Schweitzer Fachinformationen
Wenn es um professionelles Wissen geht, ist Schweitzer Fachinformationen wegweisend. Kunden aus Recht und Beratung sowie Unternehmen, öffentliche Verwaltungen und Bibliotheken erhalten komplette Lösungen zum Beschaffen, Verwalten und Nutzen von digitalen und gedruckten Medien.
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.
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.
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 1Ali 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 49Sohom 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 67Ramij 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 79Sabrieh 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 93Behrouz 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 131Mahoor 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 149Ali 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 171Mohammed 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 189Meghana 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 209Yingqi 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 255Jinpeng 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 313Yan 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 323Zejia 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 363Zejia 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 417Zejia 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 449Pouria 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 471Vassilis 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 501Xieli 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 525Ali 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
Ali Parizad, Postdoctoral Associate, Virginia Tech, Advanced Research Institute (ARI), Virginia, USA
Ali Parizad is a Postdoctoral Associate at Virginia Tech's Advanced Research Institute. His tenure at Virginia Tech involves leveraging machine learning (ML) to enhance energy efficiency within smart grids, under the mentorship of Professor Saifur Rahman, IEEE President 2023. Ali's academic foundation was laid at Southern Illinois University, where he obtained his PhD from the Electrical and Computer Engineering Department in 2021. His doctoral research, which was honored with the Dissertation Research Award for the 2020-2021 academic year, focused on pioneering solutions for modern power systems and smart grids. Specifically, he developed innovative software for Ameren Electric Company, aimed at optimizing distribution system planning with an emphasis on distributed energy resources (DERs) to boost the performance of electric distribution networks. His PhD dissertation emphasized the application of machine/deep learning algorithms for load forecasting, alongside exploring cyber-security and false data detection methods within power systems.
Before embarking on his PhD, Ali joined MAPNA Electric and Control Engineering and Manufacturing Company, Iran's premier power company, as a Power Systems Analysis Engineer in 2010. His roles expanded to include Energy Management System and Supervisory Control and Data Acquisition (SCADA) engineer, as well as Commissioning Supervisor in substation and power plant projects in collaboration with ABB and SIEMENS companies. His innovative work in the realm of real-time simulators culminated in the registration of a patent for a real-time islanded simulator for industrial power plants.
Ali's research interests are extensive, covering the application of artificial intelligence, deep learning, big data, information theory techniques in modern power systems and smart grids, distributed generation, renewable energies, and the operation and control of power systems. He has also explored the potential applications of real-time simulators in enhancing power system operations.
His contributions to the field are substantial, with three books, two book chapters, a patent, and numerous papers in reputable power systems journals to his name. Ali is a valued peer reviewer for several prestigious academic journals, including IEEE Transactions on Power Delivery, IEEE Transactions on Power Electronics, and IEEE Access, among others. His work not only contributes to the academic community but also to the advancement of practical solutions for power systems and smart grid challenges.
As a Senior Data Scientist in the Information and Data Analytics (IDA), Data Science & Machine Learning department at Shell Energy, Ali applied his profound expertise to develop and implement advanced data science solutions for energy demand forecasting and electric vehicle charging station analysis. This role underscored his commitment to leveraging data analytics and machine learning to solve complex challenges in the energy sector, marking his transition from academia to a leading role in industry innovation. Continuing on this path, he holds the position of Staff Power Systems Machine Learning Engineer at Thinklabs AI, where he is dedicated to furthering his impact by addressing critical power systems challenges through state-of-the-art AI technologies.
Hamid Reza Baghaee, Faculty of Electrical and Computer Engineering (ECE) at Tarbiat Modares University (TMU), Tehran, Iran
Hamid Reza Baghaee (SM' 2008, M' 2017) received his PhD in Electrical Engineering from Amirkabir University of Technology (AUT) (Center of Excellence in Power Engineering and the most prestigious university of Iran in electrical power engineering) in 2017. From 2007 to 2017, he was a teaching and research assistant in the Department of Electrical Engineering at AUT. He is the author of three books, three published book chapters, 85 ISI-ranked journal papers (mostly published in IEEE, IET, and Elsevier journals), 70 conference papers, and the owner of one registered patent. Additionally, he has presented 20 workshops and 15 invited talks at national and international conferences and scientific events. His book entitled Microgrids and Methods of Analysis was selected as the best book of the year in the power and energy industry of Iran by the technical committee of the Iran Ministry of Energy (MOE) in November 2021 and the winner of the Distinguished Author of the International Books Award in the AUT in December 2021. He has many HOT and HIGHLY-CITED papers in his journal and conference papers, based on SciVal and Web of Science (WoS) statistics. His special fields of interest are micro- and smart grids, cyber-physical power systems, power system cyber security and cyber-resiliency, application of artificial intelligence (AI) and machine learning (ML) and big data analytics in power systems, real-time simulation of power systems, distributed generation, and renewable energy resources, FACTS, HVDC and custom power devices, power electronics applications in power systems, Power Electronics-Dominated Grids (PEDGs), power quality, real-time simulation of power systems, and power system operation, control, monitoring, and protection.
