Overview

A practical roadmap to the application of artificial intelligence and machine learning to power systems In an era where digital technologies are revolutionizing every aspect of power systems, Smart Cyber-Physical Power Systems, Volume 2: Solutions from Emerging Technologies 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. This 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. For 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. Welcome 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.

Full Product Details

Author:   Ali Parizad (Virginia Polytechnic Institute and State University, VA, USA) ,  Hamid Reza Baghaee (Tarbiat Modares University, Tehran, Iran) ,  Saifur Rahman (Virginia Polytechnic Institute and State University, VA, USA)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-IEEE Press
Dimensions:   Width: 18.50cm , Height: 3.80cm , Length: 25.70cm
Weight:   1.225kg
ISBN:  

9781394334568

ISBN 10:   1394334567
Pages:   624
Publication Date:   27 May 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

About the Editors xxi List of Contributors xxv Foreword (John D. McDonald) xxxi Foreword (Massoud Amin) xxxiii Preface for Volume 2: Smart Cyber-Physical Power Systems: Solutions from Emerging Technologies xxxvii Acknowledgments xxxix 1 Information Theory and Gray Level Transformation Techniques in Detecting False Data Injection Attacks on Power System State Estimation 1 Ali Parizad and Constantine Hatziadoniu 1.1 Introduction 1 1.2 Cyber-attacks on the State Variables of the Power System 2 1.3 Information Theory 4 1.4 Gray Level Transformation 6 1.5 Linear Transformation 7 1.6 Logarithmic Transformations 7 1.7 Power-Law Transformations 7 1.8 Simulation Results 8 1.9 Conclusion 44 References 45 2 Artificial Intelligence and Machine Learning Applications in Modern Power Systems 49 Sohom Datta, Zhangshuan Hou, Milan Jain, and Syed Ahsan Raza Naqvi 2.1 The Need for AI/ML in Modern Power Systems 49 2.2 AL/ML Algorithms in Power System Applications 49 2.3 AI/ML-Based Applications in the Electricity Grid 52 2.4 Future of AI/ML in Power Systems 61 References 62 3 Physics-Informed Deep Reinforcement Learning-Based Control in Power Systems 67 Ramij Raja Hossain, Qiuhua Huang, Kaveri Mahapatra, and Renke Huang 3.1 Introduction 67 3.2 Overview of RL/DRL 69 3.3 Grid Control Perspectives 70 3.4 Importance of Physics-Informed DRL in Grid Control and Different Methods 71 3.5 Grid Control Applications of Physics-Informed DRL 72 3.6 Discussion and Research Directions 74 3.7 Conclusions 75 References 75 4 Digital Twin Approach Toward Modern Power Systems 79 Sabrieh Choobkar 4.1 Digital Twin Concept 79 4.2 Digital Twin: The Convergence of Recent Technologies 84 4.3 Cyber-Physical System and Digital Twin 87 4.4 Novelties and Suggestions of Digital Twin to Smart Grid Subsystems 88 4.5 Conclusions 90 References 90 5 Application of AI and Machine Learning Algorithms in Power System State Estimation 93 Behrouz Azimian, Reetam Sen Biswas, and Anamitra Pal 5.1 Introduction 93 5.2 Motivation and Theoretical Background 95 5.3 DNN Architecture for DSSE and TI 97 5.4 SMD Measurement Selection for DSSE and TI 98 5.5 Smart Meter Data Consideration 104 5.6 Implementation of DNN-Based TI and DSSE 114 5.7 Conclusion 126 Acknowledgment 127 Appendix 127 References 128 6 ANN-Based Scenario Generation Approach for Energy Management of Smart Buildings 131 Mahoor Ebrahimi, Mahan Ebrahimi, Miadreza Shafie-khah, Hannu Laaksonen, and Pierluigi Siano 6.1 Introduction 131 6.2 Problem Formulation 132 6.3 Application of AI in Energy Management of Smart Homes 137 6.4 Simulation and Results 139 6.5 Conclusion 145 References 146 7 Protection Challenges and Solutions in Power Grids by AI/Machine Learning 149 Ali Bidram 7.1 Introduction 149 7.2 Zonal Setting-Less Modular Protection Using ml 150 7.3 Traveling Wave Protection of dc Microgrids Using ml 159 7.4 Conclusion 168 References 168 8 Deep and Reinforcement Learning for Active Distribution Network Protection 171 Mohammed AlSaba and Mohammad Abido 8.1 Introduction and Motivation 171 8.2 Problem Statement 173 8.3 Proposed Methodology for Fault Detection and Classification 177 8.4 Case Study and Implementation 178 8.5 Results and Discussion 180 8.6 Hardware in-the-Loop Testing 186 8.7 Conclusion 186 Acknowledgments 187 References 187 9 Handling and Application of Big Data in Modern Power Systems for Planning, Operation, and Control Processes 189 Meghana Ramesh, Jing Xie, Monish Mukherjee, Thomas E. McDermott, Anjan Bose, and Michael Diedesch 9.1 Introduction 189 9.2 Intelligent Modeling and Its Applications 190 9.3 Case Study 193 9.4 Conclusions 206 Acknowledgment 206 References 207 10 Handling and Application of Big Data in Modern Power Systems for Situational Awareness and Operation 209 Yingqi Liang, Junbo