Power Systems Engineering Research Center

T-63 Project Summary 

Harnessing the Power of Artificial Intelligence (AI) for Transmission & Distribution Operations

Summary This project aims to harness the potential of artificial intelligence (AI) and machine learning (ML) technology for several applications in power systems that are either impossible or extremely costly to solve by the state of the art. To this end, we stress the importance of embedding power system physical characteristics in AI/ML solution frameworks. The specific topics that we focus on are (1) machine learning tools for congestion and contingency forecasting, (2) discriminative and generative learning for equipment failure prediction, and (3) deep learning for state estimation and control of systems with limited observability and unmodeled dynamics.
Academic Team Members

Project Leader: Yang Weng (Arizona State University, yang.weng@asu.edu)
Team Members: Lang Tong (Cornell University, ltong@ece.cornell.edu) Anamitra Pal (Arizona State University, anamitra.pal@asu.edu

Industry Team Members Evangelos Farantatos (EPRI), Mahendra Patel (EPRI), Yingchen Zhang (NREL), Santosh Veda (NREL), Mark Westendorf (MISO), Matthew Rhodes (SRP), Di Shi, (GEIRI), Zhe Yu (GEIRI, ), Jianzhong Tong (PJM), Harvey Scribner (SPP), Bajarang Agrawal (APS), George Stefopoulos (NYPA), Atena Darvishi (NYPA), Honggang Wang (GE), Ming Jin (GE), Phil Hart (GE), Zhongyu Wu (MISO), Chetan Mishra (Dominion Energy)
Project Period July 1, 2019 to August 31, 2021