电力设备的运维管理主要包括设备的故障分析、主动预警和差异化运维． 在面对 电网运行过程中多时间尺度、多时空维度的海量数据背景下，文中将数据挖掘技术应用到 电力设备的运行管理上． 文中利用 K-means 聚类算法挖掘历史运行数据信息，进行单维状 态量故障特征提取;利用 Apriori 算法挖掘不同故障模式下关联规则，建立关键性能矩阵， 借助高维随机矩阵理论分析设备故障的时空特性;利用 D-S 证据理论对单维与多维诊断 结果进行信息合成，获得设备故障的诊断判据． 同时，综合考虑系统运行状态和电力用户 差异性，建立设备健康度指数以及重要度指数，显著降低设备运维决策风险． 仿真案例证 明了本文方法的有效性．

The operation and maintenance management of the power equipment (PE) mainly includes fault analy- sis，active early-warning and differentiated operation and maintenance． In the context of massive data with multiple time scales and multiple time and space dimensions in the process of grid operation，data mining technology was ap- plied for PE operation and maintenance management． The one-dimensional fault feature was extracted from fault in- formation by K-means clustering algorithm． Then，Apriori algorithm was employed to mine association rules of dif- ferent failure modes and establish key performance matrix． The spatial-temporal characteristics were analyzed based on high-dimensional random matrix theory (ＲMT)． Afterwards，one-dimensional and multi-dimensional fault fea- tures were combined based on D-S evidence theory so that the fault diagnosis criteria of PE was obtained． At the same time，comprehensively considering the PE operating state and the variation for power users，health index and importance index of equipment were established，which could help to significantly reduce the decision-making risk of PE operation and maintenance． The result of simulation proves the effectiveness of the proposed method．