer’s disease is a progressive degenerative disease that affects cognition and memory. The affected person becomes increasingly unable to remember events, recognize things and people, retain the meaning of words and exercise judgment over time. Furthermore, as a person with Alzheimer’s disease becomes weaker and more vulnerable to physical and moral threats, living alone and independently is no longer an option. She thus becomes dependent on her family members and caregivers. However, the emergence of home automation and artificial intelligence, as well as its deployment in the sphere of health and well-being, has proven to be effective and practical. Thus, remote monitoring and assistance have made it possible to regain autonomy and independence. In this paper, we survey recent and relevant works that combine artificial intelligence techniques, namely Machine Learning and Deep Learning, with smart homes to ensure Alzheimer’s inhabitants safety while performing their daily activities. Keywords: Smart homes · Alzheimer’s disease · Machine Learning · Deep Learning · Inhabitant safety

1 Introduction Alzheimer’s disease (AD) is the most common form of dementia, accounting for nearly half of cases, with major societal and individual repercussions (Mozer 1998). Since its discovery in 1906, the number of Alzheimer’s cases has continued to grow. Despite advanced technologies and the various scientific research conducted on this disease, no effective treatment to stop it definitively has yet been found. Indeed, according to the World Health Organization (WHO), a new case is detected every four seconds, which represents 7.7 million new cases each year. A world day of celebration has therefore been set up aimed at raising awareness of AD, in the world in general and among people over 65 in particular, being the age group most affected by this disease. Typically, memory loss is the first and most noticeable symptom of AD (Gauthier et al. 2021). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 398–407, 2023. https://doi.org/10.1007/978-3-031-21216-1_42

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Patients with AD indeed tend to forget what they were doing a few minutes earlier, putting them in an unpleasant and sometimes life-threatening situation. Forgetting to turn off the stove can start a house fire, leaving the faucets open can lead to flooding and slipping, and leaving gas-powered appliances running for an extended period can cause occupants to asphyxiate. Also, if an Alzheimer’s patient goes out shopping alone, he may not recognize the way home. For all these reasons, patients with this disease must be constantly accompanied and cared for. This however often demoralizes them, causes them discomfort and robs them of a sense of autonomy and intimacy. The recent global health crisis has imposed even more constraints on these patients, including the lack of caregivers, the obligation to limit contact with others, and the isolation of patients’ relatives in the event of contamination, forcing them to remain alone at home, leading to a shortage of places in retirement homes, and so on. Many researchers are focused on finding an effective treatment for AD. Also, several works focus on methods for detecting and predicting the disease, but little is carried out on the protection of the personal and physical life of people suffering from this disease. Patients with dementia, including AD, require ongoing monitoring and may need assistance with Activities of Daily Living (ADLs) such as preparing a meal, eating, dressing, etc. This situation worsens considerably in the later stages of AD and patients become more dependent on the help of caregivers. To monitor ADLs and minimize human intervention while ensuring the privacy of patient’s life and help overburdened hospitals and healthcare staff to reduce operating costs and efficiently utilize resources, the concept of Smart Homes (SHs) and monitoring mechanisms based on Artificial Intelligence (AI) algorithms can play an important role. In fact, the concept of integrated remote medical assistance via mobile and desktop apps, the permanent monitoring of SH inhabitants via sensors and actuators, and the integration of AI in the SH ecosystem seem to attract more attention. Hence, several studies have been carried out with the aim of bringing more comfort, protection and safety to patients with AD by combining different SH technologies with AI techniques such as Machine Learning (ML) and Deep Learning (DL) techniques. This paper aims to provide an overview of the field by presenting a literature review of remote monitoring and assistance systems based on AI techniques to ensure the safety of patients with AD in SHs. In particular, the main contributions of this paper are as follows: • We survey some recent and relevant research works on remote monitoring and assistance of AD patients and compare them according to different criteria that we have defined. • We highlight the main limitations of the surveyed solutions and discuss some recommendations that we believe should be considered for the proposal of new remote monitoring and assistance systems for AD patients in SHs. The remainder of this paper is organized as follows. In Sect. 2, we outline the evolution of SHs from past to present and describe how to monitor and support patients with AD using SH technology and AI algorithms. In Sect. 3, we review some relevant research works on the protection and monitoring of patients with AD, and in Sect. 4, we discuss their major limits. Finally, we conclude the paper in Sect. 5.

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2 Smart Homes for Alzheimer’s Patients: Monitoring and Assisting SHs are not a brand-new notion. When it first appeared in 1970, the primary goal of domotic was to automate the home; manage the opening and closing of shutters, electric gates, heating and lighting. Then, with the development of the Internet and connected networks, wireless technologies such as Wi-Fi and Bluetooth, as well as the miniaturization of electronic components, the introduction of mobile devices and the invasion of touch screens and connected televisions, home automation devices have become much more powerful and easier to use, and our homes have become increasingly smarter ever since. In addition to energy management applications (e.g., wireless and battery-free sensors can capture a small amount of energy and produce the data needed for decision making), home automation today has a plethora of other uses such as security, health, air quality, assistance to vulnerable people, and much other services as shown in (Fig. 1), and it is slowly but steadily infiltrating many other aspects of our daily lives. Thus, SHs can program themselves to observe the habits of their occupants and learn to anticipate and respond to their expectations (Mozer 1998). Furthermore, in response to the aging of the population, SHs for health are proposed as a promising solution. They promote aging in place by allowing elderly people to stay in their homes while remaining safe and independent through the use of smart technologies, which integrate sensors, RFID, Smartphones and cameras in a variety of locations and equipment. SHs modeled for dementia care require the extension of the activity recognition system to identify anomalies in resident behavior. Correspondingly, we note that the concept of activity recognition is used in the majority of research works dealing with this subject. In fact, it is one of the most effective options for tracking patient activities and detecting any changes in their behavior (Campo et al. 2012; Fikry et al. 2021). Another example is the hazard prediction system proposed in (Aissani et al. 2021), which combines a prediction algorithm with a multi-agent architecture to ensure the safety of people with AD in a SH. The system detects four potential hazards for the elderly and AD patients, namely floods, fires, gas leaks and short circuits.

Fig. 1. Smart home services.

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Recent advancements in linked products and AI are paving the way for a revolution in home living. Indeed, these advances enable a more in-depth assessment of the living environment of the house’s occupants. With this understanding of the living context, the behavior of the house can be modified in complex ways according to the habits and needs of its inhabitants. Patients with AD-like disorders require constant monitoring and occasional support with ADLs. In the later stages of Alzheimer’s, the illness deteriorates significantly and individuals become increasingly dependent on the support of healthcare professionals (Raza et al. 2019). Therefore, follow-up is the most popular way to support and assist people with dementia in their daily lives, according to the systematic review (Fikry et al. 2021). ML classification techniques for activity recognition can summarize the recorded data to recognize daily activities and compare the performance of various algorithms in order to draw the best conclusion.

3 Survey of AI-Based Monitoring and Assistive Systems SH is a field of research at the crossroads of home automation, the Internet of Things (IoT) and AI, integrating multiple technologies with the ultimate promise of improving the safety and comfort of its inhabitants, anticipating their needs, and supporting them in carrying out certain daily tasks. In this section, we review new research on monitoring and assisting the elderly, especially patients with AD. We categorize this research according to the two fundamental AI techniques used, namely ML and DL. 3.1 ML Algorithms-Based Systems Gayathri and Easwarakumar (2016) have proposed a framework for aiding people with dementia who experience memory loss in a SH by combining domain knowledge modeling with probabilistic modeling. The Markov Logic Network (MLN), a statistical relational learning approach, models uncertain data and domain knowledge. MLN- based activity recognition is used to simulate the processing of incomplete event sequences as well as the modeling of occupant-specific knowledge, given by caregivers/doctors. As a result, they are creating an intelligent decision support system that detects when an occupant’s activity deviates from normal daily routines and determines relevant notifications to handle emergency situations. In fact, the system considers three types of alarm, namely low, high, and emergency alarm, depending on the level of immediate attention required to ensure the safety of the occupants. Low alarms are simple alarms that alert the occupant to certain tasks they have forgotten to perform during their routine activity, high alarms require the attention of the occupant’s supervisor to restore the occupant in their normal state, and emergency alarms that require medical intervention to make the occupant recover from critical state. Pirzada et al. (2018) have proposed the design and development of an interactive multi-platform user interface to monitor the health of elderly people using sensor data from a SH. The implementation of this SH project is divided into four stages, the first of which involves installing sensors throughout the house to measure ADLs. In the second stage, the analog data is converted into digital data and sent through a gateway to devices/applications over wireless or wired internet. In stage 3, the application server

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receives raw data for processing and analysis, which includes filtering, clustering, and anomaly detection. In order to determine anomalies, the KNN algorithm is used in pattern recognition. Data storage completes stage 4. This also results in the creation of a data warehouse, which can be used to predict trends based on the huge amount of data collected. Each individual’s ADLs can be recorded and behavioral patterns can be inferred. Along with the diagnosis of AD, Raza et al. (2019) have proposed a sensor-based framework to recognize and record daily life events using wireless sensor networks and a cloud-based classification system to help healthcare professionals to establish appropriate patient needs. The ADLs classification system not only enables automated recognition and recording of essential activities of daily living, but also provides patients with appropriate assistance in performing their daily tasks and achieving their daily goals. This can help people live longer and slow down the deterioration of brain tissue. It also provides more accurate input to qualified physicians, allowing them to diagnose patient needs and the level of assistance required with greater accuracy. Acceleration and angular velocity data from various ADLs (e.g., sitting, standing, walking, lying, etc.) are recorded by body-worn inertial measurement unit (IMU) sensors and transmitted wirelessly to a local data accumulation unit. The data is immediately available to emergency services, medical personnel and caregivers in the event of irregularities. The data collected from the sensors is periodically transferred to a cloud server for further processing and ADLs classification. The authors, in particular, provide a framework to identify/diagnose ADLs and AD-like disorders using both ML and DL techniques namely Support Vector Machine (SVM) and Convolutional Neural Network (CNN). Besides, a modified architecture of AlexNet (Krizhevsky et al. 2012) is used in the process of diagnosing AD-like diseases. For sustainable and successful management of care services designed for the patient, the proposed system creates medical records, activity logs and nursing care plans. Medical practitioners and caregivers can access patient sensory data, activity profile, and medical records generated through a cloud-based ML and classification system. Aljojo et al. (2020) have developed an Arabic Smartphone application to enable AD patients in Saudi Arabia who are in the early or intermediate stages of the disease to carry out their daily routines and activities, while allowing them to interact with their family and friends. The “Alzheimer Assistant” app has several features, including patient tracking and sending reminders and to-do notifications throughout the day, capture and add images of people interacting with the patient and display information about the person and their relationship using facial recognition and ML, and notify caregivers of Alzheimer’s patients when they leave the safety zone through the location of a Smartwatch or bracelet. Caregivers can also use the app to add reminders and tasks to the patient’s schedule. The Realtime Face Recognizer (RFR) library was used to implement the app’s facial recognition functionality. Ibrahim Mamun et al. (2021) have proposed an AD detection and assistance system for Alzheimer’s patients named “AlziHelp” in a SH using 5G, IoT and ML approaches. Smart IoT devices (e.g., smart watches, smart phones, and smart shoes) are used to collect data on SH residents to determine whether or not they have AD. In fact, a resident’s

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position and activities are linked to a particular time. The system then looks for inconsistencies in actions and timing for a person’s position and actions and will determine the best possible action using the K-Nearest Neighbor (KNN). The system has two separated but connected parts, the detection part of AD and the assistive part. Using a flag value, the system will turn on the second part after detecting AD. Using smart wireless led signals and a sonic buzzer, this part can help a person with AD perform their daily tasks. 3.2 DL Algorithms-Based Systems Sukor et al. (2019) have proposed a strategy to detect anomalies in the behavior of SH residents and identify any deviations from their routines. The use of two DL models known as Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN), which are widely used in pattern recognition communities, was investigated in this study. The proposed network uses three hidden layers (i.e., deep MLP), each with the same number of units. The activation function is the Rectified-Liner Unit (ReLU), which corresponds to a linear transformation. Long Short-Term Memory (LSTM) is the most commonly used model in RNN. It enables long-term temporal dependency by replacing hidden nodes with gated memory cells. Three gates are introduced in LSTMs: the input gate, the forget gate, and the output gate. The input gate regulates new values entering memory, the forget gate determines how long the value is retained in memory, and the output gate computes the LSTM memory block’s output activation. The learned models are then used to categorize normal and abnormal circumstances. The results of comparing MLP to LTSM reveal that MLP is a very efficient anomaly detection method compared to LTSM. A DL-based Internet of Health Framework for patients with AD, known as DeTrAs, has been proposed by Sharma et al. (2020). The proposed framework is designed in three phases: (1) an RNN method based on sensory movement data to predict AD is proposed, (2) an ensemble approach for abnormality tracking for Alzheimer’s patients is designed, which consists of two parts: a CNN-based emotion detection scheme and a timestamp-window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for patients with AD is also proposed. This is done to limit the amount of inaccurate detections made by the proposed technique since the weights applied to distinct detection input sources would eliminate outliers. If the total weight of the sensory alarm generation exceeds the recommended level, the alarm is regarded a true alarm, and the Alzheimer’s patient assistance procedures are activated. The third phase of the scheme includes the use of IoT devices to provide various forms of assistance to the Alzheimer’s patient. In corrective help, the Alzheimer’s patient is informed of an inaccurate decision that requires action or needs to be reversed. In reinforcing assistance, the method chosen for a previously incorrectly done activity is repeated in order to perform it successfully in this case. Reinforcement aid is used to provide cognitive stimulation in order to slow the progression of AD. When an Alzheimer’s sufferer uses an app, supportive help is required. Instead of the above, IoT-based aid can be categorized into ADLs, social cognition, and cognitive stimulation therapy based on the complexity of trigger and support mechanisms.

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In Table 1, we summarize and compare the surveyed solutions according to various criteria, namely the AI methods used, the category of users considered, the evaluation metrics measured, the accuracy rate, and the dataset employed. Table 1. Comparison of the different solutions examined.

ML-based algorithms

DL-based algorithms

Reference

Method

User

Evaluation metrics

Accuracy

Dataset

Gayathri and Easwarakumar (2016)

MLN

Demented people

Precision, recall and f-measure

96%

Real dataset (Ordóñez et al. 2013)

Pirzada et al. (2018)

KNN

Elderly people

10 k-fold cross validation

/

MIT (Tapia et al. 2004)

Raza et al. (2019)

CNN, SVM

Alzheimer’s patient

10-fold cross validation

95%

OASIS (Marcus et al. 2010) ADNI (Jack Jr. et al. 2008)

Aljojo et al. (2020)

RFR

Alzheimer’s patient

Usability and accessibility

28.5%

7 different persons

Ibrahim Mamun et al. (2021)

KNN

Alzheimer’s patient

/

/

/

Sukor et al. (2019)

MLP, RNN

Elderly people

Leave-One-Out 92.2% Cross Validation (LOOCV)

Public SH dataset

Sharma et al. (2020)

RNN

Alzheimer’s patient

Precision, recall and f-score

Daphnet (Bächlin et al. 2010) MMI (Mollahosseini et al. 2016) Fer (Goodfellow et al. 2013) Snew (Dhall et al. 2016) (Chaffar and Inkpen 2011)

88.63%

4 Discussion and Limits In a single-occupant smart home, gathering accurate information does not present much of complications. However, we cannot guarantee the solitude of the inhabitant within this house; people with AD and the elderly may have regular visits from their caregivers and relatives. Thus, the systems that are preconfigured for the collection and processing of information from a single person will consider that all the information captured within the

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house comes from the same and unique person. The majority of the works studied in this survey do not consider such scenario. Because people with AD are exposed to hazards on a daily basis while performing daily living tasks, hazard detection is insufficient to ensure their safety. Setting up a real-time alert system for family members and caregivers is crucial in order to save them in case of threat or danger, which was not considered in most of the studied works. It is essential for the scientific community to specify the algorithms and methods used in research work, as well as the metrics for evaluating performance and results. However, the ML algorithm used for face recognition was not specified in (Aljojo et al. 2020), performance evaluation metrics and results were not mentioned in (Ibrahim Mamun et al. 2021), and no performance evaluation estimate was provided in (Pirzada et al. 2018). Finally, the accuracy in (Aljojo et al. 2020) is very low, this can impact the functionality of the application and may not ensure effective remote monitoring of Alzheimer’s patients by caregivers.

5 Conclusion and Future Directions The global population of elderly people is undeniably growing. As people age, they become increasingly vulnerable to indicators of aging such as loss of memory, loss of bodily autonomy, and so on. These elderly people can be our parents, grandparents, relatives or friends, and they are likely to be exposed to the hazards of ordinary life, thus requiring special care. In this paper, we have proposed a survey examining recent research works dealing with different systems proposed to monitor and assist elderly people and more specifically people with AD in SH in order to predict any suspected life-threatening event. To ensure the accuracy of detecting anomalies in the behavior of SH residents, most work has focused on AI algorithms, namely ML and DL algorithms. In the following, some recommendations that we believe should be considered when proposing new monitoring and assistive systems to ensure the safety of Alzheimer’s patients within SHs are suggested: • Using AI algorithms to improve the accuracy of fall detection in a SH: leaving water taps running can lead to flooding and increase the risk of falls. Screams, inactivity, and high humidity levels could indicate patient falling due to flooding in the SH. • Implementation and evaluation of a real-time alert system for caregivers and relatives of Alzheimer’s patients in the event of detection of unusual behavior in a SH such as an intrusion: people with AD who live alone are at risk of having their property stolen or abused, putting their lives in jeopardy if no action is taken promptly. Therefore, detecting any unusual activity inside the house is critical in order to notify relatives or the authorities who can intervene in time. • Implementation of a system based on DL algorithms capable of predicting aggressive behavior in Alzheimer’s patients before it happens: the main goal is to try to prevent another crisis from occurring by intervening upstream. If the hostility is fueled by pain or a physical need, a caregiver or referent will step in to help the patient meet their needs and avoid the situation from becoming dangerous to the patient or family members.

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References Aissani, C., Akroun, Y.-F., Yazid, M., Bouchelaghem, S.: Smart home danger prediction system to ensure people with Alzheimer’s disease safety. In: Proceedings of 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-Being (IHSH), pp. 86– 91. Boumerdes, Algeria (2021). https://doi.org/10.1109/IHSH51661.2021.9378728 Aljojo, N., et al.: Alzheimer assistant: a mobile application using machine learning. Rom. J. Inf. Technol. Autom. Control 30(4), 7–26 (2020) Bächlin, M., et al.: Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom. IEEE Trans. Inf. Technol. Biomed. 14(2), 436–446 (2010) Campo, É., Estève, D., Chan, M.: Conception d’un habitat adapté pour l’aide à l’autonomie des personnes âgées. Les Cahiers de l’année Gérontologique 4(4), 356–363 (2012). https://doi.org/ 10.1007/s12612-012-0313-7 Chaffar, S., Inkpen, D.: Using a heterogeneous dataset for emotion analysis in text. In: Butz, C., Lingras, P. (eds.) AI 2011. LNCS (LNAI), vol. 6657, pp. 62–67. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21043-3_8 Dhall, A., Goecke, R., Lucey, S., Gedeon, T.: Static facial expression analysis in tough conditions: data, evaluation protocol and benchmark. In: Proceedings of IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2106–2112. Barcelona, Spain (2016). https://doi.org/10.1109/ICCVW.2011.6130508 Fikry, M., Hamdhana, D., Lago, P., Inoue, S.: Activity recognition for assisting people with dementia. In: Ahad, M.A.R., Mahbub, U., Rahman, T. (eds.) Contactless Human Activity Analysis. ISRL, vol. 200, pp. 271–292. Springer, Cham (2021). https://doi.org/10.1007/978-3-03068590-4_10 Gauthier, S., Rosa-Neto, P., Morais, J.A., Webster, C.: World Alzheimer Report 2021: journey through the diagnosis of dementia. Alzheimer’s Disease International (2021) Gayathri, K.S., Easwarakumar, K.S.: Intelligent decision support system for dementia care through smart home. Procedia Comput. Sci. 93, 947–955 (2016) Goodfellow, I.J., et al.: Challenges in representation learning: a report on three machine learning contests. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013. LNCS, vol. 8228, pp. 117–124. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-42051-1_16 Ibrahim Mamun, M., Rahman, A., Mridha, M.F., Hamid, M.A.: AlziHelp: an Alzheimer disease detection and assistive system inside smart home focusing 5G using IoT and machine learning approaches. In: Singh Pundir, A.K., Yadav, A., Das, S. (eds.) Recent Trends in Communication and Intelligent Systems. AIS, pp. 105–113. Springer, Singapore (2021). https://doi.org/10.1007/ 978-981-16-0167-5_12 Jack, C.R., Jr., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685–691 (2008) van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: Human activity recognition from wireless sensor network data: benchmark and software. In: Chen, L., Nugent, C., Biswas, J., Hoey, J. (eds) Activity Recognition in Pervasive Intelligent Environments. Atlantis Ambient and Pervasive Intelligence, vol. 4, pp. 165–186. Atlantis Press (2011). https://doi.org/10.2991/978-94-9121605-3_8 Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012) Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci. 22(12), 2677–2684 (2010) Mollahosseini, A., Chan, D., Mahoor, M.H.: Going deeper in facial expression recognition using deep neural networks. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–10. Lake Placid, NY (2016)

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Mozer, M.C.: The neural network house: an environment hat adapts to its inhabitants. In: Proceedings of AAAI Spring Symposium: Intelligent Environments, vol. 58 (1998) Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) Pervasive 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24646-6_10 Ordóñez, F., De Toledo, P., Sanchis, A.: Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. Sensors 13(5), 5460–5477 (2013) Pirzada, P., White, N., Wilde, A.: Sensors in smart homes for independent living of the elderly. In: Proceedings of the 5th International Multi-Topic ICT Conference (IMTIC), pp. 1–8. Jamshoro, Pakistan (2018) Raza, M., Awais, M., Ellahi, W., Aslam, N., Nguyen, H.X., Le-Minh, H.: Diagnosis and monitoring of Alzheimer’s patients using classical and deep learning techniques. Expert Syst. Appl. 136, 353–364 (2019) Sharma, S., Dudeja, R.K., Aujla, G.S., Bali, R.S., Kumar, N.: DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients. Neural Comput. Appl. 1–13 (2020). https://doi.org/10.1007/s00521-020-05327-2 Sukor, A.A., Zakaria, A., Rahim, N.A., Kamarudin, L.M., Nishizaki, H.: Abnormality detection approach using deep learning models in smart home environments. In: Proceedings of the 7th International Conference on Communications and Broadband Networking, pp. 22–27. Nagoya, Japan (2019)

A New Transformer Condition Monitoring Based on Infrared Thermography Imaging and Machine Learning Amine Mahami(B) , Toufik Bettahar, Chemseddine Rahmoune, Foudil Amrane, Mohamed Touati, and Djamel Benazzouz Solid Mechanics and Systems Laboratory (LMSS), University M’hamed Bougara, 35000 Boumerdes, Algeria [email protected]

Abstract. Electrical systems maintenance is becoming a crucial and an important part in the economic policies and that’s due their deep implication in the majority of the industrial installations. Electrical transmission and distribution relay mainly on transformers. Electrical transformers condition monitoring plays a major role in increasing their availability, enhancing their reliability and preventing further major failures and high cost maintenance. A new non-contact and non-intrusive method is adopted in this paper to monitor electrical transformers and diagnose their faults based on infrared thermography imaging techniques (IRT). When thermographs are obtained using an infrared camera for different states of the studied transformer, a dataset is then prepared for the following step. Features extraction was applied on the considered infrared images to be used later as input indicators for an automatic classification and identification of transformer’s healthy and several faulty states based machine learning methods (LS-SVM). This method was applied and compared with several IA techniques in order to select the most efficient one in term of accuracy and stability to be relied on in this purpose. The proposed technique, which is mainly based on IRT, features extraction and machine learning, has shown a remarkable efficiency in transformers condition monitoring and an accurate faults diagnosis, and can be generalized as a reliable and powerful tool in such problematics. Keywords: Infrared thermography images · Electrical transformer · Faults diagnosis · Feature extraction · Machine learning methods · Faults classification stability

1 Introduction Electrical transformers condition monitoring plays a major role in the durability, availability and maintenance policies of such equipment. Based on user’s experience, what can be considered a reliable transformer must require reduced maintenance costs and time over its operating cycle (at least 40 years) without any efficiency loss. New technologies were embedded for the aim to minimize maintenance interventions and shutdowns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 408–418, 2023. https://doi.org/10.1007/978-3-031-21216-1_43

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Unfortunately, the long lifetime of a transformer has an impact on its users in term of the rapid technological changes and their maintenance policies. It had been observed over time that one of the most common failures of electrical transformers the external short circuit [1]. Such problem had led companies and researchers to invest and develop more reliable monitoring techniques and fast diagnosis tools with low cost manner. Consequently, scholars were able to handle many issues related to transformer winding short circuit faults, by developing various techniques such as low voltage impulse method (LVI) [2, 3], short-circuit reactance measurement (SCR) [4], frequency response analysis technique (FRA) [5], sweep frequency impedance method (SFI) [6], and Dissolved Gas Analysis (DGA) [7, 8]. These techniques have some non-negligible disadvantage among which we may sit: their being offline methods and require power cuts, DGA need oil sampling and analysing which requires time and health risks. In the last decade, novel methods had been improved and suited to be utilized as non-destructive and non-invasive analysis techniques such as the acoustic ultrasound emissions for winding short-circuit fault diagnosis, which can be applied while the machine or the equipment is running. This approach has lately been widely used to monitor electrical transformers and diagnose their faults [9, 10]. Nonetheless, it is not suitable in acoustic emission environments where other nearby electrical devices are found where acoustic noise is mainly caused by the magneto-astrictive action of the core, Barkhausen noise, oil pumps, and cooling fans. Generally, this technique that uses acoustic propagation signal has shown its limitations when it comes to transformers fault diagnosis. Most of the above mentioned methods are not flexible for electromechanical systems diagnosis, especially electrical transformer. This led us to search for an efficient and a fast approach that can overcome their limitations and reliably do the monitoring and the diagnosis job. From a physical perspective, temperature is considered a crucial indicator of almost any system’s state, and due to the difficulty, the lack of space and the high cost of thermal sensors establishment. Thermal infrared imaging which is a non-contact, nonintrusive measurement technique is adopted in this work. It can indicate and visualize the monitored system components temperature variations on a real time mode. These advantages promoted thermography to be one of the leading non-destructive examination techniques [11], it had shown it power in many field like medical science [12], defence [13], and automotive [14]. In recent years, and due to the extend use of this method, it has been discovered that IRT images contain valuable information about the monitored system [15]. These data, if properly exploited, present a rich source of features to be extracted. Furthermore, a machine learning method (LS-SVM) was employed for the classification task of the studied subject’s condition based on those previously extracted features. This paper presents a new approach to diagnose electrical transformers faults based on IRT images, features extraction, and Machine learning methods. Moreover, a fair comparison was performed between the employed machine learning method (LS-SVM) and some others (SVM, DT, RF, KNN) in order to select the most reliable one in term of

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accuracy and stability. It has been found that LS-SVM is the most reliable AI technique where it had shown its superiority over the rest of the tested ones.

2 Experimental Data Experiments were run on healthy electrical transformers and eight (8) faulty ones. The unhealthy states are represented by eight different short circuit defects in the common core winding. A thermal camera was used to capture images to be used as thermographic data of those cases. All generated defects in this dataset are internal faults; they depend on neither external pieces nor failure in initial setup electrical components. The obtained IRT images will be exploited as a source material for the transformers condition monitoring and faults diagnosis. IRT data acquisition was performed on an Electrical Machines Laboratory workbench [16] under an environmental temperature of 23°. The technical characteristics of the thermal camera and the tested transformer represented in Table 1. Table 1. Thermal camera characteristics and electrical transformer specifications [16]. Thermal camera properties

Transformer specifications

Dali-tech T8 TIC

Phase 1

Detector resolution 384 * 288

Power 1 KW

Measurement accuracy ±2° or ±2% (of reading, which is great) Voltage 220 V Imaging NETD  0.04°@30°

Input Current 1.5 A

Measuring range −20°– +650°

Operating Voltage 220–660

Imaging Frame Rate 50/60 Hz

Frequency 50–60 Hz

A health state and eight different short circuit faulty conditions of the transformer are labelled by nine sets of images (classes). Table 2 displays the amount of short-circuit rounds and percentage of each state, along with its corresponding fault’s class labels. Table 2. General description of the nine considered states in the transformer diagnostic. Fault class (rounds)

Healthy

80

160

240

320

400

480

560

600

Percentage

0%

13%

26%

40%

53%

66%

80%

93%

100%

Label

1

2

3

4

5

6

7

8

9

Thermal images a healthy and eight short circuit transformer’s faults are shown in Fig. 1. It is clearly noticeable that the detection of defective stats when compared to a healthy one is possible through direct observation of the thermographs, but, it does not seem feasible to differentiate between the various faults stages. This can be attributed to

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the fact that thermal images differentiation is based on high level classification methods, and one’s bare eyes could be misleading. Therefore, the need to develop new methods based images feature extraction and machine learning techniques has become inexorable.

Fig. 1. Thermal image of electrical transformer conditions: (P1) healthy, (P2) 13% fault, (P3) 26% fault, (P4) 40% fault, (P5) 53% fault, (P6) 66% fault, (P7) 80% fault, (P8) 93% fault, (P9) 100% fault.

In this context, a new transformer condition monitoring method based on image’s feature extraction and least square support vector machine (LS-SVM) Classifier is proposed in this paper. A sequential flowchart is presented in Fig. 2 where the main steps of

Fig. 2. Flowchart of the transformer’s short circuit faults diagnosis proposed method.

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the proposed approach are illustrated. After infrared image thermography (IRT) collection, their features are extracted and set to be used as inputs for LS-SVM classification program. The parameters of this pattern recognition supervised learning method [17] will be fixed at the training phase. The testing step will be run based on the adopted parameters and samples will be classified accordingly.

3 Feature Extraction Infrared images are considered as raw data, and since faults detection, localization and identification can’t be adequately done when counting only on bare eyes, feature extraction identifies the most discriminating characteristics in an image signal or data [18]. In this work, ten of the most utilized and reliable features in image processing [19] are extracted from both healthy and faulty transformer’s IRT images in order to be used as an input for automatic machine learning. The ten following features are considered in this study: Entropy (H), Edge (E) [20], Intensity (I), Mean (HEI), Mean LBP, Mean red, Mean blue, Mean green, Mean (RGB), Mean (grad(RGB)). The ten extracted feature from the whole data set are graphically presented in Fig. 3 where a total number of 160 thermal images that belongs to nine 9 different states of an electrical transformer were analysed in the aim to estimate their efficiency in faults identification and try to find a clear pattern that separates between the studied cases. It is noticeable in the features graphs that the distinction between the transformer conditions is not adequate and cannot be relied on, despite the fact that a case separation tendency exists in some features graphs such as Mean LBP, Mean (RGB) and Mean (grad (RGB)). This lack of accuracy is considered an obstacle in the way that reliable and efficient diagnosis should be done. That why, a resort to more sophisticated step is mandatory.

4 Feature Classification Using Least Square Support Vector Machines (LS-SVM) Least Square Support Vector Machine (LS-SVM) is machine learning technique that was introduced by Suykens and Vandewalle [21] as an extension to Support Vector (SVM) Machine Method by changing the inequality constrains into equality constrains. This enhancement had shown a great impact on the computational time and complexity, and considerably facilitates Lagrange multiplier solution by simplifying the quadratic programming into a linear equations problem. LS-SVM is used in order to solve multiple scales problems and reduce the computing time. The quadratic programming that is used SVM method is replaced by the least square linear system which represent the loss function in LS-SVM where equality constrains replace the inequality constrains that used to be found in the simple SVM method. This steps are what makes LS-SVM a simpler and more low cost and less time consuming technique [23].

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Fig. 3. Extracted features: a-Entropy, b-Edge, c-Intensity, d-mean (H + E + I), e-Mean of LBP, f-Mean Red, g-Mean Green, h-Mean Blue, i-Mean (R + G + B), j-Mean (gradient(RGB)).

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5 Classification Results Analysis and Discussion Since the main purpose of this study is to evaluate and demonstrate the robustness of LS-SVM method in electrical transforms faults diagnosis, a fair comparison with other classification tools was executed. Four techniques that include k-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision tree (DT) and Random forest (RF) were tested along with LS-SVM in the aim to compare the accuracy and the stability of each one of them and estimate their efficiency as transformer’s defects classifiers. The considered dataset was divided in a fashion that 70% goes to the training step and 30% is kept for the test. The stability of all methods was analysed based on the standard deviation of ten repeated test. Furthermore, in order for the contingency impact to be reduced, average, maximum and minimum values are taken. In standard SVM, the penalty factor equals to 100, and the kernel function is 0.01. DTs minimum number of father nodes is 5. K = 5 is taken as the nearest neighbour number of KNN. Table 3. Classification results of the five tested methods: KNN, RF, DT, SVM and LS-SVM. KNN

RF

DT

SVM

LS-SVM

Max

100

100

100

100

100

Min

98,6842

98,6842

96,0526

89,4736

100

Mean

99,8684

99,4736

98,5526

96,3157

100

STD

0,4160

0,6794

1,5752

3,2704

0

The listed classification results in Table 3 show that LS-SVM gave the best performance in terms of accuracy and stability. It should be mentioned that the rest of the employed methods had given some decent outcome as well with a maximum value of 100% for all of them and a minimum accuracy of 89,4736 that corresponds to SVM classifier. Stability is also an important indicator of the adopted method’s robustness; it was evaluated based on the standard deviation computation of ten repetitive accuracy tests for each method. LS-SVM gave the best STD that equals to zero indicating its superiority over the rest of the classifiers. Inversely, SVM was found to be the less stable among all the methods with a STD of 3,2704. In order to offer an intuitive illustration of the classification effects resulting from these techniques, a graphical representation is respectively shown in Figs. 4 and 5. Confusion matrices in Fig. 4 illustrate the accuracy of each classification method and highlight the amount of the misclassified thermal images with respect to their original classes. Such ability allows to show the weakness in faults diagnosis of each classifier in term of the misclassified faults and the wrong classes that they were put in. The shown matrices belong all to the tenth classification test, as one can notice KNN gave, for this time, an accuracy of 100% but this doesn’t mean it had always been like this. On the other hand, LS-SVM was stable at this value (100%) for the whole ten executions of the program.

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Fig. 4. Confusion matrix of the employed methods plotted at the 10th experiment. ((a): KNN, (b): RF, (c): DT, (d): SVM and (e): LS-SVM).

In the classification charts that are presented in Fig. 5 we can say the interpretation as in confusion matrices, except that, this time, the graphical representation is different. It illustrates the mismatch and divergence between the output class and the target class in a gradual fashion depending on the number of samples that belongs to each class of transformer’s defects. This confusion confirms the difficulty to distinguish between the studied cases counting only on bare eyes or other less sophisticated image classifiers. LS-SVM had clearly proved its high reliability and adequate performance when it comes to transformer’s thermography fault’s diagnosis.

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Fig. 5. Classification results of the employed methods plotted at the 10th experiment ((a): KNN, (b): RF, (c): DT, (d): SVM and (e): LS-SVM).

6 Conclusion Fault diagnosis of electrical transformers based on infrared thermography images was discussed in this paper. Feature extraction and LS-SVM classifier were used to identify and diagnose different stages of short circuits defects in the common core winding. These techniques were employed in the aim to demonstrate the effectiveness of this non-contact, non-intrusive approach by proving its accuracy, high sensitivity and its stability.

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The efficiency of the proposed method was validated by identifying nine sorts of electrical transformer. It was then compared with other existing classifiers, IRT images classification based on LS-SVM had indicated a 100% accuracy in the whole ten repeated tests this confirms a high stability of this classifier compared to the other considered methods. Therefore, LS-SVM for transformer’s IRT images faults classification can be rated as an alternative to monitor electrical transformers. Under the premise of the same input, LS-SVM classifier had proved its superiority since the classification effect is the highest and the most stable over KNN, RF, DT and standard SVM.

References 1. Shi, Y., Ji, S., Zhang, F., Ren, F., Zhu, L., Lv, L.: Multi-Frequency acoustic signal under shortcircuit transient and its application on the condition monitoring of transformer winding. IEEE Trans. Power Delivery 34(4), 1666–1673 (2019). https://doi.org/10.1109/TPWRD.2019.291 8151 2. Khrennikov, A.Yu.: Fault detection of electrical equipment. Diagnostic methods. Int. J. Autom. Control Eng. 2(1) (2013) 3. Drobyshevski, A.A.: Assessment of transformer winding mechanical condition by lowvoltage impulse method. In: 2003 IEEE Bologna Power Tech Conference Proceedings, vol. 2, p. 6 pp. (2003). https://doi.org/10.1109/PTC.2003.1304636 4. Ou, X.-B., Ji, S.-C., Wang, C.-J., Luo, Y.-Y.: Simulation of transformer short-circuit reactance with FEM by coupling magnetic field with electric circuit 46, 59–63 (2010) 5. Bagheri, M., Naderi, M.S., Blackburn, T., Phung, T.: Frequency response analysis and shortcircuit impedance measurement in detection of winding deformation within power transformers. IEEE Electr. Insul. Mag. 29(3), 33–40 (2013). https://doi.org/10.1109/MEI.2013. 6507412 6. Liu, Y., et al.: A study of the sweep frequency impedance method and its application in the detection of internal winding short circuit faults in power transformers. IEEE Trans. Dielectr. Electr. Insul. 22(4), 2046–2056 (2015). https://doi.org/10.1109/TDEI.2015.004977 7. Bacha, K., Souahlia, S., Gossa, M.: Power transformer fault diagnosis based on dissolved gas analysis by support vector machine. Electr. Power Syst. Res. 83(1), 73–79 (2012) 8. Cui, H., Chen, D., Zhang, Y., Zhang, X.: Dissolved gas analysis in transformer oil using Pd catalyst decorated MoSe2 monolayer: a first-principles theory. Sustain. Mater. Technol. 20, e00094 (2019) 9. Sikorski, W.: Development of acoustic emission sensor optimized for partial discharge monitoring in power transformers. Sensors (Basel, Switzerland) 19(8), 1865 (2019). https://doi. org/10.3390/s19081865 10. Kucera, M., Brncal, P., Cefer, V., Jarina, R., Gutten, M.: Analysis of acoustic and electromagnetic emission of traction transformers. Przegl˛ad Elektrotechniczny. https://doi.org/10. 15199/48.2021.05.19. ISSN 0033-2097, R. 97 NR 5/2021 11. Jeffali, F., Ouariach, A., Bachir, E., Nougaoui, A.: Diagnosis of three-phase induction motor and the impact on the kinematic chain using Non Destructive Technique of Infrared Thermography. Infrared Phys. Technol. 102 (2019). https://doi.org/10.1016/j.infrared.2019. 07.001 12. Bahramian, F., Mojra, A.: Thyroid cancer estimation using infrared thermography data. Infrared Phys. Technol. 104, 103126 (2019). https://doi.org/10.1016/j.infrared.2019.103126 13. Akula, A., Ghosh, R., Sardana, H.K.: Thermal imaging and its application in defence systems, vol. 1391 (2011). https://doi.org/10.1063/1.3643540

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14. Xia, C., et al.: Infrared thermography-based diagnostics on power equipment: state-of-the-art. High Volt. 6 (2020). https://doi.org/10.1049/hve2.12023 15. Jia, Z., Liu, Z., Vong, C., Pecht, M.: A rotating machinery fault diagnosis method based on feature learning of thermal images. IEEE Access 7, 12348–12359 (2019). https://doi.org/10. 1109/ACCESS.2019.2893331 16. Najafi, M., Baleghi, Y., Mirimani, S.M.: Thermal images_1-phase_ dry type_Transformer. Mendeley Data, V1 (2020) 17. Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999) 18. Kumar, G., Bhatia, P.: A detailed review of feature extraction in image processing systems (2014). https://doi.org/10.1109/ACCT.2014.74 19. Kumar, G., Bhatia, P.K.: A detailed review of feature extraction in image processing systems. In: 2014 Fourth International Conference on Advanced Computing & Communication Technologies, pp. 5–12 (2014). https://doi.org/10.1109/ACCT.2014.74 20. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986) 21. Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process Lett. 9, 293–300 (1999) 22. Vapnik, V.N., Vapnik, V.: Statistical Learning Theory, vol. 1. Wiley, New York (1998) 23. Gao, X., Wei, H., Li, T., Yang, G.: A rolling bearing fault diagnosis method based on LSSVM. Adv. Mech. Eng. (2020). https://doi.org/10.1177/1687814019899561

A Robust Decoupled Control of Electric Vehicle Using Type-2 Fuzzy Logic Controller Mohamed Kabir Billal Boumegouas(B) , Katia Kouzi, and M. Birame Laboratory of Semiconductors and Functional Materials, Amar Telidji University of Laghouat, P. O. B. 37G, Route de Ghardaïa, 03000 Laghouat, Algeria [email protected]

Abstract. For environmental reasons, Electric Vehicle (EV) is arousing renewed interest because it has the advantage of being non-polluting. As known Electric Vehicle (EV) is a complicated system due to non-linearity’s and unmeasured disturbance, which make it a challenge that faces the controller’s designer to improve the performance of the EVs. Intelligent controllers are frequently used in many areas and provide good results. These controllers have special calculation features for solving specific problems. Basis on this, we propose in this paper Type-2 Fuzzy Logic Control (T2FLC) for speed EV regulation. The main features of the suggested controller were compared with those of the classical type-1 FLC by simulation tests. Besides, a real model of the EV’s presented and evaluated under Artemis driving cycle. The obtained simulations results have proven that the T2FLC has an attractive result in terms of fast response and rapidity of rejection the perturbations, which confirm the robustness and high performances assured by the T2FLC. Keywords: Electric vehicle · Type-2 fuzzy logic controller · Type-1 fuzzy logic control · Six-phase PMSM · Artemis

1 Introduction Due to the numerous advantages offered by electric vehicles, such as B. reduction of gas emissions in the air, high efficiency. In recent years, electric vehicles have become a global challenge [1]. Permanent magnet synchronous motors (PMSMs) are an attractive option to meet EV needs such as large torque density, high efficiency moreover maintenance-free and little size [2, 3]. Compared to traditional PMSM, the six-phase PMSM increases power density, also reducing torque ripple, Moreover, fault tolerance is a surety of the EV safety [4]. To ameliorate the reliability, accuracy, and distribution rejection of the traction chain system of the EV, it has to suggest a robust control has a suitable impact. Hence, one of the most popular techniques currently consists of the use of fuzzy logic for the control of nonlinear systems [5, 6]. It is a type of automatic control based on heuristic reasoning that can be converging any nonlinear function with a specific level of precision [7]. Fuzzy logic is described as a collection of rules that may utilize to describe the behavior of complex systems that is not easy to insert mathematically. This control defines © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 419–426, 2023. https://doi.org/10.1007/978-3-031-21216-1_44

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as a mathematical translation of oral representations based on expert experience into a computer environment. Depending on these, FLC has attracted major interest in several fields such as modern technology developed in data analysis, nonlinear methods also production engineering, and pattern recognition. Information technology. Nevertheless, the type 2 fuzzy logic controller (T2FLC) structure is known as one of the compelling last research topics. Furthermore, the hardness of realization of the T2FLC structure hasn’t encouraged the researchers and scientists in this control. Nevertheless, it has been numerous research on this due to the model uncertainty of T2FLC is better than T1FLC which offers a high-performance control versus parameter changes and system uncertainties that is ensured by its robust structure [8, 10]. This paper has introduced the fuzzy logic control type 2 and contrasted it to fuzzy type 1 based on the traction chain of EV associated with six-phase PMSM. We began with the model in dq frame of the six-phase PMSM. Secondly, we present T2FLC and T1FLC as well. Then, moving to introduce the dynamic model of the EV. Fourthly, the results of the two controllers have been carried out and compared under the same conditions in terms of effectiveness and robustness. Finally, we have a conclusion of the work presented.

