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Full 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. 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.
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.
Readers will develop new strategies and techniques for leveraging data towards real-world outcomes.
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