Dr. Baghaee is also the winner of four national and international prizes, as the best dissertation award, from the Iran Scientific Organization of Smart Grids (ISOSG) in December 2017, the Iranian Energy Association (IEA) in February 2018, Amirkabir University of Technology in December 2018, and the IEEE Iran Section in May 2019 for his PhD dissertation. After pursuing his post-doctoral fellowship in AUT (October 2017-August 2019), in August 2019, he joined AUT as an Associate Research Professor in the Department of Electrical Engineering. He is the Project Coordinator of the AUT pilot microgrid project, one of the sub-projects of the Iran grand (National) Smart Grid Project. He has been a co-supervisor and consulting professor of more than 15 PhD and 20 MSc students since 2017. In 2022, he joined the Faculty of Electrical and Computer Engineering (ECE) at Tarbiat Modares University (TMU), Tehran, Iran, where he is now an Assistant Professor. In December 2023, has was selected as a distinguished researcher at TMU for the reputation and citations of his research among papers and patents. He also was a short-term scientist with CERN and ABB Switzerland. Besides, Dr. Baghaee is a member and Vice-Chairperson of the IEEE Iran Section Power Chapter (since 2022), a member and secretary-chair of the IEEE Iran Section Communication Committee (from 2020 to 2023), and a member of the IEEE, IEEE Smart Grid Community, IEEE Internet of Things Technical Community, IEEE Big Data Community, IEEE Smart Cities Community, and IEEE Sensors Council. Since August 2021, he has been elected as a member of the board and chairperson of the committee on publication and conferences at the ISOSG, the Vice-Chairperson and international representative of CIGRE Iran C6 working group on "Active distribution systems and distributed energy resources," a member of the IEE Transmission and Distribution (TD) Committee, IEEE PES Transmission Sub-Committee and its working groups of Reliability impacts of Inverter-based Resources, Generation and Energy Storage Integration, Voltage Optimization, and Transmission Power System Switching, and also IEEE PES Subcommittee on Big Data Analytics for Power Systems, and IEEE PES Task Force on Application of Big Data Analytic on Transmission System Dynamic Security Assessment, IEEE PES Task Force on Resilient and Secure Large-Scale Energy Internet Systems (RSEI), and IEEE Task Force on Microgrid Design. He is also the reviewer of several IEEE, IET, and Elsevier journals, and Guest Editor of several special issues in IEEE, IET, and Elsevier, MDPI, and a scientific program committee member of several IEEE conferences. Since December 2020, he served as an Associate Editor and Energy Section Editor of the IET Journal of Engineering. He has also been selected as the best and outstanding reviewer of several journals, such as IEEE Transactions on Power Systems (Top 0.66 of reviewers, among more than 8000 reviewers in 2020), Elsevier Control Engineering Practice (in 2018, 2019, and 2020), Wiley International Transaction on Electrical Energy Systems in 2020, and the Pablon best and listed among top 1 of the reviewers in Engineering (in 2018) and both Engineering and Cross-Field (in 2019). He was selected as the Star Reviewer of the IEEE JESTPE and IEEE Power Electronics Society (PELS) in 2020, commemorated and presented during the IEEE ECCE 2021 conference in Vancouver, Canada. He has also been listed in 2020, 2021, and 2022 editions of the top 2% of scientists in the field of Energy, Electrical Engineering, and Enabling and Strategic Technologies according to the Science-Wide Citation Indicators (reported by Stanford University, USA), and mentioned among World's top 1% of Elite Scientists according to Web of Science (WoS) and Essential Science Indicators (ESI) ranking since 2020.
Prof. Saifur Rahman, Director, Virginia Tech Advanced Research Institute, Virginia, USA 2023 IEEE President and CEO
Professor Saifur Rahman is the founding director of the Advanced Research Institute at Virginia Tech, USA, where he is the Joseph R. Loring professor of electrical and computer engineering. He also directs the Center for Energy and the Global Environment at the University. He is a Life Fellow of the IEEE and an IEEE Millennium Medal winner. He was the 2023 IEEE President and CEO. He was the IEEE Power and Energy Society (PES) President in 2018 and 2019. He is the founding...
Dateiformat: ePUBKopierschutz: Adobe-DRM (Digital Rights Management)
Systemvoraussetzungen:
Das Dateiformat ePUB ist sehr gut für Romane und Sachbücher geeignet – also für „fließenden” Text ohne komplexes Layout. Bei E-Readern oder Smartphones passt sich der Zeilen- und Seitenumbruch automatisch den kleinen Displays an. Mit Adobe-DRM wird hier ein „harter” Kopierschutz verwendet. Wenn die notwendigen Voraussetzungen nicht vorliegen, können Sie das E-Book leider nicht öffnen. Daher müssen Sie bereits vor dem Download Ihre Lese-Hardware vorbereiten.Bitte beachten Sie: Wir empfehlen Ihnen unbedingt nach Installation der Lese-Software diese mit Ihrer persönlichen Adobe-ID zu autorisieren!
Weitere Informationen finden Sie in unserer E-Book Hilfe.
