Artificial intelligence-based smart power systems:
Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studiesArtificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connect...
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Hoboken, New Jersey
Wiley
[2023]
Piscataway, NJ IEEE Press |
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| Zusammenfassung: | Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studiesArtificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution. It covers many new topics such as distribution phasor measurement units, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. The text also explores the potential consequences of artificial intelligence and advanced technologies in smart power systems in the forthcoming years.To enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies.Artificial Intelligence-based Smart Power Systems includes specific information on topics such as:* Modeling and analysis of smart power systems, covering steady state analysis, dynamic analysis, voltage stability, and more* Recent advancement in power electronics for smart power systems, covering power electronic converters for renewable energy sources, electric vehicles, and HVDC/FACTs* Distribution Phasor Measurement Units (PMU) in smart power systems, covering the need for PMU in distribution and automation of system reconfigurations* Power and energy management systemsEngineering colleges and universities, along with industry research centers, can use the in-depth subject coverage and the extensive supplementary learning resources found in Artificial Intelligence-based Smart Power Systems to gain a holistic understanding of the subject and be able to harness that knowledge within a myriad of practical applications |
| Beschreibung: | xxii, 378 Seiten Illustrationen, Diagramme 938 grams |
| ISBN: | 9781119893967 |
Internformat
MARC
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| 245 | 1 | 0 | |a Artificial intelligence-based smart power systems |c edited by Sanjeevikumar Padmanaban, Sivaraman Palanisamy, Sharmeela Chenniappan, Jen Bo Holm-Nielsen |
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| 520 | |a Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studiesArtificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution. It covers many new topics such as distribution phasor measurement units, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. The text also explores the potential consequences of artificial intelligence and advanced technologies in smart power systems in the forthcoming years.To enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies.Artificial Intelligence-based Smart Power Systems includes specific information on topics such as:* Modeling and analysis of smart power systems, covering steady state analysis, dynamic analysis, voltage stability, and more* Recent advancement in power electronics for smart power systems, covering power electronic converters for renewable energy sources, electric vehicles, and HVDC/FACTs* Distribution Phasor Measurement Units (PMU) in smart power systems, covering the need for PMU in distribution and automation of system reconfigurations* Power and energy management systemsEngineering colleges and universities, along with industry research centers, can use the in-depth subject coverage and the extensive supplementary learning resources found in Artificial Intelligence-based Smart Power Systems to gain a holistic understanding of the subject and be able to harness that knowledge within a myriad of practical applications | ||
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Datensatz im Suchindex
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| adam_text | v Contents Editor Biography xv List of Contributors 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 1.11 2 2.1 2.2 2.2.1 2.2.1.1 2.2.1.2 2.2.1.3 2.2.2 2.2.2.1 2.2.2.2 2.2.3 2.3 2.4 2.4.1 2.4.2 2.5 2.6 2.6.1 2.6.2 2.6.2.1 xvii 1 Siváromon Palanisamy, Zahira Rahiman, and Sharmeela Chenniappan Problems in Conventional Power Systems ì Distributed Generation (DG) 1 Wide Area Monitoring and Control 2 Automatic Metering Infrastructure 4 Phasor Measurement Unit 6 Power Quality Conditioners 8 Energy Storage Systems 8 Smart Distribution Systems 9 Electric Vehicle Charging Infrastructure 10 Cyber Security 11 Conclusion 11 References 11 Introduction to Smart Power Systems 15 Madhu Palati, Sagar Singh Prathap, and Nagesh Haiasahalli Nagaraju Introduction 15 Modeling of Equipment’s for Steady-State Analysis 16 Load Flow Analysis 16 Gauss Seidel Method 18 Newton Raphson Method 18 Decoupled Load Flow Method 18 Short Circuit Analysis 19 Symmetrical Faults 19 Unsymmetrical Faults 20 Harmonic Analysis 20 Modeling of Equipments for Dynamic and Stability Analysis 22 Dynamic Analysis 24 Frequency Control 24 Fault Ride Through 26 Voltage Stability 26 Case Studies 27 Case Study 1 27 Case Study 2 28 Existing and Proposed Generation Details in the Vicinity of Wind Farm 29 Modeling and Analysis of Smart Power System
vi Contents 2.6.2.2 2.6.2.3 2.6.2.4 2.6.2.5 2.6.2.6 1A3..7 2.6.2.8 2.7 Ъ Power Evacuation Study for 50 MW Generation ЗО Without Interconnection of the Proposed 50 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation 31 Observations Made from Table 2.6 31 With the Interconnection of Proposed 50 MW Generation from Wind Farm on 66 kV level of 220/66 kV Substation 31 Normal Condition without Considering Contingency 32 Contingency Analysis 32 With the Interconnection of Proposed 60 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation 33 Conclusion 34 References 34 Muttilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy 37 Marimuthu Marikannu, Wjayalakshmi Subramanian, Paranthagan Balasubramanian, Jayakumar Narayanasamy Nisha C. Rani, and Devi Vigneshwari Balasubramanian Introduction 37 Multilevel Cascaded Boost Converter 40 Modes of Operation of MCBC 42 Mode-1 Switch Ѕл Is ON 42 Mode-2 Switch Ѕл Is ON 42 Mode-3-Operation ֊ Switch SA Is ON 42 Mode-4-Operation ֊ Switch SA Is ON 42 Mode-5-Operation ֊ Switch SA Is ON 42 Mode-6-Operation - Switch SA Is OFF 42 Mode-7-Operation - Switch SA Is OFF 42 Mode-8-Operation ֊ Switch SA Is OFF 43 Mode-9-Operation ֊ Switch SA Is OFF 44 Mode 10-Operation ֊ Switch SA is OFF 45 Simulation and Hardware Results 45 Prominent Structures of Estimated DC-DCConverter with Prevailing Converter 49 Voltage Gain and Power Handling Capability 49 Voltage Stress 49 Switch Count and Geometric Structure 49 Current Stress 52 Duty Cycle Versus Voltage Gain 52 Number of Levels in the Planned Converter 52 Power
