Susceptibility assessment of wildfire-induced transmission line tripping using a physical-Bayesian modeling approach.

The escalating frequency and intensity of wildfires, driven by extreme climate events and anthropogenic activities, pose severe threats to the security and reliability of electrical power systems. Wildfires in proximity to high-voltage transmission lines can lead to a critical reduction in air insulation strength, caused by elevated temperatures, smoke particulate matter, and highly conductive flames. These conditions often precipitate insulation breakdown, line tripping, and potentially widespread power outages. This paper proposes an integrated physical-Bayesian modeling framework for assessing the susceptibility of transmission lines to wildfire-induced tripping. The framework combines a Bayesian network model, which estimates wildfire ignition probability by incorporating geographic, meteorological, fuel, and anthropogenic variables, with a physics-based model that evaluates the risk of insulation failure under wildfire conditions, accounting for both flame bridging and smoke effects. Key influencing features are identified through the Relief algorithm for feature selection. Applied to the Guangdong Power Grid in China, the model identifies distance to settlements, vegetation type, and monthly rainfall as the dominant factors affecting ignition probability. Spatial analysis reveals strong agreement between high-risk zones and historical fire events, and predicted tripping risks are consistent with actual outage records. The approach supports differentiated wildfire prevention strategies and enhances grid resilience, offering a scientifically robust and practical tool for proactive risk management in power systems exposed to wildfire hazards.