Advanced Machine Learning for Synchrophasor Technology

Develop a suite of new Grid-Modeling aware Machine Learning (ML) tools to monitor the transmission grid during its normal operations (task 1) and also localize significant frequency events in seconds after they occur (task 2). They will utilizes (a) advanced optimization and computation methods and algorithms for ML and data analytics; (b) the state-of-the-art, industry-grade frequency monitoring software; (c)  phasor measurement unit (PMU) measurements at the transmission level; (d) aggregated micro-synchrophasors (uPMU) measurements at the distribution level; and (e) modern map-visualization tools and approaches. They will build new ML software to provide situational awareness, computational, and map-visualization extensions of the PNNL & BPA Power Plant Model Validation (PPMV) software.