Enze Zhou

This study proposes a predictive model for assessing the spatiotemporal risk of wildfire occurrence in transmission corridors, with an emphasis on the role of meteorological factors in short-term wildfire dynamics. A comprehensive set of 17 factors across four categories is considered. Following factor selection via the Random Forest (RF) algorithm, the predictive model is constructed using the key subset of wildfire factors. The Auto Regressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) algorithms are employed to predict dynamic meteorological factor data, while the Dynamic Bayesian Network (DBN) is used to explore the interrelationships among wildfire factors across different time periods. The result shows that the DBN-based wildfire risk assessment model at a 3-day time scale achieves a high accuracy of 86.39%; when utilizing meteorological data predicted by ARIMA-GARCH, the wildfire risk prediction model still reaches an accuracy of 79.64%. Additionally, wildfire risk distribution maps for typical high-risk periods in Guangdong Province are generated using the model, revealing that 80.00%, 100.00%, 70.00%, and 72.72% of actual fire points, respectively, fall within high-risk and very high-risk areas, demonstrating the model's ability to provide accurate short-term predictions. This model offers significant value for decision-making in wildfire management, particularly for policymakers, grid operators, and fire management teams, enhancing the efficiency of risk mitigation efforts in critical transmission corridors.

With the intensification of global climate change, the frequency of wildfires has markedly increased, presenting an urgent challenge in assessing tripping failures for power systems. This paper proposes an innovative method to evaluate the spatial wildfire-induced tripping risk of transmission lines based on a flame combustion model. Firstly, Bayes theory is employed to assess the spatial probability of wildfire occurrence. Subsequently, Wang Zhengfei’s flame combustion model is utilized to estimate the potential flame height of wildfires along the transmission corridor. Thirdly, the insulation breakdown risk of the transmission line is calculated based on the relative height difference between the flame and the transmission line. Finally, the spatial wildfire-induced tripping risk of the transmission line is then determined by combining the wildfire occurrence probability and the insulation breakdown risk. A case study conducted in Guizhou province, China validates the accuracy of the proposed model. Utilizing ArcGIS, the wildfire occurrence probability distribution in Guizhou is visualized to enhance the efficiency of operation and maintenance. The results indicate that over 80% of wildfire incidents occurred in areas with occurrence probabilities exceeding 50%.

Considering the complexity of the physical model of wildfire occurrence, this paper develops a method to evaluate the wildfire risk of transmission-line corridors based on Naïve Bayes Network (NBN). First, the data of 14 wildfire-related factors including anthropogenic, physiographic, and meteorologic factors, were collected and analyzed. Then, the relief algorithm is used to rank the importance of factors according to their impacts on wildfire occurrence. After eliminating the least important factors in turn, an optimal wildfire risk assessment model for transmission-line corridors was constructed based on the NBN. Finally, this model was carried out and visualized in Guangxi province in southern China. Then a cost function was proposed to further verify the applicability of the wildfire risk distribution map. The fire events monitored by satellites during the first season in 2020 shows that 81.8% of fires fall in high- and very-high-risk regions.