Zhao, and Dipti Srinivasan 10.1 Introduction 209 10.2 Challenges for Using Big Data Techniques in Smart Grids 209 10.3 Solutions Using Big Data Techniques for Smart Grid Situational Awareness 211 10.4 Applications of Big Data Techniques for Smart Grid Operation 228 10.5 Numerical Results 231 10.6 Concluding 250 References 251 11 Data-Driven Methods in Modern Power System Stability and Security 255 Jinpeng Guo, Georgia Pierrou, Xiaoting Wang, Mohan Du, and Xiaozhe Wang 11.1 Introduction 255 11.2 Data-Driven Wide-Area Damping Control 256 11.3 Data-Driven Wide-Area Voltage Control 266 11.4 Data-Driven Inertia Estimation for Frequency Control 274 11.5 A Data-Driven Polynomial Chaos Expansion Method for Available Transfer Capability Assessment 284 11.6 Using PCE to Assess the Ramping Support Capability of a Microgrid 297 References 305 12 Application of Quantum Computing for Power Systems 313 Yan Li, Ganesh K. Venayagamoorthy, and Liang Du 12.1 Quantum Computing in Renewable Energy Systems 313 12.2 Quantum Approximate Optimization Algorithm for Renewable Energy Systems 316 12.3 Typical Applications of Quantum Computing 319 Acknowledgment 320 References 320 13 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 1 Principles and Concepts 323 Zejia Jing, Ali Parizad, and Saifur Rahman 13.1 Introduction 323 13.2 Principles and Concepts of Building Hourly Energy Consumption Forecasting 325 13.3 Conclusion 359 References 359 14 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 2 Simulation and Experimental Results 363 Zejia Jing, Ali Parizad, and Saifur Rahman 14.1 Introduction 363 14.2 Case Study and Result of Building Hourly Energy Consumption Forecasting 364 14.3 Building Occupancy Measurement 394 14.4 Conclusion 409 15 PV Energy Forecasting Applying Machine Learning Methods Targeting Energy Trading Systems 417 Zejia Jing, Ali Parizad, and Saifur Rahman 15.1 Introduction 417 15.2 PV Energy Forecasting 418 15.3 Conclusion 447 References 447 16 An Intelligent Reinforcement-Learning-Based Load Shedding to Prevent Voltage Instability 449 Pouria Akbarzadeh Aghdam, Hamid Khoshkhoo, and Ahmad Akbari 16.1 Introduction 449 16.2 Stability Control Methods 450 16.3 Characteristics of Optimal Stability Controller 451 16.4 Utilizing Reinforcement Learning for Enhancing Voltage Stability 452 16.5 Taxonomy of RL 455 16.6 Proposed Algorithm 456 16.7 Reinforcement Learning Algorithm Components 456 16.8 Algorithm Implementation Process 458 16.9 Simulations and Results 460 16.10 Scenario I 462 16.11 Scenario II 463 16.12 Scenario III 465 16.13 Conclusion 466 References 466 17 Deep Learning Techniques for Solving Optimal Power Flow Problems 471 Vassilis Kekatos and Manish K. Singh 17.1 Introduction 471 17.2 Sensitivity-Informed Learning for OPF 473 17.3 Deep Learning for Stochastic OPF 487 17.4 Conclusions 497 References 497 18 Research on Intelligent Prediction of Spatial–Temporal Dynamic Frequency Response and Performance Evaluation 501 Xieli Sun, Longyu Chen, and Xiaoru Wang 18.1 Introduction 501 18.2 Modeling Process and Evaluation Method 503 18.3 Case Study 515 18.4 Conclusion 522 References 522 19 Emerging Technologies and Future Trends in Cyber-Physical Power Systems: Toward a New Era of Innovations 525 Ali Parizad, Hamid Reza Baghaee, Vahid Alizadeh, and Saifur Rahman 19.1 Introduction 525 19.2 Paradigm Shifts in Power Transmission and Management 526 19.3 Innovations in Electric Mobility and Sustainable Transportation 530 19.4 Digital Transformation and Technological Convergence in Cyber-Physical Power Systems 530 19.5 Cyber-Physical Systems Enhancing Societal Well-Being 539 19.6 Toward a Decentralized and Automated Future 540 19.7 Overcoming Challenges with Advanced Technologies 541 19.8 Revolutionizing Modern Power Systems with Real-Time Simulators 547 19.9 Emerging Trends Shaping the Future Energy Landscape 549 19.10 Conclusion 552 References 553 Index 567

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Author Information

Ali Parizad, PhD, is a Postdoctoral Associate at the Advanced Research Institute (ARI) of Virginia Polytechnic Institute and State University, VA, USA. Leveraging his extensive academic background, he served as a Senior Data Scientist in the IDA Data Science & Machine Learning (DSML) Department at Shell Energy. He holds the position of Staff Power Systems Machine Learning Engineer at Thinklabs AI, where he tackles critical challenges in power systems with cutting-edge AI applications. Hamid Reza Baghaee, PhD, is an Associate Research Professor at Amirkabir University of Technology, Tehran, Iran. Saifur Rahman, PhD, is the founding director of the Advanced Research Institute at Virginia Tech, where he is the Joseph R. Loring Professor of Electrical and Computer Engineering.

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