Electronic Converters for Renewable Energy Sources (Applications of MLCB) 54 MCBC Connected with PV Panel 54 Output Response of PV Fed MCBC 54 H-Bridge Inverter 54 Modes of Operation 55 Mode 1 55 Mode 2 55 Mode 3 56 Mode 4 56 Mode 5 56 Mode 6 56 Mode 7 58 Mode 8 58 Mode 9 59 Mode 10 59 Applications 3.2 3.3 3.3.1 3.3.2 3.3.3 3.3.4 3.3.5 3.3.6 3.3.7 3.3.8 3.3.9 3.3.10 3.4 3.5 3.5.1 3.5.2 3.5.3 3.5.4 3.5.5 3.5.6 3.6 3.6.1 3.6.2 3.6.3 3.7 3.7.1 3.7.2 3.7.3 3.7.4 3.7.5 3.7.6 3.7.7 3.7.8 3.7.9 3.7.10
Contents 3.8 3.9 3.10 3.11 Simulation Results of МСВС Fed Inverter 60 Power Electronic Converter for E-Vehicles 61 Power Electronic Converter for HVDC/Facts 62 Conclusion 63 References 63 4 Recent Advancements in Power Electronics for Modern Power Systems-Comprehensive Review on DC-Link Capacitors Concerning Power Density Maximization in Power Converters 65 Naveenkumar Marati, Shariq Ahammed, Kathirvei Karuppazaghi, Balraj Vaithilingam, Gyan R. Biswal, Phaneendra B. Bobba, Sanjeevikumar Padmanaban, and Sharmeela Chenniappan Introduction 65 Applications of Power Electronic Converters 66 Power Electronic Converters in Electric Vehicle Ecosystem 66 Power Electronic Converters in Renewable Energy Resources 67 Classification of DC-Link Topologies 68 Briefing on DC-Link Topologies 69 Passive Capacitive DC Link 69 Filter Type Passive Capacitive DC Links 70 Filter Type Passive Capacitive DC Links with Control 72 Interleaved Type Passive Capacitive DC Links 74 Active Balancing in Capacitive DC Link 75 Separate Auxiliary Active Capacitive DC Links 76 Integrated Auxiliary Active Capacitive DC Links 78 Comparison on DC-Link Topologies 82 Comparison of Passive Capacitive DC Links 82 Comparison of Active Capacitive DC Links 83 Comparison of DC Link Based on Power Density, Efficiency, and Ripple Attenuation 86 Future and Research Gaps in DC-Link Topologies with Balancing Techniques 94 Conclusion 95 References 95 4.1 4.2 4.2.1 4.2.2 4.3 4.4 4.4.1 4.4.1.1 4.4.1.2 4.4.1.3 4.4.2 4.4.2.1 4.4.2.2 4.5 4.5.1 4.5.2 4.5.3 4.6 4.7 5 5.1 5.2 5.3 5.4 5.4.1 5.5 5.5.1 5.5.2 5.5.3 5.5.4 5.5.5 5.6 5.6.1
5.6.2 5.6.3 5.6.4 5.6.5 5.7 Energy Storage Systems for Smart Power Systems 99 Siváromon Palanisamy Logeshkumar Shanmugasundaram, and Sharmeela Chenniappan Introduction 99 Energy Storage System for Low Voltage Distribution System 100 Energy Storage System Connected to Medium and High Voltage 101 Energy Storage System for Renewable Power Plants 104 Renewable Power Evacuation Curtailment 106 Types of Energy Storage Systems 109 Battery Energy Storage System 109 Thermal Energy Storage System 110 Mechanical Energy Storage System 110 Pumped Hydro 110 HydrogenStorage 110 Energy Storage Systems for Other Applications 111 Shift in Energy Time 111 Voltage Support 111 Frequency Regulation (Primary, Secondary, and Tertiary) 112 Congestion Management 112 Blackstart 112 Conclusion 112 References 113 vii
viii I Contents 6 6.1 6.2 6.2.1 6.3 6.3.1 6.4 6.5 7 7.1 7.2 7.3 7.3.1 7.3.2 7A 7A.1 7.4.2 7.4.3 7.5 7.6 8 8.1 8.2 8.3 8.3.1 8.3.2 8.4 8.4.1 8.4.2 8.4.3 8.4.4 8.4.5 8.4.6 8.4.7 8.4.8 8.4.9 8.4.10 8.4.11 8.5 Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage Thamatapu Eswararao, Sundaram Elango, Umashankar Subramanian, Krishnamohan Tatikonda, Garika Gantaiahswamy, and Sharmeela Chenniappan Introduction 115 Structure of Supercapacitor 117 Mathematical Modeling of Supercapacitor 117 Bidirectional Buck-Boost Converter 118 FPGA Controller 119 Experimental Results 120 Conclusion 123 References 125 Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator Rania Moutchou, Ahmed Abbou, Bouazza Jabri, Salah E. Rhaili, and Khalid Chigane Introduction 129 Proposed MPPT Control Algorithm 130 Wind Energy Conversion System 131 Wind Turbine Characteristics 131 Model of PMSG 132 Fuzzy Logic Command for the MPPT of the PMSG 133 Fuzzification 134 Fuzzy Logic Rules 134 Defuzzification 134 Results and Discussions 135 Conclusion 139 References 139 A Novel Nearest Neighbor Searching-Based Fault Distance Location Method for HVDC Transmission Lines 141 Aleena Swetapadma, Shobha Agarwal, Satarupa Chakrabarti, and Soham Chakrabarti Introduction 141 Nearest Neighbor Searching 142 Proposed Method 144 Power System Network Under Study 144 Proposed Fault Location Method 145 Results 146 Performance Varying Nearest Neighbor 147 Performance Varying Distance Matrices 147 Near Boundary Faults 148 Far Boundary Faults 149 Performance During High Resistance
Faults 149 Single Pole to Ground Faults 150 Performance During Double Pole to Ground Faults 151 Performance During Pole to Pole Faults 751 Error Analysis 152 Comparison with Other Schemes 153 Advantages of the Scheme 154 Conclusion 154 Acknowledgment 754 References 154 129 115
Contents 9 Comparative Analysis of Machine Learning Approaches in Enhancing Power System 157 Md. I. H. Pathan, Mohammad S. Shahriar, Mohammad Μ. Rahman, Md. Sanwar Hossain, Nadia Awatif, and Md. Shafiullah Introduction 157 Power System Models 159 PSS Integrated Single Machine Infinite Bus Power Network 159 PSS-UPFC Integrated Single Machine Infinite Bus Power Network 160 Methods 161 Group Method Data Handling Model 161 Extreme Learning Machine Model 162 Neurogenetic Model 162 Multigene Genetic Programming Model 163 Data Preparation and Model Development 165 Data Production and Processing 165 Machine Learning Model Development 165 Results and Discussions 166 Eigenvalues and Minimum Damping Ratio Comparison 166 Time-Domain Simulation Results Comparison 170 Rotor Angle Variation Under Disturbance 170 Rotor Angular Frequency Variation Under Disturbance 171 DC-Link Voltage Variation Under Disturbance 173 Conclusions 173 References 174 Stability 9.1 9.2 9.2.1 9.2.2 9.3 9.3.1 9.3.2 9.3.3 9.3.4 9.4 9.4.1 9.4.2 9.5 9.5.1 9.5.2 9.5.2.1 9.5.2.2 9.5.2.3 9.6 10 10.1 10.2 10.3 10.3.1 10.3.2 10.3.3 10.3.4 10.3.5 10.4 10.5 10.5.1 10.5.2 10.5.3 10.6 10.7 10.7.1 10.8 10.8.1 10.8.2 10.8.3 10.8.4 10.8.5 10.8.6 10.9 Augmentation of PV-Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System 179 Jyoti Shukla, Basanta K. Panigrahi, and Monika Vardia Introduction 179 PV-Wind Hybrid Power Generation Configuration 180 Proposed Systems Topologies 181 Structure of PV System 181 The MPPTs Technique 183 NN Predictive Controller Technique 183 ANFIS
