Description
Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids.- Provides expert analysis of the latest developments by global authorities- Contains detailed references for further reading and extended research- Provides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformatics- Focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data
Table of Contents
SECTION 1 Harness the Big Data From Power Systems1. A Holistic Approach to Becoming a Data-Driven Utility2. Emerging Security and Data Privacy Challenges for Utilities: Case Studies and Solutions3. The Role of Big Data and Analytics in Utility Innovation4. Frameworks for Big Data Integration, Warehousing, and Analytics SECTION 2 Harness the Power of Big data5. Moving Toward Agile Machine Learning for Data Analytics in Power Systems6. Unsupervised Learning Methods for Power System Data Analysis7. Deep Learning for Power System Data Analysis8. Compressive Sensing for Power System Data Analysis 9. Time-Series Classification Methods: Review and Applications to Power Systems DataSECTION 3 Put the Power of Big Data into Power Systems10. Future Trends for Big Data Application in Power Systems11. On Data-Driven Approaches for Demand Response12. Topology Learning in Radial Distribution Grids13. Grid Topology Identification via Distributed Statistical Hypothesis Testing14. Supervised Learning-Based Fault Location in Power Grids15. Data-Driven Voltage Unbalance Analysis in Power Distribution Networks16. Predictive Analytics for Comprehensive Energy Systems State Estimation17. Data Analytics for Energy Disaggregation: Methods and Applications18. Energy Disaggregation and the Utility-Privacy Tradeoff
