IoT, Machine Learning and Blockchain Technologies for Renewable Energy and Modern Hybrid Power Systems RIVER PUBLISHERS SERIES IN INFORMATION SCIENCE AND TECHNOLOGY Series Editors: K.C. CHEN, National Taiwan University, Taipei, Taiwan and University of South Florida, USA SANDEEP SHUKLA, Virginia Tech, USA and Indian Institute of Technology Kanpur, India The “River Publishers Series in Computing and Information Science and Technology” covers research which ushers the 21st Century into an Internet and multimedia era. Networking suggests transportation of such multimedia contents among nodes in communication and/or computer networks, to facilitate the ultimate Internet. Theory, technologies, protocols and standards, applications/services, practice and implementation of wired/wireless The “River Publishers Series in Computing and Information Science and Technology” covers research which ushers the 21st Century into an Internet and multimedia era. Networking suggests transportation of such multimedia contents among nodes in communication and/or computer networks, to facilitate the ultimate Internet. Theory, technologies, protocols and standards, applications/services, practice and implementation of wired/wireless networking are all within the scope of this series. Based on network and communication science, we further extend the scope for 21st Century life through the knowledge in machine learning, embedded systems, cognitive science, pattern recognition, quantum/biological/molecular computation and information processing, user behaviors and interface, and applications across healthcare and society. Books published in the series include research monographs, edited volumes, handbooks and textbooks. The books provide professionals, researchers, educators, and advanced students in the field with an invaluable insight into the latest research and developments. Topics included in the series are as follows: • Artificial intelligence • Cognitive Science and Brian Science • Communication/Computer Networking Technologies and Applications • Computation and Information Processing • Computer Architectures • Computer networks • Computer Science • Embedded Systems • Evolutionary computation • Information Modelling • Information Theory • Machine Intelligence • Neural computing and machine learning • Parallel and Distributed Systems • Programming Languages • Reconfigurable Computing • Research Informatics • Soft computing techniques • Software Development • Software Engineering • Software Maintenance For a list of other books in this series, visit www.riverpublishers.com IoT, Machine Learning and Blockchain Technologies for Renewable Energy and Modern Hybrid Power Systems Editors C. Sharmeela Anna University, India P. Sanjeevikumar Aarhus University, Denmark P. Sivaraman Vestas Technology R&D Chennai Pvt. Ltd, India Meera Joseph Independent Institute of Education, South Africa River Publishers Published 2023 by River Publishers River Publishers Alsbjergvej 10, 9260 Gistrup, Denmark www.riverpublishers.com Distributed exclusively by Routledge 605 Third Avenue, New York, NY 10017 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN IoT, Machine Learning and Blockchain Technologies for Renewable Energy and Modern Hybrid Power Systems / by C. Sharmeela, P. Sanjeevikumar, P. Sivaraman, Meera Joseph. © 2023 River Publishers. All rights reserved. No part of this publication may be reproduced, stored in a retrieval systems, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Routledge is an imprint of the Taylor & Francis Group, an informa business ISBN 978-87-7022-724-7 (print) ISBN 978-10-0082-440-7 (online) ISBN 978-10-0336-078-0 (ebook master) While every effort is made to provide dependable information, the publisher, authors, and editors cannot be held responsible for any errors or omissions. Contents Preface xiii Acknowledgments xv List of Figures xvii List of Tables xxiii List of Contributors xxv List of Abbreviations xxix 1 Introduction to IoT 1 Asim Maharjan and Saju Khakurel 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 ApplicationsofIoT . . . . . . . . . . . . . . . . . . . . . . 6 1.3.1 DomesticApplications . . . . . . . . . . . . . . . . 6 1.3.2 ApplicationsinHealthcare . . . . . . . . . . . . . . 7 1.3.3 ApplicationsinE-commerce . . . . . . . . . . . . . 8 1.3.4 IndustrialApplications . . . . . . . . . . . . . . . . 9 1.3.5 ApplicationsinEnergy . . . . . . . . . . . . . . . . 