Description
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
Section One: Harness the Big data from Power Systems 1. A Holistic Approach to Becoming a Data-driven Utility 2. Security and Data Privacy Challenges for Data-driven Utilities 3. The Role of Big Data and Analytics in Utilities Innovation 4. Big Data integration for the digitalisation and decarbonisation of distribution grids Section Two: Put the Power of Big data into Power Systems 5. Topology Detection in Distribution Networks with Machine Learning 6. Grid Topology Identification via Distributed Statistical Hypothesis Testing 7. Learning Stable Volt/Var Controllers in Distribution Grids 8. Grid-edge Optimization and Control with Machine Learning 9. Fault Detection in Distribution Grid with Spatial-Temporal Recurrent Graph Neural Networks 10. Distribution Networks Events Analytics using Physics-Informed Graph Neural Networks 11. Transient Stability Predictions in Power Systems using Transfer Learning 12. Misconfiguration Detection of Inverter-based Units in Power Distribution Grids using Machine Learning 13. Virtual Inertia Provision from Distribution Power Systems using Machine Learning 14. Electricity Demand Flexibility Estimation in Warehouses using Machine Learning 15. Big Data Applications in Electric Power Systems: The Role of Explainable Artificial Intelligence (XAI) in Smart Grids 16. Photovoltaic and Wind Power Forecasting Using Data-Driven Techniques: an overview and a distribution-level case study 17. Grid resilience against wildfire with Machine Learning
