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 Systems1. A Holistic Approach to Becoming a Data-driven Utility2. Security and Data Privacy Challenges for Data-driven Utilities3. The Role of Big Data and Analytics in Utilities Innovation4. Big Data integration for the digitalisation and decarbonisation of distribution gridsSection Two: Put the Power of Big data into Power Systems5. Topology Detection in Distribution Networks with Machine Learning6. Grid Topology Identification via Distributed Statistical Hypothesis Testing7. Learning Stable Volt/Var Controllers in Distribution Grids8. Grid-edge Optimization and Control with Machine Learning9. Fault Detection in Distribution Grid with Spatial-Temporal Recurrent Graph Neural Networks10. Distribution Networks Events Analytics using Physics-Informed Graph Neural Networks11. Transient Stability Predictions in Power Systems using Transfer Learning12. Misconfiguration Detection of Inverter-based Units in Power Distribution Grids using Machine Learning13. Virtual Inertia Provision from Distribution Power Systems using Machine Learning14. Electricity Demand Flexibility Estimation in Warehouses using Machine Learning15. Big Data Applications in Electric Power Systems: The Role of Explainable Artificial Intelligence (XAI) in Smart Grids16. Photovoltaic and Wind Power Forecasting Using Data-Driven Techniques: an overview and a distribution-level case study17. Grid resilience against wildfire with Machine Learning
