Join the Grid Analytics Community on Wednesday, September 20, 2023 at 1:00 PM CT for a presentation with Q&A and open discussion on, "Utility Data Analytics via Machine Learning," led by Yang Weng, Assistant Professor, School of Electrical, Computer and Energy Engineering at Arizona State University.
Session Description:
Deep penetration of distributed energy resources (DERs) calls for improved monitoring of power systems against reliability and security issues. For example, the low observability in some distribution grids makes monitoring DERs hard due to limited investment and vast coverage of distribution grids. Past methods proposed machine learning models with limited explainability. However, critical energy infrastructure needs assurance. For such a need, this talk shows how to design assured machine learning for power system monitoring via a twin structure of two learning agents, namely the AI-based and physics-guided models. The twins will collaborate adaptively to minimize the learning error while maximizing physical consistency. The structure ensures good generalization properties of the learned models. We then illustrate how twin models can be used to design cyber-attacks that can bypass the chi-squared test on bad data without the system information and discuss how to monitor and mitigate such attacks. Finally, we will demonstrate how the proposed methods are validated using our utility-connected hardware-in-the-loop microgrid.
Members of the Grid Analytics Community will receive a Microsoft Teams link prior to this meeting. Not a member of this community? UAI Utility Members can register for any of our communities by using our Request To Join link.