Intelligent Renewable Energy Systems: Integrating Artificial Intelligence Techniques and Optimization Algorithms

473 Pages·2021·14.299 MB·English

by  Neeraj Priyadarshi, Akash Kumar Bhoi, Sanjeevikumar Padmanaban, S. Balamurugan, Jens Bo Holm-Nielson

Intelligent Renewable
Energy Systems
Scrivener Publishing
100 Cummings Center, Suite 541J
Beverly, MA 01915-6106
Artificial Intelligence and Soft Computing for Industrial Transformation
Series Editor: Dr S. Balamurugan ([email protected])
Scope: Artificial Intelligence and Soft Computing Techniques play an impeccable role in industrial
transformation. The topics to be covered in this book series include Artificial Intelligence, Machine Learning,
Deep Learning, Neural Networks, Fuzzy Logic, Genetic Algorithms, Particle Swarm Optimization,
Evolutionary Algorithms, Nature Inspired Algorithms, Simulated Annealing, Metaheuristics, Cuckoo
Search, Firefly Optimization, Bio-inspired Algorithms, Ant Colony Optimization, Heuristic Search
T echniques, Reinforcement Learning, Inductive Learning, Statistical Learning, Supervised and Unsupervised
Learning, Association Learning and Clustering, Reasoning, Support Vector Machine, Differential Evolution
Algorithms, Expert Systems, Neuro Fuzzy Hybrid Systems, Genetic Neuro Hybrid Systems, Genetic Fuzzy
Hybrid Systems and other Hybridized Soft Computing Techniques and their applications for Industrial
Transformation. The book series is aimed to provide comprehensive handbooks and reference books
for the benefit of scientists, research scholars, students and industry professional working towards next
generation industrial transformation.
Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Intelligent Renewable
Energy Systems
Edited by
Neeraj Priyadarshi
Akash Kumar Bhoi
Sanjeevikumar Padmanaban
S. Balamurugan
and
Jens Bo Holm-Nielsen
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-78627-6
Cover image: Pixabay.com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Contents
Preface xv
1 Optimization Algorithm for Renewable Energy Integration 1
Bikash Das, SoumyabrataBarik, Debapriya Das
and V. Mukherjee
1.1 Introduction 2
1.2 Mixed Discrete SPBO 5
1.2.1 SPBO Algorithm 5
1.2.2 Performance of SPBO for Solving
Benchmark Functions 8
1.2.3 Mixed Discrete SPBO 11
1.3 Problem Formulation 12
1.3.1 Objective Functions 12
1.3.2 Technical Constraints Considered 14
1.4 Comparison of the SPBO Algorithm in Terms of CEC-2005
Benchmark Functions 17
1.5 Optimum Placement of RDG and Shunt Capacitor to the
Distribution Network 18
1.5.1 Optimum Placement of RDGs and Shunt
Capacitors to 33-Bus Distribution Network 25
1.5.2 Optimum Placement of RDGs and Shunt
Capacitors to 69-Bus Distribution Network 29
1.6 Conclusions 33
References 34
2 Chaotic PSO for PV System Modelling 41
Souvik Ganguli, Jyoti Gupta and Parag Nijhawan
2.1 Introduction 42
2.2 Proposed Method 43
2.3 Results and Discussions 43
2.4 Conclusions 72
References 72
v
vi Contents
3 Application of Artificial Intelligence and Machine Learning
Techniques in Island Detection in a Smart Grid 79
Soham Dutta, Pradip Kumar Sadhu, Murthy Cherikuri
and Dusmanta Kumar Mohanta
3.1 Introduction 80
3.1.1 Distributed Generation Technology in Smart Grid 81
3.1.2 Microgrids 81
3.3.1.1 Problems with Microgrids 81
3.2 Islanding in Power System 82
3.3 Island Detection Methods 83
3.3.1 Passive Methods 83
3.3.2 Active Methods 85
3.3.3 Hybrid Methods 86
3.3.4 Local Methods 87
3.3.5 Signal Processing Methods 87
3.3.6 Classifer Methods 88
3.4 Application of Machine Learning and Artificial Intelligence
Algorithms in Island Detection Methods 89
3.4.1 Decision Tree 89
3.4.1.1 Advantages of Decision Tree 91
