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

Abstract

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.