{"product_id":"artificial-intelligence-based-smart-power-systems-isbn-9781119893967","title":"Artificial Intelligence-based Smart Power Systems","description":"\u003cp\u003e\u003cb\u003eARTIFICIAL INTELLIGENCE-BASED SMART POWER SYSTEMS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAuthoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studies\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eArtificial Intelligence-based Smart Power Systems\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eTo enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eArtificial Intelligence-based Smart Power Systems \u003c\/i\u003eincludes specific information on topics such as:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eModeling and analysis of smart power systems, covering steady state analysis, dynamic analysis, voltage stability, and more\u003c\/li\u003e \u003cli\u003eRecent advancement in power electronics for smart power systems, covering power electronic converters for renewable energy sources, electric vehicles, and HVDC\/FACTs\u003c\/li\u003e \u003cli\u003eDistribution Phasor Measurement Units (PMU) in smart power systems, covering the need for PMU in distribution and automation of system reconfigurations\u003c\/li\u003e \u003cli\u003ePower and energy management systems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eEngineering colleges and universities, along with industry research centers, can use the in-depth subject coverage and the extensive supplementary learning resources found in \u003ci\u003eArtificial Intelligence-based Smart Power Systems\u003c\/i\u003e to gain a holistic understanding of the subject and be able to harness that knowledge within a myriad of practical applications.\u003c\/p\u003e \u003cp\u003eEditor Biography xv\u003c\/p\u003e \u003cp\u003eList of Contributors xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Smart Power Systems 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSivaraman Palanisamy, Zahira Rahiman, and Sharmeela Chenniappan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Problems in Conventional Power Systems 1\u003c\/p\u003e \u003cp\u003e1.2 Distributed Generation (DG) 1\u003c\/p\u003e \u003cp\u003e1.3 Wide Area Monitoring and Control 2\u003c\/p\u003e \u003cp\u003e1.4 Automatic Metering Infrastructure 4\u003c\/p\u003e \u003cp\u003e1.5 Phasor Measurement Unit 6\u003c\/p\u003e \u003cp\u003e1.6 Power Quality Conditioners 8\u003c\/p\u003e \u003cp\u003e1.7 Energy Storage Systems 8\u003c\/p\u003e \u003cp\u003e1.8 Smart Distribution Systems 9\u003c\/p\u003e \u003cp\u003e1.9 Electric Vehicle Charging Infrastructure 10\u003c\/p\u003e \u003cp\u003e1.10 Cyber Security 11\u003c\/p\u003e \u003cp\u003e1.11 Conclusion 11\u003c\/p\u003e \u003cp\u003eReferences 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Modeling and Analysis of Smart Power System 15\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMadhu Palati, Sagar Singh Prathap, and Nagesh Halasahalli Nagaraju\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 15\u003c\/p\u003e \u003cp\u003e2.2 Modeling of Equipment’s for Steady-State Analysis 16\u003c\/p\u003e \u003cp\u003e2.2.1 Load Flow Analysis 16\u003c\/p\u003e \u003cp\u003e2.2.1.1 Gauss Seidel Method 18\u003c\/p\u003e \u003cp\u003e2.2.1.2 Newton Raphson Method 18\u003c\/p\u003e \u003cp\u003e2.2.1.3 Decoupled Load Flow Method 18\u003c\/p\u003e \u003cp\u003e2.2.2 Short Circuit Analysis 19\u003c\/p\u003e \u003cp\u003e2.2.2.1 Symmetrical Faults 19\u003c\/p\u003e \u003cp\u003e2.2.2.2 Unsymmetrical Faults 20\u003c\/p\u003e \u003cp\u003e2.2.3 Harmonic Analysis 20\u003c\/p\u003e \u003cp\u003e2.3 Modeling of Equipments for Dynamic and Stability Analysis 22\u003c\/p\u003e \u003cp\u003e2.4 Dynamic Analysis 24\u003c\/p\u003e \u003cp\u003e2.4.1 Frequency Control 24\u003c\/p\u003e \u003cp\u003e2.4.2 Fault Ride Through 26\u003c\/p\u003e \u003cp\u003e2.5 Voltage Stability 26\u003c\/p\u003e \u003cp\u003e2.6 Case Studies 27\u003c\/p\u003e \u003cp\u003e2.6.1 Case Study 1 27\u003c\/p\u003e \u003cp\u003e2.6.2 Case Study 2 28\u003c\/p\u003e \u003cp\u003e2.6.2.1 Existing and Proposed Generation Details in the Vicinity of Wind Farm 29\u003c\/p\u003e \u003cp\u003e2.6.2.2 Power Evacuation Study for 50 MW Generation 30\u003c\/p\u003e \u003cp\u003e2.6.2.3 Without Interconnection of the Proposed 50 MW Generation from Wind Farm on 66 kV Level of 220\/66 kV Substation 31\u003c\/p\u003e \u003cp\u003e2.6.2.4 Observations Made from Table 2.6 31\u003c\/p\u003e \u003cp\u003e2.6.2.5 With the Interconnection of Proposed 50 MW Generation from Wind Farm on 66 kV level of 220\/66 kV Substation 31\u003c\/p\u003e \u003cp\u003e2.6.2.6 Normal Condition without Considering Contingency 32\u003c\/p\u003e \u003cp\u003e2.6.2.7 Contingency Analysis 32\u003c\/p\u003e \u003cp\u003e2.6.2.8 With the Interconnection of Proposed 60 MW Generation from Wind Farm on 