Technique 184 Training Data 186 Wind Power Generation Plant 187 Pitch Angle Control Techniques 189 PI Controller 189 NARMA-L2 Controller 190 Fuzzy Logic Controller Technique 192 Proposed DVRs Topology 192 Proposed Controlling Technique of DVR 193 ANFIS and Pl Controlling Technique 193 Results of the Proposed Topologies 196 PV System Outputs (MPPT Techniques Results) 196 Main PV System outputs 196 Wind Turbine System Outputs (Pitch Angle Control Technique Result) Proposed PMSG Wind Turbine System Output 199 Performance of DVR (Controlling Technique Results) 203 DVRs Performance 203 Conclusion 204 References 204 198 ix
Contents 207 Deepak Yadav, Saad Mekhilef Brijesh Singh, and Muhyaddin Rawa Abbreviations 207 11.1 Introduction 208 11.2 Deep and Reinforcement Learning for Decision-Making Problems in Smart Power Systems 210 11.2.1 Reinforcement Learning 210 11.2.1.1 Markov Decision Process (Μ DP) 210 11.2.1.2 Value Function and Optimal Policy 211 11.2.2 Reinforcement Learnings to Deep Reinforcement Learnings 212 11.2.3 Deep Reinforcement Learning Algorithms 212 11.3 Applications in Power Systems 213 11.3.1 Energy Management 213 11.3.2 Power Systems’Demand Response (DR) 215 11.3.3 Electricity Market 216 11.3.4 Operations and Controls 217 11.4 Mathematical Formulation of Objective Function 218 11.4.1 Locational Marginal Prices (LMPs) Representation 219 11.4.2 Relative Strength Index (RSI) 219 11.4.2.1 Autoregressive Integrated Moving Average (ARIMA) 219 11.5 Interior-point Technique KKT Condition 220 11.5.1 Explanation of Karush-Kuhn-Tucker Conditions 220 11.5.2 Algorithm for Finding a Solution 221 11.6 Test Results and Discussion 221 11.6.1 Illustrative Example 221 11.7 Comparative Analysis with Other Methods 223 11.8 Conclusion 224 11.9 Assignment 224 Acknowledgment 225 References 225 11 12 12.1 12.1.1 12.1.2 12.1.3 12.1.4 12.1.5 12.1.6 12.1.7 12.1.8 12.1.9 12.1.10 12.2 12.2.1 12.2.2 12.2.3 12.2.3.1 12.2.4 12.2.5 Deep Reinforcement Learning and Energy Price Prediction Power Quality Conditioners in Smart Power System 233 Zahira Rahiman, Lakshmi Dhandapani, Ravi Chengalvarayan Natarajan, Promila Vallikannan, Siváromon Palanisamy, and Sharmeela Chenniappan Introduction 233 Voltage Sag 234
Voltage Swell 234 Interruption 234 Under Voltage 234 Overvoltage 234 Voltage Fluctuations 234 Transients 235 Impulsive Transients 235 Oscillatory Transients 235 Harmonics 235 Power Quality Conditioners 235 STATCOM 235 SVC 235 Harmonic Filters 236 Active Filter 236 UPS Systems 236 Dynamic Voltage Restorer (DVR) 236
Contents 12.2.6 12.2.7 12.2.8 12.3 12.4 12.5 12.6 12.7 12.8 12.9 12.10 12.10.1 12.10.2 12.10.3 12.10.4 12.10.4.1 12.10.4.2 12.11 12.12 Enhancement of Voltage Sag 236 Interruption Mitigation 237 Mitigation of Harmonies 241 Standards of Power Quality 244 Solution for Power Quality Issues 244 Sustainable Energy Solutions 245 Need for Smart Grid 245 What Is a Smart Grid? 245 Smart Grid: The “Energy Internet” 245 Standardization 246 Smart Grid Network 247 Distributed Energy Resources (DERs) 247 Optimization Techniques in Power Quality Management 247 Conventional Algorithm 248 Intelligent Algorithm 248 Firefly Algorithm (FA) 248 Spider Monkey Optimization (SMO) 250 Simulation Results and Discussion 254 Conclusion 257 References 257 13 259 Sanjeevikumar Padmanaban, Mostafa Azimi Nasab, Mohammad Ebrahim Shiri, Hamid Haj Seyyed Javad i, Morteza Azimi Nasab, Mohammad Zand, and Tina Samavat Introduction 259 Internet of Things Technology 260 Smart House 261 Different Parts of Smart Home 262 Proposed Architecture 264 Controller Components 265 Proposed Architectural Layers 266 Infrastructure Layer 266 Information Technology 266 Information and Communication Technology 266 Electronics 266 Collecting Data 267 Data Management and Processing 267 Service Quality Management 267 Resource Management 267 Device Management 267 Security 267 Services 267 Applications 268 Conclusion 269 References 269 13.1 13.2 13.2.1 13.3 13.4 13.5 13.6 13.6.1 13.6.1.1 13.6.1.2 13.6.1.3 13.6.2 13.6.3 13.6.3.1 13.6.3.2 13.6.3.3 13.6.3.4 13.7 13.8 13.9 14 14.1 14.2 The Role of Internet of Things in Smart Homes 273
Sanjeevikumar Padmanaban, Tina Samavat, Mostafa Azimi Nasab, Morteza Azimi Nasab, Mohammad Zand, and Fatemeh Nikokar Introduction 273 Smart City 275 Electric Vehicles and loT in Smart Cities xi
xii Contents 14.2.1 14.3 14.4 14.4.1 14.4.2 14.4.3 14.4.4 14.5 14.6 14.6.1 14.6.2 14.6.3 14.6.4 14.7 14.7.1 14.7.2 14.7.3 14.7.4 14.7.5 14.7.6 14.7.7 14.7.8 14.7.9 14.7.10 14.8 14.9 14.9.1 14.9.2 14.10 Internet of Things and Smart City 275 The Concept of Smart Electric Networks 275 loT Outlook 276 loT Three-layer Architecture 276 View Layer 276 Network Layer 277 Application Layer 278 Intelligent Transportation and Transportation 278 Information Management 278 Artificial Intelligence 278 Machine Learning 279 Artificial Neural Network 279 Deep Learning 280 Electric Vehicles 281 Definition of Vehicle-to-Network System 281 Electric Cars and the Electricity Market 281 The Role of Electric Vehicles in the Network 282 V2G Applications in Power System 282 Provide Baseload Power 283 Courier Supply 283 Extra Service 283 Power Adjustment 283 Rotating Reservation 284 The Connection between the Electric Vehicle and the Power Grid 284 Proposed Model of Electric Vehicle 284 Prediction Using LSTM Time Series 285 LSTM Time Series 286 Predicting the Behavior of Electric Vehicles Using the LSTM Method 287 Conclusion 287 Exercise 288 References 288 15 291 Gunapriya Devarajan, Puspaiatha Naveen Kumar, Muniraj Chinnusamy, Sabareeshwaran Kanagaraj, and Sharmeela Chenniappan Introduction 291 Classification of Hardware in the Loop 291 Signal HIL Model 297 Power HIL Model 292 Reduced-Scaled HIL Model 292 Points to Be Considered While Performing HIL Simulation 293 Applications of HIL 293 Why HIL Is Important? 293 Hardware-ln-The-Loop Simulation 294 Simulation Verification and Validation 295