2 Modeling of Six-Phase PMSM The six-phase PMSM stator winding is shifted by thirty electrical degrees as shown in Fig. 1. These latest are fed by two three-phase voltage source inverters. We have a tendency to use Park transformation matrix in order to control the six-phase PMSM by transferring the (abc-def) winding to synchronous rotating coordinate system.

Fig. 1. Configuration of asymmetric six-phase PMSM drive.

The model of a six phase PMSM is delineate in dq frame as follows [1] and [7]: d id 1 dt d id 1 dt d id 1 dt d id 1 dt

 1  −Rs id 1 + ωe Lq1 iq1 + Vd 1 Ld 1    1  −Rs id 1 − ωe Ld 1 id 1 + ϕf + Vq1 = Ld 1  1  −Rs id 2 + ωe Lq2 iq2 + Vd 2 = Ld 1    1  −Rs id 1 − ωe Ld 1 id 1 + ϕf + Vq1 = Ld 1 =

A Robust Decoupled Control of Electric Vehicle

ωe =

P ωr 2

421

(1)

Torque developed by six-phase PMSM can be writing as:          Te = 1.5 Ld 1 id 1 + ϕf iq1 + Ld 1 − Lq1 id 1 iq1 + Ld 1 id 1 + ϕf iq1 + Ld 1 − Lq1 id 1 iq1

(2)

Six-phases PMSM mechanical dynamic equation is given: Te = J

d ωr + Bωr + TL dt

(3)

3 Fuzzy Logic Control T1FLC sets can model the uncertainty in a single user’s semantic concept, that is, the uncertainty within the individual. T1FLC successfully used in machine learning as well as controls. T1FLC has two linguistic input variables: the rotational velocity error (e) and its variations (e) and one linguistic output variable is electromagnetic torque variation Tem . The inputs T1FLC equation are as follow:   e(k) = Ke ref (k) − Dsim (k) , e(k) = Ke (e(k) − e(k − 1)) (4)

4 Structure of T2FLC Compared to T1FLC, the T2FLC has shown a preferable performance, due to its setting that can contemporaneously, model multiple in vivo uncertainty and inter-individual uncertainty. It has been some works that have proven that T2FLC performance better than T1FLC [12, 14]: • The model of uncertainty intra/inter individual can be ensured simultaneously by sets of T2FLC. However, T1FLC sets only model intra-individual doubts. • To attain an equivalent function, the quantity of rules needed by the interval T2FLC is a fewer than T1FLC one. • The T2FLC robustness improved due to the soft surface of the interval when it is near the steady-state. Triangular membership functions are chosen in this study as presented in Fig. 2. Moreover, two inputs and one output as same as T1FLC are selected for the design of T2FLC. Hence, Table 1 is giving the rules obtained by T2FLC structure.

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The sum-product inference algorithm was chosen to finalize the fuzzy procedure. Meanwhile, gravity center method been introduced to defuzzification process. In both FLC types, five sets are given respectively: NB: Negative Big; NS: Negative Small; EZ: Zero; PS: Positive Small; PB: Positive Big. as result, 25 rules are obtained.

Fig. 2. T2FLC control Inference and Membership functions, (a) Table of Inference rules, (b) Membership functions of inputs and output of T2FLC.

5 Dynamic Model of Electric Vehicle In this part, we have a trend to introduce an electrical vehicle model in order to check the robustness of the suggested control (Fig. 3).

Fig. 3. Forces applied on electric vehicle.

The power of EV is given in equation below in function of all forces applied on surface of the EV as follow: PVE = FT VVE

(5)

  d roue 2 2 PVE = MVE .g.(C0 + C1 ).VVE VVE + MVE .g. sin α + 0.5.ρ.Cx .VVE + MVE .r. dt (6) The Table 1 shows the parameters of the EV [15], and six-phase PMSM parameters as well.

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Table 1. Parameters of EV & the six-phase PMSM. Parameter EV EV Weight Force of gravity Radius of the wheel Density of the air Front surface(S) Coefficient of air penetration(Cx) Coefficient of rolling resistance in the dynamic state (C0) Coefficient of Rolling Resistance to Static State (C1) Slope of the road(α)

Quantity 820 kg 9.81 m/s2 0.33 m 1.2 kg/m3 2.75 0.3 1.6e-6

Parameter six-phase PMSM

0.008

Quantity 1.9 Ω 0.000835 H 0.000835 H 4 0.015 0.0954 0.353 Wb 800 V

Rs Ld Lq P J B φf Vdc

2.5%

6 Simulations Results To explain the advantages given by the proposed control, various simulation for different operating conditions have been carried out. This simulation is done using MATLAB/Simulink. Over the simulation, the reference speed is given as 50 rad/s and change to 100 Rad/s at 0.2 s, as for the load torques is 30 N.m and varied to 120 N.m at 0.3 s.

(a)

(b)

Fig. 4. Performance of suggested control T2FLC & T1FLC, (a) speed performance; (b) Torque performance.

Table 2. Comparative analysis of the T2FLC & T1FLC. Response time Overshoot pics Rejection of the perturbation Torque ripple Fuzzy type1 6.5 ms

50.5 rad/s

0.4 ms

6.3%

Fuzzy type2 4.12 ms

52.7 rad/s

0.11 ms

8.27%

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Fig. 5. Speed performance to T2FLC.

Fig. 6. Torque performance to T2FLC.

Fig. 7. Power performance of suggested control T2FLC.

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7 Discussion Figure 4a illustrate that the speed developed by T2FLC & T1FLC has following the reference speed greatly. The T2FLC has offered an acceptable overshoot also a fast time response, and a fast rejection of perturbation compared to T1FLC. Meanwhile, T1FLC is given a less overshoot compared to T2FLC but a very long time of rejection of the perturbations as shown in the zoom Fig. 4a. As for torque response, the T2FLC & T1FLC have offered good results during the simulation. Nevertheless, T2FLC has shown a good performance compared to T1FLC in the pics as fast response to changes that appeared such as speed change and applied of load torque. as for the torque ripple, they both presented an approximately the same performances ans shows in Fig. 4b. The Table 2 has summed the comparative between the two controllers and proven that the robustness and high performance has been guaranteed by using T2FLC over T1FLC. Based on the results obtained above and the Table 2 that shows T2FLC is a better controller for the traction chain, Hence, to check the effectiveness of the suggested controller, a real model of EV under the Artemis driving cycle is introduced. From gained results, it’s obvious that T2FLC keeps tracking the reference instructions of EVs. As apparently, the T2FLC shows very few errors in acceleration and deceleration phases which are almost nonexistent for speed performance. As for torque developed by the T2FLC, the ripples are reduced. Meanwhile, for the power, it’s evident that the power supply by the six-phase PMSM controlled by T2FLC has following the power demand by the EV with reduced ripples and no losses in acceleration and deceleration. Which confirmed by the Figs. 5, Fig. 6 and Fig. 7.

8 Conclusion In this study; it was proposed robust control based on type 2 fuzzy logic control technique to enhance the dynamic and static performance of EV propelled by six-phase PMSM. From the results acquired, the following conclusions may be drawn: T2FLC has been precisely demonstrated in EV associated with six-phase PMSM showing highperformance dynamic performance. Besides, it greatly improves the dynamic performance and the effectiveness of lightweight electric vehicles propelled by six-phase PMSM. The T2FLC exhibits high robustness in terms of noise suppression speed and torque ripple reduction.

References 1. Boumegouas, M.K.B., Kouzi, K.: Novel synergetic control of electric vehicle propelled by six phases permanent magnet synchronous motor. In: Hatti, M. (ed.) IC-AIRES 2021. LNNS, vol. 361, pp. 633–642. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-92038-8_63 2. Sant, A.V., Khadkikar, V., Xiao, W., Zeineldin, H.H.: Four-axis vector-controlled dual-rotor PMSM for plug-in electric vehicles. IEEE Trans. Ind. Electron. 62(5), 3202–3212 (2014) 3. Lara, J., Xu, J., Chandra, A.: Effects of rotor position error in the performance of fieldoriented-controlled PMSM drives for electric vehicle traction applications. IEEE Trans. Ind. Electron. 63(8), 4738–4751 (2016)

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4. Tong, C., Wu, F., Zheng, P., Sui, Y., Cheng, L.: Analysis and design of a fault-tolerant six-phase permanent-magnet synchronous machine for electric vehicles. In: 2014 17th International Conference on Electrical Machines and Systems (ICEMS), pp. 1629–1632. IEEE (2014) 5. Kouzi, K., Naït-Saïd, M.S.: Adaptive fuzzy logic speed-sensorless control improvement of induction motor for standstill and low speed operations. COMPEL - Int. J. Comput. Math. Electr. Electron. Eng. (2007) 6. Lakhdar, M., Katia, K.: Influence of fuzzy adapted scaling factor on the performance of a fuzzy logic controller based on an indirect vector control for induction motor drive. J. Electr. Eng. 55(7–8), 188–194 (2004) 7. Boumegouas, M.K.B., Kouzi, K.: A new synergetic scheme control of electric vehicle propelled by six-phase permanent magnet synchronous motor. Adv. Electr. Electron. Eng. 20(1), 1–14 (2022) 8. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 8(3), 199–249 (1975) 9. Mendel, J.M., John, R.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002) 10. Kumbasar, T., Eksin, I., Guzelkaya, M., Yesil, E.: Type-2 fuzzy model based controller design for neutralization processes. ISA Trans. 51(2), 277–287 (2012) 11. Np, A.: Speed and torque control of permanent magnet synchronous motor using hybrid fuzzy proportional plus integral controller. J. Vib. Control 21(3), 563–579 (2015) 12. Liu, J., Zhao, T., Dian, S.: General type-2 fuzzy sliding mode control for motion balance adjusting of power-line inspection robot. Soft. Comput. 25(2), 1033–1047 (2020). https://doi. org/10.1007/s00500-020-05202-1 13. Kaya, ˙I, Turgut, A.: Design of variable control charts based on type-2 fuzzy sets with a real case study. Soft. Comput. 25(1), 613–633 (2020). https://doi.org/10.1007/s00500-020-051 72-4 14. Bennaoui, A., Saadi, S., Ameur, A.: Invasive weed optimization algorithm for tuning transitioning from Type-1 to interval Type-2 fuzzy logic controller for boost DC-DC converters. Journal Européen des Systèmes Automatisés 53(2), 195–202 (2020) 15. Bendjedia, B., Bouchafaa, F., Rizoug, N., Boukhnifer, M.: Comparative study between battery and supercapacitor hybridization with fuel cells for automotive applications. In: 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 0833–0838. IEEE (2017)

Analysis Techno-Economic of a Stand-Alone Photovoltaic System Using a Specialized Advanced Simulation Software for Different Zones in Adrar Region T. Touahri1(B) , B. Berbaoui2 , R. Maouedj1 , and S. Laribi2 1 Unité de Recherche en Energies renouvelables en Milieu Saharien UERMS, Centre de

Développement des Energies Renouvelables, CDER, 01000 Adrar, Algeria [email protected] 2 LDDI–Laboratory, Faculty of Science and Technology, University Ahmed Draia of Adrar, 01000 Adrar, Algeria

Abstract. Renewable energy sources such as wind and solar energy are an alternative to fossil fuels used to generate electricity because they are inexhaustible sources, clean, and contribute to sustainable development. In this study, the performance of a stand-alone photovoltaic system is studied through technical and economic analysis. The photovoltaic system is designed to produce about 3.52 kW of electricity to generate the power needed by an independent house in three remote areas in southwestern Algeria. The techno-economic analysis of autonomous PV systems in the three regions was estimated using the HOMER software. The techno-economic valuation and production of solar energy are analysed, in this study, by utilizing daily solar radiation and average temperature data in the Adrar region. The techno-economic valuation and production of solar energy are analysed, in this study, by utilizing daily solar radiation and average temperature data in the Adrar region. The results show that regions of the south of Algeria are excellent regions for investment in the system photovoltaics in the electricity production, where the average cost of energy (COE) is 0.797 e/KWh and the average net cost is 18001.21 e for the three zones. Keywords: Photovoltaic · Power · Stand-alone · Performance · Homer

1 Introduction The exploitation of renewable energies in the electrification of buildings in the isolated sites in our country represents a solution to the problem of connecting the electrical grid to remote areas, which cost the state large financial burdens [1]. The photovoltaic system knows a great growth all over the world, in Algeria because of the new version of the national program for the development of renewable energies raises production capacity from renewable sources up to 22,000 MW instead of 12,000 MW as agreed in the previous program. The solar photovoltaic and wind power sectors are particularly © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 427–436, 2023. https://doi.org/10.1007/978-3-031-21216-1_45

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favored. For the needs of the national market, over the period 2015–2030, 22,000 MW will be achieved instead of 12,000 in the 2011 program. This major project will produce by 2030 [2]. The production of clean energy in sufficient quantities for countries has become a current problem, for this reason, the efficient use of renewable energy and clean renewable energy resources alongside existing energy sources will be an effective and practical option for meeting energy needs [3]. The great use of PV systems replaces or reinforces traditional central stations of electricity production as the investment in the grid has a positive overall financial effect, the electric power generated by the PV system has a special value when it synchronizes with the high demand like during summer, especially in the afternoon when the wide use of air conditioning complements this, solar PV systems can also improve the reliability of the system, and can also minimize transmission and distribution losses as they generate the electricity relative to the place where it is consumed [4, 5]. The Homer program is developed by the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL), the program is used in feasibility studies, design and optimizing energy systems. Where the user chooses the site, loads and database for the various components then the program simulates all parameters of the system [6]. In this work, a techno-economic study of a stand-alone PV system to supply electricity for a house in isolated area in three zones Adrar state (Adrar, Tinerkouk, and Timiaouine). This study use the Homer program to simulate the PV system.

2 Methodology 2.1 Description of the Sites The geographical location of the three zones is shown in Fig. 1 and Table 1 presents their coordinate data. Adrar region is situated in the southwest of Algeria at 27° 49’ N and 00°17’ W with a height of 263 m above sea level. It is characterized by a hot desert climate and periodic wind [7].

Fig. 1. Geographical location of the three zones in Adrar [8].

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Table 1. Geographical coordinates of the study zones Zone

Latitude (°N)

Longitude (°W)

Altitude (m)

Surface (km2 )

Adrar

28° 1’

0°16’

258

633

Tinerkouk

29° 41’

0°41’

482

20.131

Timiaouine

20° 26’

1°48’

582

12.553

2.2 The Meteorological Data Algeria has a large stock of sun energy due to its geographical location. Sun exposure exceeds 2,000 h annually across the country [9]. Adrar zone has rich solar radiation throughout the year, the average solar radiation is between 3 and 8 kWh/m2 [10]. The Fig. 2 shows the monthly average solar radiation of Adrar, Tinerkouk, and Timiaouine.

Fig. 2. The monthly average solar radiation of Adrar, Tinerkouk, and Timiaouine.

The data of ambient temperature are essential for defining the present output power of the PV panels Fig. 3 shows the monthly average ambient temperature for the proposed sites [11]. The minimum and maximum ambient temperatures were 11.9 °C, 10.5 °C, and 16.67 °C (January) and 37.57 °C, 34.51 °C, and 33.9 °C (July) for Adrar, Tinerkouk, and Timiaouine respectively.

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Fig. 3. The daily temperature (°C) of Adrar, Tinerkouk, and Timiaouine.

2.3 House Electric Load Demand The scaled annual average load is 4.79 kWh/day with a daily and a maximum payload of 0.74 kW. Figure 4 illustrate the daily load profile of house for different seasons in the three regions. The low demand is from 02:00 to 12:00 h and the highest electricity consumption occurred from 13:00 to 15:00.

Fig. 4. Profile of electrical load

2.4 Structure of the System Figure 5 represents a HOMER system configuration, there are four main components of the system; Photovoltaic panels, inverter, load and batteries.

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Fig. 5. Stand-Alone PV system model in HOMER.

In the proposed PV energy system, Solar PV panels have been incorporated to use the available solar radiation in the study zones. Batteries have been considered for electricity storage to supply electricity in the absence of sunlight. For the project, an annual interest rate of 10% and project life time of 25 years. 2.5 Economic Model The economic estimate is a fundamental section of the HOMER software because of its essential aim (minimize the cost). The top solution for different system models is evaluated using NPC and COE. In addition, the best system component collection is ranked founded on the lowest lifespan price [12]. The cost of energy (COE) is the average cost of electrical energy produced by the system, COE is the total annual cost (Cat ) divided by the total electricity load served (Eser ), as follows [13]: COE =

Cat Eser

(1)

The total net present cost (NPC) is the principal economic output for the system; it is based of the total annualized cost, and the capital recovery of the system. The net present cost is calculated according to the following equation [14, 15]: NPC =

Cat CRF(i, Rp)

CRF(i, Rp) = where: CRF: capital recovery factor i interest rate (%) Rp: project lifetime (yr)

i(i + 1)Rp (i + 1)Rp − 1

(2) (3)

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The annual interest rate is calculated by the following equation [16]: i=

in − f 1+f

(4)

where In : is the nominal interest rate (%), f: is the annual inflation rate (%).

3 Results and Discussions Utilizing HOMER input data for optimizing the configuration of the system to meet the load demand of 4.79 kWh/day with a stand-alone PV system including PV panels, inverter, and battery. The system consists of Generic flat plate PV modules, system converter, and Generic 1KWh lead acid batteries. The COE and NPC of the three zones are 0.770 e and 17 388,86 e for Adrar, 0,733 e and 16 555,82 e for Tinerkouk, and 0,888 e and 20 058,95 e for timiaouine. Table 2 present the detailed cost analysis. Table 2. Economic parameters of optimal PV system in the study zones Component Capital Operating Replacement Salvage cost cost (e) cost (e) (e) (e) Adrar

Tinerkouk

Battery

3900

1681

3445

Resource Total (e) ($)

−467.14 0

8559

PV

7691

397.70

0

0

0

8089

Inverter

417.61

179.96

177.18

−33.35

0

741.41

System ($)

12009

2258

3623

−500.49 0

17389

Battery

3900

1681

3445

−467.14 0

8559

PV

6960

359.91

0

0

0

7320

Inverter

381.33

164.32

161.79

−30.45

0

676.98

System ($)

11241

2205

3607

−497.59 0

16556

5700

2456

5036

−682.74 0

12509

Timiaouine Battery PV

6770

350.09

0

0

0

7120

Inverter

241.92

104.25

102.64

−19.32

0

429.48

System ($)

12712

2911

5138

−702.06 0

20059

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The monthly average electric production of systems in the study zones is shown in Fig. 6, The maximum monthly average electric production is in April (0.68 KW) for Tinerkouk, while the minimum is in December (0.35 KW) for Timiaouine. Monthly average electric production values are relatively higher in April, and August for the three zones all year.

Adrar

Tinerkouk

Timiaouine

Elctric Production (KW)

0.8

0.6

0.4

0.2

0

Jan Fab Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months

Fig. 6. The monthly average electric production of systems

PV array output is significantly dependent on data meteorological such as temperature and solar irradiance and can be calculated by the following equation [17]: PPV = PPVrated .fPV

  IT  1 + KC TC − Tref IS

(5)

where PPVrated : the PV rated power at standard test conditions (kW), fPV : the PV derating factor (%), IT : global solar radiation incident (kW/m2 ), IS : the solar radiation at standard temperature conditions (IS = 1 kW/m2 ), Kc : the temperature coefficient of the PV array (%/ºC), Tc : the PV cell temperature (ºC), Tref : the PV cell temperature under standard test conditions (25 ºC). Figure 7, 8, and 9 show the effect of electricity production, NPC, and COE of stand-alone PV systems for Adrar, Tinerkouk, and Timiaouine respectively. As the electricity production increases, NPC and COE values of the PV systems for the three zones increase.

T. Touahri et al.

NPC

16720

COE

0.74

16680

NPC (€)

0.736

16600 16560

COE (€/kWh)

0.738 16640

0.734 16520 0.732

16480 5111

5127

5160

4179

5191

Electricity production (KWh/yr)

Fig. 7. The effect of electricity production, NPC, and COE for Tinerkouk NPC

COE

17520

0.775

0.773 17440 17400 0.771

COE (€/kWh)

NPC (€)

17480

17360 0.769

17320 5555

5569

5578

5594

5611

Electricity production (KWh/yr)

Fig. 8. The effect of electricity production, NPC, and COE for Adrar NPC

COE

20200

0.894 0.893

20160 20120

0.89 0.889

20080

0.888

COE (€/kWh)

0.892 0.891

NPC (€)

434

0.887

20040

0.886 0.885

20000 4639

4649

4675

4699

4721

Electricity production (KWh/yr)

Fig. 9. The effect of electricity production, NPC, and COE for Timiaouine

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4 Conclusion In this study, the techno-economic analysis of stand-alone PV power systems with battery storage for a building in the three zones of the Adrar region was examined. The results showed that PV system with battery system is a good solution for remote areas. The COE and NPC of the three zones are 0.770 e and 17 388,86 e for Adrar, 0,733 e and 16 555,82 e for Tinerkouk, and 0,888 e and 20 058,95 e for timiaouine, the NPC and COE values of the PV systems for the three zones increased with the electric production increased. From an economic perspective, Tinerkouk, with its climate conditions, may be especially suitable for PV power generation in the considered zones. It is expected that the results will help policymakers in future energy planning in the region as well as providing an alternative option for the government in electrifying remote areas. Due to the rapid growth of the building sector in Algeria in terms of both power generation capacity as well as the extension of the grid, technical and economic analysis is needed to contribute to the state’s prospective work.

References 1. Koussa, D., Alem, M., Belhamel, M.: Système hybride (eolien, solaire) pour l’alimentation electrique d’une charge à usage domestique. Rev. Energ. Ren. Zones Arides 1–8 (2002) 2. Le société Sonelgaz: Énergies renouvelables. http://www.sonelgaz.dz/?page=article&idb=3 3. O˘guz, Y., Özsoy, M.F.: Sizing, design, and installation of an isolated wind-photovoltaic hybrid power system with battery storage for laboratory general illumination in Afyonkarahisar, Turkey. J. Energy South. Africa 26(4), 70–80 (2015) 4. Singh, S.N., Singh, B., Ostergaard, J.: Renewable energy generation in India: present scenario and future prospects. In: 2009 IEEE Power & Energy Society General Meeting, pp. 1–8. IEEE (2009) 5. Jamil, M., Kirmani, S., Rizwan, M.: Techno-economic feasibility analysis of solar photovoltaic power generation: a review smart grid and renewable. Energy 2012(3), 266–274 (2012) 6. Rezzouk, H., Mellit, A.: Feasibility study and sensitivity analysis of a stand-alone photovoltaic–diesel–battery hybrid energy system in the north of Algeria. Renew. Sust. Energ. Rev. 43, 1134–1150 (2015) 7. Hammaoui, K., Hamouda, M., Touahri, T.: A technical and economic evaluation of renewable energy in the Sahara: a case study of Adrar, Algeria. PONTE Int. J. Sci. Res. 76(6), 20–30 (2020). https://doi.org/10.21506/j.ponte.2020.6.3 8. Kholladi, M.K.: SIG pour l’Etude de l’Evolution de la Répartition de la Population de la Wilaya d’Adrar. In: 4th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications – TUNISIA (2007) 9. Touahri, T., Aoun, N., Maouedj, R., Laribi, S., Ghaitaoui, T.: Design of stand-alone PV system to provide electricity for a house in Adrar, Algeria. In: Hatti, M. (ed.) ICAIRES 2018. LNNS, vol. 62, pp. 225–232. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-04789-4_25 10. Laaboudi, A., Slama, A.: Potential evapotranspiration estimation from piche evaporimeter measurements based on adaptive neuro fuzzy inference system technique. PONTE 75(2), 92–107 (2019) 11. Li, C., Zhou, D., Zheng, Y.: Techno-economic comparative study of grid-connected PV power systems in five climate zones, China. Energy 165, 1352–1369 (2018)

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12. Aderemi, B.A., Chowdhury, S.D., Olwal, T.O., Abu-Mahfouz, A.M.: Techno-economic feasibility of hybrid solar photovoltaic and battery energy storage power system for a mobile cellular base station in Soshanguve, South Africa. Energies 11(6), 1572 (2018). https://doi. org/10.3390/en11061572 13. Louafi, N., Khaldi, F.: Techno-economic study of photovoltaic pumping system for a remote area in Algeria. Revue des matériaux et énergies renouvelables 2(1), 21–27 (2017) 14. Hiendro, A., Kurnianto, R., Rajagukguk, M., Simanjuntak, Y.M.: Techno-economic analysis of photovoltaic/wind hybrid system for onshore/remote area in Indonesia. Energy 59, 652–657 (2013) 15. Demiroren, A., Yilmaz, U.: Analysis of change in electric energy cost with using renewable energy sources in Gokceada, Turkey: an island example. Renew. Sust. Energ. Rev. 14, 323–333 (2010) 16. Dursun, B.: Determination of the optimum hybrid renewable power generating systems for Kavakli campus of Kirklareli University, Turkey. Renew. Sust. Energ. Rev. 16(8), 6183–6190 (2012) 17. Das, B.K., Hoque, N., Mandal, S., Pal, T.K., Raihan, M.A.: A techno-economic feasibility of a stand-alone hybridpower generation for remote area application in Bangladesh. Energy 134, 775–788 (2017)

Convolution Neural Network Deployment for Plant Leaf Diseases Detection Dalila Cherifi(B) , Meroua Bayou, Assala Benmalek, Ines Mechti, Abdelghani Bekkouche, Belkacem Bekkour, Chaima Amine, and Halak Ahmed Institute of Electrical and Electronic Engineering, University of Boumerdes, Boumerdes, Algeria [email protected], [email protected], [email protected], [email protected]

Abstract. The automated identification of plant diseases based on plant leaves is a huge breakthrough. Furthermore, early and accurate detection of plant diseases positively impacts crop productivity and quality. However, managing the accessibility of early plant disease detection is crucial. This work has environmental goals aiming to save plants from different threatening diseases by providing early detection of the affected leaves. We studied the performance of different Convolutional Neural Network (CNN) architectures in predicting 26 diseases for 14 plant species. The work studied the complexity of the system and compared the two main deep learning frameworks, TensorFlow and PyTorch, to get the most accurate results with higher accuracy. Using the “New PlantVillage Dataset” from Kaggle [1], the TensorFlow models achieved an accuracy of 90,94% for the basic CCN architecture, and 95,59% for the Transfer Learning architecture with VGG19. Whereas the PyTorch models achieved an accuracy of 93,47% for the basic CCN architecture, and 98,53% for the Transfer Learning architecture with ResNet34. Finally, after examining the feasibility of the model’s implementation and discussing the main problems that may be encountered, the models were deployed in a mobile application using the Tflite and torch mobile flutter SDK to let them as an internal feature in the mobile without the need for any access to the cloud, which is known as edge AI. Keywords: Plant leaf diseases detection · Convolutional Neural Network (CNN) · Transfert learning

1 Introduction By 2050, the global population is predicted to reach about 10 billion people [2]. This means that the market demand for food will continue to grow. Additionally, projections show that feeding the world by that time would require raising food production by 70% [3, 4]. In other words, the agriculture sector will face multiple challenges. However, one of the most dangerous reasons that may decrease the food production rate is infectious plant diseases. 80% of the food consumed by humans is primarily provided by plants. Unfortunately, plant diseases affect food production with a range of up to 30% loss continuously. These losses may lead populations to become reliant on imported goods. The incorrect examination and the late detection of these diseases lead to their spread © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 437–447, 2023. https://doi.org/10.1007/978-3-031-21216-1_46

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infecting other healthy plants, thus destroying crops and causing losses of up to onethird of crop production annually [5]. Hence, adopting new agricultural technologies and methodologies is required since the traditional ways are no more efficient. The preliminary stage of plant disease diagnosis is a significant task, where farmers need periodic monitoring by professionals, which might be costly and time-consuming. Here comes the role of artificial intelligence in improving the agriculture sector by replacing traditional methods with quick, less costly, and precise ways to smartly detect diseases from the indicators that look to be on the plant leaves. This paper aims to build a flutterbased mobile app for plant leaf disease recognition using deep learning algorithms. A comparative study is one based on different experiments. The work covers deep learning-based models, with both the TensorFlow and PyTorch frameworks and different architectures. This article consists of four sections, the second section covers information and the related work on plant disease detection. The third section presents how we used the Convolutional Neural Networks and their parameters in plant leaf disease recognition. The fourth section includes the experimental parts and the results, in addition to the model deployment on a mobile App, followed by a discussion and a conclusion.

2 Related Work on Plant Diseases Recognition Plant diseases are one of the most common problems in agriculture. However, treatment of the affected plants is frequently done in a late stage using chemical control after the disease has spread. In the world of agriculture, the automated diagnosis of plant diseases using plant leaves is a big step forward. In addition, early and accurate detection of plant diseases improves crop output and quality. Even an agriculturist and pathologist may fail to identify diseases in plants by viewing disease-affected leaves due to the cultivation of a vast variety of crop items. However, in underdeveloped countries’ rural areas, visual inspection is still the major method of illness detection. It also necessitates expert monitoring regularly. Farmers in rural places may have to travel a long distance to visit an expert, which is both time-consuming and costly [6]. In general, a plant gets diseased when a causal element affects its normal structure, development, function, or other activities, resulting in an atypical physiological process. Undesirable symptoms or conditions result when one or more of a plant’s vital physiological or biochemical processes are disrupted. Depending on the type of primary causative agent, plant diseases are classified as infectious or noninfectious. Unfavorable growing conditions, such as temperature extremes, poor moisture-oxygen connections, poisonous compounds in the soil or atmosphere, and an excess or shortage of an important mineral, cause noninfectious plant illnesses [7, 8]. In-plant disease identification and detection as part of many agricultural applications, the extraction of data required for analysis are done through image processing methods and machine learning applied to images of plant leaves [9]. The process of the general structure of plant disease identification via image processing begins with acquiring images through a camera or available databases, and the captured images are in RGB (Red, Green, and Blue), for which a color transformation structure is created. Then, different image preprocessing techniques are used to remove noise in an image; it is required to resize the leaf images from high resolution to low resolution to extract the

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interesting region, and this is done through the process of interpolation (converting the input image into a grayscale image with color conversion). The photos are then enhanced and smoothed to get a higher clarity. After that, the image segmentation approach divides the image into several segments based on comparable properties for further examination. Various methods are available for this technique, such as region and edge-based methods, boundary and spot detection algorithms, Otsu’s method, k-means clustering, and thresholding techniques. Feature extraction, which is the process of evaluating only the interesting area of a picture as a compressed feature set, will be applied using the dimensions reduction approach. Various features, such as texture, edges, color, and morphology can be extracted to identify the plant disease. Lastly, classifiers are used to categorize the different diseases that occur on plant leaves based on obtained features [10]. The most commonly used classifiers in earlier work to detect diseases in plants are Support Vector Machines (SVM), K-nearest neighbors (K-NN), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) [11]. In 2016, Parikh et al. [12] proposed a system that detects one of the main diseases in India using cascades of KNN classifiers and multiple training sets. The authors collected 150 natural images of cotton plants from a university and achieved an accuracy of 82.5%. On the other hand, Badol et al. [13] used 137 images of grapes to detect and classify leaf diseases of grapes using the SVM classification technique. First, images are preprocessed to remove noise using a gaussian filter, and unwanted components are removed using thresholding. Then, the diseased region is found using segmentation by K-means clustering, and color and texture features are extracted. Finally, SVM is a classification approach that is used to determine the kind of leaf disease. The system provided an accuracy of 88.89%. In 2017, Ramcharan et al. [14] used transfer learning from the CNN model for sugarcane disease identification. The classification of the disease has been done in three ways: KNN, SVM, and the softmax layer of Inception v3. The dataset contained 11,670 images, and the obtained accuracy was 73% by applying KNN and 91% by using SVM. Moreover, Singh et al. used 500 images collected from an agricultural research and extension center in India for the detection of fungal rust disease in the Pea Plant. The Support Vector Machine classifier was employed to detect pea plant leaf disease, and it was shown to be 89.60% accurate in detecting and examining illness. Finally,Suresha et al. [15] have identified two diseases seen in paddy plants, Blast and Brown Spot. The system took about 300 images as inputs. KNN and Otsu were employed for identification, and the thresholding approach was utilized for fragmenting the abnormal pictures, resulting in an overall accuracy of 76.59%. Kumar et al. in 2018 Developed in this study a relatively new exponential spider monkey optimization approach (ESMO) for feature selection and many techniques for the classification of healthy and diseased leaves: KNN, SVM, ZeroR, and LDA with a dataset containing 1000 images of Potatoes and Apples from PlantVillage dataset. SVM classifier performed better than the other classifiers, with 92.12% accuracy. In 2019, Hossain et al. [16] performed color and texture-based detection and classification of plant leaf diseases. The dataset comprises 237 leaf images and the performance of the classification using KNN provides 96.76% accuracy. Moreover, Abdulridha et al. studied two steps for the classification process of laurel wilt in avocado, Neural Network Multilayer Perceptron (MLP) and K-NN. The MLP approach obtained higher classification values than the K nearest neighbor and reached up to 99%. Finally,

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Aruraj et al. worked on Banana plant disease classification using 123 images from the PlantVillage dataset. The technique of local binary pattern has been used for texture analysis and SVM as a classifier. The maximum accuracy that the proposed work attained was 90.9% [17]. In a previously published work [18], the authors’ conclusion was that there are numerous developed ways for identifying and classifying plant diseases. But there is still no reliable and practical commercial approach for diagnosing the illnesses. Hence after experiencing the different deep learning models (InceptionV3, InceptionResnetV2, MobileNetV2, EfficientNetB0) they concluded that the most optimized model that limits the parameter number and operations as much as possible is the MobileNetV2 which makes it possible to be easily run-on mobile devices. In another work [19] the author used a MobileNet pre-trained model in plant leaf disease classification using TensorFlow. The deployment was done only on Android edge devices. In this paper, we are proposing different deployment approaches using TensorFlow and PyTorch frameworks for both Android and iOS. Then, in order to compare their results, we deployed these models after optimizing them as tflite and torch models in order to make them a practical solution for plant disease detection for both Android and iOS devices using flutter SDK.

3 Plant Leaf Diseases Recognition Using Convolutional Neural Networks Methodology To solve the problem of plant leaf diseases, we considered using different deep learning approaches and creating a model that can identify plant diseases by picturing a single leaf. CNN algorithms are used to detect diseases in plant leaves by extracting the colors and textures of disease-specific lesions. We trained both TensorFlow and PyTorch models by using specific parameters, which are: Adam optimizer, 64 batch size, Categorical_crossentropy loss function, Accuracy as a metric, and the early stopping to stop the training at the best accuracy to avoid overfitting. We applied a series of transformations to the input images using transformers that provide various features as a first step of the preprocessing, before using the CNN. First, we standardized the pixel values of our input images to get the same pixel intensity and dimensions for all images. Then we resized them from 256 to 128 px for faster processing (see Fig. 1). We also used ImageDataGenerator() with the input preprocessing function to rescale our image data automatically when we used transfer learning with VGG19. Finally, we deployed our models after quantizing them with special flutter SDKs to see which of these quantized models fit the mobile memory requirement.

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Fig. 1. Output of the first step of data pre-processing

3.1 Convolutional Neural Networks A convolutional neural network (CNN) is included among the deep learning computer vision networks that can recognize and classify pictured features. One of the fundamental applications in image classification with deep learning or computer vision, in general, is the Convolutional Neural Networks. Since the 1980s convolution techniques have been developed and improved, and the usage of multilayered neural networks has increased since 2012. Hence, it became the most commonly applied to analyze visual imagery. After the fast improvement of convolution usage, the availability of large sets of ImageNet data is crucial to be more specific and get more accurate results [20]. 3.2 Transfer Learning It is a machine learning technique in which a pre-trained model is utilized as a base for another model to solve new challenges and problematic situations. It aims to improve the performance of the target learner on target domains by transferring information from various but related domains. We use Transfer Learning algorithms in the case of having a complicated learning model so we cannot train it from scratch using the initial concepts. It is used also when we do not have sufficient training data, even if it will be available, the training process with a large dataset will take a lot of time [21]. • VGG19 Architecture: VGG-19 is a 19-layer deep convolutional neural network that has been trained on over a million photos from ImageNet. It consists of 16 convolution layers, 3 fully connected layers, 5 MaxPool layers, and 1 SoftMax layer. This network can categorize photos into 1000 different object categories. We have used this architecture for our second experiment on the TensorFlow model due to the accessibility of its weights on Keras and its good performance when it comes to image classification and features extraction [22]. • ResNet34 Architecture: We applied the residual network pre-trained model ResNet34 to train our PyTorch model. It is a set of 34 convolutional layers based on the VGG19 architecture, with the addition of “Residual blocks,” which are shortcut or skip connections. This is among the most effective Neural network architectures since it helps to ensure a low error rate far deeper in the network. As a result, it has proven to perform well in applications that demand deep neural networks, such as feature extraction and semantic segmentation [23].

