The product from this project will be a prototypical situational awareness tool. This tool will provide informative data visualizations and identify anomalous grid behavior (see Grid Mod MYPP Tasks 3.4.1 and 3.4.2). It will be interactive, allowing the user the opportunity to explore any of the data. Our research will look for precursors to unusual grid behavior and, as this research shows promise, machine-learning algorithms will be applied to help better understand what happens before, during, and possibly even after unusual grid behaviors.
Interactions with industry partners will help fine-tune these algorithms. LBNL will help facilitate these interactions, as well as provide additional data related insight. The University of Illinois will provide tool development support, as well as look for other related avenues to pursue the application of situational awareness tools. This project will advance the state-of-the-art and provide insights that can benefit the industry.