{"product_id":"smart-cyber-physical-power-systems-volume-2-solutions-from-emerging-technologies-hardback-9781394334568","title":"Smart Cyber-Physical Power Systems, Volume 2; Solutions from Emerging Technologies (Hardback) 9781394334568","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eSmart Cyber-Physical Power Systems, Volume 2\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eSolutions from Emerging Technologies\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAli Parizad (Edited by), A Parizad (Author), Hamid Reza Baghaee (Edited by), Saifur Rahman (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394334568, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 27 May 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e624 pages\u003cbr\u003e25.6 x 18.5 x 3.8 cm, 1.225 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eA practical roadmap to the application of artificial intelligence and machine learning to power systems\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn an era where digital technologies are revolutionizing every aspect of power systems, \u003ci\u003eSmart Cyber-Physical Power Systems, Volume 2: Solutions from Emerging Technologies\u003c\/i\u003e shifts focus to cutting-edge solutions for overcoming the challenges faced by cyber-physical power systems (CPSs). By leveraging emerging technologies, this volume explores how innovations like artificial intelligence, machine learning, blockchain, quantum computing, digital twins, and data analytics are reshaping the energy sector. \u003c\/p\u003e\n\u003cp\u003eThis volume delves into the application of AI and machine learning in power system optimization, protection, and forecasting. It also highlights the transformative role of blockchain in secure energy trading and digital twins in simulating real-time power system operations. Advanced big data techniques are presented for enhancing system planning, situational awareness, and stability, while quantum computing offers groundbreaking approaches to solving complex energy problems. \u003c\/p\u003e\n\u003cp\u003eFor professionals and researchers eager to harness cutting-edge technologies within smart power systems, Volume 2 proves indispensable. Filled with numerous illustrations, case studies, and technical insights, it offers forward-thinking solutions that foster a more efficient, secure, and resilient future for global energy systems, heralding a new era of innovation and transformation in cyber-physical power networks. \u003c\/p\u003e\n\u003cp\u003eWelcome to the exploration of Smart Cyber-Physical Power Systems (CPPSs), where challenges are met with innovative solutions, and the future of energy is shaped by the paradigms of AI\/ML, Big Data, Blockchain, IoT, Quantum Computing, Information Theory, Edge Computing, Metaverse, DevOps, and more.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eAbout the Editors xxi\u003c\/p\u003e \u003cp\u003eList of Contributors xxv\u003c\/p\u003e \u003cp\u003eForeword (John D. McDonald) xxxi\u003c\/p\u003e \u003cp\u003eForeword (Massoud Amin) xxxiii\u003c\/p\u003e \u003cp\u003ePreface for Volume 2: Smart Cyber-Physical Power Systems: Solutions from Emerging Technologies xxxvii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Information Theory and Gray Level Transformation Techniques in Detecting False Data Injection Attacks on Power System State Estimation 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAli Parizad and Constantine Hatziadoniu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Cyber-attacks on the State Variables of the Power System 2\u003c\/p\u003e \u003cp\u003e1.3 Information Theory 4\u003c\/p\u003e \u003cp\u003e1.4 Gray Level Transformation 6\u003c\/p\u003e \u003cp\u003e1.5 Linear Transformation 7\u003c\/p\u003e \u003cp\u003e1.6 Logarithmic Transformations 7\u003c\/p\u003e \u003cp\u003e1.7 Power-Law Transformations 7\u003c\/p\u003e \u003cp\u003e1.8 Simulation Results 8\u003c\/p\u003e \u003cp\u003e1.9 Conclusion 44\u003c\/p\u003e \u003cp\u003eReferences 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Artificial Intelligence and Machine Learning Applications in Modern Power Systems 