Table Of ContentIoT, Machine Learning and Blockchain
Technologies for Renewable Energy
and Modern Hybrid Power Systems
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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
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Distributed exclusively by Routledge
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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
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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
