IoT, machine learning and blockchain technologies for renewable energy and modern hybrid power systems
This edited book comprises chapters that describe the IoT, machine learning, and blockchain technologies for renewable energy and modern hybrid power systems with simulation examples and case studies. After reading this book, users will understand recent technologies such as IoT, machine learning te...
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| Other Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
[United States] :
River Publishers,
[2022]
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| Series: | River Publishers series in computing and information science and technology.
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| Subjects: | |
| ISBN: | 9788770227117 877022711X 9788770227247 |
| Physical Description: | 1 online resource. |
Cover
Table of contents
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| 001 | kn-on1345065298 | ||
| 003 | OCoLC | ||
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| 020 | |a 9788770227117 |q (electronic bk.) | ||
| 020 | |a 877022711X |q (electronic bk.) | ||
| 020 | |z 9788770227247 | ||
| 035 | |a (OCoLC)1345065298 | ||
| 245 | 0 | 0 | |a IoT, machine learning and blockchain technologies for renewable energy and modern hybrid power systems / |c editors, C. Sharmeela, P. Sanjeevikumar, P. Sivaraman, Meera Joseph. |
| 264 | 1 | |a [United States] : |b River Publishers, |c [2022] | |
| 264 | 4 | |c ©2022 | |
| 300 | |a 1 online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a River Publishers series in computing and information science and technology | |
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a This edited book comprises chapters that describe the IoT, machine learning, and blockchain technologies for renewable energy and modern hybrid power systems with simulation examples and case studies. After reading this book, users will understand recent technologies such as IoT, machine learning techniques, and blockchain technologies and the application of these technologies to renewable energy resources and modern hybrid power systems through simulation examples and case studies. | ||
| 505 | 0 | |a 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 1.1 Introduction 2 1.2 History 5 1.3 Applications of IoT 6 1.3.1 Domestic Applications 6 1.3.2 Applications in Healthcare 7 1.3.3 Applications in E-commerce 8 1.3.4 Industrial Applications 9 1.3.5 Applications in Energy 10 1.4 Technical Details of IoT 11 1.4.1 Sensors 11 1.4.2 Actuators 15 1.4.3 Processing Topologies 16 1.4.4 Communication Technologies 18 1.5 Recent Developments 20 1.6 Challenges 22 1.7 Conclusion 23 References 23 2 IoT and its Requirements for Renewable Energy Resources 29 2.1 Introduction 30 2.1.1 IoT and its Necessity 30 2.1.2 Challenges in RES 30 2.1.3 Integration of IoT in RES and Benefits 32 2.2 Industrial IoT 32 2.2.1 Architecture of IoT 33 2.2.2 IoT Components 34 2.3 RESandIoT 36 2.3.1 IoT Controls for RES 36 2.3.2 Challenges in IoT Implementation 38 2.4 Challenges of IoT in EMS Post-implementation 39 2.4.1 Privacy Issues 39 2.4.2 Security Concerns 41 2.4.3 Data Storage Issues 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 Solution to IoT Challenges 45 2.5.1 Blockchain Methodology 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 Cloud Computing 48 2.5.2.1 Reference architecture 49 2.5.2.2 Network communication and its challenge 51 2.5.2.3 Privacy and security 51 2.5.2.4 Background information 53 2.5.2.5 Big data analytics 53 2.5.2.6 Provision of program quality 53 2.5.2.7 IPv4 addressing limit 54 2.5.2.8 Legal aspects and social facts 55 2.5.2.9 Service detection 56 2.6 Conclusion 56 References 57 3 Power Quality Monitoring of Low Voltage Distribution System Toward Smart Distribution Grid Through IoT 61 3.1 Introduction 62 3.2 Introduction to Various PQ Characteristics 63 3.3 Introduction to IoT 64 3.4 Smart Monitoring using IoT for the Low Voltage Distribution System 65 3.5 Power Quality Monitoring of Low Voltage Distribution System ⁰́₃ Case Study 67 3.5.1 Undervoltage 69 3.5.2 Overvoltage 69 3.5.3 Interruption 71 3.5.4 Overload in Branch Circuit 72 3.6 Conclusion 74 References 75 4 Health Monitoring of a Transformer in a Smart Distribution System using IoT 79 4.1 Introduction 80 4.2 Introduction to the Transformer 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 Oil Level Monitoring Sensor 84 4.4.3 Current Sensor and Voltage Sensor 84 4.4.4 Microcontroller 85 4.4.5 LCD or Monitor 85 4.4.6 Communication System 85 4.4.7 Central Monitoring and Control 86 4.5 Case Study 86 4.6 Conclusion 89 References 89 5 Introduction To Machine Learning Techniques 93 5.1 Why and What is Machine Learning? 