data for "Dynamic risk assessment of wildfire-induced transmission line breakdown based on data assimilation method"

Wildfires represent an escalating threat to critical infrastructure, particularly transmission lines, leading to severe power outages and significant economic repercussions. This study addresses the urgent need for effective risk assessment methods in the face of rapidly evolving wildfire dynamics. By leveraging data assimilation techniques, a novel dynamic risk assessment framework is proposed, utilizing a real-world wildfire case study. Observational data is seamlessly integrated into traditional wildfire propagation simulations through an ensemble transform Kalman filter, enhancing predictive accuracy of fire line positions and their associated uncertainties. The use of multi-parameter updates further refines the data assimilation process, avoiding the limitations of only updating the position of the fire line or updating a single parameter of a certain type. Additionally, a Monte Carlo simulation-based approach is developed to dynamically calculate the probability of wildfire arrival, coupled with a robust quantitative method for assessing the likelihood of transmission line failures under extreme fire scenarios. The fire line intensity, determined under the worst-case scenario principle, serves as the input for the quantitative assessment framework. By synthesizing wildfire arrival and transmission line failure probabilities, this research offers a comprehensive real-time risk assessment tool, thereby providing a fresh perspective on managing the interface between wildfires and critical infrastructure.