Wednesday, November 7, 2018 - 3:00pm to 4:00pm
Event Calendar Category
LIDS & Stats Tea
Texas A&M University
Building and Room Number
This talk concerns about developing physically interpretable algorithms for large dynamical systems such as the power grid. The problem of source localization is formulated as one of identifying the sparse component in robust principal component analysis. The physical interpretation of the effectiveness of the proposed robust PCA algorithm is provided by examining the underlying low rank property of the resonance component. Real-world examples are tested in the context of power systems with high renewable energy penetration.
Tong Huang received the B.E. degree in Electric Power Engineering and its Automation from North China Electric Power University, Baoding, China, in 2013 and the M.S. degree in Electrical Engineering from Texas A&M University, College Station, TX, USA, in 2017, where he is currently working toward the Ph.D. degree. His advisor is Prof. Le Xie. He worked as an intern researcher at ISO New England from January to May of 2018. He is a visiting Ph.D. student in the Laboratory for Information and Decision Systems at Massachusetts Institute of Technology since September 2018. His research focuses on data analytics in power systems.