Big Data Application in Power Systems

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 Xi’an Jiaotong University, Xi’an, China, in 2009, the Diplome d’Ingénieur 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 Zhou’s research interests include statistical learning theory and paradigms for modern information-rich, large-scale, and human-involved systems.

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