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4 Experiments and Results In this section, we will go over the different experiments done to achieve the best plant disease prediction, we will explore the different results, discuss the difference between each model, and end up with a conclusion. We will also go over phone application development, and see how we can deploy our model into a mobile application. The used dataset is the “New PlantVillage Dataset” which is an open-source dataset from Kaggle which contains about 87 k images of healthy and unhealthy crop leaves. It is categorized into 38 different classes of 14 species of plant leaves, divided into 24 types of diseases and 14 types of healthy plants. The dataset is split into train, test, and valid with an 80/20 ratio. 4.1 Experiment 1: Plant Disease Detection Using CNN and TensorFlow We began our experimentation with a basic CNN model architecture built with TensorFlow. We built 5 CNN blocks using convolutional layers with the ReLU activation function, and max-pooling layers with (2,2) pooling sizes. The convolutional layers are used to extract features, and the max-pooling layers are applied to the output of the convolutional layers to calculate the max value of each feature map. We passed these layers by a flattening layer, then a dense layer with 38 units with a Softmax activation function. We compiled and trained our model over 10 epochs provided with an early stopping and model checkpoint to avoid overfitting. From this experiment, we got an accuracy of 90.94% over 8001 s of execution time. 4.2 Experiment 2: Plant Disease Detection Using Transfer Learning and TensorFlow Based on these results, we did the second experiment by building a CNN model using transfer learning and TensorFlow to increase the accuracy and decrease the execution time. We started building the model with the transfer learning architecture using VGG19. First, we defined the VGG19 as a base. We passed it through a flattening layer and a dense layer of 38 units with a softmax activation function, and then we transferred the learning to our plant disease detection model. We compiled and trained our model over 50 epochs, providing it with an early stopping and model checkpoint. The early stopping will stop the training at the fifth epoch. We obtained from this experiment an accuracy of 95.59% over 8700 s of execution time. The accuracy has increased, but the execution time was close to the resultant time of the previous experiment. 4.3 Experiment 3: Plant Disease Detection Using CNN and PyTorch To see if these are the best results that we can conduct from this work, we performed the same previous experiments using PyTorch. We built our model using 5 CNN blocks containing convolutional and pooling layers in this experiment. Then, we passed them to the fully connected layer with a final output size equal to the number of classes in our data. We trained the model over 15 epochs with a learning rate of 0.001 and got an accuracy of 93.47% over 2464 s of execution time from this experiment.

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4.4 Experiment 4: Plant Disease Detection Using Transfer Learning and PyTorch As a last experiment, we built a CNN with transfer learning architecture using ResNet 34, which is very useful for extracting features and getting cutting-edge results within a very short time in computer vision. We first loaded the ResNet model that we obtained from the touchvision library and then changed the parameters of the final layer to fit the data. We trained the model using the same hyperparameters as in the last experiment and got this time a significant increase in terms of accuracy which was equal to 98.53% over 4860 s of execution time. Among all the experiments that we conducted the following Table 1 summarizes the obtained results. Table 1. Comparative results of the experiments

Tensor flow

PyTorch

Experiment

Accuracy

Loss

Validation accuracy

Validation loss

Execution time

Plant leaf disease detection using CNN

90.94%

25.30%

90.93%

27.29%

8001 s

Plant leaf disease detection using CNN and transfer learning (VGG19)

95.59%

3.49%

95.59%

3.43%

8700 s

Plant leaf disease detection using CNN

93.47%

3.13%

92.54%

3.63%

2464 s

Plant leaf disease detection using CNN and transfer learning (Resnet34)

98.53%

1.52%

98.53%

5.05%

4860 s

4.5 Experiment 5: Model Deployment on an EDGE Device Our plant disease detector’s user interface is developed as an inner mobile application with flutter. We used the previous CNN models in this experiment and implemented them in our iOS/Android app. When users launch the app once it has been installed, it

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runs an orientation handler, which runs as a background service in the app to ensure that users receive the appropriate usage instructions and acquire the desired outcomes. The user has the option of immediately uploading the diseased leaf image from the gallery or taking a photo using the phone’s camera presented in Figs. 2 and 3.

Fig. 2. Plant disease detector app workflow.

Fig. 3. Output of the TensorFlow model deployment.

4.6 Discussion There is no doubt that prediction models built using Transfer learning, whether employing a pre-trained VGG19 or Resnet34 model, perform better in terms of accuracy than the models trained from scratch. In terms of execution time, the PyTorch models were faster than the TensorFlow models, and they also produced superior results. In reality, CNN models trained from scratch perform better when we deal with a small number of classes, such as training the model to predict leaf diseases of only one type of crop. Otherwise, it may result in overfitting. Collecting a large amount of data is one technique to avoid overfitting. A convolutional neural network may contain up to a million parameters, and adjusting them needs millions of training instances of uncorrelated data, which is not always achievable. Choosing the proper number of epochs, on the other hand, can be a solution. Overfitting the training dataset can come from using too many epochs, whereas using too few might result in an underfit model. Early stopping is a technique that allowed us to provide an unlimited large number of training epochs and then stop training when the model’s performance on a holdout validation dataset stops increasing. Furthermore, while the model’s training takes several hours on a high-performance GPU, the classification is rapid (less than a second on a CPU), so it can be simply implemented on a smartphone. This paves the way for the widespread use of smartphone-assisted crop disease diagnostics on a worldwide scale. After deploying the models built based on TensorFlow and PyTorch frameworks, the mobile application works offline and the model takes a few seconds to determine the results and provide the output to the user, however, using an old edge device or a non-powerful smartphone may prevent the app from running and the user cannot upload images for detection. For PyTorch, the plug-in is still being developed therefore there are many limitations to our deployment. While it is true that deep learning-based plant disease detection using a mobile application is a beneficial tool for early plant diagnosis, it has many limitations. By using 38 groups that include both crop species and disease states, we’ve made the problem more difficult than it needed to be from

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a practical standpoint because producers are supposed to know which crops, they are planting. Limiting the classification task to disease state will have no discernible effect due to the great accuracy of the PlantVillage dataset. However, on real-world datasets, we can see huge progress in terms of accuracy. Generally, the proposed methods perform well with a wide range of crop species and pathogens, and it is projected to improve significantly with further training data. The second constraint is that we are now limited to recognizing single leaves that are facing up against a uniform backdrop. While these are simple settings, an application should be able to classify photographs of a disease as it appears on the plant in the real world. Many illnesses manifest themselves not just on the upper side of leaves but also on other plant areas. As a result, future picture collection operations should aim to acquire photographs from a variety of angles to be in a situation as realistic as possible. For the TensorFlow mobile SDK, the tflite conversion causes the model to have less accuracy because of the post-training quantization which converts the weights from float32 to integers. As a result, the model might give wrong results for the user as shown in the figure. However, using dynamic quantization (float16) or aware-training quantization can cause the tflite file to not be supported by flutter SDK. In terms of improvement, since the model works offline the mobile application is not able to implement new and updated features on its model. The PyTorch mobile SDK, it is only supported Android and not iOS, it also results in many errors during the deployment since it hasn’t been updated as the other dependencies.

5 Conclusion Plant diseases are the main cause of losses in plant production. To reduce the damage caused by this phenomenon, we used in our study different deep learning techniques to identify the plant disease from just one leaf, which provides early protection for the plants from different infections. In the proposed work, we have developed CNN-based models with different architectures using TensorFlow and PyTorch frameworks. This work detailed the development and operation of a deep learning plant disease detector that allows farmers to diagnose the most prevalent 38 diseases in 14 plant species. We trained our CNN models using an imaging collection comprising more than 70 000 images of healthy and pathological plant leaves in a variety of positions and lighting conditions. We created a mobile app that would allow farmers with limited funds to detect plant illnesses in their early stages and minimize the application of inappropriate fertilizers, which can harm the health of both plants and soil. By using both Pytorch and TensorFlow, we were able to derive the best accuracies, where we got 95.59% for the CNN models built with TensorFlow and 98.53% for the CNN model built with PyTorch model, and then deployed them on a mobile (Edge AI) using flutter SDK. We concluded through the different experiments that deep learning models have numerous characteristics and perform differently, which helped us to choose the best model that can be deployed on the edge with the least harm to the model’s performance. This work may be further expanded by enhancing the mobile application to become a plant care app, in which the user will receive guidance and plant protection instructions based on the status of their plant. Moreover, data can be collected from our users, which will expand our dataset by increasing its size and adding new plant species and diseases. This will improve the accuracy of the results.

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References 1. Bhattarai, S.: New plant diseases dataset. PlantVillage Dataset (2018) 2. Global Agriculture towards 2050. https://www.fao.org/fileadmin/user_upload/lon/HLE F2050_Global_Agriculture.pdf. Accessed 19 May 2022 3. United Nations: The world population is projected to reach 9.8 billion in 2050, and 11.2 billion in 2100. United Nations. https://www.un.org/en/desa/world-population-projected-reach-98billion-2050-and-112-billion-2100. Accessed 19 May 2022 4. University of California: How do we sustainably feed 8 billion people by 2025? Global Food Systems Forum. https://food2025.ucanr.edu/. Accessed 19 May 2022 5. Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016) 6. Identification of plant diseases using machine learning. ResearchGate. Accessed 12 Apr 2022 7. Wikimedia Foundation: Great famine (Ireland). Wikipedia. https://en.wikipedia.org/wiki/ Great_Famine_(Ireland). Accessed 13 Apr 2022 8. Encyclopædia Britannica: Epiphytotics. Encyclopædia Britannica. https://www.britannica. com/science/plant-disease/Epiphytotics. Accessed 13 Apr 2022 9. Wikimedia Foundation: Pathogen. Wikipedia. https://en.wikipedia.org/wiki/Pathogen. Accessed 13 Apr 2022 10. Chaudhary, S., Kumar, U., Pandey, A.: A review: crop plant disease detection using image processing. Intl. J. Inn. Tech. Expl. Eng. 8(1), 472–477 (2019) 11. Harrison, O.: Machine learning basics with the K-nearest neighbors algorithm. Medium (2019). https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighb ors-algorithm-6a6e71d01761. Accessed 6 Sept 2022 12. Parikh, A., Raval, M.S., Parmar, C., Chaudhary, S.: Disease detection and severity estimation in cotton plant from unconstrained images. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 594–601. IEEE (2016) 13. Padol, P.B., Yadav, A.A.: SVM classifier based grape leaf disease detection. In: 2016 Conference on Advances in Signal Processing (CASP), pp. 175–179. IEEE (2016) 14. Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., Hughes, D.P.: Deep learning for image-based cassava disease detection. Front. Plant Sci. 8, 1852 (2017) 15. Suresha, M., Shreekanth, K.N., Thirumalesh, B.V.: Recognition of diseases in paddy leaves using kNN classifier. In: 2017 2nd International Conference for Convergence in Technology (I2CT), pp. 663–666. IEEE (2017) 16. Hossain, E., Hossain, M.F., Rahaman, M.A.: A color and texture based approach for the detection and classification of plant leaf disease using KNN classifier. In: 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6. IEEE (2019) 17. Abdulridha, J., Ehsani, R., Abd-Elrahman, A., Ampatzidis, Y.: A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses. Comput. Electron. Agric. 156, 549–557 (2019) 18. Hassan, S.M., Maji, A.K., Jasi´nski, M., Leonowicz, Z., Jasi´nska, E.: Identification of plant-leaf diseases using CNN and transfer-learning approach. Electronics 10(12), 1388 (2021) 19. Obam, Y.S.: Plant Disease Classification with TensorFlow Lite on Android Part 1 (2019). https://medium.com/@yannicksergeobam/plant-diseaseclassification-with-tensor flow-lite-on-android-part-2-c2d47371cea3 20. Mahmood, H.: Gradient descent. Medium (2019). https://towardsdatascience.com/gradientdescent-3a7db7520711. Accessed 13 Apr 2022 21. Transfer learning: Understanding transfer learning for deep learning. Analytics Vidhya (2021). https://www.analyticsvidhya.com/blog/2021/10/understanding-transfer-lea rning-for-deep-learning/. Accessed 9 June 2022

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Study and Implementation of U-Net Encoder-Decoder Neural Network for Brain Tumors Segmentation Dalila Cherifi(B) , Abdelghani Bekkouche, Meroua Bayou, Assala Benmalek, Ines Mechti, Belkacem Bekkour, Chaima Amine, and Halak Ahmed Institute of Electrical and Electronic Engineering, University of Boumerdes, Boumerdes, Algeria [email protected], [email protected]

Abstract. Emerging advanced technologies have seen a revolution of applications into medical field, in all its aspects and sides, this has helped healthcare practitioners and empowered them in achieving accurate diagnosis and treatment, specifically with the evolution of computer Aided Diagnosis systems which use image processing techniques, Computer vision,and deep learning applied on different medical images in order to diagnose the image, or sections of the image with particular diseases or illnesses. Medical images of multiples organs or parts of the body (Liver, brain, kidney, skin, etc...) can today be visualized thanks to the advanced medical imaging techniques that exists in the market (MRI, CT, etc…) these technologies uses high energy in order to acquire high quality images but high energy can harm human cells, this is why we us low energy and with this used we get slightly low quality medical images, and here technology intervenes where we can use preprocessing techniques in order to increase image resolution prior to perform diagnosis either by doctor or CAD system. We present in this paper a computer aided diagnosis system that provides an automated brain tissue segmentation applied on 3D MRI images with its four different modalities (T1, T1C, T2, T2 weighted) of BRatS 2020 challenge dataset, by implementing a U-Net like deep neural network which provides information about classification of brain tissue into healthy tissue, Edema, Enhancing tumour, Non enhancing tumour. The model achieved an accuracy of 99.01% and dice coefficient of 47.95% after 35 epochs of training. Keywords: Segmentation · Deep learning · Brain tumors · Medical image segmentation · Medical image analysis

1 Introduction During the last decade we have seen great impact brought by implementation of deep learning and machine learning approaches to healthcare sector, which revolutionized the field of medical imaging which refers to several different technologies that are used to view the human body in order to diagnose, monitor, or treat medical conditions. The involvement of the latest technologies has helped healthcare workers specifically © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 448–456, 2023. https://doi.org/10.1007/978-3-031-21216-1_47

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physicians and doctors in the diagnostic and early detection of critical diseases, like computer aided diagnosis systems who assisted doctors in interpretation of medical images and in getting a second opinion. Medical imaging broadly deals with information in image that the medical practitioner and doctors must evaluate and analyze abnormality in short time, analysis of imaging in medical field is a very crucial task because imaging is basic modality to diagnose any diseases at the earliest, but acquisition of images is not to harm the human body. Imaging techniques like MRI, x-ray, endoscopy, ultrasound, etc. If acquired with high energy will provide good quality image but they will harm the human body; hence, images are taken in less energy and therefore, the images will be bad in quality and low contrast. Cad systems are used to improve the quality of the image, which helps to interpret the medical images correctly and process the images for highlighting the conspicuous parts to achieve an accurate diagnosis. Cad is a technology that includes multiple elements like concepts of artificial intelligence (ai), computer vision, and medical image processing, which all have a common goal which is accurately detecting and diagnosing an abnormality in human body. Segmentation of abnormality within medical imaging is one of the prime challenges like brain tumor segmentation, cardiac ventricle segmentation, abdominal organ segmentation, and cells segmentation in biological images. In this work, we will implement a computer aided diagnosis system to achieve segmentation of 3D MRI modality-based brain tumors, tumors with an encoderdecoder deep neural network, which performs specific classification of brain tissues and cells into healthy tissues, edema, enhancing tumor, and non-enhancing tumor. The tumor detection is major challenging task in brain tumor quantitative evaluation. In recent years, owing to non-invasive and strong soft tissue comparison, magnetic resonance imaging (MRI) has gained great interest. MRI is a commonly used image modality technique to locate brain tumors. Heterogeneity, isointense and hypointense tumor properties restrict manual segmentation in a fair period, thus restricting the use of reliable quantitative measures in clinical practice [1, 2]. This article consists of four sections, the second section covers an overview about the medical image segmentation. The third section presents U-Net architecture. The fourth section includes the experimental parts and the results followed by a conclusion.

2 Medical Image Segmentation Overview Previous work and methods of medical image segmentation includes a varied range of techniques that are grouped into two main groups which are Classical and non-classical techniques, Classical methods include Thresholding, Clustering, Edge detection, Region based segmentation, Graph based segmentation, watershed method, and deformable model. Non classical methods are self-learning methods based on Artificial Neural Networks. In thresholding an image is converted into binary image, it chose a threshold in order to divide the image into several regions [3]. Clustering the process of grouping similar objects within an image, clustering algorithms include K-Means, adaptive KMeans, Fuzzy C-Means, etc. [4]. Images can be segmented with detection of edges by finding boundaries of objects in an image which obtained by identifying sharp changes or discontinuity in brightness [5–7]. Another way to segment an image is to predefine criteria on which the images will be partitioned into similar regions [8]. Subgraphs can

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also be used in segmentation process of an image by using matrices [9]. If we observe an image as a topographic landscape with its ridges and valleys, we can apply concepts of mathematical morphology [10]. Deformable models are curves or surfaces defined within an image domain that can move under the influence of internal forces, which are defined within the curve or surface itself, and external forces, which are computed from the image data [11]. In Artificial Neural Network, which are adaptive models for the analysis of data which are inspired by the functioning processes of the human brain. They are systems which are able to adjust their internal structure in relation to a function objective and are particularly suited for solving nonlinear problems, being able to reconstruct the approximate rules that put a certain set of data [12].

3 Brain Tumors Segmentation Using U-Net Medical image segmentation has been extensively studied in image analysis community due to the fact that manual, dense labeling of large amounts of medical images is a hard tedious task subjected to a high rate of error. Accurate and reliable solutions are desired to increase clinical workflow efficiency and support decision making through fast and automatic extraction of quantitative measurements. With the advancement of Convolutional Neural Network (CNNs), near radiologist performance can be achieved with the help of Computer aided diagnosis systems, thanks to the great evolution of emerging technologies like deep learning and machine learning, the thing that helped advance healthcare sector, by helping doctors, and clinicians achieve accurate and precise diagnosis of MRI brain scans. In this paper we propose a U-Net like network that performs segmentation of brain scans by providing a contracting path that captures context and a symmetric expanding path that enables precise localization, the model will take as input an MRI brain image and will classify it pixel wise which will divide it into homogeneous regions which correspond to: Healthy tissue, Edema, Enhancing Tumor, Non-Enhancing Tumor. In this work the architecture of the deep learning model implemented for segmentation of medical brain images is a U-Net like fully connected network (shown in Fig. 1) that constitute of an encoder or a down-sampling path that extracts features, and a decoder network or an expanding path that perform classification of the features extracted. We are interested to use U-Net in order to segment brain tumors from 3D MRI dataset, The essential idea is to supplement and provide typical contracting network by successive layers, where pooling operations are replaced by up-sampling operators, and this increases the resolution of the output, a successive convolutional layer can then learn to acquire a precise output based on this information. There are a large of feature map channels in the upsampling part, the thing that allows the network to propagate information to higher resolution layer, as a sequence the expanding path is symmetric to the contracting path which yields the U shape of the network. The network consists of a contracting path and an expansive path, which gives it the ushaped architecture. The contracting path is a typical convolutional network that consists of repeated application of convolutions, each followed by a rectified linear unit (ReLU) and a max pooling operation. During the contraction, the spatial information is reduced while feature information is increased. The expansive pathway combines the feature

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and spatial information through a sequence of up-convolutions and concatenations with high-resolution features from the contracting path [13]. U-Net network is composed of U channel and skip-connection. The U channel is similar to the encoder-decoder structure of SegNet. The encoder has four submodules, each of which contains two convolutional layers. After each submodule, there is a max pool to realize downsampling. The decoder contains four submodules. The resolution is increased successively by upsampling. Then it gives predictions for each pixel. The network structure is shown in Fig. 4. The input is 572 × 572, and the output is 388 × 388. The output is smaller than the input mainly because of the need for segmentation in the medical field, which is more accurate. It can be seen from the figure that this network has no fully connected layer, only convolution and downsampling. The network also uses a skip connection to connect the upsampling result to the output of submodule with the same resolution in the encoder as the input of next submodule in the decoder. Throughout the development of our model we have used the following operations: Convolution Layer, Rectified Linear Unit (Relu), Max pooling, Upsampling, Dropout Layer, Softmax [13].

Fig. 1. U-Net neural network architecture [13].

4 Experiments and Results In this section we will the details of the implementation step by step, starting with exploring data up to training of the deep learning model and results obtained. For the experiments we have used BraTS 2020 dataset [14] that provides 3D MRI brain volume, with four different modalities: native (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (T2-FLAIR). We have used the following metrics in ordes to measure the prefomrance of the implemented algorithms: • Dice Similarity Coefficient(DSC), also known as the Sorensen–Dice index or simply dice coefficient, is a statistical tool which measures the similarity between two sets of data.

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• Specificity is defined as the proportion of actual negatives, which got predicted as the negative (or true negative). • Sensitivity is a measure of the proportion of actual positive cases that got predicted as positive (or true positive). • Accuracy is defined as ‘the degree to which the result of a measurement conforms to the correct value or a standard’ and essentially refers to how close a measurement is to its agreed value. Precision is defined as ‘the quality of being exact’ and refers to how close two or more measurements are to each other, regardless of whether those measurements are accurate or not. It is possible for precision measurements to not be accurate. • Mean Intersection over Union (MIoU) is the area of overlap between the predicted segmentation and the ground truth divided by the area of union between the predicted segmentation and the ground truth. 4.1 Data Visualization In order to get to know more about our dataset we need to visualize the images provided by BraTS 2020 dataset, Fig. 2 shows subplots of a brain scan for the four MRI modalities.

Fig. 2. Brain scan visualization with corresponding mask

4.2 Tumor Segments Visualization Using the plotting effects, we can accurately visualize the position of tumors segments in the MRI brain scans. We can view the entire 3D brain scan by creating a class to generate a 3D plot, that we can view in the Fig. 4 (Fig. 3).

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Fig. 3. Tumor segments visualization

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Fig. 4. 3D Brain scan visualization

4.3 Model Building and Training In this experiment we build our U-Net model with its different layers encoder and decoder constituting the whole architecture of the model as shown previously in Fig. 1. After building the model architecture, we compile it and execute it, the results of the training process using the number of epochs for the respectively values of 15, 25, 35 are given in the following table: Table 1. Training metrics results Number of epochs Metrics Loss

Accuracy MIoU

Dice-coef Precision Sensitivity Specificity

15 epochs

0.0502 0.9850

0.5496 0.3246

0.9889

0.9822

0.9963

25 epochs

0.0290 0.9899

0.7190 0.4918

0.9921

0.9872

0.9973

35 epochs

0.0300 0.9901

0.4367 0.4795

0.9924

0.9875

0.9974

For every class of our pixel classification and segmentation class we get the per class dice coefficient as represented in the Table 2: Table 2. Dice coefficient per class training results Number of epochs

Dice-coefficient per class Dice-Coef-NECROTIC

Dice-Coef-EDEMA

Dice-Coef-ENHANCING

15 epochs

0.1817

0.2731

0.1714

25 epochs

0.3371

0.6160

0.5641

35 epochs

0.3489

0.5598

0.5644

Regarding validation step of our deep learning model, the values of metrics are shown in the following tables (Tables 3 and 4):

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Number of epochs Metrics Loss

Accuracy MIoU

Dice-coef Precision Sensitivity Specificity

15 epochs

0.0594 0.9842

0.6229 0.3298

0.9894

0.9799

0.9964

25 epochs

0.0368 0.9881

0.6380 0.4611

0.9895

0.9858

0.9965

35 epochs

0.0385 0.9883

0.6369 0.4917

0.9907

0.9856

0.9968

Table 4. Dice coefficient per class validation results Number of epochs

Dice-coefficient per class Dice-Coef-NECROTIC

Dice-Coef-EDEMA

Dice-Coef-ENHANCING

15 epochs

0.2121

0.2708

0.3669

25 epochs

0.3279

0.5940

0.5372

35 epochs

0.2312

0.4659

0.5793

Observing the different tables, we see that the three trained models achieve good results, with a highest accuracy, dice coefficient, mean-io-u,precision,and sensitivity for the model trained with 35, the case where we had 15 epochs of training resulted in a low value of dice-coef which is an important metric to measure the performance of a segmentation model, for the 25 epochs we have achieved good accuracy, dice coefficient and other metrics, but 35 epochs of training results override and we conclude that, when our model is trained with 35 epochs it has better performance. 4.4 Model Evaluation and Testing We evaluate and test our trained model with the same metrics over testing dataset and we get the following results (Table 5): Table 5. Evaluation metrics Metrics

Loss

Accuracy

MIoU

Dice-Coef

Precision

Sensitivity

Specificity

Results

0.0151

0.9947

0.8302

0.6351

0.9948

0.9934

0.9983

Another way to evaluate our model is applying it to real brain scans and comparing it with the ground truth we get the Fig. 4 where multi classification is performed, and in Fig. 5 classification per class is performed (Fig. 6). In this section we have studied and implemented our deep learning model for segmentation of medical images. We successfully attained an accuracy of 99.01 and Dice

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Fig. 5. Testing the model on brain scan

Fig. 6. Evaluating segmentation model

coefficient of 0.47 for the U-Net model that we have trained with Brats 2020 training and validation dataset.

5 Conclusion In this paper we presented a brain tissue segmentation model that deals directly with MRI brain scans. We have demonstrated that the model is able to produce segmentation results that are in good agreement with the ground truth for the four different classes: healthy tissue, Edema, Non-Enhancing tumor (necrotic), and Enhancing tumor with the good metrics results obtained over three tries with different number of training epochs (15, 25, and 35), and we chose to take the trained model with best values combination of metrics, which was the model trained with 35 epochs (see Table 1) with an accuracy of 99.01%, Dice-coeff of 47.95, and a loss of 3%. The U-Net like network developed in accordance with a different input image dimensions from the original network that was for microscopic images. Further work in upgrading the model performance may include adding dense layers between the encoder and decoder to reduce the semantic gaps between the feature maps of the two subnetworks as the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar.

References 1. Frackowiak, R.S.J.: Human Brain Function. Elsevier (2004) 2. Friston, K.J., Jezzard, P., Turner, R.: Analysis of functional MRI time-series. Hum. Brain Mapp. 1(2), 153–171 (1994)

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3. Al-Amri, S.S., Kalyankar, N.V., et al.: Image segmentation by using threshold techniques. arXiv preprint arXiv:1005.4020 (2010) 4. Ambeth Kumar, V.D., et al.: Exploration of an innovative geometric parameter based on performance enhancement for footprint recognition. J. Intell. Fuzzy Syst. 38(2), 2181–2196 (2020) 5. Lakshmi, S., Sankaranarayanan, V., et al. A study of edge detection techniques for segmentation computing approaches. In: IJCA Special Issue on “Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications”, CASCT, pp. 35–40 (2010) 6. Hien, N.M., Binh, N.T., Viet, N.Q.: Edge detection based on fuzzy c means in medical image processing system. In: 2017 International Conference on System Science and Engineering (ICSSE), pp. 12–15. IEEE (2017) 7. Chakraborty, S., Roy, M., Hore, S.: A study on different edge detection techniques in digital image processing. In: Feature Detectors and Motion Detection in Video Processing, pp. 100– 122. IGI Global (2017) 8. Wazarkar, S., Keshavamurthy, B.N., Hussain, A.: Region-based segmentation of social images using soft kNN algorithm. Procedia Comput. Sci. 125, 93–98 (2018) 9. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004) 10. Digabel, H., Lantu´Ejoul, C.: Iterative algorithms. In: Proceedings of 2nd European Symposium Quantitative Analysis of Microstructures in Material Science, Biology and Medicine, vol. 19, p. 8. Riederer Verlag (1978) 11. McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis. In: Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 171–180. IEEE (1996) 12. Jeevitha, K., Iyswariya, A., RamKumar, V., Mahaboob Basha, S., Praveen Kumar, V.: A review on various segmentation techniques in image processsing. Eur. J. Mol. Clin. Med. 7(4), 1342–1348 (2020) 13. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep con- volutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481– 2495 (2017) 14. https://www.med.upenn.edu/cbica/brats2020/data.html

Bayesian Regularized Backpropagation Neural Network Model to Estimate Resilient Modulus of Unbound Granular Materials for Pavement Design K. Sandjak1(B) , M. Ouanani2 , and T. Messafer1 1 Faculté de Technologie, Université M’hamed Bougara, 35000 Boumerdes, Algeria

[email protected] 2 Faculté des Sciences et de la Technologie, Université Ziane Achour, 3117 Djelfa, Algeria

Abstract. The resilient modulus of unbound granular materials is one of the main input design parameters for pavements, especially for low-volume roads. Almost, laboratory and/or in-situ tests have been carried out to determine the resilient modulus of unbound granular materials used as base and subbase layers in road construction. Nevertheless, these operations are usually expensive, time-consuming, and complex. Hence, the prediction of the resilient modulus of unbound granular materials using machine learning techniques has been commonly used in recent years. In this study, Backpropagation Neural Network model coupled with Bayesian regularization method is used to estimate the resilient modulus of unbound granular materials based on 260 specimens collected from an experimental database. The performance of the model is assessed by specific statistical criteria, including the Pearson correlation coefficient (R) and mean square error (MSE). The results show that the proposed algorithm performed well in the prediction of the resilient modulus of unbound granular materials. Thus, it can be concluded that the Bayesian regularized backpropagation neural network model is a rationally accurate and practical prediction tool for pavement engineers. Keywords: Bayesian regularization · Backpropagation algorithm · Resilient modulus · Unbound granular materials

1 Introduction The road network, in Algeria, is one of the largest in North Africa; the length is currently estimated at 133741 km, secondary roads cover more than 50% of the total road network. These road structures generally consist of thin asphalt layers and significant unbound granular base and subbase layers over subgrade soil to bear the traffic load [1]. For this pavement structure, the resilient modulus of unbound granular materials (UGMs) is one of the main input design parameters.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 457–468, 2023. https://doi.org/10.1007/978-3-031-21216-1_48

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The Mechanistic Empirical Pavement Design Guide (MEPDG) [2] defines three levels of reliability, the first level (equivalent to Algerian road network class 1) needs input from resilient modulus test to provide the highest level of reliability, while for the second and the third levels (levels equivalent to Algerian road network class 2 and secondary roads classified as medium to low volume roads respectively) the use of correlations and local database is allowed. In the last decades, many challenges facing the spread of the Algerian Mechanistic Empirical Pavement Design Guide method [3], since it is based essentially on laboratory characterisation of pavements materials and the use of a large materials database. However, due to the complex nature of materials used in road construction, laboratory determination of the resilient modulus of unbound aggregate materials is time-consuming, requires capital investment and special training [4]. Currently, several local transport agencies in Algeria have not the required testing capabilities to determine the resilient modulus of their UGMs. To deal with this situation, many practitioners tend to use empirical formulas to relate the resilient modulus of UGMs to different basic engineering properties via coefficients determined by classical regression methods [5]. However, despite their simplicity, empirical methods have several limitations; for example, the degree of nonlinearity and the effect of all influencing parameters cannot be considered; some empirical relationships also give weak correlations using classical regression methods [6]. In this case, the application of soft computing techniques such as artificial neural networks could be beneficial [7]. Over the past decades, Artificial Neural Networks models have been used in geotechnical engineering to predict many geomaterials complicated behaviour and relate inputs to targets of several experimental datasets. ANN method has also been recently implemented to predict resilient modulus of subgrade soils and granular base layers from basic engineering properties and stress state conditions for analysis and pavement design [8]. The backpropagation algorithm (BP) is extensively utilized to adjust ANN’s parameters. This algorithm uses a set of input and output values to find the relevant weight and bias of the neural network. Nonetheless, in traditional BP networks, there are some shortcomings, such as the low convergence speed and an easy drop to the local minimum [4]. Hence, to minimize the error associated to the backpropagation algorithm, some generalization methods as Bayesian regularization (BR) [9] and Levenberg–Marquardt (LM) [10] are employed, due to their advantage in reaching a lower mean squared error. Several researchers noticed that BR achieved better than LM [11, 12]. Furthermore, BR algorithm has been well used in many domains, including data mining, stock price volatility prediction, and engineering [13, 14]. This research article focuses on estimating the resilient modulus of unbound granular materials generally used as base and subbase materials in the Northern region of Algeria. Common evaluation indicators, such as the Pearson correlation coefficient (R) and mean square error (MSE) are used to evaluate the performance of the proposed model. A local database containing 260 experimental results is used to develop the ANN model. Simulations results indicate that backpropagation neural network model coupled with Bayesian regularization algorithm have good accuracy in predicting the resilient modulus of unbound granular materials.

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2 Materials and Methods The present study is carried out based on the proposed methodology that incorporates three main stages: (1) data preparation, (2) model development, and (3) validation of the proposed model. In the first stage, the data obtained from laboratory tests are used to create two datasets: the testing and training datasets. The first dataset is generated from 80% of the total data, whereas the second dataset is built from 20% of the remaining data. To develop the ANN model based on the Bayesian regularization backpropagation algorithm, the training dataset is considered. In this stage, the effect of varying the number of neurons in the hidden layer is investigated. In the third stage, the testing dataset is adopted to validate the proposed model. Statistical indicators, including MSE and R, are employed. 2.1 Data Preparation The resilient modulus of unbound aggregate materials (Mr) is influenced by many factors. However, this research will focus on the main factors that significantly affect the resilient modulus to reduce the model complexity. An experimental database is utilized to generate the ANN models of indirect estimation of UGMs resilient modulus based on material type, basic engineering characteristics and loading conditions. The database consists of 260 experimental datasets obtained from RLT tests performed by the laboratory of the Central Transportation Agency located in Algiers, Algeria [15]. These RLT tests were carried out on different types of UGMs resulting from the quarry crushing of three types of massive rocks: granite, limestone, and diabase available in different deposits located in the central region of northern Algeria. Based on this experimental database, several input parameters are selected, namely, Aggregate Mineralogical Nature or rock type (AMN), Coefficient of Uniformity (CU), Coefficient of Curvature (CU), Fine content (Fc), Liquidity Limit (LL), Plasticity Index (PI), Maximum Dry Density (MDD), Water content (Wc) and two loading components: confining pressure σ3 noted (SIG3), the deviator of stress σd noted (SIGD). The output of these parameters in modelling is the resilient modulus of unbound aggregate materials (Mr). Detailed definitions and how to determine the input variables from laboratory tests can be found in [16, 17]. Note that the AMN variable is an index that takes three values: 1 for granite, 2 for limestone and 3 for diabase. Table 1 details the symbol, unit, as well as statistical analysis of the inputs and output continuous variables. 2.2 Bayesian Regularization Algorithm An artificial neural network (ANN) is an efficient machine learning-based data analysis algorithm. This machine learning approach tries to simulate the process of knowledge achievement and assumption occurring in the human brain [18]. ANN has been commonly used to resolve nonlinear regression analysis problems. It has been proven that an ANN with a hidden layer can simulate extremely complicated nonlinear functions [19]. To create a reliable model, adequate training of a neural network is the most important

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Variable

Total count

Mean

StDev

Minimum

Maximum

Range

Cu

260

58.28

29.22

4.17

112.00

107.83

Cc

260

3.875

1.860

0.880

6.920

6.040

Fc (%)

260

9.461

1.991

8.000

14.080

6.080

LL (%)

260

28.456

4.932

19.000

37.000

18.000

PI (%)

260

7.079

2.613

2.000

12.000

10.000

MDD (g/cm3 )

260

2233.2

67.0

2109.0

2341.0

232.0

MC (%)

260

6.5363

1.1292

3.6000

8.5000

4.9000

SIG3 (kPa)

260

102.85

71.34

10.00

250.00

240.00

SIGD (kPa)

260

334.8

160.4

150.0

600.0

450.0

Mr (MPa)

260

267.23

128.19

46.60

590.31

543.71

phase. Backpropagation is an algorithm generally used to train neural networks. Typical backpropagation networks usually apply a gradient descent algorithm with a slow convergence rate [20]. Therefore, one of the algorithms that enhance the convergence or learning rate of the neural network is the backpropagation training network coupled with the Bayesian regularization algorithm. Bayesian regularization is the linear combination of Bayesian methods and ANN to calculate the optimal regularization parameters automatically. Unlike conventional network training, in which the optimal weight set is selected by minimizing the error function, the Bayesian approach requires the probability distribution of network weights. Thus, the network predictions are also a probability distribution [21]. In the training process, a general performance function is used for calculating the distance between real and predicted data, namely the mean sum of squared network errors: N 2 1  Mro,i − Mrt,i F = Ed = N

(1)

i=1

where F is the target function, N is the number of samples in the database, Mro,i and Mrt,i are the predicted and the measured experimental values respectively. To enhance the generalization of the model, the gradient-based optimization algorithm is chosen to minimize the target. The target function in Eq. (1) expanded with the addition of a term Ew which is the sum of the squares of the lattice weights: F = αEw + βEd

(2)

Here, the α and β are parameters that are to be optimized in the Bayesian framework of MacKay [22]. To search for the optimum regularization parameters, a Bayesian regularization method is utilized. The optimal regularization parameters can so be obtained automatically. Bayesian optimization of the regularization parameters needs the computation of the Hessian matrix of the objective function. Nevertheless, the optimal regularization technique requires the costly computation process of the Hessian matrix.

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2.3 Statistical Indicators of Performance Assessing the model accuracy is a fundamental part of the process of creating machine learning models to explain how well the model is operating. In this research, the mean square error (MSE) and Pearson correlation coefficient (R) are used to evaluate the predicted error rate and model performance. The mean square error (MSE) indicates the level of scattering that the ANN model produces, as shown in Eq. (3) [23]. R is an important indicator of regression analysis which represents the correlation between the predicted results and the actual output, varying from − 1 to 1, as shown in Eq. (4). The closer the absolute value of R is to 1, the better the model is. The level of correlation is considered acceptable if the R values exceed 0.8 [24]. MSE =    R=

N 2 1  Mro,i − Mrt,i N

(3)

i=1

  N  i=1 Mro,i − Mro Mrt,i − Mrt 2 N  2 N  i=1 Mro,i − Mro i=1 Mrt,i − Mrt

(4)

where Mro , Mro are the measured experimental value and the average measured experimental value, Mrt and Mrt are the predicted value and the average predicted value.

3 Results and Discussion 3.1 Effect of the Number of Neurons in the Hidden Layer on Model Performance The performance of the ANN model relies upon the structure of the neural network, specifically, the number of hidden layers and the number of neurons in each hidden layer. Once the number of inputs and outputs is fixed, the unspecified architecture parameters are the number of the hidden layer and the number of neurons in each hidden layer. The number of hidden layers is usually determined firstly, based on the complication of the relationship between input and output. The process of trial-and-error test is used to create the network structure. Some studies have shown that most specific problems using only one hidden layer can be sufficient to efficiently solve the complex nonlinear relationship between input and output [25]. In this study, after conducting several runs, it was observed that the transfer function form had a negligible on the computed MSE values. Therefore, the commonly used neuron activation function, Tansig (Eq. 5) was used and a linear function namely ‘purelin’ (Eq. 6) was selected for transferring data between the hidden layer and the output layer. f 1 (x) =

2 −1 1 + e−2x

f 2 (x) = x

(5) (6)

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The optimisation approach proposed by Soleimanbeigi and Hataf [26] was adopted to select the optimum number of hidden layer nodes. For this, the hidden layer nodes were increased until no further enhancement was obtained over the testing data set. Figure 1 represents the optimisation procedure for selecting the number of hidden nodes. It can

Fig. 1. The optimisation procedure for selecting the number of hidden nodes.

Fig. 2. Optimal architecture of the selected ANN model.

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be observed that the lowest mean square error (MSE = 14.41) is obtained at five hidden nodes. The optimal structure of the ANN model is illustrated in Fig. 2. A training dataset with 215 samples with 10 input parameters and 1 output parameter is utilized to build the Bayesian Regularized-Backpropagation Neural Network model (BR-BPNN). In this research work, the impact of training iterations on the Neural Network model accuracy is analysed. The number of iterations ranges from 100 to 500. Table 2. reproduces the effect of the number of iterations on the values of the performance indicators. The results show that considering the value of MSE, 100 iterations provide the lowest error, and the highest value of R compared to the results of the other number of iterations. Overall, choosing 100 iterations is the optimal selection to have the best prediction results. Table 2. Number of iterations versus BR-BPNN model performance Number of iterations

MSE

R

100

14.41

0.9994

200

26.02

0.9991

300

36.43

0.9988

400

43.01

0.9987

500

31.24

0.9991

3.2 Performance Analysis of the Proposed Model The performance of the proposed BR-BPNN model to predict the resilient modulus of unbound granular materials (UGMs) is established by checking the equilibrium between training and testing patterns. A network model that makes an accurate prediction on testing data can predict the new resilient modulus for any other inputs of unbound granular materials. However, the accuracy of predicting resilient modulus is dependent on the kind of experimental data utilized for network training. Figure 3 shows the error histogram obtained after training and testing the network model. The error on the x-axis indicates how the predicted resilient modulus (output) differs from the experimentally measured resilient modulus (target). Instances on the y-axis define the number of unbound granular materials specimens in the training or testing dataset with a specific error. Many of the errors after training and testing with the proposed BR-BPNN model lie in the range of −4.907 to 4.369. In addition, the proposed model can predict the resilient modulus for most unbound granular materials specimens with an error between −2.583 to 2.05, which is near the zero-error line. Figure 4 shows the pattern of the MSE performance of the BR-BPNN model for epochs during the training and testing phase. The results show that, when the epochs are increased, BR-BPNN model can predict UGMs resilient modulus with the lowest MSE due to adequate training.