66 kV Level of 220\/66 kV Substation 33\u003c\/p\u003e \u003cp\u003e2.7 Conclusion 34\u003c\/p\u003e \u003cp\u003eReferences 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Multilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy Applications 37\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMarimuthu Marikannu, Vijayalakshmi Subramanian, Paranthagan Balasubramanian, Jayakumar Narayanasamy, Nisha C. Rani, and Devi Vigneshwari Balasubramanian\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 37\u003c\/p\u003e \u003cp\u003e3.2 Multilevel Cascaded Boost Converter 40\u003c\/p\u003e \u003cp\u003e3.3 Modes of Operation of MCBC 42\u003c\/p\u003e \u003cp\u003e3.3.1 Mode-1 Switch S A Is ON 42\u003c\/p\u003e \u003cp\u003e3.3.2 Mode-2 Switch S A Is ON 42\u003c\/p\u003e \u003cp\u003e3.3.3 Mode-3-Operation – Switch S A Is ON 42\u003c\/p\u003e \u003cp\u003e3.3.4 Mode-4-Operation – Switch S A Is ON 42\u003c\/p\u003e \u003cp\u003e3.3.5 Mode-5-Operation – Switch S A Is ON 42\u003c\/p\u003e \u003cp\u003e3.3.6 Mode-6-Operation – Switch S A Is OFF 42\u003c\/p\u003e \u003cp\u003e3.3.7 Mode-7-Operation – Switch S A Is OFF 42\u003c\/p\u003e \u003cp\u003e3.3.8 Mode-8-Operation – Switch S A Is OFF 43\u003c\/p\u003e \u003cp\u003e3.3.9 Mode-9-Operation – Switch S A Is OFF 44\u003c\/p\u003e \u003cp\u003e3.3.10 Mode 10-Operation – Switch S A is OFF 45\u003c\/p\u003e \u003cp\u003e3.4 Simulation and Hardware Results 45\u003c\/p\u003e \u003cp\u003e3.5 Prominent Structures of Estimated DC–DC Converter with Prevailing Converter 49\u003c\/p\u003e \u003cp\u003e3.5.1 Voltage Gain and Power Handling Capability 49\u003c\/p\u003e \u003cp\u003e3.5.2 Voltage Stress 49\u003c\/p\u003e \u003cp\u003e3.5.3 Switch Count and Geometric Structure 49\u003c\/p\u003e \u003cp\u003e3.5.4 Current Stress 52\u003c\/p\u003e \u003cp\u003e3.5.5 Duty Cycle Versus Voltage Gain 52\u003c\/p\u003e \u003cp\u003e3.5.6 Number of Levels in the Planned Converter 52\u003c\/p\u003e \u003cp\u003e3.6 Power Electronic Converters for Renewable Energy Sources (Applications of MLCB) 54\u003c\/p\u003e \u003cp\u003e3.6.1 MCBC Connected with PV Panel 54\u003c\/p\u003e \u003cp\u003e3.6.2 Output Response of PV Fed MCBC 54\u003c\/p\u003e \u003cp\u003e3.6.3 H-Bridge Inverter 54\u003c\/p\u003e \u003cp\u003e3.7 Modes of Operation 55\u003c\/p\u003e \u003cp\u003e3.7.1 Mode 1 55\u003c\/p\u003e \u003cp\u003e3.7.2 Mode 2 55\u003c\/p\u003e \u003cp\u003e3.7.3 Mode 3 56\u003c\/p\u003e \u003cp\u003e3.7.4 Mode 4 56\u003c\/p\u003e \u003cp\u003e3.7.5 Mode 5 56\u003c\/p\u003e \u003cp\u003e3.7.6 Mode 6 56\u003c\/p\u003e \u003cp\u003e3.7.7 Mode 7 58\u003c\/p\u003e \u003cp\u003e3.7.8 Mode 8 58\u003c\/p\u003e \u003cp\u003e3.7.9 Mode 9 59\u003c\/p\u003e \u003cp\u003e3.7.10 Mode 10 59\u003c\/p\u003e \u003cp\u003e3.8 Simulation Results of MCBC Fed Inverter 60\u003c\/p\u003e \u003cp\u003e3.9 Power Electronic Converter for E-Vehicles 61\u003c\/p\u003e \u003cp\u003e3.10 Power Electronic Converter for HVDC\/Facts 62\u003c\/p\u003e \u003cp\u003e3.11 Conclusion 63\u003c\/p\u003e \u003cp\u003eReferences 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Recent Advancements in Power Electronics for Modern Power Systems-Comprehensive Review on DC-Link Capacitors Concerning Power Density Maximization in Power Converters 65\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNaveenkumar Marati, Shariq Ahammed, Kathirvel Karuppazaghi, Balraj Vaithilingam, Gyan R. Biswal, Phaneendra B. Bobba, Sanjeevikumar Padmanaban, and Sharmeela Chenniappan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 65\u003c\/p\u003e \u003cp\u003e4.2 Applications of Power Electronic Converters 66\u003c\/p\u003e \u003cp\u003e4.2.1 Power Electronic Converters in Electric Vehicle Ecosystem 66\u003c\/p\u003e \u003cp\u003e4.2.2 Power Electronic Converters in Renewable Energy Resources 67\u003c\/p\u003e \u003cp\u003e4.3 Classification of DC-Link Topologies 68\u003c\/p\u003e \u003cp\u003e4.4 Briefing on DC-Link Topologies 69\u003c\/p\u003e \u003cp\u003e4.4.1 Passive Capacitive DC Link 69\u003c\/p\u003e \u003cp\u003e4.4.1.1 Filter Type Passive Capacitive DC Links 70\u003c\/p\u003e \u003cp\u003e4.4.1.2 Filter Type Passive Capacitive DC Links with Control 72\u003c\/p\u003e \u003cp\u003e4.4.1.3 Interleaved Type Passive Capacitive DC Links 74\u003c\/p\u003e \u003cp\u003e4.4.2 Active Balancing in Capacitive DC Link 75\u003c\/p\u003e \u003cp\u003e4.4.2.1 Separate Auxiliary Active Capacitive DC Links 76\u003c\/p\u003e \u003cp\u003e4.4.2.2 Integrated Auxiliary Active Capacitive DC Links 78\u003c\/p\u003e \u003cp\u003e4.5 Comparison on DC-Link Topologies 82\u003c\/p\u003e \u003cp\u003e4.5.1 Comparison of Passive Capacitive DC Links 82\u003c\/p\u003e \u003cp\u003e4.5.2 Comparison of Active Capacitive DC Links 83\u003c\/p\u003e \u003cp\u003e4.5.3 Comparison of DC Link Based on Power Density, Efficiency, and Ripple Attenuation 