Fault Diagnosis Method of Transmission and Transformation Equipment Based on Big Data Mining Technology

被引：0
作者：
Hu J. [1 ]
Yin L. [2 ]
Li Z. [1 ]
Guo L. [2 ]
Duan L. [1 ]
Zhang Y. [2 ]
机构：
[1] State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments, Department of Electrical Engineering, Tsinghua University, Beijing
[2] Electric Power Research Institute, Guangxi Power Grid Company, Nanning
来源：
Gaodianya Jishu/High Voltage Engineering | 2017年 / 43卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Apriori association; Big data analysis; fault diagnosis; k-means clustering algorithm; Silhouette coefficient; Tanimoto coefficient;
D O I：
10.13336/j.1003-6520.hve.20171031026
中图分类号：
学科分类号：
摘要：
The traditional faulty diagnosis method of power transmission and transformation equipment has the disadvantages of being susceptible to experts' subjectivity and model's ossification. In this paper, a new method of equipment fault diagnosis based on big data mining was proposed. Key technologies of this method were introduced, including clustering algorithm of fault patterns, analysis of relevance among status parameters and fault diagnosis based on correlation matrix. The fault cases of an operation oil immersed transformer bushing in recent 10 years were used as big data mining object. The k-means clustering algorithm together with silhouette coefficient could be used to classify fault pattern. Combination of Apriori association algorithm and Tanimoto coefficient could characterize the strength of the relationship between statuses. Fault diagnosis matrix built by Pearson correlation coefficient could precisely evaluate the fault patterns, which was consistent with actual maintenance results. The results of this study show that the inherent law of the recorded data could be obtained based on big data mining, and an adaptive and more accurate device fault diagnosis could be achieved. © 2017, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：3690 / 3697
页数：7
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