Simulation Computer Hardware 295 Benefits of Using Hardware֊In-The-Loop Simulation 296 HIL for Renewable Energy Systems (RES) 296 Introduction 296 Hardware in the Loop 297 Electrical Hardware in the Loop 297 15.1 15.1.1 15.1.1.1 15.1.1.2 15.1.1.3 15.1.2 15.1.3 15.2 15.2.1 15.2.2 15.2.3 15.2.4 15.3 15.3.1 15.3.2 15.3.2.1 Modeling and Simulation of Smart Power Systems Using HIL
Contents 15.3 .2.2 Mechanical Hardware in the Loop 297 15.4 HIL for HVDC and FACTS 299 15.4.1 Introduction 299 15.4.2 Modular Multi Level Converter 300 15.5 HIL for Electric Vehicles 301 15.5.1 Introduction 301 15.5.2 EV Simulation Using MATLAB, Simulink 302 15.5.2.1 Model-Based System Engineering (MBSE) 302 15.5.2.2 Model Batteries and Develop BMS 302 15.5.2.3 Model Fuel Cell Systems (FCS) and Develop Fuel Cell Control Systems (FCCS) 15.5.2.4 Model Inverters, Traction Motors, and Develop Motor Control Software 304 15.5.2.5 Deploy, Integrate, and Test Control Algorithms 304 15.5.2.6 Data-Driven Workflows and AI in EV Development 305 15.6 HIL for Other Applications 306 15.6.1 Electrical Motor Faults 306 15.7 Conclusion 307 References 308 16 16.1 16.2 16.3 16.4 16.5 16.6 16.6.1 16.6.2 16.6.3 16.7 16.7.1 16.7.1.1 16.7.2 16.7.2.1 16.7.3 16.7.4 16.7.5 16.8 17 17.1 17.2 17.2.1 17.2.2 17.2.2.1 17.2.2.2 17.2.2.3 17.2.2.4 17.2.3 303 311 Geethanjali Muthiah, Meenakshi Devi Manivannan, Hemavathi Ramadoss, and Sharmeela Chenniappan Introduction 311 Comparison of PMUs and SCADA 312 Basic Structure of Phasor Measurement Units 313 PM U Deployment in Distribution Networks 314 PMU Placement Algorithms 315 Need/Significance of PMUs in Distribution System 315 Significance of PMUs- Concerning Power System Stability 316 Significance of PMUs in Terms of Expenditure 316 Significance of PMUs in Wide Area Monitoring Applications 316 Applications of PMUs in Distribution Systems 317 System Reconfiguration Automation to Manage Power Restoration 317 Case Study 317 Planning for High DER Interconnection
(Penetration) 319 Case Study 319 Voltage Fluctuations and Voltage Ride-Through Related to DER 320 Operation of Islanded Distribution Systems 320 Fault-Induced Delayed Voltage Recovery (FIDVR) Detection 322 Conclusion 322 References 323 Distribution Phasor Measurement Units (PMUs) in Smart Power Systems Blockchain Technologies for Smart Power Systems 327 4. Gayathri, S. Saravan an, P. Pandiyan, and V. Rukkumani Introduction 327 Fundamentals of Blockchain Technologies 328 Terminology 328 Process of Operation 329 Proof of Work (PoW) 329 Proof of Stake (PoS) 329 Proof of Authority (PoA) 330 Practical Byzantine Fault Tolerance (PBFT) 330 Unique Features of Blockchain 330 xiii
xiv Contents 17.2.4 17.2.4.1 17.2.4.2 17.2.4.3 17.3 17.3.1 17.3.2 17.3.3 17.3.4 17.3.5 17.4 17.4.1 17.4.2 17.4.3 17.5 17.6 17.6.1 17.6.2 17.6.3 17.6.4 17.6.5 17.6.6 17.7 17.8 17.9 Energy with Blockchain Projects 330 Bitcoin Cryptocurrency 331 Dubai: Blockchain Strategy 331 Humanitarian Aid Utilization of Blockchain 331 Blockchain Technologies for Smart Power Systems 331 Blockchain as a Cyber Layer 331 Agent/Aggregator Based Microgrid Architecture 332 Limitations and Drawbacks 332 Peer to Peer Energy Trading 333 Blockchain for Transactive Energy 335 Blockchain for Smart Contracts 336 The Platform for Smart Contracts 337 The Architecture of Smart Contracting for Energy Applications Smart Contract Applications 339 Digitize and Decentralization Using Blockchain 340 Challenges in Implementing BJockchain Techniques 340 Network Management 341 Data Management 341 Consensus Management 341 Identity Management 341 Automation Management 342 Lack of Suitable Implementation Platforms 342 Solutions and Future Scope 342 Application of Blockchain for Flexible Services 343 Conclusion 343 References 344 338 349 Subrat Sahoo 18.1 Introduction 349 18.1.1 Geopolitical Situation 349 18.1.2 Covid-19 Impacts 350 18.1.3 Climate Challenges 350 18.2 Definition and Constituents of Smart Power Systems 351 18.2.1 Applicable Industries 352 18.2.2 Evolution of Power Electronics-Based Solutions 353 18.2.3 Operation of the Power System 355 18.3 Challenges Faced by Utilities and Their Way Towards Becoming Smart 18.3.1 Digitalization of Power Industry 359 18.3.2 Storage Possibilities and Integration into Grid
360 18.3.3 Addressing Power Quality Concerns and Their Mitigation 362 18.3.4 A Path Forward Towards Holistic Condition Monitoring 363 18.4 Ways towards Smart Transition of the Energy Sector 366 18.4.1 Creating an All-Inclusive Ecosystem 366 18.4.1.1 Example of Sensor-Based Ecosystem 367 18.4.1.2 Utilizing the Sensor Data for Effective Analytics 368 18.4.2 Modular Energy System Architecture 370 18.5 Conclusion 371 References 373 18 Power and Energy Management in Smart Power Systems Index 377 356
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v Contents Editor Biography xv List of Contributors 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 1.11 2 2.1 2.2 2.2.1 2.2.1.1 2.2.1.2 2.2.1.3 2.2.2 2.2.2.1 2.2.2.2 2.2.3 2.3 2.4 2.4.1 2.4.2 2.5 2.6 2.6.1 2.6.2 2.6.2.1 xvii 1 Siváromon Palanisamy, Zahira Rahiman, and Sharmeela Chenniappan Problems in Conventional Power Systems ì Distributed Generation (DG) 1 Wide Area Monitoring and Control 2 Automatic Metering Infrastructure 4 Phasor Measurement Unit 6 Power Quality Conditioners 8 Energy Storage Systems 8 Smart Distribution Systems 9 Electric Vehicle Charging Infrastructure 10 Cyber Security 11 Conclusion 11 References 11 Introduction to Smart Power Systems 15 Madhu Palati, Sagar Singh Prathap, and Nagesh Haiasahalli Nagaraju Introduction 15 Modeling of Equipment’s for Steady-State Analysis 16 Load Flow Analysis 16 Gauss Seidel Method 18 Newton Raphson Method 18 Decoupled Load Flow Method 18 Short Circuit Analysis 19 Symmetrical Faults 19 Unsymmetrical Faults 20 Harmonic Analysis 20 Modeling of Equipments for Dynamic and Stability Analysis 22 Dynamic Analysis 24 Frequency Control 24 Fault Ride Through 26 Voltage Stability 26 Case Studies 27 Case Study 1 27 Case Study 2 28 Existing and Proposed Generation Details in the Vicinity of Wind Farm 29 Modeling and Analysis of Smart Power System
