Decision Analysis Method for Operation and Maintenance Management of Power Equipment Based on Data Mining

被引：1
|
作者：
Cai Z. [1 ]
Ma G. [1 ]
Sun Y. [1 ]
Huang Y. [1 ]
机构：
[1] School of Electric Power, South China University of Technology, Guangzhou, 510640, Guangdong
来源：
Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2019年 / 47卷 / 06期
关键词：
Association rule mining; Data mining; Decision-making risk; Operation and maintenance management; Power equipment;
D O I：
10.12141/j.issn.1000-565X.180399
中图分类号：
学科分类号：
摘要：
The operation and maintenance management of the power equipment (PE) mainly includes fault analysis, 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 applied for PE operation and maintenance management. The one-dimensional fault feature was extracted from fault information by K-means clustering algorithm. Then, Apriori algorithm was employed to mine association rules of different 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 features 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. © 2019, Editorial Department, Journal of South China University of Technology. All right reserved.
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页码：57 / 64and71
页数：6414
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