Enhance data quality, event detection, and situational awareness with machine learning algorithms using synchrophasor and advanced metering infrastructure data

The Regents of the University of California at Riverside received a $1.47 million Cooperative Agreement from the Department of Energy's Office of Electricity Delivery and Energy Reliability under the Electricity Research, Development and Analysis program (CFDA 81.122) for the period October 1, 2025 through September 30, 2028. The project delivers advanced machine learning-based analytics solutions designed to enhance power system reliability and resilience through the processing of synchrophasor and Advanced Metering Infrastructure (AMI) data. Primary deliverables include enhanced sensor data quality protocols, improved event detection and classification methodologies, and augmented distribution system operator situational awareness capabilities at the grid edge.

The research initiative focuses on streaming sensor data analytics to address critical grid modernization objectives supported by the Electricity Research, Development and Analysis program. By leveraging machine learning algorithms applied to synchrophasor and AMI datasets, the project aims to improve the operational intelligence available to distribution system operators, thereby supporting broader Department of Energy priorities in grid strengthening and modernization. The work will be performed at UC Riverside's facility in Riverside, California, and represents the university's continued engagement in federally-funded energy research aligned with national grid resilience priorities.

The primary goal of this project is to enhance power system reliability and resilience through cutting-edge machine learning-based streaming sensor data analytics. Specifically, the recipient aims to enhance sensor data quality, improve event detection, labeling, and classification, and boost distribution system operator situational awareness at the grid edge.