An Optimization of UAV-Based Remote Monitoring for Improving Wildfire Response in Power Systems

Abstract

Wildfires lead to colossal losses on territory, local, state and federal levels, affecting critical infrastructure, the economy, decarbonization goals, social sustainability and more. Although wildfire impacts highlight the urgent need for resilience-comprehensive methods in power system wildfire response, existing techniques often focus on a single phase, usually wildfire progression. In this work, a comprehensive approach is proposed to provide optimal and real-time information toward mitigating wildfire risk in all resilience phases, necessary to decompartmentalize wildfire response. This paper focuses on the optimal routing of the remote monitoring resources for a self-sufficient low-cost wildfire mitigation model (SL-PWR), which utilizes predicted spatio-temporal wildfire probability maps of the utility service area and optimized unmanned aerial vehicle (UAV) monitoring trees to obtain input images for training the SL-PWR modules. Results show that optimizing the SL-PWR’s UAV monitoring using predicted wildfire threat parameters can improve situational awareness and rapidity of detection during wildfire incidents.

Keywords

• Resilient wildfire response
• wildfire mitigation
• UAV monitoring trees
• resource optimization
• deep learning