49\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSohom Datta, Zhangshuan Hou, Milan Jain, and Syed Ahsan Raza Naqvi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 The Need for AI\/ML in Modern Power Systems 49\u003c\/p\u003e \u003cp\u003e2.2 AL\/ML Algorithms in Power System Applications 49\u003c\/p\u003e \u003cp\u003e2.3 AI\/ML-Based Applications in the Electricity Grid 52\u003c\/p\u003e \u003cp\u003e2.4 Future of AI\/ML in Power Systems 61\u003c\/p\u003e \u003cp\u003eReferences 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Physics-Informed Deep Reinforcement Learning-Based Control in Power Systems 67\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRamij Raja Hossain, Qiuhua Huang, Kaveri Mahapatra, and Renke Huang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 67\u003c\/p\u003e \u003cp\u003e3.2 Overview of RL\/DRL 69\u003c\/p\u003e \u003cp\u003e3.3 Grid Control Perspectives 70\u003c\/p\u003e \u003cp\u003e3.4 Importance of Physics-Informed DRL in Grid Control and Different Methods 71\u003c\/p\u003e \u003cp\u003e3.5 Grid Control Applications of Physics-Informed DRL 72\u003c\/p\u003e \u003cp\u003e3.6 Discussion and Research Directions 74\u003c\/p\u003e \u003cp\u003e3.7 Conclusions 75\u003c\/p\u003e \u003cp\u003eReferences 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Digital Twin Approach Toward Modern Power Systems 79\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSabrieh Choobkar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Digital Twin Concept 79\u003c\/p\u003e \u003cp\u003e4.2 Digital Twin: The Convergence of Recent Technologies 84\u003c\/p\u003e \u003cp\u003e4.3 Cyber-Physical System and Digital Twin 87\u003c\/p\u003e \u003cp\u003e4.4 Novelties and Suggestions of Digital Twin to Smart Grid Subsystems 88\u003c\/p\u003e \u003cp\u003e4.5 Conclusions 90\u003c\/p\u003e \u003cp\u003eReferences 90\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Application of AI and Machine Learning Algorithms in Power System State Estimation 93\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBehrouz Azimian, Reetam Sen Biswas, and Anamitra Pal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 93\u003c\/p\u003e \u003cp\u003e5.2 Motivation and Theoretical Background 95\u003c\/p\u003e \u003cp\u003e5.3 DNN Architecture for DSSE and TI 97\u003c\/p\u003e \u003cp\u003e5.4 SMD Measurement Selection for DSSE and TI 98\u003c\/p\u003e \u003cp\u003e5.5 Smart Meter Data Consideration 104\u003c\/p\u003e \u003cp\u003e5.6 Implementation of DNN-Based TI and DSSE 114\u003c\/p\u003e \u003cp\u003e5.7 Conclusion 126\u003c\/p\u003e \u003cp\u003eAcknowledgment 127\u003c\/p\u003e \u003cp\u003eAppendix 127\u003c\/p\u003e \u003cp\u003eReferences 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 ANN-Based Scenario Generation Approach for Energy Management of Smart Buildings 131\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMahoor Ebrahimi, Mahan Ebrahimi, Miadreza Shafie-khah, Hannu Laaksonen, and Pierluigi Siano\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 131\u003c\/p\u003e \u003cp\u003e6.2 Problem Formulation 132\u003c\/p\u003e \u003cp\u003e6.3 Application of AI in Energy Management of Smart Homes 137\u003c\/p\u003e \u003cp\u003e6.4 Simulation and Results 139\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 145\u003c\/p\u003e \u003cp\u003eReferences 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Protection Challenges and Solutions in Power Grids by AI\/Machine Learning 149\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAli Bidram\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 149\u003c\/p\u003e \u003cp\u003e7.2 Zonal Setting-Less Modular Protection Using ml 150\u003c\/p\u003e \u003cp\u003e7.3 Traveling Wave Protection of dc Microgrids Using ml 159\u003c\/p\u003e \u003cp\u003e7.4 Conclusion 168\u003c\/p\u003e \u003cp\u003eReferences 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Deep and Reinforcement Learning for Active Distribution Network Protection 171\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMohammed AlSaba and Mohammad Abido\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction and Motivation 171\u003c\/p\u003e \u003cp\u003e8.2 Problem Statement 173\u003c\/p\u003e \u003cp\u003e8.3 Proposed Methodology for Fault Detection and Classification 177\u003c\/p\u003e \u003cp\u003e8.4 Case Study and Implementation 178\u003c\/p\u003e \u003cp\u003e8.5 Results and Discussion 180\u003c\/p\u003e \u003cp\u003e8.6 Hardware in-the-Loop Testing 186\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 186\u003c\/p\u003e \u003cp\u003eAcknowledgments 187\u003c\/p\u003e \u003cp\u003eReferences 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Handling and Application of Big Data in Modern Power Systems for Planning, Operation, and Control Processes 189\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMeghana Ramesh, Jing Xie, Monish Mukherjee, Thomas E. McDermott, Anjan Bose, and Michael Diedesch\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 189\u003c\/p\u003e \u003cp\u003e9.2 Intelligent Modeling and Its Applications 190\u003c\/p\u003e \u003cp\u003e9.3 Case Study 193\u003c\/p\u003e \u003cp\u003e9.4 Conclusions 206\u003c\/p\u003e \u003cp\u003eAcknowledgment 206\u003c\/p\u003e \u003cp\u003eReferences 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Handling and Application of Big Data in Modern Power Systems for Situational Awareness and Operation 209\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYingqi Liang, Junbo Zhao, and Dipti Srinivasan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 209\u003c\/p\u003e \u003cp\u003e10.2 Challenges for Using Big Data Techniques in Smart Grids 209\u003c\/p\u003e \u003cp\u003e10.3 Solutions Using Big Data Techniques for Smart Grid Situational Awareness 211\u003c\/p\u003e \u003cp\u003e10.4 Applications of Big Data Techniques for Smart Grid Operation 228\u003c\/p\u003e \u003cp\u003e10.5 Numerical Results 231\u003c\/p\u003e \u003cp\u003e10.6 Concluding 250\u003c\/p\u003e \u003cp\u003eReferences 251\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Data-Driven Methods in Modern Power System Stability and Security 255\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJinpeng Guo, Georgia Pierrou, Xiaoting Wang, Mohan Du, and Xiaozhe Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 255\u003c\/p\u003e \u003cp\u003e11.2 Data-Driven Wide-Area Damping Control 256\u003c\/p\u003e \u003cp\u003e11.3 Data-Driven Wide-Area Voltage Control 266\u003c\/p\u003e \u003cp\u003e11.4 Data-Driven Inertia Estimation for Frequency Control 274\u003c\/p\u003e \u003cp\u003e11.5 A Data-Driven Polynomial Chaos Expansion Method for Available Transfer Capability Assessment 284\u003c\/p\u003e \u003cp\u003e11.6 Using PCE to Assess the Ramping Support Capability of a Microgrid 297\u003c\/p\u003e \u003cp\u003eReferences 305\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Application of Quantum Computing for Power Systems 313\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYan Li, Ganesh K. Venayagamoorthy, and Liang Du\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Quantum Computing in Renewable Energy Systems 313\u003c\/p\u003e \u003cp\u003e12.2 Quantum Approximate Optimization Algorithm for Renewable Energy Systems 316\u003c\/p\u003e \u003cp\u003e12.3 Typical Applications of Quantum Computing 319\u003c\/p\u003e \u003cp\u003eAcknowledgment 320\u003c\/p\u003e \u003cp\u003eReferences 320\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 1 Principles and Concepts 323\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eZejia Jing, Ali Parizad, and Saifur Rahman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 323\u003c\/p\u003e \u003cp\u003e13.2 Principles and Concepts of Building Hourly Energy Consumption Forecasting 325\u003c\/p\u003e \u003cp\u003e13.3 Conclusion 359\u003c\/p\u003e \u003cp\u003eReferences 359\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 2 Simulation and Experimental Results 363\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eZejia Jing, Ali Parizad, and Saifur Rahman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 363\u003c\/p\u003e \u003cp\u003e14.2 Case Study and Result of Building Hourly Energy Consumption Forecasting 364\u003c\/p\u003e \u003cp\u003e14.3 Building Occupancy Measurement 394\u003c\/p\u003e \u003cp\u003e14.4 Conclusion 409\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 PV Energy