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Title: Event Identification on Power Systems via Graph Neural Networks and Phasor Measurement Unit
Abstract: With the increasing penetration of distributed energy resources, power systems are gradually transforming from traditional grids to smart grids. This calls for developing advanced monitoring systems to ensure a reliable and resilient electricity supply. Phasor measurement units (PMUs) are being widely installed on power systems, providing a unique opportunity to enhance wide-area situational awareness in grid transition. One essential application is the use of PMU data for real-time event identification. However, the high granularity and non-stationary nature of PMU data and imperfect data quality could bring great technical challenges for real-time system event identification. Besides, how to take full advantage of all PMU data in event identification is still an open problem. In this talk, I will present two novel data-driven approaches by leveraging robust Graph Neural Networks (GNNs) and Phasor Measurement Units (PMU) to learn the dynamic interactions of electrical grid components in order to improve the power system resilience. I will also cover other aspects such as the end-to-end mapping relationship between Markov Transition Filed (MTF)-based graphs and the event types.
Bio: Yanchao Wang is a Ph.D. candidate in the Department of Electrical and Computer Engineering at Iowa State University supervised by Dr. Matthew Darr and Dr. Zhaoyu Wang. He received the B.S. degree in optical information and technology from the Beijing Institute of Technology, in 2014, and start the Ph.D. degree in electrical and computer engineering from Iowa State University, in 2018. His research interests include PMU event identification in power systems, Deep Learning, Graph Neural Networks (GNNs), and data analytics.
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