Analysis of risk factors of power grid faults based on association rule mining technology
Extreme weather and meteorological disasters,such as heavy rain,typhoon,high temperature,and lightning,may bring great challenges to the safe and stable operation of power system.Therefore,exploring the correlation between power grid failure risk and relative factors is an effective means to enhance the level of power safety of power grid.In this study,the technical process of power grid fault risk factor association analysis is constructed based on fuzzy clustering and frequent item set association rule mining algorithms.Taking Guangzhou City as an example,the power grid fault maintenance order data,meteorological data and typhoon disaster data of Guangzhou City are adopted to explore the association rules between power grid faults risk and features of relative factors on the basis of meteorological and disaster dataset generalization.Results show that there are significant association rules between the low-voltage power grid faults and factors such as high temperature,heavy rain,strong wind and typhoon disasters occurred in Guangzhou City.With these findings,power grid safety measures and suggestions can be specifically proposed.
power grid faultmeteorological factorsassociation rule miningfrequent itemset algorithm
NETL
NSTL
万方数据