86\u003c\/p\u003e \u003cp\u003e4.6 Future and Research Gaps in DC-Link Topologies with Balancing Techniques 94\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 95\u003c\/p\u003e \u003cp\u003eReferences 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Energy Storage Systems for Smart Power Systems 99\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSivaraman Palanisamy, Logeshkumar Shanmugasundaram, and Sharmeela Chenniappan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 99\u003c\/p\u003e \u003cp\u003e5.2 Energy Storage System for Low Voltage Distribution System 100\u003c\/p\u003e \u003cp\u003e5.3 Energy Storage System Connected to Medium and High Voltage 101\u003c\/p\u003e \u003cp\u003e5.4 Energy Storage System for Renewable Power Plants 104\u003c\/p\u003e \u003cp\u003e5.4.1 Renewable Power Evacuation Curtailment 106\u003c\/p\u003e \u003cp\u003e5.5 Types of Energy Storage Systems 109\u003c\/p\u003e \u003cp\u003e5.5.1 Battery Energy Storage System 109\u003c\/p\u003e \u003cp\u003e5.5.2 Thermal Energy Storage System 110\u003c\/p\u003e \u003cp\u003e5.5.3 Mechanical Energy Storage System 110\u003c\/p\u003e \u003cp\u003e5.5.4 Pumped Hydro 110\u003c\/p\u003e \u003cp\u003e5.5.5 Hydrogen Storage 110\u003c\/p\u003e \u003cp\u003e5.6 Energy Storage Systems for Other Applications 111\u003c\/p\u003e \u003cp\u003e5.6.1 Shift in Energy Time 111\u003c\/p\u003e \u003cp\u003e5.6.2 Voltage Support 111\u003c\/p\u003e \u003cp\u003e5.6.3 Frequency Regulation (Primary, Secondary, and Tertiary) 112\u003c\/p\u003e \u003cp\u003e5.6.4 Congestion Management 112\u003c\/p\u003e \u003cp\u003e5.6.5 Black Start 112\u003c\/p\u003e \u003cp\u003e5.7 Conclusion 112\u003c\/p\u003e \u003cp\u003eReferences 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage 115\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eThamatapu Eswararao, Sundaram Elango, Umashankar Subramanian, Krishnamohan Tatikonda, Garika Gantaiahswamy, and Sharmeela Chenniappan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 115\u003c\/p\u003e \u003cp\u003e6.2 Structure of Supercapacitor 117\u003c\/p\u003e \u003cp\u003e6.2.1 Mathematical Modeling of Supercapacitor 117\u003c\/p\u003e \u003cp\u003e6.3 Bidirectional Buck–Boost Converter 118\u003c\/p\u003e \u003cp\u003e6.3.1 FPGA Controller 119\u003c\/p\u003e \u003cp\u003e6.4 Experimental Results 120\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 123\u003c\/p\u003e \u003cp\u003eReferences 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator 129\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRania Moutchou, Ahmed Abbou, Bouazza Jabri, Salah E. Rhaili, and Khalid Chigane\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 129\u003c\/p\u003e \u003cp\u003e7.2 Proposed MPPT Control Algorithm 130\u003c\/p\u003e \u003cp\u003e7.3 Wind Energy Conversion System 131\u003c\/p\u003e \u003cp\u003e7.3.1 Wind Turbine Characteristics 131\u003c\/p\u003e \u003cp\u003e7.3.2 Model of PMSG 132\u003c\/p\u003e \u003cp\u003e7.4 Fuzzy Logic Command for the MPPT of the PMSG 133\u003c\/p\u003e \u003cp\u003e7.4.1 Fuzzification 134\u003c\/p\u003e \u003cp\u003e7.4.2 Fuzzy Logic Rules 134\u003c\/p\u003e \u003cp\u003e7.4.3 Defuzzification 134\u003c\/p\u003e \u003cp\u003e7.5 Results and Discussions 135\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 139\u003c\/p\u003e \u003cp\u003eReferences 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 A Novel Nearest Neighbor Searching-Based Fault Distance Location Method for HVDC Transmission Lines 141\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAleena Swetapadma, Shobha Agarwal, Satarupa Chakrabarti, and Soham Chakrabarti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 141\u003c\/p\u003e \u003cp\u003e8.2 Nearest Neighbor Searching 142\u003c\/p\u003e \u003cp\u003e8.3 Proposed Method 144\u003c\/p\u003e \u003cp\u003e8.3.1 Power System Network Under Study 144\u003c\/p\u003e \u003cp\u003e8.3.2 Proposed Fault Location Method 145\u003c\/p\u003e \u003cp\u003e8.4 Results 146\u003c\/p\u003e \u003cp\u003e8.4.1 Performance Varying Nearest Neighbor 147\u003c\/p\u003e \u003cp\u003e8.4.2 Performance Varying Distance Matrices 147\u003c\/p\u003e \u003cp\u003e8.4.3 Near Boundary Faults 148\u003c\/p\u003e \u003cp\u003e8.4.4 Far Boundary Faults 149\u003c\/p\u003e \u003cp\u003e8.4.5 Performance During High Resistance Faults 149\u003c\/p\u003e \u003cp\u003e8.4.6 Single Pole to Ground Faults 150\u003c\/p\u003e \u003cp\u003e8.4.7 Performance During Double Pole to Ground Faults 151\u003c\/p\u003e \u003cp\u003e8.4.8 Performance During Pole to Pole Faults 151\u003c\/p\u003e \u003cp\u003e8.4.9 Error Analysis 152\u003c\/p\u003e \u003cp\u003e8.4.10 Comparison with Other Schemes 153\u003c\/p\u003e \u003cp\u003e8.4.11 Advantages of the Scheme 154\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 154\u003c\/p\u003e \u003cp\u003eAcknowledgment 154\u003c\/p\u003e \u003cp\u003eReferences 154\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability 157\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMd. I. H. Pathan, Mohammad S. Shahriar, Mohammad M. Rahman, Md. Sanwar Hossain, Nadia Awatif, and Md. Shafiullah\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 157\u003c\/p\u003e \u003cp\u003e9.2 Power System Models 159\u003c\/p\u003e \u003cp\u003e9.2.1 PSS Integrated Single Machine Infinite Bus Power Network 159\u003c\/p\u003e \u003cp\u003e9.2.2 PSS-UPFC Integrated Single Machine Infinite Bus Power Network 160\u003c\/p\u003e \u003cp\u003e9.3 Methods 161\u003c\/p\u003e \u003cp\u003e9.3.1 Group Method Data Handling Model 161\u003c\/p\u003e \u003cp\u003e9.3.2 Extreme Learning Machine Model 162\u003c\/p\u003e \u003cp\u003e9.3.3 Neurogenetic Model 162\u003c\/p\u003e \u003cp\u003e9.3.4 Multigene Genetic Programming Model 163\u003c\/p\u003e \u003cp\u003e9.4 Data Preparation and Model Development 165\u003c\/p\u003e \u003cp\u003e9.4.1 Data Production and Processing 165\u003c\/p\u003e \u003cp\u003e9.4.2 Machine Learning Model Development 165\u003c\/p\u003e \u003cp\u003e9.5 Results and Discussions 166\u003c\/p\u003e \u003cp\u003e9.5.1 Eigenvalues and Minimum Damping Ratio Comparison 166\u003c\/p\u003e \u003cp\u003e9.5.2 Time-Domain Simulation Results Comparison 170\u003c\/p\u003e \u003cp\u003e9.5.2.1 Rotor Angle Variation Under Disturbance 170\u003c\/p\u003e \u003cp\u003e9.5.2.2 Rotor Angular Frequency Variation Under Disturbance 171\u003c\/p\u003e \u003cp\u003e9.5.2.3 DC-Link Voltage Variation Under Disturbance 173\u003c\/p\u003e \u003cp\u003e9.6 Conclusions 173\u003c\/p\u003e \u003cp\u003eReferences 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Augmentation of PV-Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System 179\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJyoti Shukla, Basanta K. Panigrahi, and Monika Vardia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 179\u003c\/p\u003e \u003cp\u003e10.2 PV-Wind Hybrid Power Generation Configuration 180\u003c\/p\u003e \u003cp\u003e10.3 Proposed Systems Topologies 181\u003c\/p\u003e \u003cp\u003e10.3.1 Structure of PV System 181\u003c\/p\u003e \u003cp\u003e10.3.2 The MPPTs Technique 183\u003c\/p\u003e \u003cp\u003e10.3.3 NN Predictive Controller Technique 183\u003c\/p\u003e \u003cp\u003e10.3.4 ANFIS Technique 184\u003c\/p\u003e \u003cp\u003e10.3.5 Training Data 186\u003c\/p\u003e \u003cp\u003e10.4 Wind Power Generation Plant 187\u003c\/p\u003e \u003cp\u003e10.5 Pitch Angle Control Techniques 189\u003c\/p\u003e \u003cp\u003e10.5.1 PI Controller 189\u003c\/p\u003e \u003cp\u003e10.5.2 NARMA-L2 Controller 190\u003c\/p\u003e \u003cp\u003e10.5.3 Fuzzy Logic Controller Technique 192\u003c\/p\u003e \u003cp\u003e10.6 Proposed DVRs Topology 192\u003c\/p\u003e \u003cp\u003e10.7 Proposed Controlling Technique of DVR 193\u003c\/p\u003e \u003cp\u003e10.7.1 ANFIS and PI Controlling Technique 193\u003c\/p\u003e \u003cp\u003e10.8 Results of the Proposed Topologies 196\u003c\/p\u003e \u003cp\u003e10.8.1 PV System Outputs (MPPT Techniques Results) 196\u003c\/p\u003e \u003cp\u003e10.8.2 Main PV System outputs 196\u003c\/p\u003e \u003cp\u003e10.8.3 Wind Turbine System Outputs (Pitch Angle Control Technique Result) 198\u003c\/p\u003e \u003cp\u003e10.8.4 Proposed PMSG Wind Turbine System Output 199\u003c\/p\u003e \u003cp\u003e10.8.5 Performance of DVR (Controlling Technique Results) 203\u003c\/p\u003e \u003cp\u003e10.8.6 DVRs Performance 203\u003c\/p\u003e \u003cp\u003e10.9 Conclusion 204\u003c\/p\u003e \u003cp\u003eReferences 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Deep Reinforcement Learning and Energy Price Prediction 207\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDeepak Yadav, Saad Mekhilef, Brijesh Singh, and Muhyaddin Rawa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 207\u003c\/p\u003e \u003cp\u003e11.1 Introduction 208\u003c\/p\u003e \u003cp\u003e11.2 Deep and Reinforcement Learning for Decision-Making Problems in Smart Power Systems 210\u003c\/p\u003e \u003cp\u003e11.2.1 Reinforcement Learning 210\u003c\/p\u003e \u003cp\u003e11.2.1.1 Markov Decision Process (MDP) 210\u003c\/p\u003e \u003cp\u003e11.2.1.2 Value Function and Optimal Policy 211\u003c\/p\u003e \u003cp\u003e11.2.2 Reinforcement Learnings to Deep Reinforcement Learnings 212\u003c\/p\u003e \u003cp\u003e11.2.3 Deep Reinforcement Learning Algorithms 212\u003c\/p\u003e \u003cp\u003e11.3 Applications in Power Systems 213\u003c\/p\u003e \u003cp\u003e11.3.1 Energy Management 213\u003c\/p\u003e \u003cp\u003e11.3.2 Power Systems’ Demand Response (DR) 215\u003c\/p\u003e \u003cp\u003e11.3.3 Electricity Market 216\u003c\/p\u003e \u003cp\u003e11.3.4 Operations and Controls 217\u003c\/p\u003e \u003cp\u003e11.4 Mathematical Formulation of Objective Function 218\u003c\/p\u003e \u003cp\u003e11.4.1 Locational Marginal Prices (LMPs) Representation 219\u003c\/p\u003e \u003cp\u003e11.4.2 Relative Strength Index (RSI) 219\u003c\/p\u003e \u003cp\u003e11.4.2.1 Autoregressive Integrated Moving Average (ARIMA) 219\u003c\/p\u003e \u003cp\u003e11.5 Interior-point Technique \u0026amp; KKT Condition 220\u003c\/p\u003e \u003cp\u003e11.5.1 Explanation of Karush–Kuhn–Tucker Conditions 220\u003c\/p\u003e \u003cp\u003e11.5.2 Algorithm for Finding a Solution 221\u003c\/p\u003e \u003cp\u003e11.6 Test Results and Discussion 221\u003c\/p\u003e \u003cp\u003e11.6.1 Illustrative Example 221\u003c\/p\u003e \u003cp\u003e11.7 Comparative Analysis with Other Methods 223\u003c\/p\u003e \u003cp\u003e11.8 Conclusion 224\u003c\/p\u003e \u003cp\u003e11.9 Assignment 224\u003c\/p\u003e \u003cp\u003eAcknowledgment 225\u003c\/p\u003e \u003cp\u003eReferences 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Power Quality Conditioners in Smart Power System 233\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eZahira Rahiman, Lakshmi Dhandapani, Ravi Chengalvarayan Natarajan, Pramila Vallikannan, Sivaraman Palanisamy, and Sharmeela Chenniappan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 233\u003c\/p\u003e \u003cp\u003e12.1.1 Voltage Sag 234\u003c\/p\u003e \u003cp\u003e12.1.2 Voltage Swell 234\u003c\/p\u003e \u003cp\u003e12.1.3 Interruption 234\u003c\/p\u003e \u003cp\u003e12.1.4 Under Voltage 234\u003c\/p\u003e \u003cp\u003e12.1.5 Overvoltage 234\u003c\/p\u003e \u003cp\u003e12.1.6 Voltage Fluctuations 234\u003c\/p\u003e \u003cp\u003e12.1.7 Transients 235\u003c\/p\u003e \u003cp\u003e12.1.8 Impulsive Transients 235\u003c\/p\u003e \u003cp\u003e12.1.9 Oscillatory Transients 235\u003c\/p\u003e \u003cp\u003e12.1.10 Harmonics 235\u003c\/p\u003e \u003cp\u003e12.2 Power Quality Conditioners 235\u003c\/p\u003e \u003cp\u003e12.2.1 STATCOM 235\u003c\/p\u003e \u003cp\u003e12.2.2 Svc 235\u003c\/p\u003e \u003cp\u003e12.2.3 Harmonic Filters 236\u003c\/p\u003e \u003cp\u003e12.2.3.1 Active Filter 236\u003c\/p\u003e \u003cp\u003e12.2.4 UPS Systems 236\u003c\/p\u003e \u003cp\u003e12.2.5 Dynamic Voltage Restorer (DVR) 236\u003c\/p\u003e \u003cp\u003e12.2.6 Enhancement of Voltage Sag 236\u003c\/p\u003e \u003cp\u003e12.2.7 Interruption Mitigation 237\u003c\/p\u003e \u003cp\u003e12.2.8 Mitigation of Harmonics 241\u003c\/p\u003e \u003cp\u003e12.3 Standards of Power Quality 244\u003c\/p\u003e \u003cp\u003e12.4 Solution for Power Quality Issues 244\u003c\/p\u003e \u003cp\u003e12.5 Sustainable Energy Solutions 245\u003c\/p\u003e \u003cp\u003e12.6 Need for Smart Grid 245\u003c\/p\u003e \u003cp\u003e12.7 What Is a Smart Grid? 245\u003c\/p\u003e \u003cp\u003e12.8 Smart Grid: The “Energy Internet” 245\u003c\/p\u003e \u003cp\u003e12.9 Standardization 246\u003c\/p\u003e \u003cp\u003e12.10 Smart Grid Network 247\u003c\/p\u003e \u003cp\u003e12.10.1 Distributed Energy Resources (DERs) 247\u003c\/p\u003e \u003cp\u003e12.10.2 Optimization Techniques in Power Quality Management 247\u003c\/p\u003e \u003cp\u003e12.10.3 Conventional Algorithm 248\u003c\/p\u003e \u003cp\u003e12.10.4 Intelligent Algorithm 248\u003c\/p\u003e \u003cp\u003e12.10.4.1 Firefly Algorithm (FA) 248\u003c\/p\u003e \u003cp\u003e12.10.4.2 Spider Monkey Optimization (SMO) 250\u003c\/p\u003e \u003cp\u003e12.11 Simulation Results and Discussion 254\u003c\/p\u003e \u003cp\u003e12.12 Conclusion 257\u003c\/p\u003e \u003cp\u003eReferences 257\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 The Role of Internet of Things in Smart Homes 259\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSanjeevikumar Padmanaban, Mostafa Azimi Nasab, Mohammad Ebrahim Shiri, Hamid Haj Seyyed Javadi, Morteza Azimi Nasab, Mohammad Zand, and Tina Samavat\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 259\u003c\/p\u003e \u003cp\u003e13.2 Internet of Things Technology 260\u003c\/p\u003e \u003cp\u003e13.2.1 Smart House 261\u003c\/p\u003e \u003cp\u003e13.3 Different Parts of Smart Home 262\u003c\/p\u003e \u003cp\u003e13.4 Proposed Architecture 264\u003c\/p\u003e \u003cp\u003e13.5 Controller Components 265\u003c\/p\u003e \u003cp\u003e13.6 Proposed Architectural Layers 266\u003c\/p\u003e \u003cp\u003e13.6.1 Infrastructure Layer 266\u003c\/p\u003e \u003cp\u003e13.6.1.1 Information Technology 266\u003c\/p\u003e \u003cp\u003e13.6.1.2 Information and Communication Technology 266\u003c\/p\u003e \u003cp\u003e13.6.1.3 Electronics 266\u003c\/p\u003e \u003cp\u003e13.6.2 Collecting Data 267\u003c\/p\u003e \u003cp\u003e13.6.3 Data Management and Processing 267\u003c\/p\u003e \u003cp\u003e13.6.3.1 Service Quality Management 267\u003c\/p\u003e \u003cp\u003e13.6.3.2 Resource Management 267\u003c\/p\u003e \u003cp\u003e13.6.3.3 Device Management 267\u003c\/p\u003e \u003cp\u003e13.6.3.4 Security 267\u003c\/p\u003e \u003cp\u003e13.7 Services 267\u003c\/p\u003e \u003cp\u003e13.8 Applications 268\u003c\/p\u003e \u003cp\u003e13.9 Conclusion 269\u003c\/p\u003e \u003cp\u003eReferences 269\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Electric Vehicles and IoT in Smart Cities 273\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSanjeevikumar Padmanaban, Tina Samavat, Mostafa Azimi Nasab, Morteza Azimi Nasab, Mohammad Zand, and Fatemeh Nikokar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 273\u003c\/p\u003e \u003cp\u003e14.2 Smart City 275\u003c\/p\u003e \u003cp\u003e14.2.1 Internet of Things and Smart City 275\u003c\/p\u003e \u003cp\u003e14.3 The Concept of Smart Electric Networks 275\u003c\/p\u003e \u003cp\u003e14.4 IoT Outlook 276\u003c\/p\u003e \u003cp\u003e14.4.1 IoT Three-layer Architecture 276\u003c\/p\u003e \u003cp\u003e14.4.2 View Layer 276\u003c\/p\u003e \u003cp\u003e14.4.3 Network Layer 277\u003c\/p\u003e \u003cp\u003e14.4.4 Application Layer 278\u003c\/p\u003e \u003cp\u003e14.5 Intelligent Transportation and Transportation 278\u003c\/p\u003e \u003cp\u003e14.6 Information Management 278\u003c\/p\u003e \u003cp\u003e14.6.1 Artificial Intelligence 278\u003c\/p\u003e \u003cp\u003e14.6.2 Machine Learning 279\u003c\/p\u003e \u003cp\u003e14.6.3 Artificial Neural Network 279\u003c\/p\u003e \u003cp\u003e14.6.4 Deep Learning 280\u003c\/p\u003e \u003cp\u003e14.7 Electric Vehicles 281\u003c\/p\u003e \u003cp\u003e14.7.1 Definition of Vehicle-to-Network System 281\u003c\/p\u003e \u003cp\u003e14.7.2 Electric Cars and the Electricity Market 281\u003c\/p\u003e \u003cp\u003e14.7.3 The Role of Electric Vehicles in the Network 282\u003c\/p\u003e \u003cp\u003e14.7.4 V2G Applications in Power System 282\u003c\/p\u003e \u003cp\u003e14.7.5 Provide Baseload Power 283\u003c\/p\u003e \u003cp\u003e14.7.6 Courier Supply 283\u003c\/p\u003e \u003cp\u003e14.7.7 Extra Service 283\u003c\/p\u003e \u003cp\u003e14.7.8 Power Adjustment 283\u003c\/p\u003e \u003cp\u003e14.7.9 Rotating Reservation 284\u003c\/p\u003e \u003cp\u003e14.7.10 The Connection between the Electric Vehicle and the Power Grid 284\u003c\/p\u003e \u003cp\u003e14.8 Proposed Model of Electric Vehicle 284\u003c\/p\u003e \u003cp\u003e14.9 Prediction Using LSTM Time Series 285\u003c\/p\u003e \u003cp\u003e14.9.1 LSTM Time Series 286\u003c\/p\u003e \u003cp\u003e14.9.2 Predicting the Behavior of Electric Vehicles Using the LSTM Method 287\u003c\/p\u003e \u003cp\u003e14.10 Conclusion 287\u003c\/p\u003e \u003cp\u003eExercise 288\u003c\/p\u003e \u003cp\u003eReferences 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Modeling and Simulation of Smart Power Systems Using HIL 291\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGunapriya Devarajan, Puspalatha Naveen Kumar, Muniraj Chinnusamy, Sabareeshwaran Kanagaraj, and Sharmeela Chenniappan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 291\u003c\/p\u003e \u003cp\u003e15.1.1 Classification of Hardware in the Loop 291\u003c\/p\u003e \u003cp\u003e15.1.1.1 Signal HIL Model 291\u003c\/p\u003e \u003cp\u003e15.1.1.2 Power HIL Model 292\u003c\/p\u003e \u003cp\u003e15.1.1.3 Reduced-Scaled HIL Model 292\u003c\/p\u003e \u003cp\u003e15.1.2 Points to Be Considered While Performing HIL Simulation 293\u003c\/p\u003e \u003cp\u003e15.1.3 Applications of HIL 293\u003c\/p\u003e \u003cp\u003e15.2 Why HIL Is Important? 293\u003c\/p\u003e \u003cp\u003e15.2.1 Hardware-In-The-Loop Simulation 294\u003c\/p\u003e \u003cp\u003e15.2.2 Simulation Verification and Validation 295\u003c\/p\u003e \u003cp\u003e15.2.3 Simulation Computer Hardware 295\u003c\/p\u003e \u003cp\u003e15.2.4 Benefits of Using Hardware-In-The-Loop Simulation 296\u003c\/p\u003e \u003cp\u003e15.3 HIL for Renewable Energy Systems (RES) 296\u003c\/p\u003e \u003cp\u003e15.3.1 Introduction 296\u003c\/p\u003e \u003cp\u003e15.3.2 Hardware in the Loop 297\u003c\/p\u003e \u003cp\u003e15.3.2.1 Electrical Hardware in the Loop 297\u003c\/p\u003e \u003cp\u003e15.3.2.2 Mechanical Hardware in the Loop 297\u003c\/p\u003e \u003cp\u003e15.4 HIL for HVDC and FACTS 299\u003c\/p\u003e \u003cp\u003e15.4.1 Introduction 299\u003c\/p\u003e \u003cp\u003e15.4.2 Modular Multi Level Converter 300\u003c\/p\u003e \u003cp\u003e15.5 HIL for Electric Vehicles 301\u003c\/p\u003e \u003cp\u003e15.5.1 Introduction 301\u003c\/p\u003e \u003cp\u003e15.5.2 EV Simulation Using MATLAB, Simulink 302\u003c\/p\u003e \u003cp\u003e15.5.2.1 Model-Based System Engineering (MBSE) 302\u003c\/p\u003e \u003cp\u003e15.5.2.2 Model Batteries and Develop BMS 302\u003c\/p\u003e \u003cp\u003e15.5.2.3 Model Fuel Cell Systems (FCS) and Develop Fuel Cell Control Systems (FCCS) 303\u003c\/p\u003e \u003cp\u003e15.5.2.4 Model Inverters, Traction Motors, and Develop Motor Control Software 304\u003c\/p\u003e \u003cp\u003e15.5.2.5 Deploy, Integrate, and Test Control Algorithms 304\u003c\/p\u003e \u003cp\u003e15.5.2.6 Data-Driven Workflows and AI in EV Development 305\u003c\/p\u003e \u003cp\u003e15.6 HIL for Other Applications 306\u003c\/p\u003e \u003cp\u003e15.6.1 