vi Contents 2.6.2.2 2.6.2.3 2.6.2.4 2.6.2.5 2.6.2.6 1A3.7 2.6.2.8 2.7 Ъ Power Evacuation Study for 50 MW Generation ЗО Without Interconnection of the Proposed 50 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation 31 Observations Made from Table 2.6 31 With the Interconnection of Proposed 50 MW Generation from Wind Farm on 66 kV level of 220/66 kV Substation 31 Normal Condition without Considering Contingency 32 Contingency Analysis 32 With the Interconnection of Proposed 60 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation 33 Conclusion 34 References 34 Muttilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy 37 Marimuthu Marikannu, Wjayalakshmi Subramanian, Paranthagan Balasubramanian, Jayakumar Narayanasamy Nisha C. Rani, and Devi Vigneshwari Balasubramanian Introduction 37 Multilevel Cascaded Boost Converter 40 Modes of Operation of MCBC 42 Mode-1 Switch Ѕл Is ON 42 Mode-2 Switch Ѕл Is ON 42 Mode-3-Operation ֊ Switch SA Is ON 42 Mode-4-Operation ֊ Switch SA Is ON 42 Mode-5-Operation ֊ Switch SA Is ON 42 Mode-6-Operation - Switch SA Is OFF 42 Mode-7-Operation - Switch SA Is OFF 42 Mode-8-Operation ֊ Switch SA Is OFF 43 Mode-9-Operation ֊ Switch SA Is OFF 44 Mode 10-Operation ֊ Switch SA is OFF 45 Simulation and Hardware Results 45 Prominent Structures of Estimated DC-DCConverter with Prevailing Converter 49 Voltage Gain and Power Handling Capability 49 Voltage Stress 49 Switch Count and Geometric Structure 49 Current Stress 52 Duty Cycle Versus Voltage Gain 52 Number of Levels in the Planned Converter 52 Power
Electronic Converters for Renewable Energy Sources (Applications of MLCB) 54 MCBC Connected with PV Panel 54 Output Response of PV Fed MCBC 54 H-Bridge Inverter 54 Modes of Operation 55 Mode 1 55 Mode 2 55 Mode 3 56 Mode 4 56 Mode 5 56 Mode 6 56 Mode 7 58 Mode 8 58 Mode 9 59 Mode 10 59 Applications 3.2 3.3 3.3.1 3.3.2 3.3.3 3.3.4 3.3.5 3.3.6 3.3.7 3.3.8 3.3.9 3.3.10 3.4 3.5 3.5.1 3.5.2 3.5.3 3.5.4 3.5.5 3.5.6 3.6 3.6.1 3.6.2 3.6.3 3.7 3.7.1 3.7.2 3.7.3 3.7.4 3.7.5 3.7.6 3.7.7 3.7.8 3.7.9 3.7.10
Contents 3.8 3.9 3.10 3.11 Simulation Results of МСВС Fed Inverter 60 Power Electronic Converter for E-Vehicles 61 Power Electronic Converter for HVDC/Facts 62 Conclusion 63 References 63 4 Recent Advancements in Power Electronics for Modern Power Systems-Comprehensive Review on DC-Link Capacitors Concerning Power Density Maximization in Power Converters 65 Naveenkumar Marati, Shariq Ahammed, Kathirvei Karuppazaghi, Balraj Vaithilingam, Gyan R. Biswal, Phaneendra B. Bobba, Sanjeevikumar Padmanaban, and Sharmeela Chenniappan Introduction 65 Applications of Power Electronic Converters 66 Power Electronic Converters in Electric Vehicle Ecosystem 66 Power Electronic Converters in Renewable Energy Resources 67 Classification of DC-Link Topologies 68 Briefing on DC-Link Topologies 69 Passive Capacitive DC Link 69 Filter Type Passive Capacitive DC Links 70 Filter Type Passive Capacitive DC Links with Control 72 Interleaved Type Passive Capacitive DC Links 74 Active Balancing in Capacitive DC Link 75 Separate Auxiliary Active Capacitive DC Links 76 Integrated Auxiliary Active Capacitive DC Links 78 Comparison on DC-Link Topologies 82 Comparison of Passive Capacitive DC Links 82 Comparison of Active Capacitive DC Links 83 Comparison of DC Link Based on Power Density, Efficiency, and Ripple Attenuation 86 Future and Research Gaps in DC-Link Topologies with Balancing Techniques 94 Conclusion 95 References 95 4.1 4.2 4.2.1 4.2.2 4.3 4.4 4.4.1 4.4.1.1 4.4.1.2 4.4.1.3 4.4.2 4.4.2.1 4.4.2.2 4.5 4.5.1 4.5.2 4.5.3 4.6 4.7 5 5.1 5.2 5.3 5.4 5.4.1 5.5 5.5.1 5.5.2 5.5.3 5.5.4 5.5.5 5.6 5.6.1
5.6.2 5.6.3 5.6.4 5.6.5 5.7 Energy Storage Systems for Smart Power Systems 99 Siváromon Palanisamy Logeshkumar Shanmugasundaram, and Sharmeela Chenniappan Introduction 99 Energy Storage System for Low Voltage Distribution System 100 Energy Storage System Connected to Medium and High Voltage 101 Energy Storage System for Renewable Power Plants 104 Renewable Power Evacuation Curtailment 106 Types of Energy Storage Systems 109 Battery Energy Storage System 109 Thermal Energy Storage System 110 Mechanical Energy Storage System 110 Pumped Hydro 110 HydrogenStorage 110 Energy Storage Systems for Other Applications 111 Shift in Energy Time 111 Voltage Support 111 Frequency Regulation (Primary, Secondary, and Tertiary) 112 Congestion Management 112 Blackstart 112 Conclusion 112 References 113 vii
viii I Contents 6 6.1 6.2 6.2.1 6.3 6.3.1 6.4 6.5 7 7.1 7.2 7.3 7.3.1 7.3.2 7A 7A.1 7.4.2 7.4.3 7.5 7.6 8 8.1 8.2 8.3 8.3.1 8.3.2 8.4 8.4.1 8.4.2 8.4.3 8.4.4 8.4.5 8.4.6 8.4.7 8.4.8 8.4.9 8.4.10 8.4.11 8.5 Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage Thamatapu Eswararao, Sundaram Elango, Umashankar Subramanian, Krishnamohan Tatikonda, Garika Gantaiahswamy, and Sharmeela Chenniappan Introduction 115 Structure of Supercapacitor 117 Mathematical Modeling of Supercapacitor 117 Bidirectional Buck-Boost Converter 118 FPGA Controller 119 Experimental Results 120 Conclusion 123 References 125 Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator Rania Moutchou, Ahmed Abbou, Bouazza Jabri, Salah E. Rhaili, and Khalid Chigane Introduction 129 Proposed MPPT Control Algorithm 130 Wind Energy Conversion System 131 Wind Turbine Characteristics 131 Model of PMSG 132 Fuzzy Logic Command for the MPPT of the PMSG 133 Fuzzification 134 Fuzzy Logic Rules 134 Defuzzification 134 Results and Discussions 135 Conclusion 139 References 139 A Novel Nearest Neighbor Searching-Based Fault Distance Location Method for HVDC Transmission Lines 141 Aleena Swetapadma, Shobha Agarwal, Satarupa Chakrabarti, and Soham Chakrabarti Introduction 141 Nearest Neighbor Searching 142 Proposed Method 144 Power System Network Under Study 144 Proposed Fault Location Method 145 Results 146 Performance Varying Nearest Neighbor 147 Performance Varying Distance Matrices 147 Near Boundary Faults 148 Far Boundary Faults 149 Performance During High Resistance
