Priyadarshi Neeraj - Intelligent Renewable Energy Systems

Scrivener Publishing
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Artificial Intelligence and Soft Computing for Industrial Transformation

Series Editor: Dr S. Balamurugan ()

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 Techniques, 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 ()
Phillip Carmical ()

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

This book presents intelligent renewable energy systems integrating artificial intelligence techniques and optimization algorithms. The first chapter describes placement of distributed generation (DG) sources including renewable distributed generation (RDGs) such as biomass, solar PV, and shunt capacitor has been considered for the study purpose. The second chapter develops a new approach to chaotic particle swarm optimization (CPSO) technique. In the third chapter, comprehensive reviews of different artificial intelligence and machine learning techniques have been explicated. To bring out its advantages over other methods used in island detection, the traditional methods are first explained and then compared with artificial intelligence and machine learning island detection techniques. The performance of the intelligent controller is found to be good under steady conditions for grid connected photovoltaic systems and has been discussed in chapter four. Chapter five explains various uses of Genetic Algorithms (GA) and Solar PV forecasting are described; further, many stimulated algorithms which have been used in optimization, controlling, and methods of supervising of power for renewable energy analysis, which include hybrid power generation strategies are discussed. Chapter six presents the integration of 100 kW solar PV source to the 25 kV AC grid by using generalized r-s based SVPWM algorithm. Chapter seven aims to discuss the idea of hybrid system configuration, dynamic modeling, energy management, and control strategies. A multi-stage planning framework is proposed in chapter eight to divide the planning period into several stages so that investments can be made in each stage as per the requirements. A unique and a novel GUI is presented to design the entire solar PV systems has been discussed in Chapter nine. Chapter ten addresses micro-grid situational awareness using micro PMU. Role of AI & ML in smart grid entities such as Home Energy Management System (HEMS), Energy Trading, Adaptive Protection, Load Forecasting and Smart Energy Meter are presented in Chapter eleven. Chapter twelve presents a new method for energy loss allocation in radial distribution network (RDN) with distributed generationin the context of deregulated power system. Chapter thirteen presents the optimization of controller parameters for FACTS and VSC based HVDC. Chapter fourteen describes Short Term load forecasting for a Captive Power Plant Using Artificial Neural Network. Chapter fifteen defines Real-time EV Charging Station Scheduling Scheme by using Global Aggregator.

Neeraj Priyadarshi
Akash Kumar Bhoi
Sanjeevikumar Padmanaban
S. Balamurugan
Jens Bo Holm-Nielsen
Editors

Bikash Das

Department of Electrical Engineering, Govt. College of Engineering and Textile Technology, Berhampore, West Bengal, India
Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, K. K. Birla Goa Campus, Goa, India
Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India
Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India