Big Data Application in Power Systems

Reza Arghandeh, Yuxun Zhou, Reza Arghandeh, Yuxun Zhou

• Publisher: Elsevier Science
• Year: 2024
• Edition: 2
• eBook ISBN:
  9780443219511
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

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. - Provides a total refresh to include the most up-to-date research, developments, and challenges - Focuses on practical techniques, including rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches for processing high dimensional, heterogeneous, and spatiotemporal data - Engages with cross-disciplinary lessons, drawing on the impact of intersectional technology including statistics, computer science, and bioinformatics - Includes five brand new chapters on hot topics, ranging from uncertainty decision-making to features, selection methods, and the opportunities provided by social network data

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Contributors
• About the editors
• Preface, objective, and overview of the book
• Acknowledgments for the second edition
• List of Illustrations
• List of Tables
• Chapter One : A holistic approach to becoming a data-driven utility
• Chapter Two : Security and data privacy challenges for data-driven utilities
• Chapter Three : The role of big data and analytics in utilities innovation
• Chapter Four : Big data integration for the digitalization and decarbonization of distribution grids
• Chapter Five : Topology detection in distribution networks with machine learning
• Chapter Six : Grid topology identification via distributed statistical hypothesis testing
• Chapter Seven : Learning stable local Volt/Var controllers in distribution grids
• Chapter Eight : Grid-edge optimization and control with machine learning
• Chapter Nine : Fault detection in distribution grid with spatial-temporal recurrent graph neural networks
• Chapter Ten : Supervised learning-based fault location in power grids
• Chapter Eleven : Transient stability predictions in modern power systems using transfer learning
• Chapter Twelve : Misconfiguration detection of inverter-based units in power distribution grids using machine learning
• Chapter Thirteen : Virtual inertia provision from distribution power systems using machine learning
• Chapter Fourteen : Electricity demand flexibility estimation in warehouses using machine learning
• Chapter Fifteen : The role of eXplainable Artificial Intelligence (XAI) in smart grids
• Chapter Sixteen : Data-driven photovoltaic and wind power forecasting for distribution grids
• 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
• Index
• A