Title: Spatio-Temporal Learning for Enhanced Situational Awareness of the Power Grid
Date: November 18, 2020
Bio: Hao Zhu is currently an Assistant Professor of Electrical and Computer Engineering (ECE) at The University
of Texas at Austin. She received the B.S. degree from Tsinghua University in 2006, and the M.Sc. and Ph.D. degrees
from the University of Minnesota in 2009 and 2012, all in electrical engineering. From 2012 to 2017, she was a
Postdoctoral Research Associate and then an Assistant Professor of ECE at the University of Illinois at Urbana-
Champaign. Her research focus is on developing innovative algorithmic solutions for problems related to learning
and optimization for future energy systems. Her current interests include physics-aware and risk-aware machine
learning for power systems, and the design of energy management system that accounts for the cyber-physical
coupling. She is a recipient of the NSF CAREER Award and the Siebel Energy Institute Seed Grant Award. She is
also the faculty advisor for three Best Student Papers awarded at the North American Power Symposium. She is
currently a member of the IEEE Power & Energy Society (PES) Long Range Planning (LRP) Committee.
Abstract:The electricity delivery systems are witnessing pressing needs to transform the operational paradigms to
embrace new grid architecture and energy resources. At the bulk interconnection scale, the decreasing level of inertia
makes it increasingly important to analyze the grid’s dynamic responses. At the local distribution level, the integration
of highly variable distributed energy resources (DERs) further reduces the level of observability of residential loads. To
address these challenges, this talk will present spatio-temporal learning approaches that harness the high-rate, high-
accuracy synchrophasor data available for monitoring both transmission and distribution grids. First, a statistical analysis
approach is developed based on cross-correlation to study the propagation of oscillations in wide-area interconnections.
Second, a subspace learning approach is introduced to reveal the characteristics of DERs such as rooftop solar and
electrical vehicles connected to distribution feeder. These learning approaches can enhance the situational awareness of
the physical assets for improving the security and resilience of power grid infrastructure.