Application of Machine Learning in Power Systems đź—“

— (IEEE SmartGrid) – overview, use of machine learning, deep reinforcement learning, power system emergency control, leverage …

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Webinar Date: Thursday, October 25, 2018
Time: 10:00 AM (PT)
Speaker: Qiuhua Huang, Pacific Northwest National Laboratory
Location: on the Web
Cost: none
RSVP: required
Event Details & Registration:
Summary: The webinar will begin with a short tutorial of machine learning and provide an overview of application of machine learning in power generation, transmission and distribution systems, including the history and the state of the art. Two projects at Pacific Northwest National Laboratory will be presented. The first project is power system emergency control using deep reinforcement learning, which powered AlphaGo to beat human Go champions. The second project is adaptive Remedial Action Scheme (RAS) settings using machine learning. The ultimate goal of these two projects is to leverage state-of-the-art machine learning technologies to make decision-makings in power system control centers—the “brain” of the grid— adaptive, robust and smart. Lastly, future work and research directions will be discussed.
Bio: Qiuhua Huang received his B.Eng. and M.S. degree in electrical engineering from South China University of Technology, Guangzhou, China, in 2009 and 2012, respectively, and his Ph.D. degree in electrical engineering from Arizona State University, Tempe, AZ, in 2016. Qiuhua Huang is currently a power system research engineer in the Electricity Infrastructure group, Pacific Northwest National Laboratory, Richland, WA, USA. His research interests include power system modeling, simulation and control, transactive energy, and application of advanced computing and machine learning technologies in power systems. Currently, he is the principal investigator/project manager of several DOE funded projects. He is co-chair of the “Deep Learning and Smart Grid Applications” panel session at PES GM 2018. He is an Associated Editor of CSEE Journal of Power and Energy Systems.