10 1.4 TechnicalDetailsofIoT . . . . . . . . . . . . . . . . . . . 11 1.4.1 Sensors . . . . . . . . . . . . . . . . . . . . . . . . 11 1.4.2 Actuators . . . . . . . . . . . . . . . . . . . . . . . 15 1.4.3 ProcessingTopologies . . . . . . . . . . . . . . . . 16 1.4.4 CommunicationTechnologies . . . . . . . . . . . . 18 1.5 RecentDevelopments . . . . . . . . . . . . . . . . . . . . . 20 1.6 Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 23 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 23 v vi Contents 2 IoT and its Requirements for Renewable Energy Resources 29 D. Gunapriya, R. Sivakumar, and K. Sabareeshwaran 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.1.1 IoTanditsNecessity . . . . . . . . . . . . . . . . . 30 2.1.2 ChallengesinRES . . . . . . . . . . . . . . . . . . 30 2.1.3 Integration of IoT in RES and Benefits . . . . . . . . 32 2.2 IndustrialIoT . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.2.1 ArchitectureofIoT . . . . . . . . . . . . . . . . . . 33 2.2.2 IoTComponents . . . . . . . . . . . . . . . . . . . 34 2.3 RESandIoT . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.3.1 IoTControlsforRES . . . . . . . . . . . . . . . . . 36 2.3.2 Challenges in IoT Implementation . . . . . . . . . . 38 2.4 Challenges of IoT in EMS Post-implementation . . . . . . . 39 2.4.1 PrivacyIssues . . . . . . . . . . . . . . . . . . . . . 39 2.4.2 SecurityConcerns . . . . . . . . . . . . . . . . . . 41 2.4.3 DataStorageIssues . . . . . . . . . . . . . . . . . . 43 2.4.3.1 Challenges in data management . . . . . . 43 2.4.3.2 Challenges in fetching data . . . . . . . . 44 2.4.3.3 Challenges in allocation . . . . . . . . . . 44 2.5 SolutiontoIoTChallenges . . . . . . . . . . . . . . . . . . 45 2.5.1 BlockchainMethodology . . . . . . . . . . . . . . . 45 2.5.1.1 Blockchain technology infrastructure features . . . . . . . . . . . . . . . . . . 47 2.5.1.2 Application domains of blockchain technology . . . . . . . . . . . . . . . . . 47 2.5.1.3 Challenges of blockchain technology . . . 47 2.5.2 CloudComputing . . . . . . . . . . . . . . . . . . . 48 2.5.2.1 Reference architecture . . . . . . . . . . . 49 2.5.2.2 Network communication and its challenge . . . . . . . . . . . . . . . . . . 51 2.5.2.3 Privacyandsecurity . . . . . . . . . . . . 51 2.5.2.4 Background information . . . . . . . . . . 53 2.5.2.5 Bigdataanalytics . . . . . . . . . . . . . 53 2.5.2.6 Provision of program quality . . . . . . . 53 2.5.2.7 IPv4addressinglimit . . . . . . . . . . . 54 2.5.2.8 Legal aspects and social facts . . . . . . . 55 2.5.2.9 Servicedetection . . . . . . . . . . . . . 56 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 56 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Contents vii 3 Power Quality Monitoring of Low Voltage Distribution System Toward Smart Distribution Grid Through IoT 61 P. Sivaraman, C. Sharmeela, S. Balaji, and P. Sanjeevikumar 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.2 Introduction to Various PQ Characteristics . . . . . . . . . . 63 3.3 IntroductiontoIoT . . . . . . . . . . . . . . . . . . . . . . 64 3.4 Smart Monitoring using IoT for the Low Voltage Distribution System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.5 Power Quality Monitoring of Low Voltage Distribution System–CaseStudy . . . . . . . . . . . . . . . . . . . . . 67 3.5.1 Undervoltage . . . . . . . . . . . . . . . . . . . . . 69 3.5.2 Overvoltage . . . . . . . . . . . . . . . . . . . . . . 69 3.5.3 Interruption . . . . . . . . . . . . . . . . . . . . . . 71 3.5.4 OverloadinBranchCircuit . . . . . . . . . . . . . . 72 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 74 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4 Health Monitoring of a Transformer in a Smart Distribution System using IoT 79 P. Sivaraman, C. Sharmeela, and P. Sanjeevikumar 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.2 IntroductiontotheTransformer . . . . . . . . . . . . . . . . 81 4.3 Failure of the Distribution Transformer . . . . . . . . . . . . 82 4.4 Transformer Health Monitoring System through IoT . . . . . 82 4.4.1 Winding and Oil Temperature Sensor . . . . . . . . 83 4.4.2 OilLevelMonitoringSensor . . . . . . . . . . . . . 84 4.4.3 Current Sensor and Voltage Sensor . . . . . . . . . . 84 4.4.4 Microcontroller . . . . . . . . . . . . . . . . . . . . 85 4.4.5 LCDorMonitor . . . . . . . . . . . . . . . . . . . 85 4.4.6 CommunicationSystem . . . . . . . . . . . . . . . 85 4.4.7 Central Monitoring and Control . . . . . . . . . . . 