3.4.1.2 Disadvantages of Decision Tree 91
3.4.2 Artificial Neural Network 91
3.4.2.1 Advantages of Artificial Neural Network 93
3.4.2.2 Disadvantages of Artificial Neural Network 93
3.4.3 Fuzzy Logic 93
3.4.3.1 Advantages of Fuzzy Logic 94
3.4.3.2 Disadvantages of Fuzzy Logic 94
3.4.4 Artificial Neuro-Fuzzy Inference System 95
3.4.4.1 Advantages of Artificial Neuro-Fuzzy
Inference System 95
3.4.4.2 Disadvantages of Artificial Neuro-Fuzzy
Inference System 96
3.4.5 Static Vector Machine 96
3.4.5.1 Advantages of Support Vector Machine 97
3.4.5.2 Disadvantages of Support Vector Machine 97
3.4.6 Random Forest 97
3.4.6.1 Advantages of Random Forest 98
3.4.6.2 Disadvantages of Random Forest 98
Contents vii
3.4.7 Comparison of Machine Learning and Artificial
Intelligence Based Island Detection Methods with
Other Methods 99
3.5 Conclusion 99
References 101
4 Intelligent Control Technique for Reduction of Converter
Generated EMI in DG Environment 111
Ritesh Tirole, R R Joshi, Vinod Kumar Yadav,
Jai Kumar Maherchandani and Shripati Vyas
4.1 Introduction 112
4.2 Grid Connected Solar PV System 113
4.2.1 Grid Connected Solar PV System 113
4.2.2 PhotoVoltaic Cell 114
4.2.3 PhotoVoltaic Array 114
4.2.4 PhotoVoltaic System Configurations 114
4.2.4.1 Centralized Configurations 115
4.2.4.2 Master Slave Configurations 115
4.2.4.3 String Configurations 115
4.2.4.4 Modular Configurations 115
4.2.5 Inverter Integration in Grid Solar PV System 115
4.2.5.1 Voltage Source Inverter 116
4.2.5.2 Current Source Inverter 117
4.3 Control Strategies for Grid Connected Solar PV System 117
4.3.1 Grid Solar PV System Controller 117
4.3.1.1 Linear Controllers 117
4.3.1.2 Non-Linear Controllers 117
4.3.1.3 Robust Controllers 118
4.3.1.4 Adaptive Controllers 118
4.3.1.5 Predictive Controllers 118
4.3.1.6 Intelligent Controllers 118
4.4 Electromagnetic Interference 118
4.4.1 Mechanisms of Electromagnetic Interference 119
4.4.2 Effect of Electromagnetic Interference 120
4.5 Intelligent Controller for Grid Connected Solar PV System 120
4.5.1 Fuzzy Logic Controller 120
4.6 Results and Discussion 121
4.6.1 Generated EMI at the Input Side of Grid SPV System 122
4.7 Conclusion 125
References 125
viii Contents
5 A Review of Algorithms for Control and Optimization for
Energy Management of Hybrid Renewable Energy Systems 131
Megha Vyas, Vinod Kumar Yadav, Shripati Vyas,
R.R. Joshi and Ritesh Tirole
5.1 Introduction 132
5.2 Optimization and Control of HRES 134
5.3 Optimization Techniques/Algorithms 135
5.3.1 Genetic Algorithms (GA) 136
5.4 Use of Ga In Solar Power Forecasting 140
5.5 PV Power Forecasting 142
5.5.1 Short-Term Forecasting 143
5.5.2 Medium Term Forecasting 144
5.5.3 Long Term Forecasting 144
5.6 Advantages 145
5.7 Disadvantages 146
5.8 Conclusion 146
Appendix A: List of Abbreviations 146
References 147
6 Integration of RES with MPPT by SVPWM Scheme 157
Busireddy Hemanth Kumar and Vivekanandan Subburaj
6.1 Introduction 158
6.2 Multilevel Inverter Topologies 158
6.2.1 Cascaded H-Bridge (CHB) Topology 159
6.2.1.1 Neutral Point Clamped (NPC) Topology 160
6.2.1.2 Flying Capacitor (FC) Topology 160
6.3 Multilevel Inverter Modulation Techniques 161
6.3.1 Fundamental Switching Frequency (FSF) 162
6.3.1.1 Selective Harmonic Elimination
Technique for MLIs 162
6.3.1.2 Nearest Level Control Technique 163
6.3.1.3 Nearest Vector Control Technique 164
6.3.2 Mixed Switching Frequency PWM 164
6.3.3 High Level Frequency PWM 164
6.3.3.1 CBPWM Techniques for MLI 164
6.3.3.2 Pulse Width Modulation Algorithms
Using Space Vector Techniques for
Multilevel Inverters 167
6.4 Grid Integration of Renewable Energy Sources (RES) 167
6.4.1 Solar PV Array 167
6.4.2 Maximum Power Point Tracking (MPPT) 169