Nanpeng Yu, an associate professor of electrical and computer engineering at UC Riverside and Eamonn Keogh, a professor of computer science, have received a $1 million grant from Department of Energy (DOE) to develop machine learning algorithms to extract more value from the vast amounts of sensor data gathered to monitor the health of the grid and support system operations.

The goal of this project is to develop scalable, multidimensional, and robust big data and machine learning algorithms for Phasor Measurement Unit (PMU) data to identify anomalous events, create a catalog of event signatures, predict asset health, and learn precursors to instability phenomenon. The algorithms will be crucial in enhancing the wide area monitoring, visualization, protection, and control applications. The project is expected to help system operators avoid grid outages, improve operations and reduce costs.