86 4.5 CaseStudy . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 89 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5 Introduction To Machine Learning Techniques 93 Saniya M. Ansari, Ravindra R. Patil, Rajnish Kaur Calay, and Mohamad Y. Mustafa 5.1 WhyandWhatisMachineLearning? . . . . . . . . . . . . 93 viii Contents 5.1.1 PhrasesinMachineLearning . . . . . . . . . . . . . 94 5.1.2 Steps Involved in Machine Learning Practices . . . . 94 5.1.3 PropertiesofData . . . . . . . . . . . . . . . . . . 94 5.1.4 Real-World Applications of Machine Learning . . . 95 5.2 Classification of Machine Learning Techniques . . . . . . . 96 5.2.1 SupervisedLearning . . . . . . . . . . . . . . . . . 96 5.2.1.1 Classification . . . . . . . . . . . . . . . 97 5.2.1.2 Regression . . . . . . . . . . . . . . . . . 98 5.2.2 UnsupervisedLearning . . . . . . . . . . . . . . . . 99 5.2.2.1 Clustering . . . . . . . . . . . . . . . . . 99 5.2.2.2 Association . . . . . . . . . . . . . . . . 100 5.2.3 ReinforcementLearning . . . . . . . . . . . . . . . 100 5.2.3.1 Crucial terms in reinforcement learning . . . . . . . . . . . . . . . . . . 101 5.2.3.2 Salient features of reinforcement learning . . . . . . . . . . . . . . . . . . 102 5.2.3.3 Types of reinforcement learning . . . . . . 102 5.2.3.4 Reinforcement learning algorithms . . . . 103 5.3 Some Crucial Algorithmic Mathematical Models in Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.1 LogisticRegression . . . . . . . . . . . . . . . . . . 104 5.3.2 DecisionTrees . . . . . . . . . . . . . . . . . . . . 105 5.3.3 LinearRegression . . . . . . . . . . . . . . . . . . 107 5.3.4 K-NearestNeighbors . . . . . . . . . . . . . . . . . 108 5.3.5 K-MeansClustering . . . . . . . . . . . . . . . . . 110 5.4 Pre-Eminent Python Libraries Intended for Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 5.4.1 Human Detection (OpenCV, HoG, SVM with Multi-Threading) . . . . . . . . . . . . . . . . . . . 113 5.4.2 Instagram Filters – (OpenCV, Matplotlib, NumPy) . 114 5.5 Machine Learning Techniques in State of Affairs of Power Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 117 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 118 6 Machine Learning Techniques for Renewable Energy Resources 121 K. Punitha, S. Anbarasi, and T. Balasubramanian 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Contents ix 6.2 OverviewofMachineLearning . . . . . . . . . . . . . . . . 126 6.3 DeepLearningArchitecture . . . . . . . . . . . . . . . . . 128 6.4 LSTMNetworkBasedPrediction . . . . . . . . . . . . . . 132 6.5 Concepts of Solar PV and its MPPT Techniques . . . . . . . 134 6.6 Simulation Results and Discussion . . . . . . . . . . . . . . 135 6.6.1 Modeling and Performance Analysis . . . . . . . . . 135 6.6.2 Prediction or Forecasting Methodology . . . . . . . 141 6.6.3 Utilizing Predicted Value in MPPT Technique . . . . 143 6.7 ConclusionandFutureDirections . . . . . . . . . . . . . . 145 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 146 7 Application of Optimization Technique in Modern Hybrid Power Systems 149 D. Lakshmi, R. Zahira, C. N. Ravi, P. Sivaraman, G. Ezhilarasi, and C. Sharmeela 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 150 7.2 ModernPowerSystem . . . . . . . . . . . . . . . . . . . . 151 7.2.1 DeregulatedPowerSystem . . . . . . . . . . . . . . 152 7.2.2 Components of Deregulation . . . . . . . . . . . . . 152 7.2.3 TypesofTransactions . . . . . . . . . . . . . . . . 154 7.2.3.1 Bilateral transactions . . . . . . . . . . . 154 7.2.3.2 DPMandAPF . . . . . . . . . . . . . . . 155 7.2.4 RenewableEnergySources . . . . . . . . . . . . . . 156 7.2.4.1 Doubly fed induction generator . . . . . . 156 7.2.4.2 DFIG in deregulated power system . . . . 158 7.3 Optimization Techniques and Proposed Technique . . . . . . 161 7.3.1 Controllers . . . . . . . . . . . . . . . . . . . . . . 161 7.3.2 PIController . . . . . . . . . . . . . . . . . . . . . 161 7.3.3 Artificial Optimization Algorithm for Tuning PI . . . 162 7.3.3.1 Differential evolution . . . . . . . . . . . 162 7.3.3.2 Flower pollination algorithm . . . . . . . 163 7.3.3.3 Hybridalgorithm . . . . . . . . . . . . . 164 7.3.3.4 Design of a hybrid DE-FPA algorithm forLFC . . . . . . . . . . . . . . . . . . 165 7.4 Simulation Results and Discussion . . . . . . . . . . . . . . 165 7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 167 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 169