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Fig. 3. Error Histogram of the BR-BPNN model for UGMs resilient modulus prediction.

Fig. 4. Mean squared error performance of BR-BPNN model for UGMs resilient modulus prediction.

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Fig. 5. Correlation between predicted and measured UGMs resilient modulus for the BR-BPNN model. (a) Training Data; (b) Testing Data; (c) All Data.

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Fig. 5. (continued)

The prediction capacity of the BR-BPNN model enhances to 250 epochs and stands constant thereafter. The best training performance in terms of lowest MSE is displayed with a circle corresponding to the prediction with MSE value of 14.41 at epoch 80. The BR-BPNN model is still capable to predict the resilient modulus on the testing dataset with an equivalent MSE, verifying the validity of the BR-BPNN model. Figure 5 presents the correlation curves achieved after applying the BR-BPNN model on training, testing, and all data of UGMs specimens. An ideal fitting is represented by a dashed line at an angle of 45 degrees where output resilient modulus meets the target resilient modulus, i.e., coefficient of correlation R = 1. The blue, green, and red lines represent the fitting for training, testing, and entire data of UGMs specimens respectively. A strong relationship between output and target resilient modulus with the same coefficient of correlation (R = 0.9995) for training, testing, and all data is reached, which illustrates very good data fitting. This shows the accuracy of the BR-BPNN model in predicting resilient modulus for any other input data of Unbound Granular Materials.

4 Conclusions In this work, an Artificial Neural Network using Backpropagation algorithm coupled with Bayesian regularization method is proposed to estimate the resilient modulus of unbound granular materials for pavement design. A total of 260 experimental datasets performed by the laboratory of the Central Transportation Agency on unbound granular materials are used to develop the BR-BPNN model.

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The input parameters are the essential basic engineering characteristics of UGMs and the loading stress components. Two statistical criteria, including, the Pearson correlation coefficient (R) and mean square error (MSE) are utilized to evaluate the correlation between the predicted and the experimentally measured values. The results show that the proposed BR-BPNN model is a good predictor in estimating the resilient modulus of unbound granular materials, with R values close to one and lower values of MSE for training and testing datasets. However, this proposed BR-BPNN model is built using a dataset collected principally from quarries located in the central northern region of Algeria. Thus, collecting additional data from other regions can aid to improve the generalization of the proposed model and to build a reliable soft computing tool for pavement design especially for low to moderate volume roads.

References 1. Mamma, F.: Réseau routier et autoroutier Algérien : consistances et perspectives. In: French, Conference on Road Safety, Algiers, Algeria (2017) 2. AASHTO: Mechanistic-Empirical Pavement Design Guide: A Manual of Practice. American Association of State and Highway Transportation Officials, USA (2008) 3. CTTP-Direction des Routes, Ministère des Travaux Publics, 2001, Catalogue de Dimensionnement des Chaussées (In French), Algeria 4. Nguyen, T., Ly, H.B., Luo, Q., Pham, B.T., Backpropagation Neural Network-Based Machine Learning Model for Prediction of Soil Friction Angle, Mathematical Problems in Engineering, vol. 2020, ID 8845768, p. 11 (2020). https://doi.org/10.1155/2020/8845768 5. Yau, A., Von Quintus, H.L.: Predicting elastic response characteristics of unbound materials and soils. Trans. Res. Rec. J. Trans. Res. Board USA 1874(1), 47–56 (2004) 6. Alnedawi, A., Al-Ameri, M., Nepal, K.P.: Neural network-based model for permanent deformation of unbound granular materials. J. Rock Mech. Geotech. Eng. 11, 1231–1242 (2019) 7. Jebur, A.A., et al.: New applications of a supervised computational intelligence (CI) approach: case study in civil engineering. In: Berry, M.W., Mohamed, A., Yap, B.W. (eds.) Supervised and Unsupervised Learning for Data Science. Unsupervised and Semi-Supervised Learning, pp. 145–182. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-22475-2_8 8. Saha, S., Gu, F., Luo, X., Lytton, R.L.: Use of an artificial neural network approach for the prediction of resilient modulus for unbound granular material, transportation research record. J. Transp. Res. Board USA 2672(52), 47–56 (2018) 9. Burden, F., Winkler, D.: Bayesian regularization of neural networks. Methods Mol. Biol. 458, 23–42 (2008) 10. Saini, L.M., Soni, M.K.: Artificial neural network based peak load forecasting using Levenberg-Marquardt and Quasi-Newton methods. IEE Proceed.-Gener. Trans. Distrib. 149(5), 578–584 (2002) 11. Kayri, M.: Predictive abilities of Bayesian regularization and Levenberg-Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Math. Comput. Appl. 21(2), 20 (2016). https://doi.org/10.3390/mca21020020 12. Kim, S.H., Young, J., Beadles, S.: Estimate of resilient modulus of graded aggregate base in flexible pavement. T& DI Congress ASCE, USA 10, 003 (2014). https://doi.org/10.1061/978 0784413586.003 13. Sariev, E., Germano, G.: Bayesian regularized artificial neural networks for the estimation of the probability of default. Quantitative Finan. 20(2), 311–328 (2020)

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14. Wu, D., et al.: A Bayesian Application of Bayesian regularization back propagation neural network in sensorless measurement of pump operational state. Energy Rep. 8, 3040–3051 (2022) 15. Sandjak, K., Ouanani, M.: Experimental characterisation and numerical modelling of the resilient behaviour of unbound granular materials for roads. J. Build. Mater. Struct. 7(2), 159–177 (2020) 16. Huang, Y.H.: Pavement Analysis and Design, 2nd Edition, USA, ISBN-13: 978–0131424739 17. Papagiannakis, A.T., Masad, E.A.: Pavement Design and Materials. John Wiley & Sons, USA (2008) 18. Braspenning, P.J., Thuijsman, F., Weijters, A.J.M.M.: Artificial neural networks: an introduction to ANN theory and practice. Springer, Berlin/Heidelberg, Germany (1995). https://doi. org/10.1007/BFb0027019 19. Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Germany (2006). https://doi.org/10.1007/978-1-4615-7566-5 20. Zhou, R., Wu, D., Fang, L., Xu, A., Lou, X.: A Levenberg-Marquardt backpropagation neural network for predicting forest growing stock based on the least-squares equation fitting parameters. Forests 9(12), 757 (2018) 21. Okut, H.: Bayesian regularized neural networks for small n big p data. In: Artificial Neural Networks - Models and Applications. IntechOpen (2016). https://doi.org/10.5772/63256 22. MacKay, D.J.C.: A practical Bayesian framework. Neural Comput. 472(1), 448–472 (1992) 23. Bruce, P., Bruce, A., Gedeck, P.: Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2nd edn. O’Reilly, USA (2020) 24. Smith, G.N.: Probability and statistics in civil engineering, Collins Professional and Technical Books, p. 244, UK (1986) 25. Amjad Raja, M.N., Shukla, S.K., Arif Khan, M.U.: An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil. Int. J. Pavement Eng. (2021). https://doi. org/10.1080/10298436.2021.1904237 26. Soleimanbeigi, A., Hataf, N.: Prediction of settlement of shallow foundations on reinforced soils using neural networks. Geosynth. Int. 13(5), 218 (2006)

Optimal Placement of Phasor Measurement Units Considering the Topology Transformation Method Abdelkader Azzeddine Laouid1(B) , Aicha Djalab2 , and Nail Alaoui3 1 Applied Automation and Industrial Diagnosis Laboratory, Faculty of Science and Technology,

Djelfa University, Djelfa, Algeria [email protected] 2 Faculty of Science and Technology, Djelfa University, Djelfa, Algeria 3 Laboratoire de Recherche Modélisation, Simulation et Optimisation des Systèmes Complexes Réels, Université Ziane Achour de Djelfa, Djelfa, Algeria

Abstract. The optimal location of phasor measurement units (PMUs) necessitates reducing both the needed number of PMUs and ensuring that the whole power system is observable. To determine if a power system is observable, it is required to know the voltages of all its buses. This study proposes selection rules for the topology transformation approach, which involves merging a bus with zero injection with one of its neighbors. The selection of a bus to merge with a bus with zero injections will alter the outcome of the merging procedure. The suggested technique would employ four principles to calculate the minimal number of PMUs necessary to provide complete observability of the power system in order to choose the most suitable bus to merge with the zero injection bus. The issue is articulated and resolved using a grey wolf optimization (GWO) strategy. The proposed GWO was implemented on the IEEE 14-bus and 24-bus. Keywords: Phasor measurement units (PMUs) · Grey wolf optimization (GWO) · Topology transformation method · Zero injection bus (ZIBs)

1 Introduction In the modern era, almost everything has been modernized to achieve greater efficiency, reliability and control independence, the traditional power grids are also in transition to become a modernized power grid, or widely known as the smart grid. The Smart Grid is designed to monitor and operate the electrical grid in the most efficient manner possible, thus enhancing its reliability and stability. There is a welcome opportunity to replace an older infrastructure with a smart grid that uses advanced technologies to achieve this vision. Among the advanced technologies used is the phasor measurement unit (PMU) [1]. The Phasor Measurement Unit (PMU) was launched in the mid-1980s as a monitoring device [2]. Using a common time source for synchronization, this device estimates the amplitude and phase angle of a quantity of electrical phasor (such as voltage or current) © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 469–481, 2023. https://doi.org/10.1007/978-3-031-21216-1_49

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in the power grid. Typically, GPS is used to synchronize time, allowing for real-time synchronized measurements at many remote places on the power system. The PMU is designed to capture samples of a waveform in a short series and reconstruct the phasor value from the magnitude and angle measurement. A synchrophasor is a resultant measurement. These time-synchronized measurements are necessary since, if either the production and consumption sides of the system are not completely aligned, frequency imbalances can lead to network stress, which is a potential cause of blackouts. The high cost of installing PMUs makes the possibility of changing all traditional measurements in the near future very unlikely. As a result, several techniques have been proposed by the researchers to solve the problem of the optimal placement of PMUs (OPP) [3]. Numerous optimization strategies have been implemented in the latest years to determine the optimal placement of PMUs in a power system, such as integer linear programming (ILP) [4–6], Binary integer linear programming (BILP) [7], biogeography based optimization [8], Cellular Learning Automata [9], Mixed Integer Linear Programming (MILP) [10], Empirical observability Gramian [11], The Gravitational Search Algorithm [12], Revised Analytical Hierarchy Process [13], The exponential binary particle swarm optimization (EBPSO) [14], integer linear programming (ILP) methodology [15], Binary cuckoo search [16], Binary integer linear programming [17]. The presence of a zero-injection bus can also contribute to reducing the number of PMUs required. Several studies have adapted the merging approach to handle with ZIB. However, the merging method has two limitations, one is to identify the exact placement of the PMUs and the other is to choose the right bus to merge. For this reason, this paper proposes three rules to deal with these limitations. Following the three rules developed, the best candidate bus to merge with ZIB will be evaluated. The results obtained by the proposed method will determine the precise location of the PMU. The main aim of this paper is to find the optimal placement of PMUs in different power systems, by using the topology transformation method, to attain full observability by maximizing the measurement redundancy (SORI). The rest of this article is arranged as following: Sects. 2 and 3 presents the mathematical formulation of PMU placement problem with and without ZIBs and topological observability rules related to each of them. Section 4 specifies the system Observability Redundancy Index (SORI to evaluate the quality of the optimal solution obtained. In Sect. 5, the suggested method is fully detailed. Section 6 describes the case study for the suggested method via the IEEE 9-bus system. In Sect. 7, an overview of the GWO approach is presented. In Sect. 8, the application of GWO to fix the OPP issue is described. Section 9 presents the simulation results, while provides the conclusion of the article.

2 The Formulation of PMUs Issue The primary aim of the OPP problem will be to gather the smallest amount of PMU with associated positions in order to provide a constantly controlled electric grid. As a result, the OPP problem’s objective function is just as follows: Min

N Bus  i=1

(ci xi )

(1)

Optimal Placement of Phasor Measurement Units

Subject to fi = AX ≥ 1ˆ

471

(2)

where: • • • •

NBus equals the overall number of network buses. ci is the full cost of PMU installation at bus i. fi is the observability function of bus i A is a binary connection matrix with the following entries:  1, if i = j or i and j are connected Aij = 0, Otherwise

(3)





• 1 is a vector which each element is a one 1 = [1, . . . , 1]. • X is a binary variable array whose entries xi determine the possibility of placing a PMU on the bus i. Its entries are specified as follows:  1, When PMU is placed on bus i xi = (4) 0, else 2.1 PMUs Placement Criteria 1. On a bus integrated with a PMU, both the voltage of its own phasor and the currents of all branches attached to it are monitored directly by the PMU. 2. Using Ohm’s law, it is easy to calculate the voltage at the end of a branch when the voltage and current at one end are known. 3. If the voltage phasors at both ends of a branch are known, it is possible to specify the branch’s current using Ohm’s law. To detail exactly how these rules work, follow Fig. 1 when a PMU is installed in bus 1, the value V 1 , I 1–2 , I 1–3 and I 1–4 can be directly obtained conforming to Rule 1.

Fig. 1. Modelling PMUs placement rule.

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Once the current branches values determined I 1–2 , I 1–3 and I 1–4 , the voltages at busses 2, 3, and 4 can be calculated by ohm’s law following rule 2. Lastly, the current phasor of branch 3–4 will be available, according to rule 3.

3 Impact of Zero Injection Bus (ZIB) The zero-injection buses are required to transport energy throughout the power grid without injection or consuming it. Therefore, The total flow along all branches connecting to the ZIB is equivalent to 0. Following is a classification of the ZIB rules: 1. When all buses adjacent to an observable ZIB are observable except one, it is possible to consider the unobservable bus as observable by applying the current Kirchhoff law in the ZIB. 2. If all adjacent buses to a non-observable ZIB are observable, the ZIB can be deemed as observable by using the node equation. Consider Fig. 2 to illustrate these rules. Here, bus 1 is a zero-injection bus, whereas buses 2, 3, 4, and 5 are its neighbors. Assume that buses 1, 2, 3 and 5 are all observable (their voltages are known) excluding bus 4. According to rule 1, when applying the KCL at bus 4 (ZIB), the current value I 1–4 can be calculated. Concerning the last rule, suppose that buses 2, 3, 4, and 5 are observable and the ZIB is not. The voltage of bus 1 may be computed via the node equation at the ZIB.

Fig. 2. Modeling the rules of ZIBs

4 System Observability Redundancy Index (SORI) The System Observability Redundancy Index (SORI) is an essential indicator to evaluate the quality of the optimal solution obtained. The set of optimal solutions is chosen on the basis of the greatest SORI value, which denotes the most reliable solution [18]:

Optimal Placement of Phasor Measurement Units

SORI =

Nbus 

Ai xi ∀i ∈ I

473

(5)

i=1

where AX corresponds to the number of times a bus is observed by the PMU.

5 The Proposed Transformation Method The method of bus merging includes the ZIB and one of its adjacent buses merging together. Consequently, the merging procedure will combine the constraints of the two buses into one single constraint, decreasing the number of requirements that must be met for every bus to be observable by PMU placement collection. According to the observability principles described before, if all buses linked to the ZIB except one are observable, the non-observable bus is also classified as observable. Consequently, the combined bus means that if it is observable, the bus chosen to combine with it will similarly be observable. The proposed method consists of four rules for which every candidate bus will be evaluated in sequence. Following are the four rules: 1. Candidate bus must not have been merged previously. 2. Merge the ZIB with its adjacent bus, which is radial bus. 3. If the rule 2 is not satisfied, merge the ZIB and its adjacent bus that has the most number of branches and one of its neighbors must be connected with the same ZIB.

Fig. 3. Flowchart for rules evaluation for candidate bus.

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4. If the above rules are not respected, the ZIB must be merged with the adjacent bus that has the most branches attached to it. The flowchart in Fig. 3 shows how to assess each bus on the basis of the above rules.

6 Grey Wolf Optimization Algorithm (GWO) The Grey Wolf Optimizer (GWO) is an innovative optimization method that simulates grey wolf behaviour. The Grey Wolves, also known as “Canis Lupus”, are among the most famous Canidae species in the world. As depicted in Fig. 4, grey wolves reside in packs of 5 to 12 individuals and adhere to a distinct social structure [19].

Fig. 4. The hierarchal order of gray wolves.

The pack is led by a pair of wolves called alphas (either alpha male or alpha female). The alpha is the most dominating member of the pack. Alpha must be the lone wolf in order to make decisions for the pack, such as waking time, hunting, and sleeping location. In addition, he is the only member of the pack permitted to reproduce. In a unique sense, the Alpha is not always the strongest member of the pack, but rather the one most suited to lead this group. Clearly, the pack’s structure and concentration are far more important than their power [20]. Beta (β) is the second tier of the grey wolf’s social structure. Beta wolves are the ones below the alpha wolf. They help the alpha wolf to make decisions and do other group tasks. The beta wolf can be female or male, and it is the ideal contender to succeed the alpha wolf in the event that the latter dies or gets extremely old. The beta wolf should show respect to the alpha, but has the authority to order lower-ranking wolves [19].

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The delta wolves (δ) occupy the third rank in the social structure of this group. These wolves get orders from the alpha pair through the beta wolves, and they exert dominance over the omega wolves (the last in the hierarchy). There are many categories of delta wolf, including scouts, sentinels, elders, hunters, and caretakers [19]. Omega wolves (ω) are the final wolves in this chain. They submit to the other wolves and serve as scapegoats. Omega wolves appear to be unimportant members of the pack, and they are the last ones permitted to feed [19]. The grey wolf hunting process (hunt, encirclement, and attack) can be considered as the following optimization procedure: 1. strategy of hunting is called “optimization”; 2. The prey denotes the “optimum”; 3. The optimal solutions are the alpha, beta, and delta wolves, with alpha being the best solution. Omega represents the remaining solutions. 4. The alpha, beta, and delta wolves guide the hunt, while omega wolves obey the dominant pack. Encircling the prey is the initial step in the hunting process. The grey wolf is capable of locating its prey and encircling it. This phase’s mathematical model is as follows:    · Xp (t) − X (t)  = C (6) D  ·D  X (t + 1) = Xp (t) − A

(7)

where t indicates the current iteration, Xp represents the position vector of the prey,  and C  are coefficient vectors and X represents the position vector of the grey wolf, A X (t + 1) represents the grey wolf’s next location. Consequently, grey wolves adjust their location by assessing the location of their prey with stochastic Eqs. (6) and (7).  and C:  The following formula determines the vectors A  = 2a · r1 − a A

(8)

 = 2 · r2 C

(9)

− → → r1 and − r2 are random vectors with elements in the interval [0,1]. Initially, the value of a is set to 2 and decreases linearly until it reaches 0 during the course of the algorithm’s iterations. The majority of the time, alpha guides the hunt. Beta and delta occasionally participate in this hunt. Alpha, beta, and omega wolves carry out the second phase of the grey wolf hunting process. Supposing that alpha, beta, and omega wolves are aware of probable prey locations. As stated by the following equations, we will record the location of these wolves and refresh the locations of the other wolves.    1 · Xα (t) − X (t)  α = C (10) D    2 · Xβ (t) − X (t)  β = C D

(11)

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   3 · Xδ (t) − X (t)  δ = C D

(12)

1 · D α X1 = Xα (t) − A

(13)

2 · D β X2 = Xβ (t) − A

(14)

3 · D β X3 = Xδ (t) − A

(15)

X1 + X2 + X3 X (t + 1) = 3

(16)

The last phase involved attacking the prey once the wolves have ceased their pursuit. This stage is represented by the drop of the value of a. The A variable has a random value within the interval [1,1], which decreases linearly from 2 to 0 as the algorithm iterates. Thus, the values of A are also decreasing. If |A| < 1, the wolves will attack the prey. This represents the exploitation process. If |A| > 1, wolves are obligated to separate themselves from their prey. This illustrates the exploratory The exploring procedure in the GWO technique is modelled mathematically using the A variable with random values higher than 1 or less than −1 to push the search agents to deviate from the prey [21]. The Pseudo code of GWO algorithm is presented as follows:

Initialize the grey wolf population. Initialize a, A, and C using equation (34). Calculate the fitness of each search agent (wolf). =the best wolf. =the second best wolf. =the third best wolf. While (maximum iteration is not reached) For (each wolf) Update the position of the current wolf by equation (36). End for Update a, A, and C using equation (34). Calculate the fitness values of all wolves. Update the three best wolves positions , , and End while Return .

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7 Application of the Suggested Algorithm The GWO technique was applied to solve the OPP problem. The OPP problem’s decision variables are PMU installation states. The location of the ith agent in a system with K agents is defined by: X = [x1 , x2 , . . . , xK ] For i = 1, 2, 3, . . . , K

(17)

The following are the different procedures for resolving the OPP problem with the GWO: 1. 2. 3.

Read the line and bus data of the power system. Accumulate the Connectivity Matrix (A). Initialize the GWO parameters as the Population Size (PS), the maximum number  and C).  of iterations Itmax and the vector coefficients (a, A 4. Identify the upper and lower limits of the control variables. 5. Generating an arbitrary population of N agents. For each agent, the initial values are randomly selected between the minimum and maximum values of the control variables. 6. In the OPP problem, the fitness values are calculated for each agent in the population. 7. Select the new leader wolves X α , X β and X δ from the repository.  and C).  8. Use Eqs. (10) and (11) to calculate the coefficient vector (a, A 9. Actualize the position of the wolves using Eq. (16). 10. Find and save non-dominated solutions in the repository. 11. Put t = t + 1. 12. Repeat steps 3 to 6 until reaching the end criterion.

8 Results and Discussion MATLAB R 2017b software is used to run the simulations. The laptop used to run simulations has an Intel i7 core processor with a speed of 2.4 GHz and 8 GB of RAM. The results of the simulation have been obtained by assuming that each PMU contains the maximum number of channels possible while all PMUs are equally expensive. The proposed method focuses on finding the minimum number of PMUs, to ensure complete monitoring of the system and maximize measurement redundancy (SORI). High SORI values imply too much reliability of the monitoring system for unexpected events (Table 2). Table 3 presents the optimal set of PMUs and its locations, as well as the measurement redundancy value in the absence of ZIBs. In the event that ZIBs exist, Table 4 displays the number of PMUs, their positions, and the SORI value. Due to the presence of ZIB, it seems from Tables 2 and 3 that fewer PMUs are required to enable comprehensive network monitoring in the ZIB situation than in the base case. In the instance of IEEE 24-bus, there are (7) PMUs when ZIBs are not included, but only (6) when ZIBs are present.

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Value

Number of iterations

200

Number of populations

10* NBus

Weight value for the number of bus observed, w1

1

Weight value for the number of PMUs, w2

−2

Weight value for the measurement redundancy c

0.01

Table 2. Test systems specifications Test system

Number of PMUs

Positions

SORI

IEEE 14-Bus

4

4, 6, 7, 9

19

IEEE 24-Bus

7

2, 3, 8, 10, 16, 21, 23

31

Table 3. Optimal PMUs placement results for case 1 Test system

Number of PMUs

Positions

SORI

IEEE 14-Bus

4

4, 6, 7, 9

19

IEEE 24-Bus

7

2, 3, 8, 10, 16, 21, 23

31

Table 4. Results of optimal PMUs placement for case 2 Test system

Number of PMUs

Locations

SORI

IEEE 14-Bus

3

2, 6, 9

16

IEEE 24-Bus

6

1, 8, 9, 10, 19, 21

29

To better estimate the performance of the proposed method, the results achieved by the simulations performed with the applied approach are compared to previous studies. The PMUs number and SORI value are compared versus the results achieved from previous studies which have employed different techniques to resolve the OPP problem and considering normal operation and ZIB. Tables 5 and 6 summarizes the comparison between the results of the suggested method and those of existing studies. All tested IEEE bus system solutions are compared. The most qualitative solution is the one with the highest SORI, which is worth mentioning once again. As mentioned in this Table, the different studies compared, even the approach proposed, succeeded in obtaining an identical number of PMUs for all the IEEE bus

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systems tested. However, the measurement redundancy values are different for some bus systems. Table 5. Comparaison results with prior studies for case 1 Test system

Parameter

IEEE 14-Bus

NPMUs SORI

IEEE 24-Bus

NPMUs SORI

Suggested approach

BPSO [22]

BPSO [23]

BSDP [24]

4

-

4

4

19

-

19

16

7

7

7

-

31

29

31

-

Table 6. Comparaison results with prior studies for case 2 Test system

Parameter

IEEE 14-Bus

NPMUs SORI

IEEE 24-Bus

NPMUs SORI

Suggested approach

MOPSO [23]

GSA [12]

ES [25]

3

3

3

3

16

15

16

16

6

6

6

6

29

29

29

27

9 Conclusion In this paper, a solution is provided to the OPP problem with the objective to reduce the number of PMUs and to improve the redundancy of measurements, on the basis of the SORI values that assess the quality of the placement of PMUs in the power system. This study investigated two distinct cases, involving ignoring and considering ZIBs. A grey wolf optimization (GWO) was utilized as an optimization means, which simulates the behavior of grey wolves in the wild. The suggested method has been validated on two different test systems, IEEE 14-bus and IEEE 24-bus. To verify the results, a comparison has been made with other recent approaches. It appears from the results obtained that the proposed method has reduced the number of PMUs and improved the power system observability.

References 1. Abd Rahman, H.N.: Optimal Allocation of Phasor Measurement Units Using Practical Constraints in Power Systems. Brunel University London (2017)

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2. Laouid, A.A., Rezaoui, M.M., Kouzou, A., Mohammedi, R.D.: Optimal PMUs Placement Using Hybrid PSO-GSA Algorithm. In: 4th International Conference on Power Electronics and their Applications (ICPEA) 2019. IEEE (2019) 3. Almasabi, S., Mitra, J.: Multistage optimal PMU placement considering substation infrastructure. IEEE Trans. Ind. Appl. 54, 6519–6528 (2018) 4. Gou, B.: Optimal placement of PMUs by integer linear programming. IEEE Trans. Power Syst. 23, 1525–1526 (2008) 5. Huang, L., Sun, Y., Xu, J., Gao, W., Zhang, J., Wu, Z.: Optimal PMU placement considering controlled islanding of power system. IEEE Trans. Power Syst. 29, 742–755 (2013) 6. Aghaei, J., Baharvandi, A., Akbari, M.-A., Muttaqi, K.M., Asban, M.-R., Heidari, A.: Multiobjective phasor measurement unit placement in electric power networks: Integer linear programming formulation. Elect. Power Comp. Syst. 43, 1902–1911 (2015) 7. Enshaee, A., Hooshmand, R.A., Fesharaki, F.H.: A new method for optimal placement of phasor measurement units to maintain full network observability under various contingencies. Elect. Power Syst. Res. 89, 1–10 (2012) 8. Jamuna, K., Swarup, K.S.: Multi-objective biogeography based optimization for optimal PMU placement. Appl. Soft Comput. 12, 1503–1510 (2012) 9. Mazhari, S.M., Monsef, H., Lesani, H., Fereidunian, A.: A multi-objective PMU placement method considering measurement redundancy and observability value under contingencies. IEEE Trans. Power Syst. 28, 2136–2146 (2013) 10. Aghaei, J., Baharvandi, A., Rabiee, A., Akbari, M.-A.: Probabilistic PMU placement in electric power networks: an MILP-based multi-objective model. IEEE Trans. Ind. Inf. 11, 332–341 (2015) 11. Qi, J., Sun, K., Kang, W.: Optimal PMU placement for power system dynamic state estimation by using empirical observability Gramian. IEEE Trans. Power Syst. 30, 2041–2054 (2014) 12. Laouid, A.A., Mohammedi, R.D., Rezaoui, M.M., Kouzou, A.: Optimal PMUs placement to ensure power system observability under various contingencies. Electrotehn. Electron. Autom. 68, 1–14 (2020) 13. SadanandanSajan, K., KumarMishra, A., Kumar, V., Tyagi, B.: Phased optimal PMU placement based on revised analytical hierarchy process. Elect. Power Comp. Syst. 44, 1005–1017 (2016) 14. Maji, T.K., Acharjee, P.: Multiple solutions of optimal PMU placement using exponential binary PSO algorithm for smart grid applications. IEEE Trans. Ind. Appl. 53, 2550–2559 (2017) 15. Pal, A., Vullikanti, A.K.S., Ravi, S.S.: A PMU placement scheme considering realistic costs and modern trends in relaying. IEEE Trans. Power Syst. 32, 552–561 (2016) 16. Babu, N.P., Babu, P.S., Sivasarma, D.V.S.S.: Binary cuckoo search based optimal PMU placement scheme for united Indian grid-a case study. Int. J. Eng. Sci. Technol. 10, 10–24 (2018) 17. Babu, R., Bhattacharyya, B.: An approach for optimal placement of phasor measurement unit for power network observability considering various contingencies. Iranian J. Sci. Technol. Trans. Elect. Eng. 42(2), 161–183 (2018). https://doi.org/10.1007/s40998-018-0063-7 18. Biswal, A., Mathur, H.: Optimized PMU stationing for wide area monitoring of power grid. Procedia Technol. 21, 2–7 (2015) 19. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014) 20. Mech, L.D.: Alpha status, dominance, and division of labor in wolf packs. Can. J. Zool. 77, 1196–1203 (1999) 21. Mirjalili, S.: How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Appl. Intell. 43(1), 150–161 (2015). https://doi.org/10.1007/s10489-014-0645-7

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22. Sharma, C., Tyagi, B.: An approach for optimal PMU placement using binary particle swarm optimization with conventional measurements. Int. J. Eng. Sci. Technol. 3, 1–8 (2011) 23. Laouid, A.A., Mohammedi, R.D., Kouzou, A., Rezaoui, M.M.: Optimal PMU placement in power system based on multi-objective particle swarm optimization. In: 15th International Multi-Conference on Systems, Signals & Devices (SSD) 2018. IEEE (2018) 24. Korres, G.N., Löfberg, J., Manousakis, N.M., Xygkis, T.C.: Optimal phasor measurement unit placement for numerical observability in the presence of conventional measurements using semi-definite programming. IET Gener. Transm. Distrib. 9, 2427–2436 (2015) 25. Roy, B.S., Sinha, A., Pradhan, A.: An optimal PMU placement technique for power system observability. Int. J. Elect. Power Energy Syst. 42, 71–77 (2012)

The Effect of the Intelligent Control System on the Tram Timetable Efficiency and Its Influence on the Road Capacity at Signalized Intersections Mouloud Khelf1(B) and Bhouri Neila2 1 Laboratory of Transportation Engineering and Environment (LITE), Department of Transport

Engineering, University of Constantine 1, Constantine, Algeria [email protected] 2 Engineering of Surface Transportation Networks and Advanced Computing Laboratory (GRETTIA), Department (COSYS), University Gustave Eiffel, Paris, France

Abstract. The main purpose of using the tram intelligent systems is not only to avoid incidents and accidents in real time, but also to improve the control of the tram performance. This aims to balance the transport supply with the real travelers’ demand by implementing an adequate timetable. The objective of this paper is to show, how the implementation of the tram timetable by using the intelligent control system efficiently is so important for the motorists at signalized intersections. The methodology used is based on the tram timetables applied from 2013 to 2021 and a recorded data in two intersections in an Algerian city. The results indicate that the tram timetable has a huge influence on the road traffic capacity. When the interval of time between the trams increases, the number of trams used in operation decreases, therefore the road capacity at the signalized intersections will be improved. The suggested recommendations are important to increase the roads capacity at multimodal intersections. Keywords: Intelligent control system · Tram · Timetable · Road capacity · Intersections · Traffic lights

1 Introduction The bottleneck is a serious problem in many towns around the world, particularly in urban zones and big cities [1]. Among the main causes that create this phenomenon are the population growth and the high utilization of private cars [2]. The principal decision that has been made to reduce the bottleneck problem in several cities is improving the service quality of the existing public transport systems and investing in the modern ones, to offer a sustainable public transport system, especially in the smart city [3]. Among the main issues to develop a modern smart city is to propose an efficient transportation means and manage effectively the multimodal road intersections, particularly in urban areas. This allows reducing pollution, saving energy, and improving the life quality of the population [4]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 482–492, 2023. https://doi.org/10.1007/978-3-031-21216-1_50

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As a modern, ecological, rapid and comfortable mode of transport, the tram is one of the interesting means that attract people to travel by public transport, because it encourages them to use public transport as an alternative mode for their trips instead of their own cars [5, 6]. The tram is a sustainable mean of transport for the smart city, because the double down of the light rail offers an efficient and sustainable transport system for citizens in modern smart cities [7, 8]. The new trams are equipped with intelligent control systems in order to improve its performance (Timetable planning, tram location, headway control, collecting data and traffic signal priority at intersections), and to enhance the safety of all users (trams and particular vehicles) [9]. The multimodal intersections are protected by traffic lights signalization system, with an absolute tram priority to ensure a high safety in the intersections [10, 11]. This system is based on several inductive sensors in the infrastructure and inside the trams (Fig. 1) [12]. The present paper will show how an efficient use of the tram intelligent control systems increase the capacity of the multimodal signalized intersections.

Fig. 1. System tram priority at signalized intersections [12].

2 Case Study 2.1 The Study Area The official operation of Constantine tram network started on July 4, 2013. The tram links the old city from Benabdelmalek Ramdane to Nouvelle Ville, along 18.5 km with 21 stop stations. The rolling stock of this transportation means is an Alstom Citadis-402 [1, 13]. 2.2 The Intelligent Control System of Constantine Tram The main intelligent control system is situated in the Centralized Control Room (CCR). This last is located at the administrative building of the operating company. The CCR regulators are present all along the daily operation time, because they permanently control the movement of trams. The CCR gives the regulators the means to monitor the service quality in real time and the respect of tram priority at road intersections.

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Any incident must be resolved in a very short time, because a disturbance on the tram or on the tramline section, may have consequences on the operation of the entire line, especially if it is located at the intersections. The main systems existing in the CCR are: • The operating support systems (OSS) and the tram’s signalization system; • The centralized technical management of the tramline, in addition to the control of the tramline signalization (Technical Energy Management, Centralized Technical Management and Automatic Train Supervision); • The CCTV image transmission system, the telephony and radio systems [10, 14]. In the CCR, supervisors can follow and control the tram fleet using the OSS. This last is composed of two operating offices and one statistical office. The role of this system is to ensure (Fig. 2): • • • •

The transportation regularity service and the tram fleet security; The timetable management and optimization (headway between trams); The trams’ movement detection and the supervising in real time; The collecting and the statistical analysis of the tram operation data [10, 15].

Fig. 2. The centralized control room (CCR).

2.3 The Tram Timetable The timetable and the number of trams applied by the company Setram Constantine from the beginning of the tram operation are presented respectively in tables (1) and (2) (cases 1, 2, and 3) [16]. In addition to the case 4 which is the optimal timetable founded by [10]. The result found in case 4 is based on the higher passengers’ demand for the most critical day in years (2019) and in the all years before the Covid-19 pandemic. Despite the intelligent control system (Centralized Control Room CCR) that exists and was presented previously, the company has not managed the tram according to the actual demand to improve the efficiency of the tram timetable. These last studies have proven that the timetable used by the operating company is not optimal because the offered

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capacity is very high compared to the passengers’ demand from the first year to almost the seven years (the real number of trams needed is almost 25% of the number used by the company). So, this proves that an unnecessary high number of trams has been used and the tram time headway has not been respected. Unfortunately, the road users have been affected by this bad tram intelligent system control [10, 16]. Table 1. Time headway between trams based on Setram data and the optimal time headway. Time

Case 1

Case 2

Case 3

Case 4

07:00 to 09:00

3

4

6

8

09:00 to 10:00

5

4

6

6

10:00 to 11:00

5

4

6

6

11:00 to 12:00

5

4

6

6

12:00 to 14:00

5

4

6

8

14:00 to 15:00

3

4

6

10

15:00 to 16:00

3

4

6

10

16:00 to 17:00

3

4

6

15

17:00 to 18:00

3

4

6

15

Table 2. Number of trams in both directions based on Setram company data and the optimal tram numbers. Time

Case 1

Case 2

Case 3

Case 4

07:00 to 09:00

80

60

40

30

09:00 to 10:00

24

30

20

20

10:00 to 11:00

24

30

20

20

11:00 to 12:00

24

30

20

20

12:00 to 14:00

48

60

40

30

14:00 to 15:00

40

30

20

12

15:00 to 16:00

40

30

20

12

16:00 to 17:00

40

30

20

8

17:00 to 18:00

40

30

20

8

3 Methodology In the first step, Tables 1 and 2 are used to calculate the tram’s passing time at the intersection per hour. Noting that the time required to cross the entire intersection is 40 s per tram. The second step focuses on an analysis of the chosen intersections data

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(intersections design, signal cycle lengths, phases, effective green time, and delay), by taking into consideration the required time for tram passage. Finally, combining the previous steps, permit the calculation of the signal cycle number by hour at intersections to evaluate the road traffic flow fluidity. The capacity of intersections, is calculated using a theoretical value of 2 s for the time headway between vehicles [17]. It allows us to show clearly the influence of the tram timetable optimization on the other road users.

4 Results 4.1 Presentation of Intersections Data The intersections chosen in this study are located in strategic positions. An important traffic flow demand exists in these two intersections, because they link the historical area of the city to two touristic and economic zones. The section bellow presents the design, the signal timing, the movement and the phases of each intersection. The first case is Palma Intersection (Fig. 3A). The cycle length (CL ) in this intersection is 60 s. The delay of car drivers at the intersection is 4 s when the light is green. CL = Geff (Phase y) + Geff (Phase z) Phase y includes only itinerary 1. The effective green time (Geff ) is 17 s. Phase z includes itineraries 2 and Itinerary 3. The effective green time (Geff ) is 43 s (Table 3). Table 3. The effective green time per itinerary at Palma Intersection with delay. Itineraries

1

2

3

Geff (second/ cycle)

13

39

39

The second case study is Fadhila SAADANE Intersection (Fig. 3B). The cycle length (CL ) is 90 s. The delay at the intersection is 4 s when the light is green. CL = Geff (Phase α) + Geff (Phase β) + Geff (Phase γ ) Phase α assembles itineraries 1, 2 and 3. The effective green time (Geff ) is 50 s. Phase β assembles itineraries 6 and 2. The effective green time (Geff ) is 20 s. Phase γ assembles itineraries 4, 5 and 2. The effective green time (Geff ) is 20 s (Table 4).

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Table 4. The effective green time per itinerary at Fadhila SAADANE Intersection with delay. Itineraries

1

2

3

4

5

6

Geff (second/ cycle)

46

46

46

16

16

16

Fig. 3. Intersection of Palma (A) and Fadhila SAADANE (B)

4.2 Results of Palma Intersection The Figs. 4 and 5 show the capacity of itineraries 1, 2 and 3 from 7 a.m. to 6 p.m, with several tram timetables cases. Compared to the case 1 that represents the old timetable, the case 2 increases the traffic flow fluidity with 43 vehicles per hour in itinerary 1, as with 130 vehicles per hour in itineraries 2 and 3 in rush hours. The case 3 improves the road capacity along the day with 87 vehicles per hour in rush hours and 17 vehicles per hour in the other hours in itinerary 1. In addition, this case increases the traffic flow with 160 vehicles per hours in rush hours and 52 vehicles per hour in the other hours. The execution of the optimal timetable that is based on the real passengers’ demand increases highly the road capacity, in rush hours the increase of traffic flow is between 104 and 139 vehicles per hour in itinerary 1, as well as between 312 and 416 vehicles in itineraries 2 and 3. In the other hours, the increase of traffic flow is respectively between 17 and 43 vehicles per hour for itinerary 1, and between 52 and 130 per hour for itineraries 2 and 3. Among the solutions that reduce the negative effects of tram timetable on the traffic roads capacity is to add a tram phase to itinerary 1, because this itinerary is in parallel of the tramline. The effective green time is 40 s. This last is the necessary time for tram to pass from Palma Intersection. The Fig. 6 shows that the use of an additional tram phase for itinerary 1 increases highly the road capacity in itinerary 1. This solution increases the capacity of this itinerary between 1.55 and 2.2 times than the old capacity in the case 1T, 1.75 times in the case 2T, almost 1.5 times the old capacity for the case 3T and between almost 1 and 1.5 times the old capacity for the case 4T. Figure 7 presents a global evaluation of the roads traffic capacity in all cases at Palma Intersection. In itinerary 1, the case 2 allows to increase the traffic road capacity with

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Fig. 4. Road traffic capacity for itinerary 1.

Fig. 5. Roads traffic capacity for itineraries 1 and 2.

Fig. 6. The road traffic capacity for itinerary 3 (with tram phase).

