EECS Power faculty candidate seminar: Anomaly Management in Massively Digitized Power Systems
About the event
The School of Electrical Engineering and Computer Science presents Anomaly Management in Massively Digitized Power Systems by Dr. Tong Huang, Massachusetts Institute of Technology (MIT)
Overview
The past century has witnessed a digitization trend of electric power grid where increasing digital solutions are being integrated into electric power grids. The digital solutions include advanced sensors (e.g., synchrophasors, and smart meters), inverter-based resources (e.g., renewables, energy storage, and electric vehicles), and grid edge intelligence (e.g., smart thermostats). These digital solutions in the grid not only provide opportunities for enhancing monitoring, control and protection of the power grid, but also pose challenges of ensuring both cyber and physical security of the grid. How to leverage the emerging opportunities and how to address pressing challenges define a key research question as we move towards a low-carbon future. This talk provides two concrete examples that directly answer this question. Specifically, to show how to leverage rich streaming synchrophasor data in bulk power transmission systems, the first example of this talk presents a purely data-driven yet physically interpretable algorithm that can pinpoint the sources of a type of anomalies that may cause large-scale blackouts, i.e., forced oscillations. The second example addresses physical security issues of power electronics-interfaced distribution systems by presenting a learning-based stability assessment framework. This talk is concluded with an interdisciplinary research agenda that aims to overcome bottlenecks of decarbonizing the electricity sector.
Bio
Dr. Tong Huang is a postdoctoral associate in the Laboratory for Information and Decision Systems (LIDS) at Massachusetts Institute of Technology (MIT). Before joining MIT, he was a postdoctoral researcher in the Department of Electrical and Computer Engineering at Texas A&M University where he obtained his Ph.D. degree in 2021. He was a visiting Ph.D. student in LIDS at MIT in 2018. His industry experience includes an internship at ISO-New England in 2018 and an internship at Mitsubishi Electric Research Laboratories in 2019. His research interest focuses on data analytics, cyber security, and the modeling and control of electric energy systems. He received the Best Paper Award at the 2020 IEEE Power & Energy Society General Meeting, the Best Paper Award at the 54-th Hawaii International Conference on System Sciences, Thomas W. Powell ’62 and Powell Industries Inc. Fellowship, and Texas A&M Graduate Teaching Fellowship.