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E-raamat: Big Data Application in Power Systems

Edited by (Director of Connectivity, Information and Intelligence Lab, Professor, Data Science and Machine Learning, Western Norway University of Applied Sciences, Norway, Research Professor, Electrical Computer Department, Florida State University), Edited by
  • Formaat: PDF+DRM
  • Ilmumisaeg: 01-Jul-2024
  • Kirjastus: Elsevier - Health Sciences Division
  • Keel: eng
  • ISBN-13: 9780443219511
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 01-Jul-2024
  • Kirjastus: Elsevier - Health Sciences Division
  • Keel: eng
  • ISBN-13: 9780443219511
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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.

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

Prof. Reza Arghandeh is the Director of Connectivity, Information & Intelligence Lab (Ci2Lab.com) and a Full Professor in Data Science and Machine Learning in the Department of Computer Science, Electrical Engineering, and Mathematical Sciences at the Western Norway University of Applied Sciences (HVL), Bergen, Norway. He is also the HVL Data Science Group (HVL.no/ai). Additionally, he is a Research Professor in the Electrical and Computer Department at Florida State University, USA, where he was an assistant professor from 2015 to 2018. Prior to FSU, he was a postdoctoral scholar at the University of California, Berkeley, EECS Dept 2013-2015. His research interests include data analysis and decision support for smart grids and smart cities. His research has been supported by IBM, the U.S. National Science Foundation, the U.S. Department of Energy, the European Space Agency, the European Commission, and the Research Council of Norway. Yuxun Zhou received his B.S. degree in electrical engineering from Xian Jiaotong University, Xian, China, in 2009, the Diplome dIngénieur degree in applied mathematics from École Centrale Paris, Paris, France, in 2012, and a Ph.D. degree from the Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA, USA, in 2017. He has been an author on over 60 research articles and conference proceedings published in peer-reviewed journals. Dr Zhous research interests include statistical learning theory and paradigms for modern information-rich, large-scale, and human-involved systems.
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