Forecasting Applying Machine Learning Methods Targeting Energy Trading Systems 417\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eZejia Jing, Ali Parizad, and Saifur Rahman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 417\u003c\/p\u003e \u003cp\u003e15.2 PV Energy Forecasting 418\u003c\/p\u003e \u003cp\u003e15.3 Conclusion 447\u003c\/p\u003e \u003cp\u003eReferences 447\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 An Intelligent Reinforcement-Learning-Based Load Shedding to Prevent Voltage Instability 449\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePouria Akbarzadeh Aghdam, Hamid Khoshkhoo, and Ahmad Akbari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 449\u003c\/p\u003e \u003cp\u003e16.2 Stability Control Methods 450\u003c\/p\u003e \u003cp\u003e16.3 Characteristics of Optimal Stability Controller 451\u003c\/p\u003e \u003cp\u003e16.4 Utilizing Reinforcement Learning for Enhancing Voltage Stability 452\u003c\/p\u003e \u003cp\u003e16.5 Taxonomy of RL 455\u003c\/p\u003e \u003cp\u003e16.6 Proposed Algorithm 456\u003c\/p\u003e \u003cp\u003e16.7 Reinforcement Learning Algorithm Components 456\u003c\/p\u003e \u003cp\u003e16.8 Algorithm Implementation Process 458\u003c\/p\u003e \u003cp\u003e16.9 Simulations and Results 460\u003c\/p\u003e \u003cp\u003e16.10 Scenario I 462\u003c\/p\u003e \u003cp\u003e16.11 Scenario II 463\u003c\/p\u003e \u003cp\u003e16.12 Scenario III 465\u003c\/p\u003e \u003cp\u003e16.13 Conclusion 466\u003c\/p\u003e \u003cp\u003eReferences 466\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Deep Learning Techniques for Solving Optimal Power Flow Problems 471\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVassilis Kekatos and Manish K. Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 471\u003c\/p\u003e \u003cp\u003e17.2 Sensitivity-Informed Learning for OPF 473\u003c\/p\u003e \u003cp\u003e17.3 Deep Learning for Stochastic OPF 487\u003c\/p\u003e \u003cp\u003e17.4 Conclusions 497\u003c\/p\u003e \u003cp\u003eReferences 497\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Research on Intelligent Prediction of Spatial–Temporal Dynamic Frequency Response and Performance Evaluation 501\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eXieli Sun, Longyu Chen, and Xiaoru Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 501\u003c\/p\u003e \u003cp\u003e18.2 Modeling Process and Evaluation Method 503\u003c\/p\u003e \u003cp\u003e18.3 Case Study 515\u003c\/p\u003e \u003cp\u003e18.4 Conclusion 522\u003c\/p\u003e \u003cp\u003eReferences 522\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Emerging Technologies and Future Trends in Cyber-Physical Power Systems: Toward a New Era of Innovations 525\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAli Parizad, Hamid Reza Baghaee, Vahid Alizadeh, and Saifur Rahman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 525\u003c\/p\u003e \u003cp\u003e19.2 Paradigm Shifts in Power Transmission and Management 526\u003c\/p\u003e \u003cp\u003e19.3 Innovations in Electric Mobility and Sustainable Transportation 530\u003c\/p\u003e \u003cp\u003e19.4 Digital Transformation and Technological Convergence in Cyber-Physical Power Systems 530\u003c\/p\u003e \u003cp\u003e19.5 Cyber-Physical Systems Enhancing Societal Well-Being 539\u003c\/p\u003e \u003cp\u003e19.6 Toward a Decentralized and Automated Future 540\u003c\/p\u003e \u003cp\u003e19.7 Overcoming Challenges with Advanced Technologies 541\u003c\/p\u003e \u003cp\u003e19.8 Revolutionizing Modern Power Systems with Real-Time Simulators 547\u003c\/p\u003e \u003cp\u003e19.9 Emerging Trends Shaping the Future Energy Landscape 549\u003c\/p\u003e \u003cp\u003e19.10 Conclusion 552\u003c\/p\u003e \u003cp\u003eReferences 553\u003c\/p\u003e \u003cp\u003eIndex 567\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Brand New","offer_id":52433817960728,"sku":"9781394334568","price":75.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394334568.jpg?v=1784853937","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/smart-cyber-physical-power-systems-volume-2-solutions-from-emerging-technologies-hardback-9781394334568","provider":"Freshly Printed Books","version":"1.0","type":"link"}