130 vehicles per day (from 7 a.m. to 6 p.m.) compared to the case 1. Then, the case 3 permits to raise the capacity of this itinerary with 607 vehicles per day. The utilization of the case 4 increases the traffic fluidity in itinerary 1 with 865 vehicles per day. Moreover, the addition to the tram phase for itinerary 1 increases extremely, the road traffic fluidity compared to the case 1. The case 1T increases the traffic road capacity with 2342 vehicles per day, the case 2T raises the road capacity of itinerary 1 with 2275 vehicles per day, the case 3T elevates the road traffic capacity with 2033 vehicles per day and the case 4T elevates the traffic road capacity in itinerary 1 with 1905 vehicles per day. In itineraries 2 and 3, and compared to the case 1, the case 2 allows to increase the traffic road capacity for every itinerary with 390 vehicles per day. Then, the case 3, permits raising the traffic road capacity with 1820 vehicles per day, and finally the case 4 elevates the traffic road capacity with 2600 vehicles per day. 4.3 Results of Fadhila Saadane Intersection The Figs. 8, 9, 10 and 11 represent the roads traffic capacity of itineraries 1, 2, 3, 4, 5 and 6 from 7 a.m. to 6 p.m. with different tram timetables cases. Compared to the case 1, the use of the case 2 increases the roads traffic capacity in the rush hours for itineraries 1, 2, 3, 4, 5 and 6 respectively with 102 vehicles per hour, 205 vehicles per hour and from 460 to 747 vehicles per hour. The case 3 allows elevating the capacity of the intersection especially in the rush hours with 205 vehicles per hour in itinerary 1, 409 vehicles per hour for itinerary 3, 71 vehicles for itineraries 4, 5 and 6. However,

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Fig. 7. Global evaluation of the roads traffic capacity at Palma Intersection.

the traffic road capacity decreases in this case with 27 vehicles per hours for itinerary 2. The utilization of the case 4 permits to raise enormously the capacity of the intersection, particularly in the rush hours. The capacity of the road improves respectively between 460 and 746 vehicles per hour, 577 and 632 vehicles per hour, 920 and 1493 vehicles per hour for itinerary 1, 2 and 3. Also, for itineraries 4, 5 and 6, the capacity of the intersection increases between 395 and 425 vehicles per hour for each itinerary.

Fig. 8. The road traffic capacity for itinerary 1.

Fig. 9. The road traffic cSapacity for itinerary 2.

As a solution to increase the road capacity against the negative influence of tram timetable is to add a tram phase to itineraries 1 and 3. These itineraries are in parallel with the tramline. The effective green time of the added phase is 40 s, that is the necessary time for tram to cross Fadhila SAADANE Intersection. The Figs. 12 and 13 show that the use of an add tram phase for itineraries 1 and 3 raise hugely the road capacity of these itineraries. This solution enhances the capacity of itinerary 1 between 1.71 and 2.57 times than the old capacity in the case 1T, 1.85 times in the case 2T, 1.56 times the old capacity for the case 3T and between almost 1.13 and 1.56 times the old capacity for the case 4T. Figure 14 presents a global evaluation of the roads traffic capacity in all cases at Fadhila SAADANE Intersection. In itinerary 1, the case 2 allows to increase the traffic road capacity with 302 vehicles per day (from 7 a.m. to 6 p.m.) compared to the case 1.

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Fig. 10. The road traffic capacity for itinerary Fig. 11. The road traffic capacity for itinerary 3. 4, 5 and 6.

Fig. 12. The road traffic capacity for itinerary Fig. 13. The road traffic capacity for itinerary 1 (Tram phase). 3 (Tram phase).

Then, the case 3 permits to raise the capacity of this itinerary with 1435 vehicles per day. The utilization of the case 4 increases the traffic fluidity in itinerary 1 with 5212 vehicles per day. Moreover, the addition of the tram phase for itinerary 1 increases enormously, the road traffic fluidity compared to the case 1. The case 1T increases the traffic road capacity with 7199 vehicles per day, the case 2T raise the road capacity of itinerary 1 with 6906 vehicles per day, the case 3T elevates the road traffic capacity with 5830 vehicles per day and the case 4T elevates the traffic road capacity in itinerary 1 with 8412 vehicles per day. Compared to the case 1, the case 2 and case 3 reduce the traffic road capacity respectively with 80 and 373 vehicles per day in itinerary 2. However, the case 4 increases highly the traffic road capacity with 4840 vehicles per day. In itinerary 3, the road traffic capacity increases respectively with 617, 2861 and 7566 vehicles per day for the cases 2, 3 and 4 compared to the case 1. Furthermore, the use of the tram phase in itinerary 3 elevates the road traffic capacity compared to the case 1 with 14397, 13813, 11662 and 16827 vehicles per hour respectively for cases 1T, case 2T, case 3T and case 4T. In itineraries 4, 5 and 6, compared to the case 1, the case 2 allows to increase the traffic road capacity for every itinerary with 107 vehicles per day. Then, the case 3, permits raising the traffic road capacity with 498 vehicles per day, and finally the case 4 elevates the traffic road capacity with 1813 vehicles per day.

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Fig. 14. Global evaluation of the roads traffic capacity at Palma Intersection.

5 Conclusion The principal role of the tram intelligent control system is to establish an efficient timetable and supervise the tram operation in real time. An analysis of the tram timetables has been done, to show the indirect influence of the tram intelligent control system on the road capacity at signalized intersections. For this, an analysis of four timetables cases applied by the operating company from 2013 to 2021 has been done, to show their influences on the road capacity at two strategic intersections in Constantine city. The results prove that in Palma Intersection, the total road traffic increase is 520 vehicles, 2427 vehicles, and 3465 vehicles, respectively, for cases 2, 3, and 4 compared to case 1, from 7 am to 6 pm. The addition of the green light phase (at the same time as the tram phase) for the parallel road itinerary of the tramline increased the capacity of the intersection, with 2665 vehicles, 3853 vehicles, and 4505 vehicles, respectively, for cases 2, 3, and 4 compared to the case 1, from 7 am to 6 pm. The vehicles increase rate is almost 32%, 26% and 8% for case 4 compared, respectively, to the cases 1, 2 and 3 in all itineraries. Also, the results have proven that the capacity of Fadhla Saadane Intersection increases highly for cases 2, 3, and 4 compared to case 1, respectively, with 1162 vehicles, 5419 vehicles, and 24109 vehicles from 7 am to 6 pm. The use of the green light with the tram phase for parallel roads itineraries has increased the capacity of the Fadhila SAADANE Intersection, respectively, with 20962 vehicles, 18615 vehicles, and 35522 vehicles for cases 2, 3, and 4 from 7 am to 6 p.m. The vehicles increase rate is almost 73%, 81% and 48% in the case 4 compared respectively to the cases 1, 2 and 3 in the itineraries 1, 3, 4, 5 and 6, and almost 28% in the itinerary 2 for all cases. It is recommended to manage the intelligent control system efficiently by the CCR staff, to establish an optimal timetable that responds efficiently to the passengers’ demand, so to reduce the negative influence of tram operation on the motorists at the signalized junctions. Also, the execution of the green light time with the tram phase for parallel roads itineraries with the tramline should be executed, because this solution permits to highly increase the road traffic fluidity at the intersections.

References 1. Mouloud, K., Salim, B.: The effect of noise on the comfort of passengers inside the tramway and its impact on traffic congestion in the urban area. J. Vibroeng. 20, 530–540 (2018) 2. Rida, K., Hummayoun, N., Sana, E., Mukhtar, F., Batool, T.: Service quality and customer satisfaction in public transport sector of Pakistan: an empirical study. Int. J. Econ. Manag. Sci.. 4, 125–130 (2010)

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3. Lu, K., Han, B., Zhou, X.: Smart urban transit systems: from integrated framework to interdisciplinary perspective. Urban Rail Transit. 4(2), 49–67 (2018). https://doi.org/10.1007/s40 864-018-0080-x 4. Nassar, A.S., Montasser, A.H., Abdelbaki, N.: A survey on smart cities’ IoT. In: Hassanien, A.E., Shaalan, K., Gaber, T., Tolba, M.F. (eds.) AISI 2017. AISC, vol. 639, pp. 855–864. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-64861-3_80 5. Yuxiong, J., Yu, T., Wei, W, Yuchuan, D.: Tram-oriented traffic signal timing resynchronization. J. Adv. Transp. 2018, 1–13 (13 pages) (2018). https://doi.org/10.1155/2018/879 6250 6. Guerrieri, M.: Tramways in urban areas: an overview on safety at road intersections. Urban Rail Transit. 4(4), 223–233 (2018). https://doi.org/10.1007/s40864-018-0093-5 7. Savchuk, I., Tymofii N.: Tramway as an indicator of the realisation of Smart City concept. In: E3S Web of Conferences, 159(3), 03006 (2020). https://doi.org/10.1051/e3sconf/202015 905013 8. Marc, B.: If we want smart cities, we need to double down on rail transit. Smart Cities Dive. https://www.smartcitiesdive.com/news/smart-cities-double-down-rail-transit-transport ation/571592/. Accessed 12 May 2022 9. Audikana, A., Kaufmann, V., Messer, M.A.: Governing the Geneva tram network: making decisions without making choices. J. Urban Technol. 22(4), 103–124 (2015) 10. Khelf, M., Boukebbab, S., Bhouri, N.: Evaluation of the tram intelligent system management by an analysis of its key performance indicators for an optimal mixed traffic control in Algeria. Int. J. Ship. Transp. Logist. 14(1/2), 33–55 (2022). https://doi.org/10.1504/IJSTL.2022. 120668 11. Robert, S., Martin S.: Optimizing traffic signal settings for public transport priority. In: Gianlorenzo D’Angelo and Twan Dollevoet. (eds) 17th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2017), vol. 59, pp. 9:1--9:15 (2017). https://doi.org/10.4230/OASIcs.ATMOS.2017.9 12. Entreprise Metro Alger (EMA) : Road Signs Description (EMA), Algeria (2008) 13. Entreprise Metro Alger (EMA): Le tramway de Constantine. http://www.metroalger-dz.com/ fr/activites.php?idAC=12&EMA=TRW. Accessed 5 Oct 2022 14. Entreprise Metro Alger (EMA): A general presentation of Constantine tram system (EMA), Algeria (2018) 15. Entreprise Metro Alger (EMA): Presentation of the signalization and telecommunication system (EMA), Algeria (2011) 16. Khelf, M., Boukebbab, S., Bhouri, N., Boulahlib, M.S.: Tram service quality and its impact on the passengers’ modal choice in Constantine City (Algeria). In: Kabashkin, I., Yatskiv (Jackiva), I., Prentkovskis, O. (eds.) RelStat 2018. LNNS, vol. 68, pp. 35–44. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12450-2_4 17. Cohen, S.: Ingénierie du Trafic Routier: Eléments de théorie du trafic et applications. Presses de l’école nationale des Ponts et chaussées, pp. 246 (1990). (in French)

Soil-Structure Interaction Effects on the Vibration Control of Building Structures Mohamed Seghir Jaballah1(B) , Salaheddine Harzallah2 , and Nail Bachir3 1 Laboratory of Development in Mechanics and Materials LDMM, Faculty of Science

and Technology, University of Djelfa, Djelfa, Algeria [email protected] 2 Built Environmental Research Lab., Civil Engineering Faculty, Sciences and Technology Department, University of Houari Boumediene, B.P 32 El Alia, 16111 Bab Ezzouar, Algiers, Algeria 3 Mechanical Engineering, Materials, and Structures Laboratory, Faculty of Science and Technology, Tissemsilt University of Tissemsilt, Tissemsilt, Algeria [email protected]

Abstract. The earthquake is a phenomenon that disturbs the stability of the constructions, and to reduce the damage it causes, man has sought to find reliable solutions. Among the latter, there is the active vibration control technique for structures which is the subject of this study. In this study, we investigated this type of control on structures to show its effectiveness in reducing seismic effects, using an active tuned mass damper mounted on the top floor of a building structure. This study showed that this type of control is a reliable solution to protect structures against earthquakes. In another part of this study, the soil-structure interaction effect (SSI) was introduced in the response of the controlled structures. This showed that in the case where the soils under the base of the structures have high deformability, it is very important to introduce the interaction effect to bring it closer to the real behavior of the structures. Keywords: Active control · Soil-Structure Interaction (SSI) · Active tuned mass damper · Base isolation

1 Introduction Earthquakes are a natural geological activity. They induce significant destruction. The human being seeks to satisfy his need to be protected against these phenomena. As a result, he thought of building shelters. Faced with the development that the world is experiencing, these have become buildings, skyscrapers, and other very dangerous structures [1]. Having arrived at this stage, the structures themselves have become a real danger in the face of influencing factors such as wind, earthquakes, and not to mention the vibrations caused by the man himself, for example, explosions. At this level, the human being has thought again of other solutions to eliminate these major risks threatening its stability. In the very beginning, he designed very solid buildings with empirical © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 493–500, 2023. https://doi.org/10.1007/978-3-031-21216-1_51

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standards but sufficiently strict to be able to guard against natural phenomena. With the development that has affected urbanization, the importance of these standards has taken a great place, especially in terms of foundations [2]. The realization of a project according to the new standards did not help much in the fight against earthquakes and the damage that they cause. This has pushed research towards other solutions, such as control. Since the sources of excitation are diverse and different in terms of intensity, origins, and influence, we will focus on the most aggressive and widespread phenomenon in the world, such as the earthquake. From a technical point of view, the latter is considered as a source of random vibration. The likelihood of it occurring differs from region to region. So, predicting the moment that comes just before the arrival of this phenomenon becomes the concern of current research [3–5]. The passive vibration control device is basically designed to reduce only a particular mode of vibration of the structure. In contrast, an active vibration control system can dampen a wide band of frequencies. Hence, the study of active control of structures is a logical extension of passive control technology. A control system is active if one or more actuators apply forces to a structure according to control law and use an external energy source for their operation. These forces can be used to add or dissipate energy from the structure to be controlled. To build such a system, there are two approaches that are radically different: the first consists in identifying the seismic excitation, which creates the vibrations to cancel it by superimposing an “inverse” excitation on it. This active control strategy is called feedforward control. It is mainly developed in acoustics [6], but it is also very useful for controlling the vibration of structures [5]. The second is to identify the response of the structure rather than the excitation that makes it vibrate. It, therefore, requires the modeling of the dynamic behavior of the structure. The vibration control work that involves this type of strategy is called feedback loop control [7].

Fig. 1. Structure with active control

The goal of this study is to simulate the application of active control to reduce the seismic response of structures, taking into consideration the effect of soil-structure interaction (SSI) in order to bring the behavior of structures closer to reality under seismic forces.

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2 Controlling Algorithm Control laws are used by the computer to calculate the force required to be applied. This control force is used to modify and reduce the structural response. The linear quadratic regulator (LQR) algorithm proved to be a very reliable controlling algorithm. This controller is used widely in the industry. It belongs to the category of optimal control algorithms. In an optimal control, a cost function called J function, indicating a performance index, is chosen. This last one will be later on used to be minimized in order to obtain an optimal input. It can be chosen to be quadratically depending on the control input or on the response of the system: [8, 9] J =

1 tf T ∫ (x Qx + uT Ru)dt 2 t0

(1)

where: Q (output) represents the weight matrix, and R(input) is the control vector. The performance index J represents the balance between the structural response, and the control energy, the purpose of this index is to reduce the response of the building structure. The performance index given in Eq. (1) is chosen to minimize the structural response and the control energy over the time interval from t0 to tf . If the elements of the matrix Q are large, the system response will be minimized at a large driving force. When the elements of the vector R are large, the driving force should be small [10–14]. u(t) = −KX (t)

(2)

K (LQR gain vector) is given by the relation: K = R−1 BT P where: P is the solution matrix of the Riccati equation.

Fig. 2. LQR control schema

(3)

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3 Mathematical Formulation We have used a 6-story building for this study. This type of building structure is largely found in our country Algeria. The different characteristics of the building are shown in Table 1. On the top floor, we mounted an active tuned mass damper (ATMD). This last one will be controlled using the LQR control algorithm. The equation of motion of our system can be presented as:   (4) [M ]{¨x} + [C] {˙x} +[K]{x} = −[M ]{r} x¨ g − {d }f (t) where: M, C, and K are the mass, damping, and rigidity matrices.

Fig. 3. Building equipped with ATMD

Table 1. Building proprieties Story

Mass (Kg)

Rigidity (KN/m)

Damping (KN.s/m)

1

3.45 x 103

3.404 x 108

34

2

3.45 x 103

3.404 x 108

34

3

3.45 x 103

3.404 x 108

34

4

3.45 x 103

3.404 x 108

34

5

3.45 x 103

3.404 x 108

34

6

3.45 x 103

3.404 x 108

34

ATMD

1050

8.56

0.763

Figure 4 shows a n-DOF structure controlled by the active system and considering the effect of SSI. The difference between the two cases, with and without the SSI effect,

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is that there are two additional forces that appear when considering the motion of the ¨ where mi and hi are the mass and height of floor foundation. They are mi x¨ 0 and mi hi ∅, i, respectively. Therefore, the equation of motion of the superstructure, in the case of structures with the SSI effect, is obtained by adding these inertial forces to the case of structures without the SSI effect. For this study, the El Centro 1940 earthquake will be used for this simulation.

Fig. 4. Soil-structure interaction

The state-space representation of the system with the SSI effect will be as follows:   x¨ (t)   ˙ Z(t) = [At ]{Z(t)} + [Bu ]{u(t)} + [Br ] g (5) 0 where:

 [At ] =

 [0] [I ] ; −[MSSI ]−1 [KSSI ] −[MSSI ]−1 [CSSI ]   [0]  ; [Bu ] = [MSSI ]−1 δas ⎡ ⎤ [0] [Br ] = ⎣ −[] ⎦ −[I2 ]

4

Results and Discussion

From these figures, it can be seen that:

(6) (7)

(8)

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without SSI with SSI

0.1

x(m)

0.05

0

-0.05

-0.1 0

10

20

30

40

50

t(s)

Fig. 5. Building fondation displacement (without and with SSI effect)

without SSI with SSI

3 2

V(N)

1 0 -1 -2 -3 -4 -5 0

10

20

30 t(s)

40

50

Fig. 6. Building basement shear force (without and with SSI effect)

• With the effect of SSI, the displacements of the structure are increased compared to those of the structure without the effect of SSI, because of the displacements of the foundation. • With the SSI effect, the inter-story displacements are reduced compared to those of the structure without the SSI effect because of the flexibility of the base, which plays the role of filtering. • With the SSI effect, the shear forces at the base are reduced by 30% compared to those of the structure, ignoring the SSI effect. • With the effect of SSI, the control forces of the structure are large compared to those of the structure without the effect of SSI, because of the importance of the displacements of the foundation, which require an additional force to reduce them.

Soil-Structure Interaction Effects 3

499

without SSI with SSI

2 1

U(N)

0

-1 -2 -3 -4

-5 0

10

20

30

40

50

t(s)

Fig. 7. Control force (without and with SSI effect)

5 Conclusion What can be concluded from these results is that the effect of soil-structure interaction is necessary to introduce it into the calculation of the seismic response of structures, it increases the displacements of the structure because of the flexibility of the base, but it reduces the displacements between floors as well as the reduction of the shear force at the base.

References 1. Pnevmatikos, N., Gantes, C.J.: Actively and Semi-Actively Controlled Structures Under Seismic Actions: Modeling and Analysis, pp. 1–24. Springer, Encyclopedia of Earthquake Engineering, New York, NY, USA (2014). https://doi.org/10.1007/978-3-642-35344-4_146 2. Manchalwar, A., Bakre, S.V.: Vibration control of structure by top base isolated storey as tuned mass damper. Int. J. Dyn. Control 8(3), 963–972 (2020). https://doi.org/10.1007/s40 435-020-00614-1 3. Xu, L., Cui, Y., Wang, Z.: Active tuned mass damper based vibration control for seismic excited adjacent buildings under actuator saturation. Soil Dyn. Earthq. Eng. 135,(2020) 4. Arfiadi, Y., Hadi, M.N.S.: Passive and active control of three dimensional buildings. Earthquake Eng. Struct. Dynam. 29, 377–396 (2000) 5. Soong, T.T., Spencer, B.F.: Active, semi-active and hybrid control of structures. Bull. N. Z. Soc. Earthq. Eng. 33(3), 387–402 (2000) 6. Fuller, C.C., Elliott, S.J., Nelson, P.A.: Active control of vibration. Academic press (1996) 7. Kelly, J.M.: The role of damping in seismic isolation. Earthquake Eng. Struct. Dynam. 28(1), 3–20 (1999) 8. Ezzraimi, M., Tiberkak, R., Melbous, A., Rechak, S.: LQR and PID algorithms for vibration control of piezoelectric composite plates. Mechanics 24(5), 734–740 (2018) 9. Heidari, A.H., Etedali, S., Javaheri-Tafti, M.R.: A hybrid LQR-PID control design for seismic control of buildings equipped with ATMD. Front. Struct. Civ. Eng. 12(1), 44–57 (2016). https://doi.org/10.1007/s11709-016-0382-6

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10. Alavinasab, A., Moharrami, H., Khajepour, A.: Active control of structures using energybased LQR method. Comput.-Aided Civil Infrastruct. Eng. 21(8), 605–611 (2006) 11. Zhang, J., He, L., Wang, E., Gao, R.: December. A LQR controller design for active vibration control of flexible structures. In: 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application , Vol. 1, pp. 127–132. IEEE (2008) 12. Jaballah, M.S., Harzallah, S., Nail, B.: A Comparative study on hybrid vibration control of base-isolated buildings equipped with ATMD. Eng. Technol. Appl. Scie. Res. 12(3), 8652– 8657 (2022) 13. Seghir, J.M., Bachir, N., Salaheddine, H., Salim, G.: Hybrid vibration control of structures using fractional order PIλ Dμ controller. In: International Conference on Artificial Intelligence in Renewable Energetic Systems, pp. 569-577. Springer, Cham (2021) 14. Harzallah, S., Chabaat, M., Saidani, M. and Moussaoui, M.: Numerical investigation of the seismic vulnerability of bridge piers strengthened with steel fibre reinforced concrete (SFRC) and carbon fibre composites (CFC). Case Stud. Constr. Mater. 17, e01235 (2022)

Robust Control of Multiphase Induction Generator Equipped with Fuzzy Flywheel Energy Storage System Derkouche Djamel and Kouzi Katia(B) Laboratoire Matériaux, Systèmes Énergétiques, Energies Renouvelables et Gestion de l’Énergie (LMSEERGE), University of Amar TelidjiLaghouat, Laghouat, Algeria [email protected]

Abstract. Controlling wind generators has become a challenging task for those interested in this field to ensure adequate and stable energy for consumers, so they worked to create solutions to the problem of wind energy fluctuation, among these solutions a flywheel energy storage system, which has proven to be effective in helping the wind generators to contribute to the grid. The aim of this work is to improve the performance of a wind energy conversion system (WECS) based on dual star induction generator (DSIG) integrated with a flywheel energy storage system based on the squirrel cage induction machine (SCIM). A novel control based on a synergetic control (SC) combined with vector control applied on the FESS machine. On the other one, a powerful optimization method is proposed to tuning the values of SC parameters. The model of the system is simulated for different wind generator operating modes using Matlab–Simulink. The results show the good performance of the studied system. Keywords: Wind energy conversion system · Dual star induction machine · Flywheel energy storage system · Vector control · Synergetic controller · Fuzzy logic Algorithm · PSO algorithm

1 Introduction Artificial intelligence technology is a crucial means to create intelligent manufacturing, which is a significant aspect of the industry to reduce time and money waste. It is apparent that industrial system optimization is important for competitiveness of the industry. Therefore, many mathematicians and engineers are interested in learning how to use advanced modeling and optimization technologies, as well as developing novel modeling and optimization methods, to build and manage complex systems. One of the most popular methods of optimization is the particle swarm optimization, which is a nature inspired by social behavior of bird flocking when searching for food. PSO proved to be a great solution to improve the control of the electrical machine drives. In this paper, A flywheel energy storage system (FESS) integrated to a wind energy conversion system (WECS) based on dual star induction generator (DSIG) incorporated with a flywheel energy storage system (FESS) based on a squirrel-cage induction machine (IM). The © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 501–510, 2023. https://doi.org/10.1007/978-3-031-21216-1_52

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Flywheel energy storage systems have advanced significantly in recent decades due to their robustness, long lives and quick response. The main concept of FESS is to convert the kinetic energy in the flywheel into electrical energy in the case of a power shortage in the grid. However, the control design of the electrical machine drives become a challenging task due to the linear-dynamics. The Synergetic Strategy to Control Theory (SACT), suggested by Kalashnikov [6, 7], is a new control approach for regulating nonlinear systems that consists of reaching a zero tracking error in a finite time using the macro variable and its first-time derivative. This work investigated the synergetic control with the flux-oriented control applied on the dual star induction machine and the Fuzzy logic controller applied on the squirrel induction machine of the Fess. The principle of FOC control approach is to align the flux vector on an axis of the reference frame linked to the rotating field to make the IM performance equal to that of a DC machine. On the other one, the Particle Swarm Optimization Algorithm is proposed to tuning the values of the synergetic controller parameters in order to improve the performance of the proposed system.

2 Mathematical Model of All Parts of the System Flywheel energy storage works by converting electrical energy into mechanical energy and storing it in the flywheel, which is then used to drive generators to produce power when it is required. The good features of the flywheel system is that have no impact on the environment, low maintenance costs and extreme power density. However, it have some disadvantages include expensive costs and the potential of self-discharge. Figure 1 illustrates the global diagram of the studied system which consists of a wind generator based on dual star induction machine, four converters power which are controlled by Pulse Width Modulation, the DC bus, the input filter, a transformer and the Fess which in turn consists of the flywheel and the induction machine. Flywheel energy storage plays the role of a power monitor in the system so that constant power must pass to the grid. Through the following equation we can determine the reference power applied on the Fess [1, 2] PFess−ref = Preg − Pwind

(1)

A Standard PI voltage controller is used to regulate the DC bus voltage and provides the value of the power required to keep the voltage at the reference value udc-ref. The reference active power is supplied via the dc link voltage control, The following is the evolution of the DC bus voltage: dUdc 1 = (idc − im − ig ) dt C

(2)

For a given inertia, the energy stored in the flywheel is proportional to the square of the rotational speed, as shown  t1 PFess−ref dt (3) Ec−ref = Ect0 + t0

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Fig. 1. Global system studied

2.1 Induction Machine Model The Park model is the most common model used in the induction machines. The following system describes the flux and currents of squirrel induction machine [1]: ⎧ Mp disd Rsr M 1 ⎪ ⎪ dt = − ρLs isd + ωs isq + ρLs Lr Tr φrd + ρLs Lr IM φrq + ρLs vsd ⎪ ⎪ ⎨ disq = −ω i − Rsr i − Mp  φ + M φ + 1 v s sd dt ρLs sq ρLs Lr IM rd ρLs Lr Tr rq ρLs sq (4) ⎪ d φrd = M isd − 1 φrd + (ωs − IM p)φrq ⎪ dt T T ⎪ r r ⎪ ⎩ d φrd = M i − 1 φ − (ω −  p)φ s IM rq dt Tr sq Tr rq 2

2

With; Rsr = Rs + M R And, ρ = 1 − LMs lr Lr 2 r The mechanical speed of the Fess is defined by Jfess

d IM = Tem−fess − fIM IM dt

(5)

By Application the vector control on the induction machine of the Fess, the system (3) will be as follows ⎧ disd Rsr = − ρL isd + ωs isq + ρLsMLr Tr φrd + ρL1 s vsd ⎪ ⎪ s ⎪ didtsq ⎨ Mp Rsr 1 dt = −ωs isd − ρLs isq − ρLs Lr IM φrd + ρLs vsq (6) d φ M 1 rd ⎪ = Tr isd − Tr φrd ⎪ ⎪ dt ⎩ The reference speed and the reference current are determined, respectively by:  2.Ec−ref Lr Tem−refIM , isq−refIM = (7) IM −ref = JIM pM ϕr−refIM

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The stator pulsation is calculated by ωsIM = pv +

MRr isq−ref Lr ϕr−ref

(8)

2.2 DSIG Model A schematic of the stator and rotor windings for a dual-stator induction machine is given in Fig. 2. Three phases are distributed in each winding of the dual star induction machine, and their magnetic axes are 120° apart. The following system in the Park frame (9) gives the electrical equations of DSIM [3–5]:

Fig. 2. Scheme of dual-stator induction machine winding.

˙ = AX + BU X

(9)

 T T  With; X = φds1 φqs1 φds2 φqs2 φdr φqr and U = vds1 vqs1 vds2 vqs2 0 0 The system matrices of DSIG are written as follows

L

With; Ts = Rs(1,2) And, Tr = RLrr s(1,2) The mechanical equation and the electromagnetic torque are calculated by: J

dr = Tr − Tem − Jr dt

(10)

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Tem = P



lm [ iqs1 + iqs2 φdr − (ids1 + ids2 )φqr (lm + lr )

505

(11)

We apply the flux oriented control on DSIG model, the electromagnetic torque became as follow Tem−ref = P

lm (iqs1 + iqs2 )φ ∗ r (lm + lr )

(12)

3 Combined Synergetic-Vector Control Applied to DSIG Machine To build the synergetic control we should choose the macro- variable and limit it, which we consider a linear function of the mechanical state variables. We choose the first group of macro variables as follow [8, 9] ψ1 = k1 x1 + k2 x2

(13)

With: x1 = r−ref − r , and x2 = φr−ref − φr ψ1 Must satisfy the following homogeneous differential equation: ˙ + ψ = 0, with T > 0 Tψ

(14)

We replace ψ1 of (14) in (12) which gives

where:

T(k1 x˙ 1 + k2 x˙ 2 ) + k1 x1 + k2 x2 = 0

(15)



dr dφr −T k1 + k2 + k1 (r−ref − r ) + k2 (φr−ref − φr ) = 0 dt dt

(16)

 d φr dt

= 1J Tem−ref − TL − kf r Rr r Lm = − Lr +L φr + LRr +L (i +isd2 ) m m sd1

dr dt

(17)

Finally, by combining (17) and (16), we obtain the control law   dφr J k1 (r−ref − r ) + k2 (φr−ref − φr ) − Tk 2 + TL + kf r = 0 Tem−ref = Tk 1 dt (18) where: k1 , k2 and T are the controller parameter.

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4 Design of Fuzzy Logic Algorithm of the Fess Machine The structure of the fuzzy logic algorithm is illustrated in Fig. 3, which is proposed by Mamdani [12] in order to control the speed of the flywheel energy storage machine, where the inputs are the speed error (e) and the change of speed (de) and the output is the electromagnetic torque Tem−ref . The fuzzy controller consists of fuzzification interface, knowledge base, inference and defuzzification. k, k e , ke are a proportional gains. For each input and output fuzzy set, the fuzzy controller provides five typical triangletype membership functions, The linguistic set proposed are; negative big (NB), negative small (NS)„ zero (EZ), positive small (PS and positive big (PB).

Fig. 3. Fess integrated to the wind generator

The inputs and the output are determined by the following equations [13]: e(k) = refIM − IM

(19)

e(k) = ke [e(k) − e(k − 1)]

(20)

Tem−ref = Tem (k − 1) + kTem ∗ Tem

(21)

5 Design of PSO Algorithm to Tuning Synergetic Control Parameters The proposed Particle Swarm Optimization Algorithm for finding the values of the synergetic controller parameters T, k1 and k2, is divided into the following steps [10, 11]: Step 1: All velocity and position of the particles are set at random within a predetermined range. Step 2: Updating the velocities and positions of all particles according to the following equations

(22) Vi (t + 1) = w.V i (t) + ϕ1 .r1 (t).(pbi (t) − xi (t))+ϕ 2 .r2 (t). pgi (t) − xi (t)

where xi and vi are the position and velocity of particle i, respectively; pbi , pgi are the best position and the best swarm position; r1 and r2 are random variables between [0,1];

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ϕ1 and ϕ2 are two accelerations constants that influence the relative weighting of linked terms; and the inertial weight w is chosen as a compromise between the local and global exploring capabilities of the swarm. Step 3: Updating the velocity and position of all particles as follows xi (t + 1) = xi (t) + Vi (t)

(23)

Step 4: When the conditions are met, the memory will update according to. pbi = pi If f (pi ) > f (p

bi ). pgi = pi If f (pi ) > f pgi . Where f(x) is the objective function optimized. Step 5: Stopping condition, the final value of T, k1, and k2 are the best controller parameters if we reach the maximum number of iterations (Fig. 4).

Fig. 4. An optimized synergetic controller by PSO algorithm

6 Simulation Results The mathematical model of the global system is implemented on MATLAB / Simulink for a variable wind speed. The simulation is performed for a period of time of 30 s. The rated power of DSIG and IM are 1.5 MW and 0.45 MW respectively, their parameters are given in Table 1. The amount of active power delivered to the grid is fixed at Preg = 0.85MW, and the amount of reactive power is set to zero (Qr* = 0). The parameters of the synergetic controller obtained by PSO are: T = 9.5751e−04, k1 = 6.3236e−04, k2 = 9.7540e−05. According to the obtained results, the optimized synergetic control of the DSIG showed a great ability to improve the system studied, where we got a very fast response of DSIG velocity as shown in Fig. 6 and 7, less than 0.02 s of short settling time, and the curve of the velocity follows its reference perfectly. The evolution of the DC bus voltage is presented in Fig. 8, where the initial voltage is equal to udc0 = 1 kV, we note that the voltage Udc kept at a constant value equal to its reference which is equal to 1.131 kV.

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Fig. 5. Wind speed profile

Fig. 6. DSIG speed

The electromagnetic torque and the speed of Fess follow the evolution of the wind profile as shown in Fig. 5 and 6, also they follow their references perfectly. The stator voltage and current waveforms are plotted in Fig. 7. According to the simulation conditions, we get a constant active power of the grid and reactive power equal zero as illustrated in Fig. 8.

Fig. 7. Zoom in DSIG speed

Fig. 8. DSIG electromagnetic torque

From the Fig. 9, we notice that the active power changes between the positive and negative case, which proves that the Fess machine works both as a motor and as a generator. If the power supplied by the wind generator Pge is higher than the power required Preg, the difference between the two will be kept in the Fess and if there is a deficit, the power kept in the Fess covers that deficit. The electromagnetic torque of Fess follows the evolution of the wind profile as shown in Fig. 10, also it follows its reference perfectly. We see through Fig. 12 the speed of Fuzzy Fess which increases when Fess stores the energy and decreases when it delivers the energy to the grid in case of deficit. The wind generator and the grid powers are presented in Fig. 13, where the reactive power of DSIG changes between 0.2 and 0.5MW while it equals zero in the grid side, we note also that the active power of DSIG is variable and at a constant value in the grid side, which equal to the desired power. Figure 14 represents the voltage and current of the grid side, it can be seen that are in opposite phases, which means that the power is constant and generated (Fig. 11).

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Fig. 9. Fess active power

Fig. 10. Fess electromagnetic torque

Fig. 11. The Fess power

Fig. 12. Fess mechanical speed

Fig. 13. The grid power

Fig. 14. Grid voltage and current

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Table 1. Parameters Type

Symbol and Value

DSIG

400 V, 50 Hz, 2 pairs of pole, rs1 = rs2 = 0.008X , ls1 = ls2 = 0.134 mH, lm = 0.0045 H, rr = 0.007X , lr = 0.0067 mH, J = 100 kg m2 , fr = 2.5 NmS/rd

IM

400 V, 50 Hz, 2 pole pairs, Rs = Rr =0.0171 , Ls = Lr =0.0173 H, M = 0.0135 H, fv = 250 NmS/rd

Turbine

Turbine: Radius = 35 m, blades = 3, hub high = 85, G = 90

7 Conclusion In this article, a model and a control strategy of a complete wind energy conversion system based on dual star induction machine with a Fuzzy flywheel energy storage system. The synergetic approach to control theory has been suggested and implemented to control the dual star induction machine. Moreover, Particle swarm optimization has been proposed to select the appropriate parameters of the synergetic control. The proposed

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control approach shows attractive results in optimizing wind turbine energy extraction and ensuring the stability of the system, and the flywheel energy storage system proves that is a good solution to integrate wind generators with the grid.

References 1. Davigny, A.: Participation AUX Services systèmes de fermes d’éoliennes à vitesse variable intégrant du stockage inertiel d’énergie. University of Lille France, PhD (2007) 2. Cimuca, G.: Système inertiel de stockage d’énergie associé à des générateurs éoliens. University of Lille France, PhD (2005) 3. Amimeur, H., Aouzellag, D., Abdessemed, R., Ghedamsi, K.: Sliding mode control of a dual-stator induction generator for wind 4. El Aimani, S.: Modelling and simulation of doubly fed induction generator for variable speed wind turbines integrated in a distribution network. In: 10th European Conference on Power Electronics and Application, Toulouse, France (2003) 5. Kouki, H., Ben Fredj, M., Rehaoulia, H.: Modeling of double star induction machine including magnetic saturation and skin effect. In: 10th International Multi-Conferences on Systems, Signals & Devices 2013 (SSD13), pp. 1–5. IEEE (2013). https://doi.org/10.1109/SSD.2013. 6564045. G. Système inertiel de stockage d’énergie associé à des générateurs éoliens. PhD, University of Lille France, 2005 6. Kolesnikov, A., et al.: A synergetic approach to the modeling of power electronic systems. In: 7th Workshop on Computers in Power Electronics COMPEL 2000, Proceedings (Cat. No.00TH8535), pp. 259–262. IEEE (2000). https://doi.org/10.1109/CIPE.2000.904726 7. Kolesnikov, A., Veselov, G.: A synergetic approach to the modeling of power electronic systems. In: Proceedings of COMPEL, Blacksburg, VA (2000) 8. Davoudi, A., Bazzi, A.M., Chapman, P.L.: Application of synergetic control theory to nonsinusoidal PMSMs via multiple reference frame theory. In: 2008 34th Annual Conference of IEEE Industrial Electronics, pp. 2794–2799. IEEE (2008). https://doi.org/10.1109/IECON. 2008.4758401 9. Guermit, H., Kouzi, K.: Investigate the performance of an optimized synergetic control approach of dual star induction motor fed by photovoltaic generator with fuzzy MPPT. In: Hatti, M. (ed.) ICAIRES 2018. LNNS, vol. 62, pp. 297–310. Springer, Cham (2019). https://doi. org/10.1007/978-3-030-04789-4_33 10. Dorigo, M., et al. (eds.): ANTS 2010. LNCS, vol. 6234. Springer, Heidelberg (2010). https:// doi.org/10.1007/978-3-642-15461-4 11. Bhubaneswar, Odisha, India: Induction motor Using PSO-ANFIS International Conference on Intelligent Computing, Communication & Convergence. https://doi.org/10.1016/J.PROCS. 2015.04.212 12. Zadeh, L.A.: The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets Syst 11, 199–227 (1983) 13. Kouzi, K., Mokrani, L., Nait, S.: High performances of fuzzy self-tuning scaling factor of PI fuzzy logic controller based on direct vector control for induction motor drive without flux measurements. In: Proceedings of IEEE International conference on Industrial Technology, 8–10 December 2004: Hammamet Tunisia, pp. 1106–1111 (2004)

Urban Flood Risk; Diagnosis and Proposed Management. A Case Study in Bechar City, South Western Algeria Bouhellala Kharfia(B) Département de Génie Civile et Hydraulique, Université de Bechar, BP 417 Route Kenadsa, 08000 Bechar, Algeria [email protected]

Abstract. The city of Bechar is confronted with the phenomena of floods which cause catastrophic floods and this constitute a major constraint for the economic and social development. This work represents a study of the hydraulic management within the framework of the protection of bechat, against the floods. The design of development and protection suggestions is necessary. For this purpose, hydrological and hydraulic simulations of the wadi of bechar is carried out. These developments were proposed for the feasibility of a dam at the level of wadi of Al Abiod as well as the rehabilitation of degraded walls and structures with narrow openings (bridge, footbridge, etc.) in the centre of the city. After using the ArcGis program which allowed us to define the morphological characteristics of the studied catchment area, we determined different flood hydrographs corresponding to a specific event at different frequencies by empirical formulas and compared them with the floods observed at the hydrometric station. These obtained flood flows were used as boundary conditions in the hydraulic model "HEC-RAS" in order to calculate the variation of the flooded water surface in the watershed in time and at several points of the Wadi. Keywords: Hydrological simulations · Hydraulics · ArcGis · HEC-RAS · Bechar

1 Introduction Floods are one of the most important natural hazards and affect almost all regions of the world. In Algeria, floods are characterized by sudden rises in wadis, which are characteristic of semi-arid regions. On 8 October 2008, with 99.3 mm (whereas the annual average recorded in the region is 100 mm), the overflowing of the Bechar wadi led to a toll of 13 deaths and more than 4,300 damaged houses. (National Water Resources Agency ANRH of Bechar) [3].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 511–525, 2023. https://doi.org/10.1007/978-3-031-21216-1_53

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The study of floods that cause catastrophic flooding has been the subject of much research. When data are available for a large number of years, frequency analysis of flows (YAHIAOUI, 1997) and hydro meteorological methods (GUILLOT and DUBAND, 1967; MARGOUM, 1992) such as GRADEX and AGREGEE are the preferred approaches to study flood regimes and obtain a predetermination of rare and extreme floods. However, the frequency analysis generally characterizes only the peak flows or maximum flow of the floods, and must be completed by treatments aiming at determining the exceeded flow thresholds for different time intervals considered critical with regard to the vulnerability of the environment. The objective of this project is the development of the wadi of Bechar watercourse, which passes through the centre of Bechar, as well as the implementation of an alert and risk forecasting strategy, through the collection and exploitation of the related information. Indeed, the determination of the outlet flow of a catchment area is of great importance for a hydrologist, especially when it comes to the control of flood phenomena, the regulation of watercourses, the dimensioning of hydraulic works, etc... [1, 2] In the case of the city of Bechar, flooding can only be random or accidental during a flood produced by exceptional rains, because it occurs when excess water cannot be evacuated by natural ways (minor river beds) or artificial ways planned for this purpose (rainwater evacuation networks). Based on these considerations, the present work aims at a study of protection against Floods of the city of Bechar, it will be based on the statistical treatment of hydrometric data and empirical formulas based on the samples of observed flows, with the fundamental aspects of the flow in order to evaluate the flows of floods and the parameters being used for the design of the protection works.