Faults 149 Single Pole to Ground Faults 150 Performance During Double Pole to Ground Faults 151 Performance During Pole to Pole Faults 751 Error Analysis 152 Comparison with Other Schemes 153 Advantages of the Scheme 154 Conclusion 154 Acknowledgment 754 References 154 129 115
Contents 9 Comparative Analysis of Machine Learning Approaches in Enhancing Power System 157 Md. I. H. Pathan, Mohammad S. Shahriar, Mohammad Μ. Rahman, Md. Sanwar Hossain, Nadia Awatif, and Md. Shafiullah Introduction 157 Power System Models 159 PSS Integrated Single Machine Infinite Bus Power Network 159 PSS-UPFC Integrated Single Machine Infinite Bus Power Network 160 Methods 161 Group Method Data Handling Model 161 Extreme Learning Machine Model 162 Neurogenetic Model 162 Multigene Genetic Programming Model 163 Data Preparation and Model Development 165 Data Production and Processing 165 Machine Learning Model Development 165 Results and Discussions 166 Eigenvalues and Minimum Damping Ratio Comparison 166 Time-Domain Simulation Results Comparison 170 Rotor Angle Variation Under Disturbance 170 Rotor Angular Frequency Variation Under Disturbance 171 DC-Link Voltage Variation Under Disturbance 173 Conclusions 173 References 174 Stability 9.1 9.2 9.2.1 9.2.2 9.3 9.3.1 9.3.2 9.3.3 9.3.4 9.4 9.4.1 9.4.2 9.5 9.5.1 9.5.2 9.5.2.1 9.5.2.2 9.5.2.3 9.6 10 10.1 10.2 10.3 10.3.1 10.3.2 10.3.3 10.3.4 10.3.5 10.4 10.5 10.5.1 10.5.2 10.5.3 10.6 10.7 10.7.1 10.8 10.8.1 10.8.2 10.8.3 10.8.4 10.8.5 10.8.6 10.9 Augmentation of PV-Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System 179 Jyoti Shukla, Basanta K. Panigrahi, and Monika Vardia Introduction 179 PV-Wind Hybrid Power Generation Configuration 180 Proposed Systems Topologies 181 Structure of PV System 181 The MPPTs Technique 183 NN Predictive Controller Technique 183 ANFIS
Technique 184 Training Data 186 Wind Power Generation Plant 187 Pitch Angle Control Techniques 189 PI Controller 189 NARMA-L2 Controller 190 Fuzzy Logic Controller Technique 192 Proposed DVRs Topology 192 Proposed Controlling Technique of DVR 193 ANFIS and Pl Controlling Technique 193 Results of the Proposed Topologies 196 PV System Outputs (MPPT Techniques Results) 196 Main PV System outputs 196 Wind Turbine System Outputs (Pitch Angle Control Technique Result) Proposed PMSG Wind Turbine System Output 199 Performance of DVR (Controlling Technique Results) 203 DVRs Performance 203 Conclusion 204 References 204 198 ix
Contents 207 Deepak Yadav, Saad Mekhilef Brijesh Singh, and Muhyaddin Rawa Abbreviations 207 11.1 Introduction 208 11.2 Deep and Reinforcement Learning for Decision-Making Problems in Smart Power Systems 210 11.2.1 Reinforcement Learning 210 11.2.1.1 Markov Decision Process (Μ DP) 210 11.2.1.2 Value Function and Optimal Policy 211 11.2.2 Reinforcement Learnings to Deep Reinforcement Learnings 212 11.2.3 Deep Reinforcement Learning Algorithms 212 11.3 Applications in Power Systems 213 11.3.1 Energy Management 213 11.3.2 Power Systems’Demand Response (DR) 215 11.3.3 Electricity Market 216 11.3.4 Operations and Controls 217 11.4 Mathematical Formulation of Objective Function 218 11.4.1 Locational Marginal Prices (LMPs) Representation 219 11.4.2 Relative Strength Index (RSI) 219 11.4.2.1 Autoregressive Integrated Moving Average (ARIMA) 219 11.5 Interior-point Technique KKT Condition 220 11.5.1 Explanation of Karush-Kuhn-Tucker Conditions 220 11.5.2 Algorithm for Finding a Solution 221 11.6 Test Results and Discussion 221 11.6.1 Illustrative Example 221 11.7 Comparative Analysis with Other Methods 223 11.8 Conclusion 224 11.9 Assignment 224 Acknowledgment 225 References 225 11 12 12.1 12.1.1 12.1.2 12.1.3 12.1.4 12.1.5 12.1.6 12.1.7 12.1.8 12.1.9 12.1.10 12.2 12.2.1 12.2.2 12.2.3 12.2.3.1 12.2.4 12.2.5 Deep Reinforcement Learning and Energy Price Prediction Power Quality Conditioners in Smart Power System 233 Zahira Rahiman, Lakshmi Dhandapani, Ravi Chengalvarayan Natarajan, Promila Vallikannan, Siváromon Palanisamy, and Sharmeela Chenniappan Introduction 233 Voltage Sag 234
Voltage Swell 234 Interruption 234 Under Voltage 234 Overvoltage 234 Voltage Fluctuations 234 Transients 235 Impulsive Transients 235 Oscillatory Transients 235 Harmonics 235 Power Quality Conditioners 235 STATCOM 235 SVC 235 Harmonic Filters 236 Active Filter 236 UPS Systems 236 Dynamic Voltage Restorer (DVR) 236
Contents 12.2.6 12.2.7 12.2.8 12.3 12.4 12.5 12.6 12.7 12.8 12.9 12.10 12.10.1 12.10.2 12.10.3 12.10.4 12.10.4.1 12.10.4.2 12.11 12.12 Enhancement of Voltage Sag 236 Interruption Mitigation 237 Mitigation of Harmonies 241 Standards of Power Quality 244 Solution for Power Quality Issues 244 Sustainable Energy Solutions 245 Need for Smart Grid 245 What Is a Smart Grid? 245 Smart Grid: The “Energy Internet” 245 Standardization 246 Smart Grid Network 247 Distributed Energy Resources (DERs) 247 Optimization Techniques in Power Quality Management 247 Conventional Algorithm 248 Intelligent Algorithm 248 Firefly Algorithm (FA) 248 Spider Monkey Optimization (SMO) 250 Simulation Results and Discussion 254 Conclusion 257 References 257 13 259 Sanjeevikumar Padmanaban, Mostafa Azimi Nasab, Mohammad Ebrahim Shiri, Hamid Haj Seyyed Javad i, Morteza Azimi Nasab, Mohammad Zand, and Tina Samavat Introduction 259 Internet of Things Technology 260 Smart House 261 Different Parts of Smart Home 262 Proposed Architecture 264 Controller Components 265 Proposed Architectural Layers 266 Infrastructure Layer 266 Information Technology 266 Information and Communication Technology 266 Electronics 266 Collecting Data 267 Data Management and Processing 267 Service Quality Management 267 Resource Management 267 Device Management 267 Security 267 Services 267 Applications 268 Conclusion 269 References 269 13.1 13.2 13.2.1 13.3 13.4 13.5 13.6 13.6.1 13.6.1.1 13.6.1.2 13.6.1.3 13.6.2 13.6.3 13.6.3.1 13.6.3.2 13.6.3.3 13.6.3.4 13.7 13.8 13.9 14 14.1 14.2 The Role of Internet of Things in Smart Homes 273
Sanjeevikumar Padmanaban, Tina Samavat, Mostafa Azimi Nasab, Morteza Azimi Nasab, Mohammad Zand, and Fatemeh Nikokar Introduction 273 Smart City 275 Electric Vehicles and loT in Smart Cities xi
