A team of researchers from The University of Texas at Arlington is combining several principles of machine learning to enable machines to control power networks and other complex dynamic systems more effectively during unexpected events.
Frank Lewis, the Moncrief-O’Donnell Professor of Electrical Engineering, Yan Wan and Ali Davoudi, associate professors of electrical engineering, are using a $220,000 Early-concept Grant for Exploratory Research, or EAGER, from the National Science Foundation to use real-time learning to create a unified theory on how to optimize microgrid capacity through DC distribution. Junfei Xie, an assistant professor of computer science at Texas A&M University—Corpus Christi, is assisting the team with data analysis.
Microgrid capacity in the United States is strained and DC, or direct current, distribution networks are emerging as alternatives to the current standard AC, or alternating current, networks. The networks are critical to the scalable integration of renewable energy resources and fleets of electric vehicles.
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