This webinar:
Covers the development of physics-informed machine learning algorithms by synergistically combining power system models and advanced machine learning techniques. The unique power system domain knowledge, information and models that have been integrated into machine learning algorithms include high/low entropy of certain power system sensor data, low-rank property of streaming data matrix, physical model for generation resources, power flow models, and power system dynamic and control models.

Presenter bio:
Dr. Nanpeng Yu received his Ph.D. degree in Electrical Engineering from Iowa State University in 2010. Before joining University of California, Riverside (UCR), Dr. Yu was a senior power system planner and project manager at Southern California Edison from 2011 to 2014. Currently, Dr. Yu is a Full Professor and Vice Chair of Electrical and Computer Engineering at UCR. Dr. Yu is the recipient Department of Energy Digitizing Utility Grand Prize. He received multiple best paper and prize paper awards from the IEEE Power and Energy Society. Dr. Yu is the director of Energy, Economics, and Environment Research Center at UC Riverside. He currently serves as the chair of distribution system operation and planning subcommittee of IEEE Power and Energy Society. Prof. Yu has led over 30 research and development projects as Principal Investigator, securing more than $18 million in funding from federal and state government agencies, non-profit organizations, national laboratories, electric utility companies, and energy service providers. Professor Yu’s research interests include physics-informed machine learning, transportation electrification, decarbonization for critical infrastructure systems, and optimization in smart grids.