xii Contents 14.2.1 14.3 14.4 14.4.1 14.4.2 14.4.3 14.4.4 14.5 14.6 14.6.1 14.6.2 14.6.3 14.6.4 14.7 14.7.1 14.7.2 14.7.3 14.7.4 14.7.5 14.7.6 14.7.7 14.7.8 14.7.9 14.7.10 14.8 14.9 14.9.1 14.9.2 14.10 Internet of Things and Smart City 275 The Concept of Smart Electric Networks 275 loT Outlook 276 loT Three-layer Architecture 276 View Layer 276 Network Layer 277 Application Layer 278 Intelligent Transportation and Transportation 278 Information Management 278 Artificial Intelligence 278 Machine Learning 279 Artificial Neural Network 279 Deep Learning 280 Electric Vehicles 281 Definition of Vehicle-to-Network System 281 Electric Cars and the Electricity Market 281 The Role of Electric Vehicles in the Network 282 V2G Applications in Power System 282 Provide Baseload Power 283 Courier Supply 283 Extra Service 283 Power Adjustment 283 Rotating Reservation 284 The Connection between the Electric Vehicle and the Power Grid 284 Proposed Model of Electric Vehicle 284 Prediction Using LSTM Time Series 285 LSTM Time Series 286 Predicting the Behavior of Electric Vehicles Using the LSTM Method 287 Conclusion 287 Exercise 288 References 288 15 291 Gunapriya Devarajan, Puspaiatha Naveen Kumar, Muniraj Chinnusamy, Sabareeshwaran Kanagaraj, and Sharmeela Chenniappan Introduction 291 Classification of Hardware in the Loop 291 Signal HIL Model 297 Power HIL Model 292 Reduced-Scaled HIL Model 292 Points to Be Considered While Performing HIL Simulation 293 Applications of HIL 293 Why HIL Is Important? 293 Hardware-ln-The-Loop Simulation 294 Simulation Verification and Validation 295
Simulation Computer Hardware 295 Benefits of Using Hardware֊In-The-Loop Simulation 296 HIL for Renewable Energy Systems (RES) 296 Introduction 296 Hardware in the Loop 297 Electrical Hardware in the Loop 297 15.1 15.1.1 15.1.1.1 15.1.1.2 15.1.1.3 15.1.2 15.1.3 15.2 15.2.1 15.2.2 15.2.3 15.2.4 15.3 15.3.1 15.3.2 15.3.2.1 Modeling and Simulation of Smart Power Systems Using HIL
Contents 15.3 .2.2 Mechanical Hardware in the Loop 297 15.4 HIL for HVDC and FACTS 299 15.4.1 Introduction 299 15.4.2 Modular Multi Level Converter 300 15.5 HIL for Electric Vehicles 301 15.5.1 Introduction 301 15.5.2 EV Simulation Using MATLAB, Simulink 302 15.5.2.1 Model-Based System Engineering (MBSE) 302 15.5.2.2 Model Batteries and Develop BMS 302 15.5.2.3 Model Fuel Cell Systems (FCS) and Develop Fuel Cell Control Systems (FCCS) 15.5.2.4 Model Inverters, Traction Motors, and Develop Motor Control Software 304 15.5.2.5 Deploy, Integrate, and Test Control Algorithms 304 15.5.2.6 Data-Driven Workflows and AI in EV Development 305 15.6 HIL for Other Applications 306 15.6.1 Electrical Motor Faults 306 15.7 Conclusion 307 References 308 16 16.1 16.2 16.3 16.4 16.5 16.6 16.6.1 16.6.2 16.6.3 16.7 16.7.1 16.7.1.1 16.7.2 16.7.2.1 16.7.3 16.7.4 16.7.5 16.8 17 17.1 17.2 17.2.1 17.2.2 17.2.2.1 17.2.2.2 17.2.2.3 17.2.2.4 17.2.3 303 311 Geethanjali Muthiah, Meenakshi Devi Manivannan, Hemavathi Ramadoss, and Sharmeela Chenniappan Introduction 311 Comparison of PMUs and SCADA 312 Basic Structure of Phasor Measurement Units 313 PM U Deployment in Distribution Networks 314 PMU Placement Algorithms 315 Need/Significance of PMUs in Distribution System 315 Significance of PMUs- Concerning Power System Stability 316 Significance of PMUs in Terms of Expenditure 316 Significance of PMUs in Wide Area Monitoring Applications 316 Applications of PMUs in Distribution Systems 317 System Reconfiguration Automation to Manage Power Restoration 317 Case Study 317 Planning for High DER Interconnection
(Penetration) 319 Case Study 319 Voltage Fluctuations and Voltage Ride-Through Related to DER 320 Operation of Islanded Distribution Systems 320 Fault-Induced Delayed Voltage Recovery (FIDVR) Detection 322 Conclusion 322 References 323 Distribution Phasor Measurement Units (PMUs) in Smart Power Systems Blockchain Technologies for Smart Power Systems 327 4. Gayathri, S. Saravan an, P. Pandiyan, and V. Rukkumani Introduction 327 Fundamentals of Blockchain Technologies 328 Terminology 328 Process of Operation 329 Proof of Work (PoW) 329 Proof of Stake (PoS) 329 Proof of Authority (PoA) 330 Practical Byzantine Fault Tolerance (PBFT) 330 Unique Features of Blockchain 330 xiii
xiv Contents 17.2.4 17.2.4.1 17.2.4.2 17.2.4.3 17.3 17.3.1 17.3.2 17.3.3 17.3.4 17.3.5 17.4 17.4.1 17.4.2 17.4.3 17.5 17.6 17.6.1 17.6.2 17.6.3 17.6.4 17.6.5 17.6.6 17.7 17.8 17.9 Energy with Blockchain Projects 330 Bitcoin Cryptocurrency 331 Dubai: Blockchain Strategy 331 Humanitarian Aid Utilization of Blockchain 331 Blockchain Technologies for Smart Power Systems 331 Blockchain as a Cyber Layer 331 Agent/Aggregator Based Microgrid Architecture 332 Limitations and Drawbacks 332 Peer to Peer Energy Trading 333 Blockchain for Transactive Energy 335 Blockchain for Smart Contracts 336 The Platform for Smart Contracts 337 The Architecture of Smart Contracting for Energy Applications Smart Contract Applications 339 Digitize and Decentralization Using Blockchain 340 Challenges in Implementing BJockchain Techniques 340 Network Management 341 Data Management 341 Consensus Management 341 Identity Management 341 Automation Management 342 Lack of Suitable Implementation Platforms 342 Solutions and Future Scope 342 Application of Blockchain for Flexible Services 343 Conclusion 343 References 344 338 349 Subrat Sahoo 18.1 Introduction 349 18.1.1 Geopolitical Situation 349 18.1.2 Covid-19 Impacts 350 18.1.3 Climate Challenges 350 18.2 Definition and Constituents of Smart Power Systems 351 18.2.1 Applicable Industries 352 18.2.2 Evolution of Power Electronics-Based Solutions 353 18.2.3 Operation of the Power System 355 18.3 Challenges Faced by Utilities and Their Way Towards Becoming Smart 18.3.1 Digitalization of Power Industry 359 18.3.2 Storage Possibilities and Integration into Grid
360 18.3.3 Addressing Power Quality Concerns and Their Mitigation 362 18.3.4 A Path Forward Towards Holistic Condition Monitoring 363 18.4 Ways towards Smart Transition of the Energy Sector 366 18.4.1 Creating an All-Inclusive Ecosystem 366 18.4.1.1 Example of Sensor-Based Ecosystem 367 18.4.1.2 Utilizing the Sensor Data for Effective Analytics 368 18.4.2 Modular Energy System Architecture 370 18.5 Conclusion 371 References 373 18 Power and Energy Management in Smart Power Systems Index 377 356 |
