Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today’s challenges in this rapidly accelerating area of power engineering.
Divided into three parts, this book begins by breaking down the big picture for electric utilities, before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes.
Including five brand new chapters on emerging technological solutions, Big Data Application in Power Systems, Second Edition remains an essential resource for the reader aiming to utilize the potential of big data in the power systems of the future.
1. Big Data Application in Power Systems - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page vi 7. About the editors - p. Page vii 8. Preface, objective, and overview of the book - p. Page vii 9. Acknowledgments for the second edition - p. Page ix 10. List of Illustrations - p. Page x 11. List of Tables - p. Page xi 12. Chapter One : A holistic approach to becoming a data-driven utility - p. Page xi 13. Chapter Two : Security and data privacy challenges for data-driven utilities - p. Page 34 14. Chapter Three : The role of big data and analytics in utilities innovation - p. Page 51 15. Chapter Four : Big data integration for the digitalization and decarbonization of distribution grids - p. Page 67 16. Chapter Five : Topology detection in distribution networks with machine learning - p. Page 84 17. Chapter Six : Grid topology identification via distributed statistical hypothesis testing - p. Page 105 18. Chapter Seven : Learning stable local Volt/Var controllers in distribution grids - p. Page 132 19. Chapter Eight : Grid-edge optimization and control with machine learning - p. Page 158 20. Chapter Nine : Fault detection in distribution grid with spatial-temporal recurrent graph neural networks - p. Page 184 21. Chapter Ten : Supervised learning-based fault location in power grids - p. Page 212 22. Chapter Eleven : Transient stability predictions in modern power systems using transfer learning - p. Page 232 23. Chapter Twelve : Misconfiguration detection of inverter-based units in power distribution grids using machine learning - p. Page 264 24. Chapter Thirteen : Virtual inertia provision from distribution power systems using machine learning - p. Page 292 25. Chapter Fourteen : Electricity demand flexibility estimation in warehouses using machine learning - p. Page 318 26. Chapter Fifteen : The role of eXplainable Artificial Intelligence (XAI) in smart grids - p. Page 343 27. Chapter Sixteen : Data-driven photovoltaic and wind power forecasting for distribution grids - p. Page 362 28. Chapter Seventeen : Grid resilience against wildfire with machine learning: Machine learning based detection, localization and mitigation of the impact of forest fires on power grids - p. Page 390 29. Index - p. Page 413
Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today’s challenges in this rapidly accelerating area of power engineering.
Divided into three parts, this book begins by breaking down the big picture for electric utilities, before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes.
Including five brand new chapters on emerging technological solutions, Big Data Application in Power Systems, Second Edition remains an essential resource for the reader aiming to utilize the potential of big data in the power systems of the future.
1. Big Data Application in Power Systems - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page vi 7. About the editors - p. Page vii 8. Preface, objective, and overview of the book - p. Page vii 9. Acknowledgments for the second edition - p. Page ix 10. List of Illustrations - p. Page x 11. List of Tables - p. Page xi 12. Chapter One : A holistic approach to becoming a data-driven utility - p. Page xi 13. Chapter Two : Security and data privacy challenges for data-driven utilities - p. Page 34 14. Chapter Three : The role of big data and analytics in utilities innovation - p. Page 51 15. Chapter Four : Big data integration for the digitalization and decarbonization of distribution grids - p. Page 67 16. Chapter Five : Topology detection in distribution networks with machine learning - p. Page 84 17. Chapter Six : Grid topology identification via distributed statistical hypothesis testing - p. Page 105 18. Chapter Seven : Learning stable local Volt/Var controllers in distribution grids - p. Page 132 19. Chapter Eight : Grid-edge optimization and control with machine learning - p. Page 158 20. Chapter Nine : Fault detection in distribution grid with spatial-temporal recurrent graph neural networks - p. Page 184 21. Chapter Ten : Supervised learning-based fault location in power grids - p. Page 212 22. Chapter Eleven : Transient stability predictions in modern power systems using transfer learning - p. Page 232 23. Chapter Twelve : Misconfiguration detection of inverter-based units in power distribution grids using machine learning - p. Page 264 24. Chapter Thirteen : Virtual inertia provision from distribution power systems using machine learning - p. Page 292 25. Chapter Fourteen : Electricity demand flexibility estimation in warehouses using machine learning - p. Page 318 26. Chapter Fifteen : The role of eXplainable Artificial Intelligence (XAI) in smart grids - p. Page 343 27. Chapter Sixteen : Data-driven photovoltaic and wind power forecasting for distribution grids - p. Page 362 28. Chapter Seventeen : Grid resilience against wildfire with machine learning: Machine learning based detection, localization and mitigation of the impact of forest fires on power grids - p. Page 390 29. Index - p. Page 413