Electrical Motor Faults 306\u003c\/p\u003e \u003cp\u003e15.7 Conclusion 307\u003c\/p\u003e \u003cp\u003eReferences 308\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Distribution Phasor Measurement Units (PMUs) in Smart Power Systems 311\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGeethanjali Muthiah, Meenakshi Devi Manivannan, Hemavathi Ramadoss, and Sharmeela Chenniappan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 311\u003c\/p\u003e \u003cp\u003e16.2 ComparisonofPMUsandSCADA 312\u003c\/p\u003e \u003cp\u003e16.3 Basic Structure of Phasor Measurement Units 313\u003c\/p\u003e \u003cp\u003e16.4 PMU Deployment in Distribution Networks 314\u003c\/p\u003e \u003cp\u003e16.5 PMU Placement Algorithms 315\u003c\/p\u003e \u003cp\u003e16.6 Need\/Significance of PMUs in Distribution System 315\u003c\/p\u003e \u003cp\u003e16.6.1 Significance of PMUs – Concerning Power System Stability 316\u003c\/p\u003e \u003cp\u003e16.6.2 Significance of PMUs in Terms of Expenditure 316\u003c\/p\u003e \u003cp\u003e16.6.3 Significance of PMUs in Wide Area Monitoring Applications 316\u003c\/p\u003e \u003cp\u003e16.7 Applications of PMUs in Distribution Systems 317\u003c\/p\u003e \u003cp\u003e16.7.1 System Reconfiguration Automation to Manage Power Restoration 317\u003c\/p\u003e \u003cp\u003e16.7.1.1 Case Study 317\u003c\/p\u003e \u003cp\u003e16.7.2 Planning for High DER Interconnection (Penetration) 319\u003c\/p\u003e \u003cp\u003e16.7.2.1 Case Study 319\u003c\/p\u003e \u003cp\u003e16.7.3 Voltage Fluctuations and Voltage Ride-Through Related to DER 320\u003c\/p\u003e \u003cp\u003e16.7.4 Operation of Islanded Distribution Systems 320\u003c\/p\u003e \u003cp\u003e16.7.5 Fault-Induced Delayed Voltage Recovery (FIDVR) Detection 322\u003c\/p\u003e \u003cp\u003e16.8 Conclusion 322\u003c\/p\u003e \u003cp\u003eReferences 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Blockchain Technologies for Smart Power Systems 327\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eA. Gayathri, S. Saravanan, P. Pandiyan, and V. Rukkumani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 327\u003c\/p\u003e \u003cp\u003e17.2 Fundamentals of Blockchain Technologies 328\u003c\/p\u003e \u003cp\u003e17.2.1 Terminology 328\u003c\/p\u003e \u003cp\u003e17.2.2 Process of Operation 329\u003c\/p\u003e \u003cp\u003e17.2.2.1 Proof of Work (PoW) 329\u003c\/p\u003e \u003cp\u003e17.2.2.2 Proof of Stake (PoS) 329\u003c\/p\u003e \u003cp\u003e17.2.2.3 Proof of Authority (PoA) 330\u003c\/p\u003e \u003cp\u003e17.2.2.4 Practical Byzantine Fault Tolerance (PBFT) 330\u003c\/p\u003e \u003cp\u003e17.2.3 Unique Features of Blockchain 330\u003c\/p\u003e \u003cp\u003e17.2.4 Energy with Blockchain Projects 330\u003c\/p\u003e \u003cp\u003e17.2.4.1 Bitcoin Cryptocurrency 331\u003c\/p\u003e \u003cp\u003e17.2.4.2 Dubai: Blockchain Strategy 331\u003c\/p\u003e \u003cp\u003e17.2.4.3 Humanitarian Aid Utilization of Blockchain 331\u003c\/p\u003e \u003cp\u003e17.3 Blockchain Technologies for Smart Power Systems 331\u003c\/p\u003e \u003cp\u003e17.3.1 Blockchain as a Cyber Layer 331\u003c\/p\u003e \u003cp\u003e17.3.2 Agent\/Aggregator Based Microgrid Architecture 332\u003c\/p\u003e \u003cp\u003e17.3.3 Limitations and Drawbacks 332\u003c\/p\u003e \u003cp\u003e17.3.4 Peer to Peer Energy Trading 333\u003c\/p\u003e \u003cp\u003e17.3.5 Blockchain for Transactive Energy 335\u003c\/p\u003e \u003cp\u003e17.4 Blockchain for Smart Contracts 336\u003c\/p\u003e \u003cp\u003e17.4.1 The Platform for Smart Contracts 337\u003c\/p\u003e \u003cp\u003e17.4.2 The Architecture of Smart Contracting for Energy Applications 338\u003c\/p\u003e \u003cp\u003e17.4.3 Smart Contract Applications 339\u003c\/p\u003e \u003cp\u003e17.5 Digitize and Decentralization Using Blockchain 340\u003c\/p\u003e \u003cp\u003e17.6 Challenges in Implementing Blockchain Techniques 340\u003c\/p\u003e \u003cp\u003e17.6.1 Network Management 341\u003c\/p\u003e \u003cp\u003e17.6.2 Data Management 341\u003c\/p\u003e \u003cp\u003e17.6.3 Consensus Management 341\u003c\/p\u003e \u003cp\u003e17.6.4 Identity Management 341\u003c\/p\u003e \u003cp\u003e17.6.5 Automation Management 342\u003c\/p\u003e \u003cp\u003e17.6.6 Lack of Suitable Implementation Platforms 342\u003c\/p\u003e \u003cp\u003e17.7 Solutions and Future Scope 342\u003c\/p\u003e \u003cp\u003e17.8 Application of Blockchain for Flexible Services 343\u003c\/p\u003e \u003cp\u003e17.9 Conclusion 343\u003c\/p\u003e \u003cp\u003eReferences 344\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Power and Energy Management in Smart Power Systems 349\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSubrat Sahoo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 349\u003c\/p\u003e \u003cp\u003e18.1.1 Geopolitical Situation 349\u003c\/p\u003e \u003cp\u003e18.1.2 Covid-19 Impacts 350\u003c\/p\u003e \u003cp\u003e18.1.3 Climate Challenges 350\u003c\/p\u003e \u003cp\u003e18.2 Definition and Constituents of Smart Power Systems 351\u003c\/p\u003e \u003cp\u003e18.2.1 Applicable Industries 352\u003c\/p\u003e \u003cp\u003e18.2.2 Evolution of Power Electronics-Based Solutions 353\u003c\/p\u003e \u003cp\u003e18.2.3 Operation of the Power System 355\u003c\/p\u003e \u003cp\u003e18.3 Challenges Faced by Utilities and Their Way Towards Becoming Smart 356\u003c\/p\u003e \u003cp\u003e18.3.1 Digitalization of Power Industry 359\u003c\/p\u003e \u003cp\u003e18.3.2 Storage Possibilities and Integration into Grid 360\u003c\/p\u003e \u003cp\u003e18.3.3 Addressing Power Quality Concerns and Their Mitigation 362\u003c\/p\u003e \u003cp\u003e18.3.4 A Path Forward Towards Holistic Condition Monitoring 363\u003c\/p\u003e \u003cp\u003e18.4 Ways towards Smart Transition of the Energy Sector 366\u003c\/p\u003e \u003cp\u003e18.4.1 Creating an All-Inclusive Ecosystem 366\u003c\/p\u003e \u003cp\u003e18.4.1.1 Example of Sensor-Based Ecosystem 367\u003c\/p\u003e \u003cp\u003e18.4.1.2 Utilizing the Sensor Data for Effective Analytics 368\u003c\/p\u003e \u003cp\u003e18.4.2 Modular Energy System Architecture 370\u003c\/p\u003e \u003cp\u003e18.5 Conclusion 371\u003c\/p\u003e \u003cp\u003eReferences 373\u003c\/p\u003e \u003cp\u003eIndex 377\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSanjeevikumar Padmanaban, PhD,\u003c\/b\u003e is a Full Professor with the Department of Electrical Engineering, IT and Cybernetics, at the University of South-Eastern Norway, Porsgrunn, Norway. He serves as an Editor\/Associate Editor\/Editorial Board Member of many refereed journals, in particular, the \u003ci\u003eIEEE Systems Journal\u003c\/i\u003e, the \u003ci\u003eIEEE Access Journal\u003c\/i\u003e, \u003ci\u003eIEEE Transactions on Industry Applications\u003c\/i\u003e, the Deputy Editor\/Subject Editor of \u003ci\u003eIET Renewable Power Generation\u003c\/i\u003e, and \u003ci\u003eIET Generation, Transmission and Distribution Journal\u003c\/i\u003e, Subject Editor of \u003ci\u003eFACETS and Energies MDPI Journal\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSivaraman Palanisamy \u003c\/b\u003eis a Program Manager - EV Charging Infrastructure in WRI India. He is an IEEE Senior Member, a Member of CIGRE, and Life Member of the Institution of Engineers (India). He is an active participant in the IEEE Standards Association.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSharmeela Chenniappan, PhD,\u003c\/b\u003e is a Professor in the Department of EEE, CEG campus, Anna University, Chennai, India. She is an IEEE Senior Member, a Life Member of CBIP, and Member of the Institution of Engineers (India), ISTE, and SSI.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJens Bo Holm-Nielsen, PhD,\u003c\/b\u003e is the Head of the Esbjerg Energy Section with the Department of Energy Technology at Aalborg University. He has been an organizer of various international conferences, workshops, and training programs.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAuthoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studies\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eArtificial Intelligence-based Smart Power Systems\u003c\/i\u003e 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.  \u003c\/p\u003e\u003cp\u003eTo enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eArtificial Intelligence-based Smart Power Systems \u003c\/i\u003eincludes specific information on topics such as: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e Modeling and analysis of smart power systems, covering steady state analysis, dynamic analysis, voltage stability, and more\u003c\/li\u003e \u003cli\u003e Recent advancement in power electronics for smart power systems, covering power electronic converters for renewable energy sources, electric vehicles, and HVDC\/FACTs\u003c\/li\u003e \u003cli\u003e Distribution Phasor Measurement Units (PMU) in smart power systems, covering the need for PMU in distribution and automation of system reconfigurations\u003c\/li\u003e \u003cli\u003e Power and energy management systems\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eEngineering colleges and universities, along with industry research centers, can use the in-depth subject coverage and the extensive supplementary learning resources found in \u003ci\u003eArtificial Intelligence-based Smart Power Systems\u003c\/i\u003e to gain a holistic understanding of the subject and be able to harness that knowledge within a myriad of practical applications.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47988764672229,"sku":"NP9781119893967","price":150.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119893967.jpg?v=1761781503","url":"https:\/\/k12savings.com\/products\/artificial-intelligence-based-smart-power-systems-isbn-9781119893967","provider":"K12savings","version":"1.0","type":"link"}