Published: Aug 17, 2022 by Daning Huang
Together with Dr. Yan Li from Electrical Engineering and Dr. John Harlim from Mathematics, we obtained an award from the AMPS program. The APUS lab will contribute by bridging the mathematical theory, domain knowledge, and deep learning implementation.
This project aims to devise compositional data-driven modeling, prediction, and control methods to ensure the transient stability of the distributed and reconfigurable renewable-energy-dominant power systems, which are inherently nonlinear, high dimensional, partially observed, and subject to heterogeneous uncertainties. This project will illuminate the machine learning advances for developing scalable and cohesive approaches to solve the fundamental challenge of in system’s operation. Specifically, the principal investigators (PIs) will (1) develop a noise-resilient compositional bilinear operator theoretic method to identify a control-amenable model for the transient dynamics of reconfigurable renewable energy systems; (2) devise a stochastic dynamics model for the partially-observed system by integrating a rigorous statistical closure formulation and a physics-informed topology-aware data-driven model; and (3) integrate the developed models with the optimal control algorithms to improve the transient stability of the distributed and reconfigurable system in a predictive manner towards a real-time autonomous operation capability. The PIs anticipate that these outcomes will substantially enrich and expand the current research on dynamic modeling and control of large-scale interconnected systems and support the development of these techniques for applications of the next-generation distribution grids.