2

Presentation of the Study Area

The wilaya of BECHAR is bordered to the North by the Wilayas of Naâma and El Bayadh, to the East by the Wilaya of Adrar, to the South by the Wilayas of Adrar and Tindouf and to the West by the Kingdom of Morocco. Its wilaya capital is the commune of Bechar located in the north of the Wilaya with a surface area of 5,050 km2, 1150 km southwest of the capital Algiers. The study site is the Wadi Bechar which is located in the wilaya of Bechar. The section intended for the elaboration of the development study of the Bechar wadi is about 17 km long (Fig. 1).

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Fig. 1. Localization of Bechar wadi (Google Earth)

3 The Hydrographic Network The Wadi Bechar originates at an altitude of 1919 m in the Djebel Grouz via the Wadi El Abiod. After a rapid descent, it receives in its course the waters of Wadi R’tem, Wadi Roknet El Betoum, as well as the waters of numerous tributaries coming from Djebel Antar, Djebel Horriet and Djebel Bechar. After a journey of about fifty kilometres, it passes through the town of Bechar and receives the waters of Wadi Tigheline. Beyond the town, the wadi runs for about a hundred kilometres in a NE-SW direction, passing from 900 m to 600 m in altitude. At the level of Ksiksou, it turns to the NNW-SSE and silts up at Daïet Tiour, at 547 m altitude. The catchment area of the Bechar region includes four hydrometric stations and eight meteorological stations (see Table 1.). Table 1. Hydrometric stations (ANRH of Bechar) Station name

Code

Longitude

Latitude

Elevation

Bechar ANRH

13 01 32 13 01 01

29°40 33 N 31°30 19 N

413

Djorf Torba

02°12 44 W 02°46 00 W 02°02 01 W 01°12 20 W

30°56 24 N 32°14 36 N

685

Taghit

13 01 31

Rosfa Taiba

13 01 34

4

729 950

Background on the Flooding of Wadi Bechar

Despite the fact that the study region has a very significant rainfall deficit, it is not uncommon for violent stormy rainfall to occur, causing significant damage to human

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life and property. The 1959 and 2008 floods are still the reference floods. Major floods occurred in 1965, 1979, 1993, 1994 and 1999, which flooded urban areas. The last major floods were in December 1999, October 2007 and October 2008, when large amounts of rainfall were recorded in a relatively short period of time. In 1959, the extent of the event was regional which experienced very heavy rainfall where the meteorological services recorded during the period from 19 to 21 March. 4.1 Consequences of the Floooding in Bechar (2008) Thirty-six hours of uninterrupted torrential rain (Wednesday and Thursday) were enough to transform the capital of the Saoura and its suburbs into gigantic torrents of mud, puddles of stagnant rainwater, bringing traffic to a standstill between several communes and isolating them from the northern regions of the country. Telephone connections were cut and road traffic with the northern cities was banned from Friday on the instructions of local authorities to prevent any danger, it is said. The mobilization of officials from the public bodies concerned, who meet at the crisis unit set up at the wilaya, is almost general. The heavy floods have caused significant material damage, leading to the collapse of several houses, usually built in adobe. The fury of the waters of the wadi of Bechar (13 km) swept away the few animals in the public garden along the wadi and caused the collapse of a primary school in the Haï Nour district. For the moment, seventy families have been evacuated to the reception centre for disaster victims created in a hurry at a youth hostel, the former CFA and the paramedical school, whereas the day before they were only 26, 20 other families from the Tinerkouk district came to join the other disaster victims on Thursday, we learnt this Friday. Since 1958, it is claimed, the waters of the wadi of Bechar have never caused such an important flood (1000 m3 of flow per second) which, in their fury, have infiltrated inside the houses bordering the wadi, even though they are far away, and have violently damaged two bridges separating the Debdaba district from the town centre. The Chouffane bridge, used by passers-by, was seriously damaged and threatened to collapse. Dozens of citizens affected by the floods gathered in front of the wilaya headquarters yesterday. It should be noted that the amount of water that fell during the two days is 90 mm, while the annual average recorded in the region is 100 mm. But the persistence of the rainy weather this Saturday suggests a deterioration that the population fears with anxiety and with consequences that could be dramatic (see Fig. 2).

Fig. 2. 2008 Photo of the damage caused by the flooding of the Wadi Bechar in 2008

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5 Climatic Overview The city of Bechar is characterized by a continental desert climate. There are two types of zones: • The transition zone: Delimited by Beni Ounif to the north and the Igli parallel to the south. Very hot in summer (+ 45 °C) and cold in winter (2 °C to 3 °C), rainfall is around 60 mm/year. The sandy winds are frequent and often violent (100 km/h). • The desert zone: extends beyond Beni Abbes. Annual rainfall is around 40 mm. Sand winds are very frequent. The climatic changes experienced in the region are mainly due to exceptional meteorological episodes where the annual average can be reached or even exceeded in a single sequence and then remain negligible for years. These upheavals lead to extreme and catastrophic events such as drought or floods [4]. 5.1

Precipitation

Rainfall is generally produced by Saharan lows. The latter can only produce rain if they are associated with moist air masses from the Atlantic or Mediterranean regions. The term precipitation is used to cover all meteorological water that falls on the earth’s surface. This meteorological parameter is the fundamental component of hydro climatology and knowledge of this water supply to the soil is essential for estimating soil water reserves and groundwater recharge. The only station in the northern zone of the Wadi Bechar basin, which led us to carry out our study, is characterised by the coordinates given in Table 2. The northern watershed of the Wadi Bechar has enabled us to identify certain geomorphological characteristics of the region, which are summarised in the following table. Table 2. Geomorphological characteristics of the northern watershed of wadi Bechar Designation

Symbole Unité

Symbole Unité

Valeur

Superficie du bassin versant

SBV

Km2

2311,12 Altitudes Maximale

Valeur

Désignation

Hmax

m

2000

Périmètre du bassin versant

PBV

Km

437,61

Moyenne

Hmoy

m

1097,12

Longueur du thalweg principal

LCP

Km

121,86

Minimale

Hmin

m

557

Coefficient de compacité

Kc



2.55

de fréquence H5% 5%

m

1570

Longueur équivalent

Lr

Km

207.68

de fréquence H95% 95%

m

840

Largeur équivalente

Ir

Km

11.13

de fréquence H50% 50%

m

1000

(continued)

516

B. Kharfia Table 2. (continued)

Designation

Symbole Unité

Valeur

Désignation

Surface du rectangle équivalent

Seq

Km2

Périmètre du rectangle équivalent

Peq

Km

437,61

Nombre des talwegs d’ordre 1

Coefficient  d’allongement



6,43

Indice de pente globale

Ig

Pente du bassin versant Indice de pente de M Roche

Symbole Unité

2311,12 Pente moyenne de B. V Lm

Valeur

%

1.10

N1



12918

Densité de drainage

Dd

Km/Km2

2.97

m/Km 0.35

Coefficient de torrentialité

Ct



16.60

Ibv

m/Km 1.30

Temps de concentration Tc

h

15

Ip

%

Vitesse de ruissèlement Vr

Km/h

8.12

0.65

6 Flood Study The objective of any hydrological study is to determine the flood hydrographs for a given catchment area at different return periods and to define the corresponding probable peak flows. In this flood analysis, for the estimation of the project flood and the evaluations of the probable peak flows of the flood of different return periods, the approach based on the frequently used empirical formulas was used [5]. The instantaneous maximum flow Qmax is the maximum flow of the project flood. The maximum instantaneous flow Qmax can be estimated for different return periods from the numerous empirical formulas applied to the conditions in Algeria (formulas of MALLET GAUTHIER, TURRAZA, POSSENTI, SAMIE, ALEXEEV and SOKOLOVSKY). These formulas use estimates of the maximum daily rainfall and that for a duration equal to the time of concentration (tc) [8]. The empirical formulas use runoff coefficients of 0.6 for the 10% frequency and 0.7 for the 1% frequency and 0.8 for the 0.1% frequency. These formulas can therefore be applied using the corresponding values of the runoff coefficient for the estimation of the maximum flood discharge of the considered wadi basins [9]. 6.1 Hydrometric Data From the hydrological study of the northern part of the wadi Bechar watershed, we deduce that the latter presents a very variable regime, and it allowed us to obtain the following results: The average annual flow is 0.467 m3 /s. The water flow is 4.15 mm. The flow deficit is about 82 mm.

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The values of the flows observed from 11 November 1993 to 1 November 1993 for an exceptional flood are shown in Table 3. Table 3. The values of the flows observed from 11 November 1993 to 1 November 1993 for an exceptional flood Date

Time

Height (cm) Flow (m3 /s) Date

Time Height (cm) Flow (m3 /s)

11-nov-93 00:00

64

16.2

12-nov-93 06:00 166

99.36

11-nov-93 01:00

64

16.2

12-nov-93 07:00 159

89.14

11-nov-93 02:00

64

16.2

12-nov-93 08:00 152

78.92

11-nov-93 03:00

64

16.2

12-nov-93 09:00 150

76

11-nov-93 04:00

64

16.2

12-nov-93 10:00 147

73.48

11-nov-93 05:00

64

16.2

12-nov-93 11:00 145

71.8

11-nov-93 06:00

90

27

12-nov-93 12:00 145

71.8

11-nov-93 07:00 120

50.8

12-nov-93 13:00 144

70.96

11-nov-93 08:00 150

76

12-nov-93 14:00 144

70.96

11-nov-93 09:00 145

71.8

12-nov-93 15:00 143

70.12

11-nov-93 10:00 135

63.4

12-nov-93 16:00 140

67.6

11-nov-93 11:00 130

59.2

12-nov-93 17:00 130

59.2

11-nov-93 12:00 150

12-nov-93 18:00 122

52.48

11-nov-93 13:00 178

116.88

76

12-nov-93 19:00 120

50.8

11-nov-93 14:00 190

134.4

12-nov-93 20:00 118

49.12

11-nov-93 15:00 200

149

12-nov-93 21:00 116

47.44

11-nov-93 16:00 230

218

12-nov-93 22:00 114

45.76

11-nov-93 17:00 270

320.8

12-nov-93 23:00 112

44.08

11-nov-93 18:00 327

492.4

13-nov-93 00:00 108

40.72

11-nov-93 19:00 372

648.72

13-nov-93 01:00 107

39.88

11-nov-93 20:00 432

887.76

13-nov-93 02:00 105

38.2

11-nov-93 21:00 466

1033.72

13-nov-93 03:00 102

35.68

11-nov-93 22:00 420

837.6

13-nov-93 04:00

98

32.6

11-nov-93 23:00 356

588.56

13-nov-93 05:00

97

31.9

12-nov-93 00:00 318

463.6

13-nov-93 06:00

96

31.2

12-nov-93 01:00 270

320.8

13-nov-93 07:00

93

29.1

12-nov-93 02:00 240

241

13-nov-93 08:00

91

27.7

12-nov-93 03:00 216

185.8

13-nov-93 09:00

90

27

12-nov-93 04:00 194

140.24

13-nov-93 10:00

85

24

12-nov-93 05:00 180

119.8

13-nov-93 11:00

70

20

518

B. Kharfia

Figure 3 shows the hydrograph of the exceptional flood of 11 November 1993 to 1 November 1993. Hydrogram of flow in 11 Nov 1993 1200

Flow m3/s

1000 800 600 400 200 0 Time (hours)

Fig. 3. Hydrograph of the exceptional flood of 11 November 1993 to 1 November 1993

The values of the frequency flows obtained are shown in Table 4. According to Table 6. and depending on the characteristics of the study area, the average flow calculated by the Mallet method and the one obtained by the Possenti method were chosen. Table 5. shows the frequency flows of the six return periods corresponding to the time of flood and decline. Table 4. Value of the flows calculated by the different empirical formulas corresponding to the time of flood and decline Fréquence

Intensité (mm/h)

Pjmax (mm)

Pluie de courte duré (mm)

Qmax,p% (m3/s) Alexeev

Sami

Sokolovsky

Méthode fréquentielle

Mallet

Turazza

Possenti

5

1,75

30.20

26.23

26,49

227,87

157,20

45,32

241,70

93,10

429,57

10

2,32

40,10

34,82

38,67

242,65

233,50

88,25

470,35

181,17

570,38

20

2,95

50,70

44,03

52,86

258,47

297,68

125,48

619,71

238,70

721,16

50

3,90

66,10

57,40

75,29

281,46

392,79

167,36

774,12

298,18

940,21

100

4,57

78,80

68,43

95,17

300,42

461,15

232,32

872,97

336,26

1120,85

1000

7,53

129,00

112,03

183,62

375,36

758,84

288,80

1141,46

439,68

1834,90

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Table 5. Value of frequency flows Time (h)

Qmax f% (m3 /s) T = 5 ans

Qmax (m3 /s) 367.64

T = 5 ans Qmax (m3 /s) 367.64

T = 5 ans Qmax (m3 /s) 367.64

Qmax (m3 /s)

0

0,00

0

0,00

0

0,00

0

1

1,63

1

1,63

1

1,63

1

2

6,54

2

6,54

2

6,54

2

3

14,71

3

14,71

3

14,71

3

4

26,14

4

26,14

4

26,14

4

5

40,85

5

40,85

5

40,85

5

6

58,82

6

58,82

6

58,82

6

7

80,06

7

80,06

7

80,06

7

8

104,57

8

104,57

8

104,57

8

9

132,35

9

132,35

9

132,35

9

10

163,40

10

163,40

10

163,40

10

11

197,71

11

197,71

11

197,71

11

12

235,29

12

235,29

12

235,29

12

13

276,14

13

276,14

13

276,14

13

14

320,26

14

320,26

14

320,26

14

15

367,64

15

367,64

15

367,64

15

16

332,09

16

332,09

16

332,09

16

17

298,90

17

298,90

17

298,90

17

18

268,01

379,35

488,74

624,87

726,75

1084,88

19

239,32

338,74

436,43

557,98

648,95

968,75

20

212,75

301,14

387,98

496,04

576,92

861,22

21

188,23

266,43

343,26

438,87

510,42

761,95

22

165,67

234,49

302,12

386,26

449,24

670,62

23

144,99

205,22

264,40

338,04

393,15

586,89

24

126,10

178,49

229,96

294,01

341,94

510,45

25

108,93

154,18

198,65

253,97

295,38

440,94

26

93,39

132,19

170,31

217,75

253,25

378,05

27

79,41

112,40

144,81

185,15

215,33

321,45

28

66,90

94,69

121,99

155,97

181,40

270,79

29

55,77

78,94

101,71

130,03

151,24

225,76 (continued)

520

B. Kharfia Table 5. (continued)

Time (h)

Qmax f% (m3 /s) T = 5 ans

Qmax (m3 /s) 367.64

T = 5 ans Qmax (m3 /s) 367.64

T = 5 ans Qmax (m3 /s) 367.64

Qmax (m3 /s)

30

45,96

65,05

83,80

107,15

124,61

186,02

31

37,36

52,89

68,14

87,11

101,32

151,24

32

29,92

42,34

54,55

69,75

81,12

121,09

33

23,53

33,30

42,91

54,86

63,80

95,24

34

18,12

25,65

33,05

42,25

49,14

73,36

35

13,62

19,27

24,83

31,75

36,92

55,12

36

9,93

14,05

18,10

23,14

26,92

40,18

37

6,97

9,87

12,71

16,25

18,90

28,22

38

4,67

6,61

8,52

10,89

12,66

18,91

39

2,94

4,16

5,36

6,86

7,98

11,91

40

1,70

2,41

3,10

3,97

4,62

6,89

41

0,87

1,23

1,59

2,03

2,36

3,53

42

0,37

0,52

0,67

0,86

1,00

1,49

43

0,11

0,15

0,20

0,25

0,30

0,44

44

0,01

0,02

0,02

0,03

0,04

0,06

Q%max(m3/s)

The flood hydrogram for the different return periods are shown in Fig. 4: 2500 2000

flood hydrograms Q100 00 Q100 0 Q100

1500 1000 500 0

Time (h)

Fig. 4. Flood flows for different return periods

Urban Flood Risk; Diagnosis and Proposed Management

521

It follows from the old flood statistics that the Wadi Bechar experiences several flood sequences each hydrological year; most of the times quite violent. The number of floods per 100 years (F = 1%) is higher in October (Table 5.). The annual variation of the months of flooding in Bechar shows two periods of maxima: one in spring, in April; the other in autumn and winter. The latter presents two maxima separated by a relative minimum in November. Two periods of minima are observed in January and July. The annual variation in the number of floods shows a similar pattern, but with more pronounced maxima in spring and autumn. Table 6. Flood statistics (source ‘Hydrologie saharienne. by Dubief J., Alger, 1959) Number o f Sept Oct Nov Dec Jan Fev Mar Apr May Jun Jul Agst Years Flow 2 3 5 8 3 3 1 5 5 9 5 8 57 For 100 years

7

71

90

45

67

15

27

42

67

27

27

9

50

537

Methodology of the Hydraulic Modelling

The purpose of the hydraulic study is to determine at what height the development works must be dimensioned to protect the city of Bechar against floods. After entering the geometrical data of each section, the next step is the hydraulic modelling by HEC-RAS (integrated software for hydraulic analysis which allows to simulate the flows at free surface. It was developed by the Hydrologic Engineering Center of the US Army Corps of Engineers) is to specify the input flows and any boundary conditions necessary to be able to perform the calculation of the water surface profiles in each section of the river system. Flow data is entered from upstream to downstream for each section. A flow value is written at the upstream end of the river system. The applied flows are the results of the hydrological simulation retained using the HEC-RAS model for the four frequencies considered (see figure below) [6]. The hydraulic simulation by HEC-RAS, allowed us to calculate the propagation of the flood wave along the Wadi Bechar system, and to plot the water surface profile [7]. Several options to visualise the calculation results are available and several types of tabulated and graphical results can be viewed and printed (Fig. 5). We understand between a real photo of the exceptional flood of 08th of August 2008 and the result of the simulation of the frequent floods in the same bridge of quarter 8. we notice that there is only about 30cm between the bridge and the free surface of the flood. on the other hand there was even an overflow of the water under two other bridge [2] (Fig. 6).

522

B. Kharfia

Fig. 5. Schematic of the 3D geometry data with simulation

Fig. 6. Bridge in Ward 8 during the exceptional flood of 2008

8

Discussions and Proposals for Different types of Development

In order to prevent these floods from occurring, it is however possible to mitigate their effects or to reduce their frequency as a priority in the most sensitive and exposed areas. These protections can be divided into two groups: direct and indirect. 8.1

Direct Protection

Direct protection consists of direct intervention on the threatened site by implementing the following actions. – Dredging to improve flow conditions by removing all obstacles and deposits that impede the flow of water in the watercourse. – Clearing of undergrowth: is also necessary at the crossing of settlements for sanitary reasons and in current sections to reduce roughness and increase flow.

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– Re-grading of watercourse sections to increase their water evacuation capacity and ensure a wetted section meeting the desired protection criteria. • Reinforcement of structures: • The creation of canals, allowing the regeneration of the initial wadi corridor. • Protection of the banks to maintain the stability of the land despite the action of the water. • The containment of wadis allows for selective protection. 8.2 Indirect Protection Indirect protection, on the other hand, consists of intervening further away from the threatened sites, outside the development perimeter, by carrying out works on the wadis responsible for the flooding: Creation of peripheral canals allowing the water to be returned to areas located outside the areas to be protected. Creation of dykes or sills in order to store and control floods upstream of the threatened areas. Development of catchment areas to combat erosion. 8.3

Proposed Development of the Wadi Bechar Basin:

• Building a dam on the Wadi El Biad The Ouakda dam is located in the north-eastern part of the catchment area and the Wadi El Abiod dam project is located in the upstream part, which is estimated as an outlet of a catchment area of 628.09 km2 and a perimeter of 187.6 km. It is surrounded by a mountainous chain (Djebel Antar) in the East and Djebel Horreit in the West. The characteristics of the Wadi El Biad catchment area are shown in Table 11 (Fig. 7). After a detailed study of the feasibility of building a dam at Wadi El Abiod, it was found that the dam will have a very significant impact on the liquid and solid input of the Ouakda catchment area – To manage sustainably the upstream of the Wadi Bechar watershed because the activities of the hydraulic domain in terms of extraction of alluvium from the wadis contribute to the modification of the regime of the watercourse by accelerating the runoff and the propagation of the floods on the watershed. – Relocate the existing crushing units on the Ouakda plain to mining sites. – Consider a control model to control the activity of sand removal by individuals in order to ensure the cleaning of the Wadi. – Prohibit the extraction of sand from the bed of the Wadi Bechar along the section that separates the town of Bechar, except for cleaning operations. – Consider the installation of speed bumps upstream of the Ouakda dyke in order to retain the coarse elements that can be used as sand traps.

524

B. Kharfia

Fig. 7. Situation of the proposed dam at Wadi El Biad

– Reinforcing the banks from Ouakda onwards, in order to protect agricultural land and the main sewage collector. – Avoiding the poor management of the wadis, which leads to the narrowing of the major bed (which in turn can cause choking and a rise in the flood level). – Maintenance and weeding of the wadi bed: Water runs off and concentrates rapidly in the watercourse, resulting in sudden and violent floods. The bed of the wadi is usually quickly clogged with sediment and dead wood can form dams. When they break, they release a huge wave, which can be deadly. – Prohibit building on the Bechar Wadi easements, as well as in the high angle river bend areas (in the middle of the river). – (in the direction of flow of the wadi).

9 Conclusion Taking into account the risk of flooding must usually be perceived as a constraint that is integrated as best we can in development and construction projects. Various flood risk prevention measures must be taken for new expansion sites in the city. On the other hand, for old sites, work will have to be undertaken in order to secure the population, property and the vulnerable environment in a general way. This consideration of flood risks must lead to a gradual change in attitude on the part of developers and be generalised in urban planning documents, particularly the POS (land use plans). Consequently, the residential areas and housing estates that have invaded the Chaaba bottoms and their flood zones without any particular precautions must be taken care of and the exposed population must be informed. The same applies to the neighbourhoods built on the lower parts.

References 1. Alaghmand, S., Bin Abdullah, R., Abustan, I., Eslamian, S.: Comparison between capabilities of HEC-RAS and MIKE11 hydraulic models in river flood risk modeling (a case Study of Sungai Kayu Ara River basin, Malaysia). Int. J. Environ. Sci. Technol. 2(3), 270–291 (2012)

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2. Alexander, M.: Aging, bioavailability, and overestimation of risk from environmental pollutants. In: Institute of Comparative and Environmental Toxicology and Department of Crop and Soil Sciences, Cornell University, Ithaca, New York 14853, Environ. Sci. Technol. 34(20), 4259–4265 (2000) 3. ANRH: Rapport Inédit sur les Ressources Hydriques dans la Région de Béchar, Agence Nationale des Ressources Hydrique à Béchar. 4. Bessiere, H.: Assimilation de données variationnelles pour la modélisation hydrologique distribuée de crue à cinétique rapide, Doctorat de l’Université de Toulouse – INPT (2008) 5. HEC-RAS: River Analysis System, Version 4.1: User’s Manuel. In: US Army Corps of Engineers, Hydrologic Engineering Center (HEC), p. 766 (2010) 6. Marc, I.: Virginie Chaouch (CETMEF), Mohamed El Fadili (CETMEF), Coût des protections contre les inondations fluviales, Cerema, Direction technique Eau, mer et fleuves Margny Lès Compiègne (France) (2014) 7. Dalezios, N.R., Eslamian, S.: Regional design storm of Greece within the flood risk management framework. Int. J. Hydrol. Sci. Technol. 6(1), 82–102 (2016)

Electromagnetic Converter for Electric Vehicles Integrated with Renewable Energy Sources for Sustainable Mobility Larbi Belkacem1,3(B) , Hatti Mustapha2 , Kouzi Katia3 , and Ghadbane Ahmed1 1 Research Nuclear Center of Birine CRNB, PO Box 180, Ain Oussera, Djelfa, Algeria

[email protected]

2 Research Center in Renewable Energy, Bousmail, Tipaza, Algeria

[email protected]

3 Electrical Department, University Amar Telidji of LAGHOUAT, Laghouat, Algeria

[email protected]

Abstract. In this article, we tried to shed light on the challenges that Algeria faced in order to establish a solid base for the automobile industry and the failures that accompanied these attempts. On the other hand, we highlighted the possibilities that our country has, especially lithium metal and rare lands that could be a trump card for riding the electric car train. On the other hand, we tried to provide a practical solution to address the problem of traffic congestion and gas emissions in major cities. We proposed the city of Tamanrasset as a model for the manufacture of an electric car with an innovative engine and charged by solar energy, and all this for the sake of sustainable development and energy transition and the establishment of the smart city principles through smart mobility And the smart grid. Keywords: Solar Cars · Axial flux PM motor · TORUS NS · Smart mobility · Smart grid

1

Introduction

The digitization of the city is underway but it is progressing very slowly, even if the subjects of SMART CITY and smart cities have entered the vocabulary, nothing has yet been written. The subjects are complex and protean and must be supported by political decisions and it was moreover Bill Clinton who was the first to speak of the concept of “SMART CITIES” in 2005 [1], Urban performance no longer depends only on the endowment of the city of physical infrastructures but also to work on the social, the services and especially the environmental, the subjects of mobility are at the center of the concerns. Transport is responsible for a quarter of CO2 emissions worldwide and increasingly strict regulations are part of the global fight against climate change. The Paris climate agreement to contain global warming below 2 degrees obliges us to “decarbonize” [2] our economies very quickly and significantly, and in particular the transport sector. C02 emissions in g/KWh for electrical consumption are considerable, Electric vehicles (EVs) are one of the most promising technologies for reducing emissions in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 526–533, 2023. https://doi.org/10.1007/978-3-031-21216-1_54

Electromagnetic Converter for Electric Vehicles

527

global transportation, but the benefits they bring depend on where the energy they use comes from. Today, too few electric vehicles are powered by renewable energy. For them to be a true green option, this needs to change. The electric vehicle revolution is upon us. According to the International Energy Agency (IEA), the number of electric passenger vehicles on the world’s roads could exceed 250 million by 2030 [3], while the International Renewable Energy Agency (IRENA) estimates that electric buses and other public transport vehicles could well number more than 10 million. In this article we propose a vision of a solar vehicle that provides services that guarantee a clean and sustainable environment using renewable energies,;. It was a small vehicle powered by solar panels and batteries with a new motor consumes less electricity a new low-speed coreless AFPM machine designed for gearless in-wheel drive for solar vehicle is proposed and the analytical design procedure to achieve high efficiency at different vehicle speeds is presented. The AFPM has a three-phase winding which can produce a rotating magnetic field in the air gap. This type of electric machine can provide high power density at low speed and therefore it is acceptable to use it as a direct drive in solar vehicles.

2

Recognized Rules for Papers Submitted for Communication in IC-AIRES2022

2.1 Situation of the automotive sector in Algeria The automotive sector, through the importation and construction of cars, has been experiencing a loss of economic efficiency in recent years. Contrary to what was expected, the automotive industry in Algeria finds itself in the midst of deadweight losses following the disruption of market supply. In fact, the current imbalance is the consequence of the freezing of the import of cars into Algeria, and the cessation of the activity of car manufacturing factories. How could the automotive industry revive the Algerian economy? With a well-coordinated project for the revival of the automotive industry, Algeria will be able to count on its local skills by taking the train of the electric vehicle industry. In this sense, the Minister of Industry, Ahmed Zeghdar affirmed that the new strategy of the automotive sector provides for the manufacture of electric and hybrid vehicles to fit in with the objectives set in terms of energy transition [4]. 2.2 Solar Cars Solar Cars’ vision is to provide services that ensure a clean and sustainable environment using renewable energy, to accelerate the use and growth of conventional renewable energy sources, and to design and build electric vehicles. to make them affordable. 2.3 Presentation of the Initiative It was a small vehicle powered by solar panels and batteries, The introduction of this prototype of Solar Cars on the Algerian market has a direct impact on the sector of “mobility and sustainable transport in Africa”. The Solar Cars project aims to introduce

528

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electric vehicles to the Algerian market to meet daily transport needs. More importantly, these electric vehicles are not charged by a traditional electricity source, but rather by solar hubs that will be built to provide the electrical energy necessary to charge these vehicles. Therefore, the reduction of vehicles producing fumes by using renewable energies to meet transport needs, which guarantees sustainable development and the energy transition by contributing to the fight against climate change by reducing greenhouse gas emissions. Tight. From there, we must have government support to look at the commercial aspect of this innovation: We must really analyze and understand the Algerian market through a series of pilot programs, based on these elements, for transformed into a full-fledged business. Able to scale the business from a town like Tamanrasset.

3 Electric Vehicles, A Promising Market in Algeria The growth of the electric vehicle market is stimulating other markets, including that of lithium-ion batteries (Fig. 1). This type of battery is widely used in the field of electric vehicles because of its high energy density. According to experts, Algeria has large deposits in the north of the country in the Mediterranean, and the lakes of the southern desert regions, it is said that lithium will replace oil, it will become a metal that we will tear ourselves away in the future. Strategic in the same 0way as uranium. Lithium will be to the electric car what petroleum is to the internal combustion engine. Thirty references.

Fig. 1. Global lithium-ion battery market 2011–2024, global electric motor market 2012–2022

The global market for electric motors is also experiencing remarkable growth linked to that of electric vehicles. It was estimated at over $79 billion in 2012. According to recent estimates, it is expected to reach $129 billion in 2020 and around $142 billion in 2022, corresponding to an average annual growth rate of 6% over ten years.

4 Motorization for Solar Cars The electric motor, a key element in the energy chain of a vehicle, has continued to evolve since “they never content” of 1899 [5] until today. The first electric vehicles were equipped with DC motors with adjustable separate excitation. They have been replaced

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since the end of the 1990s by alternating current motors, thanks to progress made in power and control electronics. Compared to their predecessors, these motors are much more robust, have higher power to weight and density, and better efficiency. Three technologies are currently used. These are the so-called rotor-excited synchronous machines, by permanent magnets or by windings, and induction (or asynchronous) machines. 4.1 Characteristics required for an electric traction motor The engine must meet a need for power and torque to satisfy at least the standard operation of the vehicle. This standardization is based on standardized operating cycles. Since September 2018, the WLTC cycle, also called WLTP (Worldwide harmonized Light vehicles Test Cycles/Procedures) is the new approval cycle for measuring fuel consumption, electric range, and CO2 pollutant emissions. It replaces the NEDC cycle dating from 1973, and updated in 1996. Figure 2 shows this class 3 cycle (for vehicles whose mass power is greater than 34 W/kg, i.e. the majority of cars) [6].

Fig. 2. WLTC class 3 cycle, source [6]

Compared to its predecessor, it more faithfully reproduces the real operation of a vehicle. It is then interesting to know what is the torque and power requirement at the drive wheel to satisfy this cycle. 4.2 Choice of Axial Flux Traction Motor for Solar Cars Axial flux permanent magnet synchronous motors (AFPM) Fig. 3 have higher efficiency due to a significant reduction in rotor losses due to a lack of excitation area. Moreover, one of the major advantages of axial flux is its high power density. The amount of power per kilogram (watts/kg) is quite good compared to other types of motors. They require fewer base materials than conventional radial flux motors. AFPM motors are superior in terms of vibration and noise levels. In addition, it has an easily adjustable air gap.

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Fig. 3. Comparison between radial flow machine and axial flow [7].

Its direct integration into the system is also an important feature for use in electric vehicles. Especially for designers who will be designing an electric vehicle with an inwheel motor mount, it’s safe to say this is a perfect fit. Axial flux permanent magnet motors can be designed from several Watts to MWs. As the output power of the axial flux permanent magnet motor increases Fig. 3, the ratio between the surface connection of the rotor and the shaft decreases. AFPM machines can be single or double sided or multi-stage, with or without armature slots/core. They have internal/external PM rotors and contain a surface mounted or internal PM. The double-sided AFPM motor is the most promising and widely used type. In this paper, the recommended AFPM is assumed to be placed inside the wheel and operated as a direct-drive machine. Therefore, the axial length of the machine should be as small as possible. Also, to get the highest yield, the amount of losses should be kept low. At the vehicle drive cycle, the desired machine can be used as the engine. The best feature in engine mode is the condition without the notch effect. Cogging torque is due to cogging effects in the stator. In the ironless stator, the notching effect tends to zero. With this interpretation, the best choice to achieve these goals in the vehicle as an in-wheel PM axial flux machine configuration is the TORUS-NS type with an ironless stator Fig. 4. This AFPM machine has the configuration with two rotors and a coreless (ironless) stator. The PM’s are bonded to the solid mild steel rotor disc surfaces. The best candidate for permanent magnets in rotors is the neodymium-iron-boron (Nd-Fe-B) sintered material.

Fig. 4. Slotted TORUS NS concept machine models; the average diameter and flux direction abd3D Flux paths [8]

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4.3 2D digital model at mean radius Finite element analysis (FEA) to solve electrical or magnetic problems. It solves electromagnetic field problems by solving Maxwell’s equations in a finite region of space with appropriate boundary conditions and user-specified initial conditions to obtain a solution with guaranteed uniqueness. Its working environment under MAXWELL ANSYS [9] software is given in Fig. 5.

Fig. 5. ANSYS Maxwell interactive interface

The mesh of the machine section is given in Fig. 6a. This one has 12692 elements. In order to minimize the calculation errors, the mesh of the air gap area has been refined as shown in Fig. 6b.

Fig. 6. Mesh of the studied structure, air gap area mesh view

Simulation Results The magneto static numerical model was opted for the AFPM motor [10], the simulation was carried out under a low value current in the stator. Thus, the existing magnetic field is essentially due to the presence of the magnets at the level of the rotors. The distribution of the flux density by means of vectors and direction of flux to know the state without load is represented in the Fig. 7 and Fig. 8 shows the numerical prediction of the 2D model under ANSYS Maxwell the air gap flux density under a pole. From this curve it can be seen that the ratio of the maximum air gap flux density is about 0.749 T and the flux density mean air gap is determined at 0.67T.

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Fig. 7. Distribution of induction in the machine

Fig. 8. Magnetic flux paths and air gap magnetic flux density under a pole (at mean diameter Dg = (Di + Do) / 2)

5 Conclusion Algerian, to a complete failure, forcing the authorities to reconsider the entire project. In this article, we have tried to contribute to how the country can access the electric car industry, and this is encouraged by our country’s availability of rare minerals that have a direct impact on the electric car industry. Ensuring sustainable development and contributing to the fight against climate change by reducing greenhouse gas emissions; this car we have recommended has a permanent magnet axial flow motor which will be integrated into the wheel to improve space in.the car and reduce electric power consumption. The transition to a smart city is necessary, and that is why we have proposed the starting point of our initiative, the city of Tamanrasset, through the successful and innovative use of smart technological systems to significantly save energy use, reduce carbon dioxide emissions, and contributes to the emergence of smart mobility.

References 1. Cathelat, P.: Smart city with societal choices 2030, Published by the United Nations Educational, Scientific and Cultural Organization, p. 26, (2019) 2. The Paris Agreement is a legally binding international treaty on climate change. It was adopted by 196 Parties at COP 21 in Paris on December 12, 2015 and entered into force (2016) 3. Alliad Market Reearch (2016) 4. APS algeria press service 5. https://www.usinenouvelle.com/article/jusqu-ou-ira-la-jamais-contente.N192726

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6. https://www.car-engineer.com/fr/les-differentss-cycles-de-conduite 7. A New Era of Axial Flux Motor Technology for Electric Vehicles – Magnax 8. Huang, S., Aydin, M., Lipo, T.A.: TORUS Concept Machines: Pre-Prototyping Design Assessment for Two Major Topologies. In: IEEE International Conference on Electrical Machines and Drives, Boston, pp.645–651 (2001) 9. ANSYS MAXWELL Version 16.0 user’s guide – Maxwell 2D/3D (2010) 10. Larbi, .B., Hatti, M., Kouzi, K., Ghadbane, A.: Design and Investigation of Axial Flux Permanent Magnet Synchronous Machine for electric vehicles. In: 2018 International Conference on Communications and Electrical Engineering (ICCEE) (2018)

Power Electronics and Grid Connected

Variability of Solar Radiation Received on Tilted Planes in Adrar Region in the South of Algeria I. Oulimar1,2(B) , K. Bouchouicha1 , N. Bailek2 , and M. Bellaoui1 1 Unité de Recherche en Energies Renouvelables en Milieu Saharien, URERMS, Centre de

Développement des Energies Renouvelables, CDER, 01000 Adrar, Algeria [email protected] 2 Materials and Energies Research Laboratory (MERL), Faculty of Science and Technology,University Amine Elokkal El Hadj Moussa Eg Akhamouk, 11000 Tamanrasset, Algeria

Abstract. The solar radiation is one of the renewable energy sources that is gaining importance day by day. To make the most effective use of solar energy, the tilt angle of the solar panel that collects solar radiation must be properly adjusted. In this study, experimental and analytical investigations have been conducted in order to identify the solar panels’ optimum tilt angles for the first time in Adrar site, located on the southwestern region of Algeria, and to recommend a general approach for any location in country. Calculations were made with both selected isotropic and anisotropic models. 11 years (2010–2020) historical data in Renewable Energy Research Unit in Saharan Medium (URER-MS) was used. Monthly, seasonal, and Annual solar panels’ optimum tilt angles were determined. It is found that the annual optimum solar panel tilt angle should be set to 28°. These findings might be generalized to locations with different coordinates at the same latitude the annual variability of solar radiation on different planes of inclination is between 6 kWh/m2 /day and 8 kWh/ m2 /day (2010–2020) all inclinations combined from horizontal to near 60° and 2 kWh/m2 to 6 kWh/m2 for a vertical plane facing plain south. Keywords: Solar energy · Global radiation · Temporal variability · Tilted plane

1 Introduction Algerian’s energy mix is heavily reliant on conventional energy sources, with a relatively little fraction of renewable energy currently in use [1], however, Algerian’s Renewable Energy Policy is directed at maximizing the utilization of the renewable energy and sets a future plan for obtaining over then 35% of total electrical energy from renewable energy sources by the year of 2030 [2, 3]. Among the various accessible renewable energy resources in Algeria, harnessing solar energy is critical for increasing energy output and playing a vital role in economic growth and social welfare. Solar PV is one of the renewable energy sources that is ideal for sustainable development goals implementation [4–6]. Clean electricity has provided enormous environmental, social, and economic advantages. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 537–546, 2023. https://doi.org/10.1007/978-3-031-21216-1_55

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Most of the locations in south Algeria regions receive abundant solar radiation, and solar energy utilization technology can be profitably applied to this regions [6–9]. Despite the potential for solar energy, the insufficient availability of solar radiation information at different locations hinders the design and efficient use of solar energy conversion devices. The most efficient use of solar radiation is determined by the time of day, the season, the inclination angles of the solar panels, and the solar panels’ installation area. The inclination angle of the solar panels is the most important of these parameters. There were multiple studies in this field trying to identify the optimal tilt angle for solar panels. These studies generally use the approach of determining the maximum values of sunlight falling on a fixed angle solar panel in the appropriate geographical region on the globe. These maximum values were determined and averaged over time intervals based on the inclination angle of solar radiation.[1, 10–16]. The tilt angle of a solar panel has a significant impact on its energy collecting potential. As a result, optimizing the tilt angle is required to maximize solar energy. Although experimental investigation of optimum tilt angles is certainly more accurate. In this context, experimental and analytical investigations have been conducted in this study, in order to identify in order to identify the optimized tilt angles for southern region of Algeria and to recommend a general approach for the estimation of opt for any location in country. Global horizontal irradiation was obtained from Renewable Energy Research Unit in Saharan Medium. A monthly data for a period of 11 years (Jan 2010 to Dec 2020) was utilized in our investigation. The applied mathematical method should be able to identify the optimal tilt angle of a solar panel at any position on the globe. The necessary parameters were calculated with the Matlab software code using the historical data between 2010 and 2020 obtained from Renewable Energy Research Unit in Saharan Medium (URER-MS), collected by Solar and Wind Energy Potentiality Team members. The annual, semi-annual, seasonal, and monthly optimal tilt angles of solar panels in Adrar were determined using the obtained data.

