Smart Cyber-Physical Power Systems, Volume 2
eBook - PDF

Smart Cyber-Physical Power Systems, Volume 2

Solutions from Emerging Technologies

  1. 627 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Smart Cyber-Physical Power Systems, Volume 2

Solutions from Emerging Technologies

About this book

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.

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Yes, you can access Smart Cyber-Physical Power Systems, Volume 2 by Ali Parizad,Hamid Reza Baghaee,Saifur Rahman in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Contents
  5. About the Editors
  6. List of Contributors
  7. Foreword (John D. McDonald)
  8. Foreword (Massoud Amin)
  9. Preface for Volume 2: Smart Cyber‐Physical Power Systems: Solutions from Emerging Technologies
  10. Acknowledgments
  11. Chapter 1 Information Theory and Gray Level Transformation Techniques in Detecting False Data Injection Attacks on Power System State Estimation
  12. Chapter 2 Artificial Intelligence and Machine Learning Applications in Modern Power Systems*
  13. Chapter 3 Physics‐Informed Deep Reinforcement Learning‐Based Control in Power Systems
  14. Chapter 4 Digital Twin Approach Toward Modern Power Systems
  15. Chapter 5 Application of AI and Machine Learning Algorithms in Power System State Estimation
  16. Chapter 6 ANN‐Based Scenario Generation Approach for Energy Management of Smart Buildings
  17. Chapter 7 Protection Challenges and Solutions in Power Grids by AI/Machine Learning
  18. Chapter 8 Deep and Reinforcement Learning for Active Distribution Network Protection
  19. Chapter 9 Handling and Application of Big Data in Modern Power Systems for Planning, Operation, and Control Processes
  20. Chapter 10 Handling and Application of Big Data in Modern Power Systems for Situational Awareness and Operation
  21. Chapter 11 Data‐Driven Methods in Modern Power System Stability and Security
  22. Chapter 12 Application of Quantum Computing for Power Systems
  23. Chapter 13 High‐Resolution Building‐Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 1 Principles and Concepts
  24. Chapter 14 High‐Resolution Building‐Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 2 Simulation and Experimental Results
  25. Chapter 15 PV Energy Forecasting Applying Machine Learning Methods Targeting Energy Trading Systems
  26. Chapter 16 An Intelligent Reinforcement‐Learning‐Based Load Shedding to Prevent Voltage Instability
  27. Chapter 17 Deep Learning Techniques for Solving Optimal Power Flow Problems
  28. Chapter 18 Research on Intelligent Prediction of Spatial–Temporal Dynamic Frequency Response and Performance Evaluation
  29. Chapter 19 Emerging Technologies and Future Trends in Cyber‐Physical Power Systems: Toward a New Era of Innovations
  30. Index
  31. EULA