Meteorological fault risk assessment of power grid based on improved association rules algorithm
Yuanchao Li, Zongjie Xia, Mingming Meng, Tao Zhang · Procedia Computer Science · 2025
In order to improve the evaluation effect of meteorological fault risk of power grid, a new method is proposed to deal with the fault risk of power grid under extreme weather conditions. This method combines the advantages of traditional association rules algorithm and makes specific improvements to identify the potential association between power grid faults and meteorological conditions more accurately. Through the comprehensive analysis of historical meteorological data and power grid fault records, the key factors affecting the stable operation of power grid are dug out, and the corresponding risk assessment model is constructed. Experiments show that the risk assessment model based on improved association rules algorithm can accurately assess the fault risk of power grid under different meteorological conditions, and the improved algorithm is obviously better than the traditional Apriori algorithm in terms of running time, which can improve the accuracy and intelligence level of power grid meteorological fault risk assessment.