基于关联规则挖掘技术的电网故障风险要素分析

Analysis of risk factors of power grid faults based on association rule mining technology

暴雨、台风、高温、雷电等极端天气与气象灾害可能对电力系统安全稳定运行带来极大挑战。因此,探究电网故障风险与相关要素的关联关系是提升电力安全水平的有效手段。基于模糊聚类和频繁项集关联规则挖掘算法,构建了电网故障风险要素关联分析技术流程。以广州市为例,基于电网故障维修订单数据、气象数据和台风灾害数据,在对气象和灾害数据集进行概化的基础上,探究了电网故障风险及相关要素特征间的关联规则。结果表明:高温、暴雨、大风、台风灾害等要素与电网低压故障存在关联规则,据此可提出有针对性的电网安全保障措施与建议。

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.