93 5.1.1 Phrases in Machine Learning 94 5.1.2 Steps Involved in Machine Learning Practices 94 5.1.3 Properties of Data 94 5.1.4 Real-World Applications of Machine Learning 95 5.2 Classification of Machine Learning Techniques 96 5.2.1 Supervised Learning 96 5.2.1.1 Classification 97 5.2.1.2 Regression 98 5.2.2 Unsupervised Learning 99 5.2.2.1 Clustering 99 5.2.2.2 Association 100 5.2.3 Reinforcement Learning 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 Logistic Regression 104 5.3.2 Decision Trees 105 5.3.3 Linear Regression 107 5.3.4 K-Nearest Neighbors 108 5.3.5 K-Means Clustering 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 ⁰́₃ (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 6.1 Introduction 122 6.2 Overview of Machine Learning 126 6.3 Deep Learning Architecture 128 6.4 LSTM Network Based Prediction 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 Conclusion and Future Directions 145 References 146 7 Application of Optimization Technique in Modern Hybrid Power Systems 149 7.1 Introduction 150 7.2 Modern Power System 151 7.2.1 Deregulated Power System 152 7.2.2 Components of Deregulation 152 7.2.3 Types of Transactions 154 7.2.3.1 Bilateral transactions 154 7.2.3.2 DPM and APF 155 7.2.4 Renewable Energy Sources 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 PI Controller 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 Hybrid algorithm 164 7.3.3.4 Design of a hybrid DE-FPA algorithm for LFC 165 7.4 Simulation Results and Discussion 165 7.5 Conclusion 167 References 169 8 Application of Machine Learning Techniques in Modern Hybrid Power Systems ⁰́₃ A Case Study 173 8.1 Introduction 174 8.2 Technical Issues in Modern Hybrid Power Systems 176 8.2.1 Power Quality 177 8.2.2 Demand-Supply Management 177 8.2.3 Synchronization and Islanding 177 8.2.4 Protective Devices, Safety, and Environment 177 8.2.5 Human Factor 178 8.3 Application of ML and Optimization Techniques in MHPS 178 8.4 A Prediction Case Study of ML in MHPS 179 8.4.1 Forecasting Irradiance of SPP 182 8.4.2 Metrics for Understanding the Performance of Predictions using ML Methods 184 8.4.3 Model-Based and Model-Free Regression Techniques 185 8.4.4 Prediction Block 186 8.4.5 Forecasting of Solar Irradiance with a Model-Based Regression Approach 187 8.4.6 Forecasting of Solar Irradiance with a Model-Free Regression Approach (ANNs) 190 8.4.7 Normalization, Training, and Testing for Model-Free Regression 191 8.5 Optimization Block in MHPS 193 8.5.1 Optimization-Assisted ML of MHPS 193 8.5.2 Experimental Setup 197 8.5.3 Validation Block 197 8.5.3.1 Thorough comparisons in voltage-magnitudes for the actual test day for model-based and model-free approaches 198 8.6 Conclusion 200 References 201 9 Establishing a Realistic Shunt Capacitor Bank with a Power System using PSO/ACCS 205 9.1 Introduction 206 9.2 Problem Statement 208 9.2.1 Power Flow Equations 209 9.2.2 Mathematical Representation 210 9.2.3 Sensitivity Calculations 211 9.3 Capacitor Bank Operation Strategies 212 9.4 Particle Swarm Optimization 214 9.5 Limitation Treatment 216 9.6 PSO Implementation for Offline Capacitor Study 216 9.7 Simulation System for Optimal Capacitor Allocation 218 9.7.1 Modified System Data 219 9.7.2 Simulation Study 221 9.8 Automatic Capacitor Control Scheme 224 9.8.1 ACCS IED Scope 225 9.8.2 ACCS Operation Logic Steps 225 9.8.3 ACCS Operation Sample 227 9.9 Conclusion 230 References 231 10 Introduction to Blockchain Technologies 235 10.1 Introduction and Classification 236 10.2 Blockchain Technology Characteristics 237 10.2.1 Multi-Centralization 237 10.2.2 Tamper-Proof, Traceable, and Transparent 237 10.2.3 High Reliability 238 10.3 Blockchain Technology Graph 238 10.3.1 Core Technology Overview 239 10.3.2 Expansion Technology Overview 248 10.3.3 Supporting Technology Overview 251 10.4 Conclusion 253 References 254 11 Blockchain Technologies for Renewable Energy Resources with Case Study: SHA⁰́₃256, 384, and 512 257 11.1 Introduction 258 11.2 Local Energy Trading and Consensus Algorithms 258 11.3 Simulation 260 11.3.1 Energy Trading Model and Case Study 260 11.3.2 Performance Result and Evaluation of the Models at Different Hash Algorithms 262 11.4 Conclusion and Recommendations 266 References 268 Index 271 About the Editors 273. | |
| 590 | |a Knovel |b Knovel (All titles) | ||
| 650 | 0 | |a Renewable energy sources. | |
| 650 | 0 | |a Hybrid power systems. | |
| 650 | 0 | |a Artificial intelligence |x Engineering applications. | |
| 650 | 0 | |a Internet of things. | |
| 650 | 0 | |a Blockchains (Databases) | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Sharmeela, C., |e editor. | |
| 700 | 1 | |a Sanjeevikumar, Padmanaban, |d 1978- |e editor. |1 https://id.oclc.org/worldcat/entity/E39PCjt9g7qKDRt7YYCbrkRmbb | |
| 700 | 1 | |a Sivaraman, P. |q (Pandarinathan), |e editor. |1 https://id.oclc.org/worldcat/entity/E39PCjvcMMvYyc8Jg9gKVmRrhd | |
| 700 | 1 | |a Joseph, Meera, |e editor. | |
| 830 | 0 | |a River Publishers series in computing and information science and technology. | |
| 856 | 4 | 0 | |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpITMLBTR1/iot-machine-learning?kpromoter=marc |y Full text |
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