Grid resilience against wildfire with machine learning

Decision-making by human operators, using system data obtained from bulk transmission systems, under adverse dynamic events should be supplemented by intelligent proactive control based on state-of-the-art machine learning (ML) algorithms. This chapter focuses on the integration of ML into transmission system operation during wildfires for resiliency-driven proactive control for load shedding, line switching, and resource allocation, considering the dynamics of the wildfire and failure propagation through the power grid to minimize impact on the system.