Prevention of wildfires from circuit device faults and failures using advanced waveform analytics the project will develop and validate unique machine learning (ML) technologies using real-life data…

Texas A&M Engineering Experiment Station received a $3.14 million Cooperative Agreement from the Department of Energy's Office of Cybersecurity, Energy Security, and Emergency Response (CESER) under the Cybersecurity, Energy Security & Emergency Response program (CFDA 81.008), awarded October 1, 2025, with completion targeted for September 30, 2028. The project will develop and validate machine learning technologies designed to prevent wildfires caused by circuit device faults and failures in electrical utility infrastructure. The solution will leverage real-world operational data collected from more than 600 utility feeders over two decades to train advanced waveform analytics systems capable of detecting, locating, and identifying failing equipment before catastrophic failure and fire ignition occurs.

The research and development effort addresses a critical national priority within CESER's Risk Management Tools and Technologies division by creating next-generation detection and monitoring capabilities for energy infrastructure protection against natural hazards. By maintaining feeders in optimal operational health through early intervention, the project directly supports the Department of Energy's mission to enhance the security and resilience of U.S. critical energy infrastructure. Work will be performed at the awardee's primary location in College Station, Texas.

Prevention of wildfires from circuit device faults and failures using advanced waveform analytics the project will develop and validate unique machine learning (ML) technologies using real-life data from more than 600 utility feeders, during routine operations (i.e., not staged or simulated), collected over a period of two decades. Risk of wildfire ignition is much reduced if a feeder is maintained in a healthy state. The overarching project goal: detect, find, and fix failing equipment before catastrophic failure and fire ignition.