2

Study Area

The data used in this study represents the historical global solar radiation data between 2010 and 2020 obtained from Renewable Energy Research Unit in Saharan Medium (URER-MS) affiliated to the renewable energy development center (CDER), the data collected and treated by Solar and Wind Energy Potentiality Team members at Adrar site (latitude: 27.88°N, longitude: − 0.27°E, Fig. 1. Geographic map of Adrar with Mean annual sum and altitude: 269 m above the of global horizontal irradiation (kWh/m2 /year) [19] sea level) located in the south of Algeria (see Fig. 1) [17, 17].

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Methodology

In theoretically, collecting more solar energy required more frequent tilt adjustment; however, changing tilt angle frequently is impracticable. As less frequent tilt adjustment with maximum solar energy capture is preferred, a trade-off between the two is required. As a result, it is necessary to evaluate the collected solar energy happens when is fixed for months, seasons, bi-annually, and yearly (fixed tilt angle throughout the year). A Matlab code was developed to optimize the tilt angle of a solar panel in the site of Adrar [20, 21], extraction of angles significant to the variation of solar radiation [22], Furthermore, a comparison of the optimum tilt angles is conducted using the experiments measurements realised using Pyranometers Kipp & Zonen CM11 pyranometers, one for measuring global Horizontal irradiance, and the others are mounted on various flat tilted surfaces and facing south. (See Fig. 2), and finally analyze the temporal variation of global solar radiation with these angles [23, 24].

Fig. 2. GIS/URER/MS experimental platform: (a) diagram of the station (b) photo of the station

3.1 Extraterrestrial Solar Radiation Variation Extraterrestrial solar radiation is evaluated at the upper atmosphere, the energy illumination due to this radiation on a plane perpendicular to the sun’s rays is on average 1367 W/m2 per year: it is the solar constant that is usually symbolized by I0 . For extraterrestrial radiation on any surface by designating by I0 the illumination of a normal screen to the rays, the illumination of any receiving surface is equal to [25]: B0 = I0 . cos θ

(1)

Or θ is the angle of incidence of rays, and I0 =1367 W/m2 (the solar constant). The energy received by the receiving surface, of supposed fixed orientation, is deduced by the integration between the two moments that delimit the presence of the sun in the space seen by the receiving surface. B0 (β, α) =

12 ω2 .I0 ∫ cos θ.d ω π ω1

(2)

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For our case the orientation is always due to the south (α = 0) and (β) the angle of inclination of the receiving surface. By integration between sunrise and sunset, the previous formula becomes [26]: 24 ωl .I0 ∫ (sin δ sin(ϕ − β) + cos δ cos(ϕ − β) cos ω).d ω π 0

(3)

 π 24  .I0 sin δ sin(ϕ − β)ωl . + cos δ cos(ϕ − β) sin ωl π 180

(4)

B0 (β, 0) = What leads to: B0 (β, 0) =

A Matlab code was developed to optimize the tilt angle for the site of Adrar. Global horizontal irradiation was obtained from Renewable Energy Research Unit in Saharan Medium. A monthly data for a period of 11 years was utilized in this study For Adrar site, we have integrated between sunrise and sunset, the total solar radiation reaching earth’s surface was computed corresponding to each value of tilt angle, witch it was changed from 0° (horizontal plane) to 90° (vertical plane) with an increment of 0.5 for each day of the year, then took the averages irradiation for monthly, seasonly and yearly of each tilt angle. Daily tilt angle was optimized at the value at which total radiation received by a tilted surface was maximum. The optimum angles of each month represent the average of the angles which corresponds to the maximum irradiation of each day of the self, detailed investigation of optimized tilt angle conditions for the Adrar site is presented (Table 1): Table 1. Monthly Optimum angles tilt for Adrar site Months

Janvier

Feb

Mars

Apr

Mai

Jun

Jul

August

Septembre

October

Novembre

Décembre

Optimum tilt angle

56

47

32

13

0

0

0

6

25

42

54

58

Comparison of captured solar energy in case of monthly, seasonal, biannual and annual tilt angle adjustment was done with daily adjusted tilt angle case (see Table 2): Table 2. Semi-annual angles tilt for Adrar site Period

January–March

April–August

September–December

Optimized tilt angles

45

0

45

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Results of optimum tilt angles have been validated using historical data conducted previously. Optimum angle was found to be varying from a maximum value of 58˚ in December to 0˚ in Mai, June and jully, and according to the results of the daily and monthly variation, we take the average of the months from 01 September to 31 March is equal to 45° and the average of the months from 01 April to 01 September with 0°. And by a simple comparaison between the extraterrestrial radiation at angles 0° and 45° we made a correction of the dates of the change of angle. That brings us to the date of September 11 instead of September 01 and March 28 instead of 31 March for the angle 45°.The rest of the year with angle zero (0°) (between March 29 and 12 September), With the same formula we can calculate the extraterrestrial rays on a southern vertical plane and we can gather the curves of the different representative angles (annual average, semi-annual, monthly, vertical) on the Fig. (3).

Fig. 3. Extraterrestrial solar radiation on different planes in Adrar

3.2 Variations of Solar Radiation at the EARTH’S Surface The measurements taken in this study are the averages of measurements companion carried out over 11 years (2010–2020) on the site study (URER/MS) presented in Fig. (4):

Fig. 4. Series of radiation measurements during the period 2010–2020

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A simple change of location in February 2017 from a roof of a hut with about 03 m of ground to a roof of a building with 10 m of ground (Table 3). Table 3. Measurement period for each angle of inclination Angles

Période

Observation



2010–2020

Change of location in 2017

27.88

2010–2020

Change of location in 2017

45

2011

February and October (2010–2011)

90

2013–2020

Change of location in 2017

Variable

2010

May to July (2010–2020)

In our experimental platform (Fig. 5), the horizontal and slope at the latitude of the site (27.88°) are measured during the entire period (2010–2020). Monthly optimal angle measurements are performed during 2010, with correction by multi-year averages for angles that have more than one measurement, namely the months of February and October for 45° (2010,2011) and May, June, July for 0° (2010–2020). The measurements for 45° angle are made during 2011 and between 2013 and 2020 for the vertical.

Fig. 5. Global solar radiation on different planes of inclination on the ground of Adrar

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Results and Discussion

The analysis of the curves of extraterrestrial solar radiation on different planes (Fig. 2) shows that there is generally a variation between eight (08) and tweleve (12) kWh/m2 per day for the horizontal and inclined planes; and it is generally less than 8 kwh/m2 for the vertical plane. For the solar radiation received in Adrar (Fig. 5), the variation is generally between 6 Kwh/m2 and 8 Kwh/m2 per day for the horizontal and inclined planes and it is less than 6 kwh/m2 for the vertical plane. The comparison between the extraterrestrial radiation and Adrar’s ground radiation shows that Adrar region soil receives an average of 70% of the extraterrestrial radiation annually, regardless of the inclination of the plane except in the case of the vertical plane which receives more than the extraterrestrial radiation in summer. And that is probably the albedo radiation from the ground. Amount of solar energy received may vary significantly from one location to the other depending upon its position relative to the sun along with atmospheric condition of particular location under consideration; however, optimum tilt angle conditions, as explained above, were found to be a strong function of latitude of location.

5

Conclusion

This study is carried out with the aim to determine the optimized tilt angles in Adrar region, located in southwestern of Algeria, and to recommend a general approach for the estimation of the angle for any location in the country. The optimal tilt angle varies each month of the year. The collected total solar radiation varies as the solar panel tilt angle is altered monthly, seasonally, and yearly. The earth received solar radiation has been determined to be the maximum in the monthly adjustments of the solar panel tilt angle. Matlab code was developed to optimize the tilt angle on daily, monthly, seasonally, biannually and annual basis. It was observed that the collected solar radiation decreased in the seasonal and annual solar panel tilt angles, respectively. It has been determined that the annual average optimum tilt angle of the solar panel is approximately equal to the value 28°. The results show that the average optimum tilt angles of the solar panels were 45°, 0° (Horizontal) and 45° in the periods of January to March, Aprit-August and Septmber to December, respectively. According to the annual, semiannual, and monthly adjusted solar panel tilt angle the annual variability of the collected solar radiations are between 6 kWh/m2 /day and 8 kWh/ m2 /day (2010–2020) all inclinations combined from horizontal to near 60° and 2 kWh/m2 to 6 kWh/m2 for vertical plane facing plain south.

References 1. Kaldellis, J., Zafirakis, D.: Experimental investigation of the optimum photovoltaic panels’ tilt angle during the summer period. Energy 38(1), 305–414 ( 2012). https://doi.org/10.1016/ j.energy.2011.11.058

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2. Khraief, N., Shahbaz, M., Mallick, H., Loganathan, N.: Estimation of electricity demand function for Algeria: revisit of time series analysis. Renew. Sustain. Energy Rev. 82, 4221– 4234 (2018). https://doi.org/10.1016/j.rser.2016.11.106 3. Bouchouicha, K., Hassan, M.A., Bailek, N., Aoun, N.: Estimating the global solar irradiation and optimizing the error estimates under Algerian desert climate. Renew Energy 139, 844–858 (2019). https://doi.org/10.1016/j.renene.2019.02.071 4. Bouchouicha, K., Razagui, A., Bachari, N.E.I., Aoun, N.: Mapping and geospatial analysis of solar resource in Algeria. Int. J. Energy Environ. Econ. 23(6), 735–751 (2015) 5. Bouchouicha, K., Bailek, N., Bellaoui, M., Oulimar, B.: Estimation of solar power output using ANN model: a case study of a 20-MW solar PV plan at Adrar, Algeria. In: Hatti, M. (ed.) Smart Energy Empowerment in Smart and Resilient Cities. Lecture Notes in Networks and Systems, vol. 102, pp. 195–203. Springer, Cham (2020). https://doi.org/10.1007/978-3030-37207-1_20 6. Bouchouicha, K., Bailek, N., Razagui, A., EL-Shimy, M., Bellaoui, M., Bachari, N.E.I.: Comparison of artificial intelligence and empirical models for energy production estimation of 20 MWp solar photovoltaic plant at the Saharan Medium of Algeria. Int. J. Energy Sector Manage. 15(1) (2021) . https://doi.org/10.1108/IJESM-12-2019-0017 7. Bouchouicha, K., Razagui, A., el Islam Bachari, N., Aoun, N.: Hourly global solar radiation estimation from MSG-SEVIRI images-case study: Algeria. World J. Eng. 13(3), 266–274 (2016). https://doi.org/10.1108/WJE-06-2016-036 8. Razagui, A., Abdeladim, K., Bouchouicha, K., Bachari, N., Semaoui, S., Hadj Arab, A.: A new approach to forecast solar irradiances using WRF and libRadtran models, validated with MERRA-2 reanalysis data and pyranometer measures. Solar Energy 221, 148–161 (2021). https://doi.org/10.1016/j.solener.2021.04.024 9. Razagui, A., Bachari, N.I., Bouchouicha, K., Hadj Arab, A.: Modeling the Global Solar Radiation Under Cloudy Sky Using Meteosat Second Generation High Resolution Visible Raw Data. Journal of the Indian Society of Remote Sensing 45(4), 725–732 (2016). https:// doi.org/10.1007/s12524-016-0628-8 10. Bakirci, K.: General models for optimum tilt angles of solar panels: Turkey case study. Renew. Sustain. Energy Rev. 16(8), 6149–6159 (2012). https://doi.org/10.1016/j.rser.2012.07.009 11. Benghanem, M.: Optimization of tilt angle for solar panel: Case study for Madinah, Saudi Arabia. Appl. Energy 88(4), 1427–1433 (2011). https://doi.org/10.1016/j.apenergy.2010. 10.001 12. Despotovic, M., Nedic, V.: Comparison of optimum tilt angles of solar collectors determined at yearly, seasonal and monthly levels. Energy Convers. Manag. 97 121–131 (2015). https:// doi.org/10.1016/j.enconman.2015.03.054 13. Khatib, T., Mohamed, A., Mahmoud, M., Sopian, K.: Optimization of the tilt angle of solar panels for Malaysia. Energy Sources, Part A: Recovery, Utilization and Environmental Effects 37(6), 606–613 (2015). https://doi.org/10.1080/15567036.2011.588680 14. Ullah, A., Imran, H., Maqsood, Z., Butt, N.Z.: Investigation of optimal tilt angles and effects of soiling on PV energy production in Pakistan. Renew. Energy 139, 830–843 (2019). https:// doi.org/10.1016/j.renene.2019.02.114 15. Jamil, B., Siddiqui, A.T., Akhtar, N.: Estimation of solar radiation and optimum tilt angles for south-facing surfaces in Humid Subtropical Climatic Region of India. Eng. Sci. Technol. Int. J. 19(4), 1826–1835 (2016). https://doi.org/10.1016/j.jestch.2016.10.004 16. Mialhe, P.: Variabilité spatiale et temporelle du rayonnement solaire global sur une topographie à relief marqué et complexe. Cas de l’\^\ile de La Réunion. Université de la Réunion (2018) 17. Bouchouicha, K., Oulimar, I.: La chaine de mesure radiométrique à l’Unité de Recherche en Energie Renouvelable en Milieu Saharien d’Adrar. In: international conference on energy and sustainable development (2013)

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18. Bellaoui, M., Bouchouicha, K., Oulimar, B.: Daily Global Solar Radiation Based on MODIS Products: The Case Study of ADRAR Region (Algeria). In: Hatti, M. (ed.) ICAIRES 2019. LNNS, vol. 102, pp. 157–163. Springer, Cham (2020). https://doi.org/10.1007/978-3-03037207-1_16 19. Bellaoui, M., Bouchouicha, K., Oulimar, I.: Estimation of daily global solar radiation based on MODIS satellite measurements: The case study of Adrar region (Algeria). Measurement (Lond) vol. 183 (2021). https://doi.org/10.1016/j.measurement.2021.109802 20. Iqbal, M.: An Introduction to Solar Radiation. Elsevier (1983). https://doi.org/10.1016/B9780-12-373750-2.X5001-0 21. Duffie, J.A.(Deceased), Beckman, W.A., Blair, N.: Solar Engineering of Thermal Processes, Photovoltaics and Wind (2020). https://doi.org/10.1002/9781119540328 22. Oulimar, I., Bouchouicha, K., Khelif, C.: Modèle Radiométrique Adéquat pour Caractériser l’Apport Energétique Optimal sur Site d’Adrar. In: International Conference on Energy and Sustainable Development ICESD’13 (2013) 23. Duffie, J.A., Beckman, W.A.: Solar Eng. Thermal Process. Fourth Edition. (2013). https:// doi.org/10.1002/9781118671603 24. Mondol, J.D., Yohanis, Y. G., Norton, B.: The impact of array inclination and orientation on the performance of a grid-connected photovoltaic system. Renew. Energy 32(1), 118–140 (2007). https://doi.org/10.1016/j.renene.2006.05.006 25. Duffie, J.A., Beckman, W.A., Worek, W.M.: Solar Engineering of Thermal Processes. In: 2nd ed., J. Sol, Energy Eng. 116(1), 944 (1994). https://doi.org/10.1115/1.2930068 26. Oulimar, I.: Diagnostic sur les différents modes de collecte énergétique. Adrar (2010)

Environmental and Financial Impact Analysis of a Tubular 850 KW Wind Turbine Tower F. Ferroudji1,2(B) , L. Saihi1 , and K. Roummani1 1 Unité de Recherche en Energies Renouvelables en Milieu Saharien, URERMS, Centre de

Développement des Energies Renouvelables, CDER, 01000 Adrar, Algeria [email protected] 2 Laboratoire de Mécanique des Structures et Matériaux, Université de Batna 2, Batna, Algeria

Abstract. Nowadays, the main importance for leading organizations is taking into consideration the environmental impact of a product at the product design stage. This study focuses on the analysis environmental and financial impact of a steel tubular tower assembly for 850 KW wind turbine using SOLIDWORKS Sustainability tool. The analyses of the tower for four materials with different steel qualities (S235, S275, S355 and S460) were performed in order to evaluate the carbon footprint, total energy consumption, air acidification and water eutrophication. The results obtained indicate that the S275 and S355 materials are more environmentally friendly and less financial impact than the reference material. Keywords: Wind turbine tower · Life cycle assessment · Eco-design · SOLIDWORKS Sustainability tool

1 Introduction In recent decades, the wind power is the most promising among all other renewable energy options, due to the development of turbine generator techniques and sustainability. Wind generators convert kinetic energy of wind to electricity energy with zero producing CO2 emissions and impact on nature during their operation [1–4]. However, there are environmental impact resulting from wind turbines manufacture, transportation, installation and dismantling at the end of their operational life [5]. Recently, in the face of a deepening environmental crisis, the necessity to eco-design (or green-design) of products is more and more growing in engineering design and it entered in the international political agenda [6]. Consequently, Life Cycle Assessment (LCA) (also known as ’cradle-to-grave analysis’) emerged in answer to the necessity to develop life cycles causing the lowest direct and indirect environmental impacts. Designers can be used LCA technique to compare environmental impacts for different products which perform the same functions, in order to ensure a sustainable design [7–10]. In order to support LCA technique, several types of Computer Aided Design (CAD) software tools such as SOLIDWORKS, CATIA, and AUTODESK are developed and could be used of them as eco-design tools in the product design phase [11–13]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 547–554, 2023. https://doi.org/10.1007/978-3-031-21216-1_56

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In horizontal axis wind turbine, the manufacturing cast of tower is considered of high importance which can correspond 30–40% of the total wind turbine costs. The tower structure appears to be the component with the highest environmental impact in the wind turbine system [14, 15]. This study focuses on the analysis of environmental (or pollution) and financial impact over the life cycle of a steel tubular tower assembly for 850 KW wind turbine. The analyses of the tower for four materials with different steel qualities (S235, S275, S355 and S460) were conducted using SOLIDWORKS Sustainability tool. Then, in order to select the suitable material, these four materials were compared with the reference material, in terms of environmental impact considering the carbon footprint (or global warming potential), total energy consumption, air acidification and water eutrophication.

2 Methodology In this present paper, for the case study, a wind turbine tower assembly from the concept of sustainability. In Fig. 1 shows the wind turbine tower assembly and its components. The tower is tube steel structure and it is 55 m high. The tower is pre-assembled into three hollow subparts which are bolted together by means flanges at either end. The tower structure is conical form and its shell thickness varying from 18mm at the bottom to 10mm at the top. Present wind turbine tower and wind turbine in detailed in Ref. [14].

Fig. 1. Wind turbine tower for sustainability analysis [14]

For assess the environmental impact of wind turbine tower, we use Sustainability tool (by Dassault Systems) [16]. This tool is an integrated part of the SOLIDWORKS environment which has the ability of evaluation of environmental and financial impact of a design by using the science of LCA of a product based on the GaBi sustainability database. From different materials and design solutions, the designers can be compared results to ensure a sustainable design [17]. The sustainable design methodology includes three basic steps: (i) using safe materials; (ii) using an efficient manufacturing process, and (iii) reducing the carbon footprint. The sustainability analysis for the tower assembly model using SOLIDWORKS Sustainability tool is presented in Fig. 2.

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At the first, by accessing the “Set Material” tab, was launched the steels materials database available (as shown in Fig. 3) which contains class steels, “Material Class” with characteristics of each material such as Elastic modulus, Yield strength, etc. We set Cast Carbon Steel [18] as the reference material for all parts of the initial assembly (or base assembly) of the tower model. From the steel database is chosen four materials with different steel qualities which are S235 [19], S275 [20], S355 [5, 14], and S460 [21] as indicated in Fig. 3. Then, we compare the environment and financial impact of these four materials with the reference material in order to select the suitable material.

Fig. 2. Sustainability analysis of tower model.

Fig. 3. Steel Materials database.

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In addition to the material’s type used to manufacture the product, there are other factors that influences on the environment. In Sustainability tool, the manufacturing region, the region where the product is transported and used and manufacturing process were taken into account [22, 23]. For this present analysis, a location in Asia was chosen as the manufacturing region (Fig. 4a), Europe was chosen as the utilization region (Fig. 4b), the distance between the both regions is based on the assumption that the tower model is shipped to site by ship over a distance of 16093 km and the manufacturing process/or technology used is a “Stamped/Formed sheetmetal”. Based on the tower’s material selected, default percentage values for end of life of the tower in the sustainability database 25, 24 and 51% are recycled, incinerated and landfill respectively (Fig. 4c). The input data in SOLIDWORKS Sustainability analysis of tower model is summarized in Table 1.

Fig. 4. (a) Manufacturing, (b) Use, and (c) Transportation and end of life.

Table 1. The input data in SOLIDWORKS sustainability analysis of tower model. Manufacturing

Use

Transportation

End of useful life

Region: Asia

Region: Europe

Distance by train: 0.00 km

Recycled: 25%

Process: Stamped/Formed sheet-metal

In use during: 1.0 year

Distance by truck: 0.00 km

Incinerated: 24%

Electricity consumption: 0.19 kWh/gm

Distance by boat: 16093 km

Landfill: 51%

Natural gas consumption: 1476.6 BTU/gm

Distance by air: 0.00 km

Scrap rate: 9.67% Reference material: Cast Carbon Steel

Material cost per unit: 0.4 USD/kg

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3 Results and Discussion The sustainability analysis was conducted after the input data in place. The Sustainability tool affords a quantitative analysis of the environmental impact of the wind turbine tower, measured by four indicators and a computation of the material’s financial impact. SOLIDWORKS Sustainability tool use the CML environmental impact assessment methodology to analyze [17]: (i) carbon footprint (kg CO2), (ii) total energy consumption (MJ), (iii) air acidification potential (kg SO2), and (iv) water eutrophication potential (kg PO4). In this present paper, the analyses are performed where the while duration of the wind turbine lifetime (all life cycle stages) is assumed to be 20 years. The results of the environmental and financial impact for the tower model are illustrated in Figs. 5, 6, 7 and 8.

Fig. 5. Environmental and financial impact for the tower model - S235 Steel material (Yield Strength: 234 Mpa).

Figure 5 displays the environmental and financial impact of the tower model with the S235 steel material. The analysis indicates that the carbon footprint, total energy consumption and water eutrophication are increased by 2% and air acidification is increased by 68%. However, the financial impact is reduced by 25%. Figure 6 displays the environmental and financial impact of the tower model with the S275 steel material. The analysis indicates that the carbon footprint, total energy consumption, water eutrophication, and air acidification are reduced by 9, 7, 5, and 25% respectively. The financial impact is reduced by 28%.

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Figure 7 displays the environmental and financial impact of the tower model with the S355 steel material. The analysis indicates that the carbon footprint, total energy consumption, water eutrophication, and air acidification are reduced by 9, 8, 6, and 25% respectively. The financial impact is reduced by 28%. Figure 8 displays the environmental and financial impact of the tower model with the S460 steel material. The analysis indicates that the carbon footprint and total energy consumption are reduced by 3%, and water eutrophication and air acidification are increased by 4 and 40% respectively. However, the financial impact is reduced by 16%.

Fig. 6. Environmental and financial impact for the tower model - S275 Steel material (Yield Strength: 282 Mpa).

Fig. 7. Environmental and financial impact for the tower model - S355 Steel material (Yield Strength: 350 Mpa)

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Fig. 8. Environmental and financial impact for the tower model – S460 Steel material (Yield Strength: 460 Mpa)

4 Conclusion This study used LCA method to analyze the environmental and financial impact of a steel tubular tower structure for 850 KW wind turbine. The analyses of the tower for four materials with different steel qualities (S235, S275, S355 and S460) were conducted using SOLIDWORKS Sustainability tool. According to the analysis results, among the four materials chosen, the S275 and S355 steel materials are more environmentally friendly and less financial impact than the reference material.

References 1. Hernandez-Estrada, E., et al.: Considerations for the structural analysis and design of wind turbine towers: a review. Renew. Sustain. Energy Rev. 137, 110447 (2021) 2. Saihi, L., Bakou, Y., Ferroudji, F., Berbaoui, B., Djilali, L.: MPPTF & pitch fuzzy controller of a wind turbine system using DFIG. In: 2019 1st International Conference on Sustainable Renewable Energy Systems and Applications (ICSRESA) (2019) 3. Ferroudji, F., Khelifi, C.: Structural strength analysis and fabrication of a straight blade of an H-Darrieus wind turbine. J. Appl. Comput. Mech. 7(3), 1276–1282 (2021) 4. Roummani, K., et al.: A new concept in direct-driven vertical axis wind energy conversion system under real wind speed with robust stator power control. Renew. Energy. 143, 478–487 (2019) 5. Stavridou, N., Koltsakis, E., Baniotooulos, C.C.: Life cycle analysis of lattice and tubular wind turbine towers. A comparative study. IOP Conf. Ser. Earth Environ. Sci. 410, 012–071 (2020) 6. Vezzoli, C.: Design for Environmental Sustainability: Life Cycle Design of Products, 2nd edn. Springer-Verlag, London (2018) 7. Cappelli, F., Delogu, M., Pierini, M.: Integration of LCA and EcoDesign guideline in a virtual cad framework. In: Proceedings of LCE2006, pp. 185–188 (2006)

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8. Pereira, L., Mafalda, R., Marconcini, J. M., Mantovani, G. L.: The use of sugarcane bagassebased green materials for sustainable packaging design. In: Chakrabarti, A. (ed.) ICoRD 2015 – Research into Design Across Boundaries Volume 2. SIST, vol. 35, pp. 113–123. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2229-3_10 9. Tseng, T.L.B., Rahman, M.F., Chiou, R., Ho, J.C.: Sustainable green design and life cycle assessment for engineering education. In: 2021 ASEE Virtual Annual Conference (2021) 10. Morbidoni, A., Favi, R., Germani, M.: CAD-integrated LCA tool: comparison with dedicated LCA software and guidelines for the improvement. In: Hesselbach, J., Herrmann, C. (eds.) Glocalized Solutions for Sustainability in Manufacturing, pp. 569–574. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19692-8_99 11. Ferroudji, F., Outtas, T., Khelifi, C.: Design, modeling and finite element static analysis of a new two axis solar tracker using SolidWorks/COSMOSWorks. Appl. Mech. Mater. 446–447, 738–743 (2014) 12. Russo, D., Rizzi, C.: Structural optimization strategies to design green products. Comput. Ind. 65(3), 470–479 (2014) 13. Ferroudji, F., Khelifi, K., Outtas, T.: Structural dynamics analysis of three-dimensional biaxial sun-tracking system structure determined by numerical modal analysis. J. Solar Energy Eng. 140(3), 031004–031011 (2018) 14. Ferroudji, F.: Numerical modal analysis of a 850 KW wind turbine steel tower. Int. Rev. Appl. Sci. Eng. 12(1), 10–18 (2021) 15. Hernandez-Estrada, E., et al.: Considerations for the structural analysis and design of wind turbine towers a review. Renew. Sustain. Energy Rev. 137, 110447 (2021) 16. SolidWorks (2016). http://www.solidworks.com/sustainability 17. Dudkowiak, A., Grajewski, D., Dostatni, E.: Analysis of selected IT tools supporting ecodesign in the 3D CAD environment. IEEE Access. 9, 134945–13956 (2021) 18. Martinez, M.A., Adam, J.M., AlvarezRabanal, F.P., del GozDíaz, J.J.: Wind turbine tower collapse due to flange failure: FEM and DOE analyses. Eng. Failure Anal. 104, 932–949 (2019) 19. Malliotakis, G., Alevras, P., Baniotopoulos, C.: Recent advances in vibration control methods for wind turbine towers. Energies 14, 7536 (2021) 20. Hu, Y., Baniotopoulos, Yang, J.: Effect of internal stiffening rings and wall thickness on the structural response of steel wind turbine towers. Eng. Struct. 81, 148–161 (2014) 21. Veljkovic, M., et al.: High-strength tower in steel for wind turbines (HISTWIN). In: European Commission Joint Research Center. Ispra, Italy (2012) 22. Toscano, A.R., Herazo, J.C.M., Millán, R.R., Palma, H.G.H., Martinez, J.A.S.: Approach methodology for the sustainable design of packaging through computational tools: case study: water bottles. Case Stud. Thermal Eng. 16, 100561 (2019) 23. Torc˘atoru, C., S˘avescu, D.: Analyzing the sustainability of an automotive component using SOLIDWORKS CAD software. In: Annual Session of Scientific Papers IMT ORADEA 2019 (2019)

Modeling of Two Five-Phase Induction Machines Connected in Series with an Open Phase Nekkaz Mohamed1 , Djahbar Abdelkader1(B) , and Benali Youcef Mohammed2 1 LGEER Laboratory, University Hassiba Benbouali of Chlef, Chlef, Algeria

{m.nekkaz,a.djahbar}@univ-chlef.dz

2 LDEE Laboratory, University USTO of Oran, Oran, Algeria

[email protected]

Abstract. In the context of a degraded mode control of a drive comprising two five-phase induction machines connected in series and controlled independently, the article focuses on the investigation of the drive’s performance in the case of phase opening, as well as the influence of the fault on the two five-phase machines and their control in normal mode and degraded mode in simulations. Keywords: Five-phase machine · Connected in series · Polyphase machine · Degraded mode

1 Introduction Electronic equipment is becoming increasingly common in airplanes and space rockets. Various studies have already listed the benefits of replacing pneumatic, mechanical, and hydraulic applications with electrical analog systems [1, 2]. However, this new technology must meet stringent reliability and safety standards without increasing neither the equipment’s weight (a major constraint in the areas discussed here), or the system’s overall cost. Fault-tolerant architectures can be used to ensure the required reliability [3–5], while weight and cost reduction can be achieved by pooling of power electronics [4–6]. In the context of what has already been mentioned, this work aims to create a practical structure that is interesting in terms of weight, cost, and reliability. Different research [6, 7] have shown how two polyphase machines connected in series can be controlled independently. Indeed, with this particular connection, it is possible. When comparing to a topology in which the machines have the same number of phases, series coupling reduces the number of transistors by 50%, provided that, the two machines are supplied separately by H-bridges. Furthermore, by connecting the inductors of the two machines in series [8, 9], the currents in degraded mode can be reduced. This research will help to increase the availability of multi-machine serially connected systems in degraded mode. In This Paper, we propose and study a drive model for two series-connected five-phase machines powered by a single inverter, knowing that this system will be evaluated in both normal mode and degraded operating mode. In order to perform this study effectively; the degradation scenario is produced by an open phase. Following that, the phase disconnection modes will be analyzed in detail. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 555–564, 2023. https://doi.org/10.1007/978-3-031-21216-1_57

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2 Five-Phase Series-Connected Two Motor Drive The basic topology of five-phase series-connected two motor drive system is shown in Fig. 1. The variable frequency (VF) source power the five-phase induction machine (Motor 1) whose stator windings are connected to the stator windings of another fivephase induction machine (Motor 2) through an appropriate phase transposition. The two machines’ rotors are separate and coupled to different mechanical loads [10].

Fig. 1. Representation of two five-phase IM in series with transposed stator phases

As a consequence of the phase transposition shown in Fig. 1, inverter phase voltages are related to individual machine phase voltages through. ⎧ ⎪ vA = vas1 + vas2 ⎪ ⎪ ⎪ ⎪ ⎨ vB = vbs1 + vcs2 (1) vC = vcs1 + ves2 ⎪ ⎪ ⎪ vD = vds1 + vbs2 ⎪ ⎪ ⎩v = v +v E es1 ds2 In general, even when both machines are five-phase, they may have different parameters. Let the index ‘1’ denote the induction machine that is directly connected to the five-phase inverter, and the index ‘2’ indicate the second induction machine that is connected to the first machine through phase transposition. The full system’s voltage equationcan be written in a compact matrix form as: v =R∗i+

d (L ∗ i) dt

(2)

where the system is of the 15th order. ⎡ inv ⎤ ⎡ inv ⎤ v i v = ⎣ 0 ⎦, i = ⎣ ir1 ⎦ ; v inv = [vA vB vC vD vE ]T ; iinv = [iA iB iC iD iE ]T ; ir1 = 0 ir2 T [iar1 ibr1 icr1 idr1 ier1 ] ; ir2 = [iar2 ibr2 icr2 idr2 ier2 ]T . where v and i denote the voltage, current, and the s and r subscripts stand for the stator and rotor variables, respectively. The a, b, c, d and e subscripts identify the five phases, and the T superscript designates the transpose operator.

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The stator and rotor resistance and inductance matrices are defined as follows: [Rs1 ] = [Rs2 ] = [Rs ].[I5 ]

(3)

[Rr1 ] = [Rr2 ] = [Rr ].[I5 ]

(4)

[Lss1 ] = [Lss2 ] = [Lls ].[I5 ] + M .[(ϑ)]

(5)

[Lrr1 ] = [Lrr2 ] = [Llr ].[I5 ] + M .[∅(ϑ)]

(6)

[Lsr ] = [Lrs ]T = M .[(θ )]

(7)

θ= ⎡ ⎢ ⎢ ⎢ (ϑ) = ⎢ ⎢ ⎣ ⎡

t

ωr dt

(8)

0

1 cos(4ϑ) cos(3ϑ) cos(2ϑ) cos(ϑ)

cos(ϑ) 1 cos(4ϑ) cos(3ϑ) cos(2ϑ)

cos(1 ) cos(2 ) ⎢ cos( ) cos( ) ⎢ 1 5 ⎢ ∅(θ ) = ⎢ cos(4 ) cos(1 ) ⎢ ⎣ cos(3 ) cos(1 ) cos(2 ) cos(1 )

⎤ cos(2ϑ) cos(3ϑ) cos(4ϑ) cos(ϑ) cos(2ϑ) cos(3ϑ) ⎥ ⎥ ⎥ 1 cos(ϑ) cos(2ϑ) ⎥ ⎥ cos(4ϑ) 1 cos(ϑ) ⎦ cos(3ϑ) cos(4ϑ) 1 ⎤ cos(3) cos(4 ) cos(5 ) cos(2 ) cos(3 ) cos(4 ) ⎥ ⎥ ⎥ cos(1 ) cos(2 ) cos(3 ) ⎥ ⎥ cos(5 ) cos(1 ) cos(2 ) ⎦ cos(4 ) cos(5 ) cos(1 )

(9)

(10)

Being [I5 ] the identity matrix of order 5, Rs and Rr are the stator and rotor resistance, respectively, M the mutual inductance, and Lls and Llr the stator and rotor leakage inductance, respectively. Finally, and ∅k are angles defined as ∅k = θ + (k − 1)ϑ, with k = {1, 2, 3, 4, 5}, where θ represents the instantaneous rotor azimuth with respect to the α-axis of the stationary reference frame and ωr is the rotor electrical speed. The Eq. (11) is verified during the normal operation of the multiphase drive. Stator phase voltages can be obtained using the switching state of each leg of the power converter (Si ), as it is stated in (12) where Si = 0 if the lower switch is ON and Si = 1 if the opposite occurs. The power converter (the two-level five-phase voltage source inverter) provides 25 = 32 voltage vectors (30 active and 2 zero), which can be mapped applying the decoupling Clarke’s transformation defined in (13) in two orthogonal subspaces (α − β and x −y) plus the zero-sequence component. Each vector is identified using the decimal number corresponding to the binary code of the switching sate [Sa; Sb; Sc; Sd ; Se]. VA + VB + VC + VD + VE = 0

(11)

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⎡ ⎤ ⎡ ⎤ 4 −1 −1 −1 −1 vas1 + vas2 VA ⎢ −1 4 −1 −1 −1 ⎢V ⎥ ⎢v + v ⎥ ⎢ ⎢ B ⎥ ⎢ bs1 cs2 ⎥ ⎢ ⎥ ⎢ ⎥ Vdc ⎢ ∗ ⎢ −1 −1 4 −1 −1 ⎢ VC ⎥ = ⎢ vcs1 + ves2 ⎥ = ⎢ ⎢ ⎥ ⎢ ⎥ 5 ⎣ −1 −1 −1 4 −1 ⎣ VD ⎦ ⎣ vds1 + vbs2 ⎦ VE ves1 + vds2 −1 −1 −1 −1 4 ⎡

⎤⎡

⎤ Sa ⎥⎢ S ⎥ ⎥⎢ b ⎥ ⎥⎢ ⎥ ⎥⎢ Sc ⎥ ⎥⎢ ⎥ ⎦⎣ Sd ⎦ Se

(12)

In order to simplify the phase-domain model, the decoupling transformation is applied. The Clark’s decoupling transformation matrix in power invariant form is [11]–[12]: ⎤ ⎡ 1 cos(α) cos(2α) cos(3α) cos(4α)

⎢ 0 sin(α) sin(2α) sin(3α) sin(4α) ⎥ ⎥ 2⎢ ⎥ ⎢ (13) [c] = ⎢ 1 cos(2α) cos(4α) cos(6α) cos(8α) ⎥ ⎥ 5⎢ ⎣ 0 sin(2α) sin(4α) sin(6α) sin(8α) ⎦ √ √ √ √ √ 1/2 1/2 1/2 1/2 1/2 By omitting the x-y and zero-sequence equation for rotor windings and the zerosequence equation of the inverter, the complete d-q model in stationary reference frame for the two five-phase series-connected machines can be written in developed form as: ⎧ diinv diinv ⎪ ⎪ Vdinv = Rs1 idinv + Ls1 dtd + Lm1 didtdr1 + Rs2 idinv + Ls2 dtd ⎪ ⎪ ⎪ diinv diinv di ⎨ inv Vq = Rs1 iqinv + Ls1 dtq + Lm1 dtqr1 + Rs2 iqinv + Ls2 dtq (14) diinv diinv ⎪ ⎪ Vxinv = Rs1 ixinv + Ls1 dtx + Rs2 ixinv + Ls2 dtx + Lm2 didtαr2 ⎪ ⎪ ⎪ diinv diinv di ⎩ inv Vy = Rs1 iyinv + Ls1 dty + Rs2 iyinv + Ls2 dty + Lm2 dtβr2 Corresponding rotor equations are: ⎧   didinv didr1 ⎪ inv + (L + L )i ⎪ 0 = R L i + L + + L + ω i (L ) r1 m1 r1 m1 1 m1 r1 m1 qr1 dr1 ⎪ q dt dt ⎪ ⎪ ⎪ diinv ⎨ di 0 = Rr1 iqr1 + Lm1 dtq + (Lr1 + Lm1 ) dtqr1 − ω1(Lm1 idinv + (Lr1 + Lm1 )idr1 ) (15) inv ⎪ 0 = Rr2 idr2 + Lm2 dix + (Lr2 + Lm2 ) didr2 + ω2 Lm2 iinv + (Lr2 + Lm2 )iqr2 ⎪ ⎪ y dt dt ⎪ ⎪ ⎪ diinv di ⎩ 0 = Rr2 iqr2 + Lm2 dty + (Lr2 + Lm2 ) dtqr2 − ω2 (Lm2 ixinv + (Lr2 + Lm2 )idr2 ) The electromagnetic torques is evaluated as:  Tr1 = P1 Lm1 (idr1 iq − id iqr1 ) Tr2 = P2 Lm2 (idr2 iy − ix iqr2 ) The mechanical equation of the two machines is described as:  d 1 = Tr1 − TL1 − fm1 1 Jm1 dt d Jm2 dt 2 = Tr2 − TL2 − fm2 2

(16)

(17)

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3 Five-Phase Series-Connected Two Motor Drive in Open-Phase Fault Operation The two machines become asymmetrical when one open-phase fault occurs, and the system model must be reviewed because the total of the healthy phase voltages is no longer zero. Since the five-phase two machines is symmetrical, it will be considered further on that phase ‘a’ is the faulty phase without-lack of generality. Hence, the induced back-emf of the phase ‘a’ is considered in the equilibrium phase voltage equations and the matrix that relates inverter and phase voltages in post-fault situation becomes (Fig. 2):

Fig. 2. Schematic diagram of five-phase series-connected two motor drive in open phase

Let us assume that phase “a” is under open circuit, with ias1 = ias2 = 0. Then, the stator voltage of the faulty phase is obtained as follows: vAs = (Rs1 + Rs2 )ias +

d d ∅as = ∅as = BackEmf a2 dt dt

(18)

