A Data-Driven Automated Mitigation Approach for Resilient Wildfire Response in Power Systems

The escalating impact of wildfires on critical power systems, including suppression and restoration costs, bankruptcy, loss of lives, necessitates a more sustainable and resilience-oriented response approach. Although power utilities have spear-headed several initiatives, the need for a comprehensive risk management approach that can be easily integrable into current power utility methods and operations cannot be overemphasized. This work proposes a self-sufficient low-cost wildfire mitigation model (SL-PWR), a tool that automates wildfire risk reduction by intelligently functioning from the pre-wildfire phase to prevent wildfires, through the wildfire progression phase for very early detection, to system restoration after damages. Hence, the SL-PWR addresses endogenous and exogenous wildfire mitigation and risk reduction in all system resilience phases, de-compartmentalizing wildfire response. The proposed SL-PWR tool advances on spatio-temporal wildfire detection through data-driven optimization and automation to provide accurate quantitative and visual real-time critical wildfire information to infrastructure operators and emergency management teams. This paper, part of a series, presents the design and development of the SL-PWR's functional processes, which further enables optimal monitoring for accuracy and rapidity in response, as well as economic decision making of the utility. Results using publicly sourced data from a synthetic utility service area show the performance of the SL-PWR is accurate, enables rapidity, and improves situational awareness during wildfire threats.