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| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03782nam a2200457 c 4500</leader><controlfield tag="001">BV048809580</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20231009 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">230209s2023 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781119893967</subfield><subfield code="c">hbk</subfield><subfield code="9">978-1-119-89396-7</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9781119893967</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1376413197</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048809580</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29T</subfield><subfield code="a">DE-739</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ZN 8510</subfield><subfield code="0">(DE-625)157629:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Artificial intelligence-based smart power systems</subfield><subfield code="c">edited by Sanjeevikumar Padmanaban, Sivaraman Palanisamy, Sharmeela Chenniappan, Jen Bo Holm-Nielsen</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hoboken, New Jersey</subfield><subfield code="b">Wiley</subfield><subfield code="c">[2023]</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Piscataway, NJ</subfield><subfield code="b">IEEE Press</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xxii, 378 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield><subfield code="c">938 grams</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studiesArtificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution. It covers many new topics such as distribution phasor measurement units, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. 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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV048809580 |
| illustrated | Illustrated |
| index_date | 2024-07-03T21:29:58Z |
| indexdate | 2024-07-10T09:46:34Z |
| institution | BVB |
| isbn | 9781119893967 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034075541 |
| oclc_num | 1376413197 |
| open_access_boolean | |
| owner | DE-29T DE-739 |
| owner_facet | DE-29T DE-739 |
| physical | xxii, 378 Seiten Illustrationen, Diagramme 938 grams |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Wiley IEEE Press |
| record_format | marc |
| spelling | Artificial intelligence-based smart power systems edited by Sanjeevikumar Padmanaban, Sivaraman Palanisamy, Sharmeela Chenniappan, Jen Bo Holm-Nielsen Hoboken, New Jersey Wiley [2023] Piscataway, NJ IEEE Press xxii, 378 Seiten Illustrationen, Diagramme 938 grams txt rdacontent n rdamedia nc rdacarrier Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studiesArtificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution. It covers many new topics such as distribution phasor measurement units, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. The text also explores the potential consequences of artificial intelligence and advanced technologies in smart power systems in the forthcoming years.To enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies.Artificial Intelligence-based Smart Power Systems includes specific information on topics such as:* Modeling and analysis of smart power systems, covering steady state analysis, dynamic analysis, voltage stability, and more* Recent advancement in power electronics for smart power systems, covering power electronic converters for renewable energy sources, electric vehicles, and HVDC/FACTs* Distribution Phasor Measurement Units (PMU) in smart power systems, covering the need for PMU in distribution and automation of system reconfigurations* Power and energy management systemsEngineering colleges and universities, along with industry research centers, can use the in-depth subject coverage and the extensive supplementary learning resources found in Artificial Intelligence-based Smart Power Systems to gain a holistic understanding of the subject and be able to harness that knowledge within a myriad of practical applications Elektrisches Energiesystem (DE-588)4134933-7 gnd rswk-swf Smart Energy (DE-588)1262853362 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Wärmetechnik, Energietechnik, Kraftwerktechnik (DE-588)4143413-4 Aufsatzsammlung gnd-content Künstliche Intelligenz (DE-588)4033447-8 s Elektrisches Energiesystem (DE-588)4134933-7 s Smart Energy (DE-588)1262853362 s DE-604 Sanjeevikumar, Padmanaban 1978- (DE-588)1220850691 edt Palanisamy, Sivaraman edt Chenniappan, Sharmeela edt Holm-Nielsen, Jens Bo (DE-588)1157033725 edt Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034075541&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Artificial intelligence-based smart power systems Elektrisches Energiesystem (DE-588)4134933-7 gnd Smart Energy (DE-588)1262853362 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4134933-7 (DE-588)1262853362 (DE-588)4033447-8 (DE-588)4143413-4 |
| title | Artificial intelligence-based smart power systems |
| title_auth | Artificial intelligence-based smart power systems |
| title_exact_search | Artificial intelligence-based smart power systems |
| title_exact_search_txtP | Artificial intelligence-based smart power systems |
| title_full | Artificial intelligence-based smart power systems edited by Sanjeevikumar Padmanaban, Sivaraman Palanisamy, Sharmeela Chenniappan, Jen Bo Holm-Nielsen |
| title_fullStr | Artificial intelligence-based smart power systems edited by Sanjeevikumar Padmanaban, Sivaraman Palanisamy, Sharmeela Chenniappan, Jen Bo Holm-Nielsen |
| title_full_unstemmed | Artificial intelligence-based smart power systems edited by Sanjeevikumar Padmanaban, Sivaraman Palanisamy, Sharmeela Chenniappan, Jen Bo Holm-Nielsen |
| title_short | Artificial intelligence-based smart power systems |
| title_sort | artificial intelligence based smart power systems |
| topic | Elektrisches Energiesystem (DE-588)4134933-7 gnd Smart Energy (DE-588)1262853362 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Elektrisches Energiesystem Smart Energy Künstliche Intelligenz Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034075541&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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