Considering (18) and further developing the stator flux term (19), the back electromotive force (EMF) after the fault can be expressed as: ∅as = ∅αs + ∅xs = (Ls1 + Ls2 )iαs + (Lm1 + Lm2 )iαr + Lls1 + Lls2 )ixs

(19)

d (Ls1 + Ls2 )iαs + (Lm1 + Lm2 )iαr + Lls1 + Lls2 )ixs dt

(20)

BackEmf a2 =

Notice that, when the fault condition appears, one degree of freedom is lost. As a consequence, a fixed relationship between α and x components are obtained: ias1 = iαs + ixs = 0 => iαs = −ixs

(21)

Consequently, further developing (20) considering (21) and the definition of the electrical parameters of the machine (Ls1,2 = Lm1,2 + Lls1,2 and Lr1,2 = Lm1,2 + Llr1,2 ), the induced back − EMF term can be written as in: BackEmf a2 =

d ((L + Lm2 )iαs2 + (Lm1 + Lm2 )iαr2 ) dt m1

(22)

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Notice that the faulty phase is aligned with the α– axis; consequently, no β– stator/rotor component appears in (22) and (23). Since (11) remains valid in the post-fault situation and the voltage of phase “a” is no longer controllable, the voltage of the neutral point oscillates due to the count-erelectromotive force of (18), and the leg-to-phase voltage relationship needs to be modified. Considering (11), the leg-to-phase voltage relationship can be obtained in terms of the neutral-to-neutral voltage (VnN ) ⎤ ⎡ ⎡ ⎤ ⎡ ⎤ 10 000 vBs VbN 2 ⎥ ⎢v ⎥ ⎢ V ⎥ ⎢ 01 000 ⎥ ⎢ Cs ⎥ ⎢ ⎥ ⎢ cN 2 ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ (23) 00 100 ⎥ ∗ ⎢ vDs ⎥ ⎢ VdN 2 ⎥=⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ ⎦ ⎣ vEs ⎦ ⎣ VeN 2 ⎦ ⎣ 00 010 BackEmf a2 VsN −1 −1 −1 −1 0 Applying the inverse transformation to (24), the phase voltages can be calculated in terms of the leg voltages by ⎡ ⎤ ⎡ ⎤ ⎤ ⎡ VbN 2 vBs 3 −1 −1 −1 −1 ⎢v ⎥ ⎥ ⎢ −1 3 −1 −1 −1 ⎥ ⎢ V ⎢ Cs ⎥ ⎥ ⎢ cN 2 ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ 1 ⎢ ⎥ (24) ⎢ vDs ⎥ = 4 ∗ ⎢ −1 −1 3 −1 −1 ⎥∗ ⎢ VdN 2 ⎥ ⎢ ⎥ ⎢ ⎥ ⎥ ⎢ ⎣ vEs ⎦ ⎦ ⎣ −1 −1 −1 3 −1 ⎦ ⎣ VeN 2 1 1 1 1 1 VsN BackEmfa2 This, in turn, can be written as ⎤⎡ ⎤ ⎡ ⎡ ⎤ ⎤ 3 −1 −1 −1 Sb VB 1 ⎥⎢ ⎥ (L + L )i + (L + L )i ⎢ ⎥ ⎢ VC ⎥ Vdc ⎢ −1 3 −1 −1 S m1 m2 αs m1 m2 αr2 ⎥ ⎢ c ⎢ ⎥= ⎢1⎥ ⎥⎢ ⎥ − ⎢ ⎣ VD ⎦ ⎣1⎦ 4 ⎣ −1 −1 3 −1 ⎦⎣ Sd ⎦ 4 VE Se 1 −1 −1 −1 3 (25) ⎡

being the second term on the right-hand side in (25) the Back-Emf of phase ‘a’, obtained from the neutral voltage evaluation [11, 12, 13], where Lm1,2 is defined as 5M 2 . Stator/rotor impedance asymmetries appear during the fault operation that lead to non-circular trajectories of the stator currents in the α − β plane. To compensate these asymmetries, a modified Clarke’s transformation is proposed in [10], see Eq. (26), and a symmetrical post-fault model of the machine can be obtained with circular trajectories of the stator currents in the α − β plane. The proposed transformation generates the same set of equations in pre and post-fault conditions in α − βandx − y coordinates. Then, the same model of the drive can be used in healthy and faulty operation, which simplifies the management of the faulty condition where the number of switching states is reduced from 25 = 32 to 24 = 16 and the voltage vectors in α − β and x − y subspaces are consequently changed, Fig. 3(b). ⎤ ⎡ ⎤ ⎤⎡ ⎡ cos(ϑ) − 1 cos(2ϑ) − 1 cos(3ϑ) − 1 cos(4ϑ) − 1 vBs vsα ⎥ ⎢ vCs ⎥ ⎢ vsβ ⎥ 2 ⎢ sin(ϑ) sin(2θ) sin(3ϑ) sin(4ϑ) ⎥ ⎢ ⎥ ⎥⎢ ⎢ ⎦ ⎣ vDs ⎦ (26) ⎣ vsx ⎦ = 5 ⎣ sin(2ϑ) sin(4ϑ) sin(6ϑ) sin(8ϑ) vsy vEs 1 1 1 1

Modeling of Two Five-Phase Induction Machines

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4 Experimental Results Two simulation environments were designed to analyze the operation of two five-phase induction machines powered by a single five-phase converter before and after a fault. The study of the fault condition taking into account phase opening ‘a’ at time t = 2.5 s. The model parameters of each machine used during the simulations are listed in Table 1. These machines have negligible mutuals between the phases and perfectly sinusoidal electromotive forces. Table 1. Machine parameters Parameters name

Parameters values

Stator resistance, Rs

6.3 

Rotor resistance, Rr

10 

Stator leakage inductance LlS

0.46 mH

Rotor leakage inductance Llr

0.46 mH

Mutual inductance, M

0.42 mH

Moment of Inertia Jm

0.01 kg.m2

Number of Poles, P

2

Rated Torque Tr

2 N.m

In the first test, the first machine start runs at a speed of 314 rad/s with a torque load applied at time (t1 = 1 s), followed by another torque applied at time (t2 = 5 s). However, the second machine runs concurrently with the first machine at 157 rad/s, with a torque load applied at time (t3 = 1.5 s), followed by another torque applied at time (t4 = 5.5 s). In the second test, the first machine start runs at a speed of 314 rad/s; then rotating in the opposite direction at a speed of (-) 314 rad/s at time (t3 = 3 s),with a torque load applied at time (t1 = 1 s), followed by another torque applied at time (t5 = 5 s). However, the second machine runs concurrently with the first machine at 157 rad/s; then rotating in the opposite direction at a speed of (-)157 rad/s at time (t4 = 3.5 s), with a torque load applied at time (t2 = 1.5 s), followed by another torque applied at time (t6 = 5.5 s) Figs. 11 and 12.

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The Figs. 3 and 4 presents the stator currents for first and second machines in degraded mode, these two figures confirm that the phase current “a“is zero when it is in default. The Figs. 5, 6, 7, 8, 9 and 10 show higher harmonics in degraded mode compared to the normal mode, In both normal and degraded modes; both machines are well controlled independently. 3

ias1 ibs1 ics1 ids1 ies1

currents M1 (A)

2 1 0 -1

ias2 ibs2 ics2 ids2 ies2

2 Currents M2 (A)

3

1 0 -1 -2

-2 -3 2.48

2.53

2.52

2.51 Times (s)

2.5

2.49

-3 2.48

2.54

2.5

2.52

2.54 Times

2.56

2.58

2.6

Fig. 3. Stator current of first machine with the Fig. 4. Stator current of second machine with phase “a” open the phase “a” open 330

Speed wr1 (rad/s)

Speed M1 (rad/s)

(c)

300

wref

250

wr1

200 150 100

wref wr1

(d)

325

350

320

Normal mode

Degraded mode

315 310

50 0 -50 0

1

2

3 Times (s)

4

5

305 1.5

6

Fig. 5. Mechanical speed of first machine with the phase “a” open

2

2.5

3 Times (s)

3.5

4

4.5

Fig. 6. Zoom of Mechanical speed 165 wref

200 Speed wr2 (rad/s)

wr2

Speed wr2 (rad/s)

150 wref wr2

100

50

160

Degraded mode

Normal mode

155

0

150 0

1

2

3 Times (s)

4

5

6

Fig. 7. Mechanical speed of second machine with the phase “a” open

2

2.2

2.4

2.6

2.8

3 Times (s)

3.2

3.4

3.6

Fig. 8. Zoom of Mechanical speed

3.8

4

Modeling of Two Five-Phase Induction Machines 12

563

20 iqs

Tr2

iys

15

8 currents (A)

Torque Tr1 and Tr2 (N.m)

Tr1 10

6 4

10

5

2 0

0 -2 0

1

2

3 Times (s)

4

5

-5 0

6

Fig. 9. Electromagnetic torque of first and second machine with the phase “a” open

1

2

3 Times (s)

4

5

Fig. 10. Curents of first and second machine with the phase “a” open 15

400

Tr1

wr1 wr2

Torques Tr1 and Tr2 (N.m)

Speed wr1 and wr2 (rad/s)

300 200 100 0 -100 -200 -300 -400 0

6

1

2

3 Times (s)

4

5

Fig. 11. Mechanical speed of the first and second machine with the phase “a” open

6

Tr2

10 5 0 -5 -10 -15 0

1

2

3 Times (s)

4

5

6

Fig. 12. Electromagnetic torque of first and second machine with the phase “a” open

5 Conclusion This paper investigates the mechanical and electrical behavior of a system with two five-phase machines connected in series; in the case of a single-phase fault (case of an open phase). It is mentioned that the series connection topology is a solution to reduce the number of transistors. Furthermore, this system can be less expensive regardless of operational constraints (higher copper losses). This work has allowed us to synthesize the effect of an open phase as a fault in the stator winding, Which in any case generates an increase in torque ripples, a decrease in speed, in torque and the mechanical efficiency.On the other hand, there is a strong correlation between electromagnetic torque, mechanical speed and efficiency, it is also found that betweenefficiency and the fundamental current there is a strong inverse correlation. By this work, it is strongly recommended to consider the operation in degraded mode for sizing a multi-machine system, with the aim of increasing the currents of healthy phases to compensate the degradation effect.

References 1. Wenping Cao, B.C., Mecrow, G.J., Atkinson, J.W.B., Atkinson, D.J.: Overview of electric motor technologies used for more electric aircraft (MEA). IEEE Trans. Ind. Electron. 59(9), 3523–3531 (2012) 2. Garcia, A., Cusido, J., Rosero, J.A., Ortega, J.A., Romeral, L.: Reliable electro-mechanical actuators in aircraft. IEEE Aerosp. Electron. Syst. Mag. 23(8), 19–25 (2008)

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3. Mavier, J.: Convertisseurs génériques à tolérance de panne Applications pour le domaine aéronautique. Thèse, INP Toulouse, Toulouse, France (2007) 4. Etayo, A.M., Bourdon, J., Prisse, L., Meynard, T., Piquet, H.: Optimization of parallelized power inverters using a direct modelling approach for more electrical aircraft. In: International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS), Aachen, 2015, pp. 1–5 (2015) 5. Wang, Y., Lipo, T.A., Pan, D.: Robust operation of double-output AC machine drive. In: IEEE 8th International Conference on Power Electronics and ECCE Asia (ICPE & ECCE), Jeju, 2011, pp. 140–144 (2011) 6. Levi, E., Jones, M., Vukosavic, S.N., Toliyat, H.A.: A novel concept of a multiphase, multimotor vector controlled drive system supplied from a single voltage source inverter. IEEE Trans. Power Electron. 19(2), 320–335 (2004) 7. Semail, E., Levi, E., Bouscayrol, A., Kestelyn, X.: Multi-machine modelling of two series connected 5-phase synchronous machines: effect of harmonics on control. In: European Conference on Power Electronics and Application, Dresden, p. 10 (2005) 8. Nekkaz, M., Djahbar, A., Talab, R.: Modeling and control of two five-phase induction machines connected in series powered by matrix converter. Int. J. Power Electron. Drive Syst. (IJPEDS) 12(2), 685–694 (2021) 9. Levi, E., Iqbal, A., Vukosavic, S.N., Toliyat, H.A.: Modelling and control of a five-phase series-connected two-motor drive. In: Proceedings of IEEE Industrial Electronics Society, Annual Meeting IECON, Roanoke, Virginia, pp. 208–213 (2003) 10. Benali Youcef, M., Djahbar, A., Mazari B.: Modeling and control the set of matrix convertertwo five-phase wheel motors (2 pmsm) for driving an electric traction system. J. Elect. Eng. (JEE). 17, 7 (2018) 11. Meinguet, F., Nguyen, N.-K., Sandulescu, P, Kestelyn, X., Semail, E.: Fault-tolerant operation of an open-end winding five-phase PMSM drive with inverter faults. In: 39th Annual Conference of the IEEE Industrial Electronics Society, IECON 2013, Vienna, November 2013, pp. 5191–5196 (2013)

Influence of Geometric Parameters on the Performance of a Vortex Type Cooling Tower A. L. Deghal Cheridi(B) , A. Bouaam, A. Dadda, and A. Dahia Nuclear Research Center of Birine, Djelfa, Algeria [email protected]

Abstract. The exlpoitation of renewable energy is growing worldwide in several applications. The vortex motor is one of the new energy concepts that create artificial vortices in the airflow to increase the turbine rotational speed for the production of electrical energy. The objective of this work is the prediction of the behavior of a vortex tower model and the analysis of the characteristics of the air flow using Relap5 code, in addition, a parametric study is performed to find out the effect of inlet openings number on the performance of the tower. A model of the tower is developed and validated using numerical and experimental results disposable in the literature, likewise by an analytical calculation using the equations of mass conservation. Simulation results showed that this configuration of vortex tower is able to generate airflow with a maximum velocity of 5.5411 m/s at a height of 0.56 m from the base. Therefore, a turbine can be attached here to exploit the airflow maximum kinetic energy. Furthermore, the results also showed that a clear tendency of the maximum airflow velocity increases by 87% when the number of air inlet openings is varied from 1 to 8. Keywords: Renewable energy · Cooling tower · Vortex generation · Modeling and simulation · Relap5 · Parametric study · air velocity

1 Introduction Nowadays, the demand for energy has increased in many activities and the need for a reliable and cleaner source of energy has become a necessity [1]. For this reason, the search for new energy sources is increasingly in demand. Hence, exploration and utilization of renewable energy; provided by sun, wind, force of the water and the plants etc.; is developed around the world in several applications including power generation [2]. The Air Vortex Engine (AVE) is a new approach to meet our needs for sustainable and renewable energy from the forces of nature. It belongs to the family of solar towers, it is a combination of the two principles of solar chimney and tornadoes, and it seems to be a promising source [3]. The AVE contributes to increasing the energy efficiency of a power plant and this is done by heating the air via a heat source which can be waste heat from industries as well as thermal or nuclear power plants [4, 5]. The idea of AVE was proposed for the first time by Louis Michaud [6]. He indicated that hot © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 565–573, 2023. https://doi.org/10.1007/978-3-031-21216-1_58

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air in the atmosphere will work when transported to high altitudes under convection of air, and that part of the hot airflow will operate while cooling in the ascending process [7–9]. It becomes possible to simulate the behavior of energy processes like AVE using computer codes [10, 11]. So, main objective of this survey is to model and simulate the thermal hydraulic behavior of the air flow through a prototype of a vortex tower during its evacuation of the heat from a warm source towards the exit of the chimney by using Relap5 code, namely the analysis of the kinematic and thermal characteristics of the air along the tower. To know the appropriate turbine location, where the kinetic energy is maximum for high production of electrical energy, the maximum of air velocity is observed and identified. As well as, a parametric study was realized to identify the inlet openings number effect on the vortex tower performance.

2 Presentation of Vortex Tower The vortex tower (Fig. 1) is a mini dry vortex cooling tower located at the Nuclear Research Center of Birine (CRNB). It has 08 tangential air inlet openings using 08 convergence chambers in order to create the convective vortex artificially and a chimney. The tower base is flared using openings of concrete outwardly raised to assure stability of the tower and allow air to be aspired. The air is heated by the hot source via two ways, the first air passage in cylindrical form from the bottom to the top of the chimney; the second is performed by the 08 inlets localised on the peripheries of the tower base.

Velocity anemometer support

Chimney

Velocity anemometer Dawn zone

Hot source

Opening (1/8)

Cone

Fig. 1. Vortex tower design diagram.

3 Operating Principale The vortex tower working principle (Fig. 2) is mainly based on the thermal draft effect which is caused by the density differences between the interior and exterior construction.

Influence of Geometric Parameters on the Performance

567

The ambient air enters the tower via 8 openings situated at the lower part of the prototype crossing a hot source, the air is heated, it expands and begins to rise and move towards the chimney, and the less hot air occupies its place to be heated by the hot source thus the process continues. The heat transfer between the hot source and the air takes place by two ways: the first passage of air in the form cylindrical from the bottom to the top of the chimney following the guide cone and which acts as a dynamic chimney which guides the second part of the hot air which admitted tangentially through 8 bladed inlets located on the convergence chamber peripheries After that, the hot air penetrates with swirling flow in the chimney base and creates a vortex along thie chimney. The thermal energy transfer in the air takes place from a high temperature zone to a lower zone by means of convective vortices where mechanical energy is produced. HEAT

KINETIC ENERGY

MECHANICAL ENERGY

ELECTRICAL ENERGY

Fig. 2. Schematic diagram of the energy conversion process in the votex tower.

4 Modeling of Vortex Tower A. Approach used We give a description of the vortex tower nodalization using Relap5 in this section. Relap5 was designed to simulate the transient behavior of LWR systems under a large variety of supposed accident cases [12]. It is a highly generic code; it can be used for hydraulic and thermal simulation of nuclear and non-nuclear systems involving steam/water mixtures and non-condensable gases [13]. The Relap5 code is based on a non-homogeneous, non equilibrium, six-equation one-dimensional model for the two-phase system. The system model is solved numerically with the finite difference technique. The code includes several component models, which general systems can be simulated. The various actions such as the opening and closing of the valves are actuated by the use of logic variables. The approach utilised to model the tower is to subdivide it into control volumes connected by junctions. [13] (Fig. 3). The eight air inlets openings are modeled by eight “Single-Vol” components: 501–508 connected respectively to the “Time-Dependent-Vol”: 401–408 to impose the atmospheric conditions. The first air collector or the heat transfer chamber is modeled by the “Brunch” component 100, and the second collector (vortex chamber) is modeled by the “Brunch” component 200. We used the “Single-Vol” components: 601–608 to model the vanes and they are connected to the Brunch 200 by the “Check valve” components 201–208 to prevent back air. The “Pipe” component 300 is used to model the chimney and it is connected to the component 500 (Time-Dependent-Vol) to impose the atmospheric conditions at the chimney exite. The “Brunch” component 700 is used to modelate the water tank (heat source). The thermal behavior of the metal structures that make up the tower namely the heat transfer between air and water, was simulated by heat structures [12, 14] connected to Branch components 700 and 100.

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Fig. 3. Vortex tower nodalization.

B. Initial and boundary conditions For an excitation of the phenomenon of natural circulation, an initial condition of air velocity at the entrance of 0.1 m/s was imposed (Table 1). The ambient temperature at 25 °C and the pressure at atmospheric pressure were also predefined at the model inlet. To suppose that there is no heat transfer from tower walls to air, adiabatic boundary conditions are used on the walls; except the water tank where the warm water transfers its heat to the air through its walls. The temperature of the water in the hot source was imposed to 60 °C.

Table 1. Bondary conditions used. Parameters

Value

Air inlet velocity: (Vin) 0.1 m/s Hot source temperature: 60 °C (TRe) Air temperature at the inlet: (Tin)

25 °C

Air inlet pressure: (pin) atmospheric pressure, bar

C. Model validation A vortex tower model is established. In order to qualify the developed model, the Relap 5 numerical results are compared with the experimental and numerical data available in the literature in terms of velocity quantities from the references M.R.

Influence of Geometric Parameters on the Performance

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Ahmed & al [15], R. Balijepali & al [16] and Pritam Das & al [3]; thus, with an analytical calculation using the steady-state mass balance for the airflow through the tower [17]. As presented in Table 2 and Fig. 4, the comparison shows that the present results showed a good agreement with the results fond in the literature.

Table 2. Validation of elaborated tower model. Reference

M.R.Ahmed & al. [15]

R.Balijepali & al. [16]

Pritam Das & al. [3]

Deghal et al. [17]

Relap5

Velocity max (m/s)

4.8

5.5

4.5

7.5

5.5411

9 8

[7 ,5 m/s ]

Velocity (m/s)

7 6 5

[5, 5 ms] [4,8 m/s]

[5,5 411 m/s] [4,5 m/s]

4 3 2 1 0

[15]

[16]

[3]

Relap5

[17]

Fig. 4. Comparison of our result with the results of Ref. [3, 15, 16] and [17].

5 Results And Discussion A. Flow parameters analysis Table 3 gives the flow parameters at the steady-state operating conditions of some parameters such as air velocity and temperature. Figure 5 shows the qualitative and quantitative air velocity evolution inside the vortex tower at different location such as 0.27 m at the entrance of the tower, 0.560 m at the entrance of the vortex creation chamber, 0.863 m at the chimney entrance, 1.62, 2.38, 3.14, 3.90, 4.66, 5.42 6.18, 6.94 7.70 and 8.46 m along and at the chimney exit. The results indicate that this tower is able to generate an airflow, the air velocity increases slowly from the inlet of the tower after suddenly rises to 5.5411 m/s at a height of 0.560 m between the chamber of heat transfer and that creating the vortex. The air passage from the tower base to the chimney narrows, where there is a significant increase in air velocity followed by a gradual decrease towards the exit of the chimney. This change is

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cuased by the kinetic energy transformation into mechanical energy. The velocity at the outlet of the vortex creation chamber and at the chimney exit equal respectively at 1.039 and 0.95 m/s. The increase in air velocity in the tower is due to the pressure gradient created due to the suction and the reduction in density of air due to the increase in air temperature by the effect of heat transfer between the air and the hot source. We also note that there is a velocity ratio of 55 times between the inlet velocity and the maximum velocity (5.5411 m/s) obtained at the height of 0.560 m. Hence, this height is the most appropriate place to install a turbine in order to extract the maximum kinetic energy from the flow.

10 9

Z= 8,463 m

8 7

Z (m)

6 5 4

Z = 0,863 m

3 2

Z= 0,5 60 m

1 0 -1 0

1

2

3

4

5

6

7

8

9

10

11

12

Vitesse (m/s)

Fig. 5. Velocity variation along the tower.

Table 3. Relap5 steady state results. Parameters

Units

Value

Chimney inlet temperature

°C

25.390

Chimney outlet temperature

°C

25.385

Narrowing temperature

°C

25.394

Tank temperature

°C

35.649

Tower inlet velocity

m/s

0.1982

Velocity at narrowing

m/s

5.5411

Chimney inlet velocity

m/s

1.0398

Chimney outlet velocity

m/s

0.9569

Influence of Geometric Parameters on the Performance

571

B. Air inlet opening number influence on air velocity We give in this section the influence of the inlet openings number on the airflow kinematic behavior inside the vortex tower prototype. Table 4 summarizes the data of the various parameters used in the cases considered in this study thus the operating conditions.

Table 4. Data of parametric study. Parameters case studied

T (°C)

D (m)

Vin (m/s)

Nb_in

Effect of inlet opening number (Nb)

25

0.720

0.1

1, 2, 3, 4, 5, 6, 7, 8

The influence of the inlet openings number is analyzed by estimating flow parameters such as air velocity at different positions along the tower. The boundary conditions and dimensions of the tower keep constant except the air inlet openings number which varies from 1 to 8. Figure 6 shows the air velocity variation at different locations of the tower. We take note that the air velocity maximum value is different from one case to another but it is located at the same position at z = 0.560 m, thus, it rises linearly with the increase in the air inlet openings number. For the number of entries nb = 1, nb = 2, nb = 3, nb = 4, nb = 5, nb = 6, nb = 7 and nb = 8, the maximum velocities are respectively equal to 0.70344 m/ s, 1.4049 m/s, 2.1074 m/s, 2.8044 m/s, 3.4872 m/s, 4.1733 m/s, 4.8583 m/s, and 5.5411 m/s. The results indicate that the air inlets number to the vortex tower has an effect on the air velocity inside the domain. Indeed, the velocity increases with the increase in the number of entrances at different heights. 9 6,0 Nb_in=1

8

Nb_in=4

4,5

Nb_in=5

4,0

Velocity (m/s)

Z (m)

5,0

Nb_in=3

6

Nb_in=6

5

Nb_in=7

4

z= 0,56m

5,5

Nb_in=2

7

Nb_in=8

3

3,5 3,0 2,5 2,0 1,5

2

1,0

1

0,5

0

0,0

0

1

2

3

4

5

6

7

Velocity (m/s)

a. Velocity along the tower

8

9

0

1

2

3

4

5

6

7

8

9

Nb_in

b. Maximum velocity evolution

Fig. 6. Effect of air inlet opening number on air velocity at different locations in the vortex tower.

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6 Conclusion In this study, the thermal-hydraulic parameters analysis of the airflow through a prototype of a vortex tower using Relap5 system code in steady state has been presented. The vortex tower model developed by the code has been established and validated by numerical and experimental results found in the literature from the references [3, 15, 16, 17], a good agreement was obtained. The simulation is carried out and flow parameters like temperature and velocity were estimated and analyzed at various locations in the tower. Since the objective of the vortex tower is to define the maximum of velocity reached in order to know the most appropriate position of a turbine fixation in order to capture more energy, the results simulation analysis showed that this vortex tower is able to generate airflow with large air velocity where there is a 55 ratio times between the tower inlet velocity (0.1 m/s) and the maximum velocity of 5.5411 m/s reached at the height of 0.560 m from the base which is located between the heat transfer chamber and that of vortex creation. Therefore, this place of the vortex tower allows a good transformation of kinetic energy into mechanical energy for the purposes of electrical energy production. Moreover, a parametric study on the impact of the inlet openings number on the vortex tower kinematic behavior was carried out. The results showed that this parameter has a significant and direct impact on the air velocity of the tower, where the maximum of air velocity is considerably improved with the increase of the air inlet openings number, when it changes from 1 to 8, the air velocity rises from 0.7034 to 5.5411 m/s, which represents an increase of 87%. In addition, it was found that in the cases considered, the position of the maximum air velocity is located at the same place at 0.56 m between the chamber of heat transfer and the vortex creation chamber. Therfore; it is the best position for capturing the maximum of kinetic energy.

References 1. Michaud, L.M.: On the energy and control of atmospheric vortices. J. Recherches Atmospheriques (1977) 2. Dhahri, A., Omri, A., Orfi, J.: Numerical Study of a solar chimney power plant. Res. J. Appl. Sci. Eng. Technol. 8(8), 1953–1965 (2014) 3. Das, P., Chandramohan, V.P.: Estimation of flow parameters and power potential of solar vortex engine by varying its geometrical configuration: a numerical study. Energy Convers. Manage. 223, 113272 (2020) 4. Michaud, L.M.: The Atmospheric Vortex Engine. AVEtec Energy Corporation (2008) 5. Church, C.R., Snow, J.T., Baker, G.L., Agee, E.M.: Characteristics of tornado like vortices as a function of swirl ratio. A laboratory investigation. J. Atmos. Sci. (1979) 6. Michaud, L.: Proposal for the use of a controlled tornado-like vortex to capture the mechanical energy produced in the atmosphere from solar energy. Bull. Am. Meteorol. Soc. 56, 530–534 (1975) 7. Zuo, L., et al.: A vortex-type solar updraft power desalination integrated system. Energy Convers. Manage. 222, 113216 (2020) 8. Michaud, L.: Thermodynamic cycle of the atmospheric upward heat convection process. Meteor. Atmos. Phys. 72, 29–46 (2000) 9. Michaud, L.: Heat to work convection during upward heat convection Part I: Carnot engine method. Atmos. Res. 39, 157–178 (1995)

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PEM Fuel Cell Emulator Based on a Buck Converter S. Gahgouhi1 , A. Hadjaissa2(B) , K. Ameur2 , A. Rabhi3 , and M. Kious1 1 LSCFM Laboratory, Amar Telidji University, BP 37G, Ghardaia Road, 03000 Laghouat,

Algeria 2 LACoSERE Laboratory, Amar Telidji University, BP 37G, Ghardaia Road, 03000 Laghouat,

Algeria [email protected] 3 MIS Laboratory, University of Picardie Jul Verne, 33 Rue Saint Leu, 80039 Amiens Cedex1, France

Abstract. This paper presents a fuel cell emulator based on a DC/DC converter. The emulator is designed to behave exactly as proton exchange membrane fuel cell (PEMFC) based on its mathematical model and experimental data. The fuel cell emulator (FC emulator) is composed of a DC/DC buck converter that is controlled by a conventional PI controller. To test our emulator, the converter is connected to a variable DC load. The scenario of load is chosen to be hard and have abrupt changes to show the performance of the emulator. The simulation results show the performance of the control on the DC/DC buck converter in terms of response time, and following the reference voltage of the mathematical model of the fuel cell. Keywords: Fuel cell · PEMFC · Fuel cell emulator · Buck converter · PI control

1 Introduction The fuel cell is a powerful and high-quality source of energy that is currently regarded as a promising green energy source [1]. As an electrochemical device, the fuel cells convert the chemical energy in a fuel directly to electrical energy, producing heat and water [2]. it does exist a lot of fuel cells types and among the various types, the Proton Exchange Membrane PEMFC is advantageous for portable power and transportation applications due to its high performance, short start-up times, low operating temperature and emission [3, 4]. In the fuel cell, the movement of hydrogen gas through an anode and oxygen via a cathode provides electrochemical energy. In normal operation, a typical fuel cell produces 0.5–0.9 V. By connecting several cells in series forms a stack capable of supplying hundreds of kilowatts [5]. Fuel cell power production is influenced by various parameters, including hydrogen gas, stack temperature, membrane humidity, and oxygen supply, which all have a direct impact on the fuel cell’s life and efficiency. For that fuel cell emulators are necessary in research since they allow researchers to safely evaluate the performance of power stages © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Hatti (Ed.): IC-AIRES 2022, LNNS 591, pp. 574–583, 2023. https://doi.org/10.1007/978-3-031-21216-1_59

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such as inverters and DC/DC converters while examining different control strategies, the crucial testing that would put the equipment at risk, etc. [6]. In this paper the fuel cell emulator uses a general mathematical model to predict the performances of the fuel cell. Many proposed emulators could be found in the literature as illustrated briefly in [7], using different ways to reproduce the fuel cell mathematical model. In this work a DC/DC buck converter used to reproduce the behavior of the fuel cell with a control system based on a classical technique of control (conventional PI controller).

2 Fuel Cell Modeling The PEM fuel cell’s electrochemical action begins at the anode surface. The anode catalyst breaks hydrogen on protons (H+ ion), which flow through the membrane to the cathode, and electrons (e− ) move to the cathode via an external electrical connection. The hydrogen protons H+ and electrons e− , as well as oxygen O2 , mix at the cathode catalyst to generate water and heat. The following equations can be used to represent the mentioned reactions [8]: H2 ⇒ 2H + + 2e− (Anode)

(1)

1 O2 + 2H + + 2e− ⇒ H2 O(Cathode) 2

(2)

The amount of chemical energy produced in these processes is determined by the hydrogen/oxygen pressure and the temperature of the fuel cell. The open voltage of a fuel cell can be defined as follows [8]: E=−

gf 2F

(3)

where where gf0 is the change in gibbs free energy at standard pressure, F is Faraday’s constant (96.487 C). The true cell voltage can be obtained using: VCell = ENernst − VAct − VOhmic − VCon

(4)

where ENernst is the cell reversible voltage, VAct and VCon are the losses due to the activation and concentration polarization respectively, VOhmic is the ohmic polarization [9]. A. Reversible Voltage The potential produced by an open circuit thermodynamic equilibrium is the cell reversible voltage (ENernst ). This latter is calculated using an updated version of the Nernst equation taking into account temperature variation [5].   1 −3 −5 ENernst = 1.229 + 0.85.10 (T − 298.15) + 4.31.10 .T ln(PH2 ) + ln(PO2 ) 2 (5)

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where, PH2/PO2 the partial pressures(atm) of hydrogen and oxygen respectively. T is the fuel cell operation temperature (K). The real cell voltage is defined as the maximum voltage and the various voltage losses, as illustrated in Eq. (4). Every losse is associated with a voltage drop and has an impact in different current density zones. Figure 1 shows the various zones and the correlated polarization effects [10]. B. Activation Losses The activation losses or activation overvoltage is caused by the slowing of the processes on the electrodes area. The following formula in [5] was used to represent the activation polarization losses. VAct = −[ξ 1 + ξ 2 + ξ 3.T .ln(CO2 ) + ξ 4.T .ln(ifc )]

(6)

where T represents the operating temperature (K), ifc defines the cell operating current, ξi represents a parametric coefficient, and CO2 is the concentration of oxygen. C. Ohmic Losses The passage of electrons via the electrodes and the various interconnections, as well as the movement of ions via the electrolyte, cause this voltage drop. This drop in voltage is directly proportional to the current density [11]. Using Ohm’s law to express this voltage drop as follow: VOhmic = ifc .ROhmic

(7)

VOhmic = ifc (Rm + Rc )

(8)

where ROhmic is the internal resistance, Rm represent the resistance of the flow of ions through the electrolyte and Rc represent the resistance of the electrons transfer through the electrodes and the interconnections. ρM .l (9) Rm = A where ρ is the membrane specific resistivity (.cm), l is the membrane thickness(cm), A is the active surface (cm2 ). The Ohmic voltage drop is represented in numerous formulas in the literature utilizing experimentally derived parameters and material parametric coefficients. The nafion membrane is commonly utilized in PEM fuel cells, and the empirical equation defining the Nafion membrane resistivity is [5]:    t 2  ifc 2.5 i ] 181.6.[1 + 0.03. Afc + 0.062. 303 A   (10) ρM =   i ] [ψ − 0.634 − 3. Afc ].exp[4.18. T − 303 T 181.6 where ψ−0.634 , is the specific resistivity (.cm) when current equal to zero and 30 °C and the exponential term is the temperature correction factor if 30 °C is not the cell temperature.

D. Concentration Losses The concentration voltage drop has an effect on hydrogen and oxygen concentrations

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[11]. The partial pressures of hydrogen and oxygen are reduced as a result. The electrical current and system characteristics influence the decreasing of oxygen and hydrogen pressures. The concentration voltage drop is described by the following formula [5].  J VCon = −B.ln 1 − (11) Jmax where B(V) is a parametric coefficient, J is the actual density of current (A/cm2 ). The voltage of the fuel cell stack obtained by the connected of the n cells in series. VStack = ncell .Vcell

(12)

3 Dynamic Model of the Fuel Cell A charge builds on the surfaces of an electrode and a proton exchange membrane when they come into contact, a phenomenon known as the “charge double layer”. This phenomenon is essential to show the dynamic behavior of the fuel cell. On the membrane’s surface, protons are gathered, while electrons are collected on the cathode side. The charge double layer therefore has a feature comparable to that of an electrical capacitor. There will be a time delay for the charge to decrease or rise when the voltage is suddenly changed, which will affect the activation and concentration voltage polarization. As a result, the cell voltage gets a dynamic response relating to the time delay of the activation and concentration voltage drops. In order to determine the dynamic voltage output, the following equations can be employed [13]. i Vd dV = − dt C Rd C Rd =

V Act + V Con i

(13) (14)

where, C is the equivalent capacity (F), Rd represent the equivalent resistance ().

4 Fuel Cell Simulation The SR-12 modular PEM Generator data was used for fuel cell modeling. The main purpose of modeling this modular is to obtain a suitable fuel cell characteristic to be used in the fuel cell emulator. The data provided by Avista laboratories [5].

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Figure 1 shows the voltage vs current of the PEM fuel cell stack model simulated using MATLAB Simulink based on Eqs. (4, 7, 8, 12, 14) and values in Table 1. The voltage decreases first due to the activation polarization, by the development of current, the voltage continues to drop linearly due to the homic polarization. Finally, with the continuous growth of the current the voltage drops rapidly due to the concentration polarization.

Fig. 1. Fuel cell polarization curve at T = 323(K). Table 1. SR-12 modular PEM Generator parameters [5]. Parameters

Value

n

48

T

323(K)

A

62.5 cm2

Thicknes (l)

25 μm

PH2

1.47268 atm

PO2

0.2095 atm

B

0.15

RC

0.003 

ξ1

−0.948

ξ2

0.00286 + 0.002.ln(A) + 4.3.10–5 .ln(CH2)

ξ3

7.22.10–5

ξ4

−1.0615.10–4

ψ

23

Jmax

672 mA/cm2

Jn

22 mA/cm2

Imax

42 A

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5 Fuel Cell Emulator In this work the objective of the emulator is to produce the electrical behavior of the fuel cell model. The emulation of the fuel cell model is done through a DC/DC converter that provide the characteristic (voltage and power) defined by the fuel cell model. The Fig. 2 shows the PEM fuel cell emulator’s design.

Fig. 2. PEM fuel cell stack emulator scheme.

It is necessary to have a precise output voltage regulation. As a reason, a buck converter is a good choice and voltage mode regulation is simple since it avoids the mode of discontinuous A. The DC/DC Buck Converter Design The fuel cell stack’s rated output power is 500 W. The converter was designed with the following requirements: The input voltage Vin = 42 V, the switching frequency f = 10 kHz (Fig. 3).

Fig. 3. Buck converter electrical scheme.

1. Mathematical model d=

ton ton = T ton + toff

1 dIL = [(Vin − VC )d + (−VC )(1 − d)] dt L

(15) (16)

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dIL Vin d = −VC + dt L  1 VC VC dVC = [ IL − d + (IL − )(1 − d )] dt C R R dVC IL VC = − dt C RC

(17) (18) (19)

The output filter of the DC/DC converter design is done based on the continuous conduction mode (CCM). The switching frequency, the input voltage Vin , and the minimum current Imin of the converter are required to calculate the output filter (L, C) values, Table 2 [15]. L≥

10−4 .42 TPWM .Vin = 0.525mH = 8Imin 8.1

(20)

The inductor value chosen for the simulation: L = 800 μH C≥

2 VOmax TPWM 41.7.(0.0002)2 = 2600μF = 8LVmax 8.(0.8).10−3 .(0.1)

(21)

B. Fuel Cell Emulator Control The control technique proposed in this paper is based on a conventional PI controller. According to Fig. 4, the PI controller uses the error between the voltage reference (Vfc_ref ) and the measured output voltage (VO ) provided by the (L, C) filter to generate a duty cycle signal that is converted to a PWM command to obtain the appropriate switching pattern for the DC-DC converter switch.

Fig. 4. Control system (FLC-PI) proposed topology.

1. PI Controller The PI controller parameters Kp and Ki are found using the Ziegler-Nichols method, with adjustment to achieve the required output voltage. The defined parameters are shown in Table 2. Transfer function of the buck converter: V in V C (p) = α(p) rC p + LC p2 + 1 where α is the duty cycle, r is the inductor resistance.

(22)

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Table 2. Kp, Ki values of the PI controller. Parameters

Value

Kp

3.5

Ki

2.5

6 Results and Discussions As shown in Fig. 5, the emulator produces the characteristics V-I of the fuel cell stack model. The current load scenario in Fig. 6 is chosen to have abrupt changes to evaluate the system performances. The PEMFC stack model generates the voltage signal that corresponds to the current load. The controller then uses the error signal, resulting from the difference between the Vfc_ref and VO , to emulate the power output characteristics of the PEMFC model on the DC/DC buck converter output.

Fig. 5. Fuel cell characteristics (mathematical model and emulated characteristics).

Fig. 6. The current load profile.

Figure 7 shows the PEMFC stack voltage and the converter output voltage. With an average error of 0.05 V. it can be seen in Fig. 7, that the converter output voltage follows the reference signal (PEMFC stack voltage).

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Fig. 7. PEMFC model and DCDC buck converter responses.

The response time when the buck converter output voltage matches the PEMFC stack voltage is approximately 0.006 s. The dynamic of the system is shown in Fig. 8, after a sudden change in voltage due to a rapid load current change at t = 0.125 s. After 0.006 s, the converter output voltage achieves the PEMFC stack voltage, with a maximum overshoot of 2.1 V.

Fig. 8. Dynamic response of the DCDC converter output voltage.

7 Conclusion The fuel cell emulator is a solution to replace a real fuel cell by a hardware system that can effectively emulate its behavior. In this paper, a simulation work has proposed and done for a feasible and reliable design based on PEM fuel cell modules and a buck converter. According to the simulation results, the PI controller shows a good performances and robustness allowing the PEMFC emulator based on DC DC buck converter to behave as the PEMFC stack response with a very good accuracy.

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