Transformer Fault Diagnosis Method Based on Association Characteristics of Characteristic Gases

被引：0
作者：
Liang Y. [1 ]
Guo H. [2 ]
Xue Y. [1 ]
机构：
[1] College of Information and Control Engineering, China University of Petroleum, Qingdao
[2] Beijing Key Laboratory of High Voltage and EMC, North China Electric Power University, Beijing
来源：
Gaodianya Jishu/High Voltage Engineering | 2019年 / 45卷 / 02期
关键词：
DGA; Fault diagnosis; Fuzzy inference system; Maximal information coefficient; Newton interpolation method; ROC curve; Transformer;
D O I：
10.13336/j.1003-6520.hve.20181205029
中图分类号：
学科分类号：
摘要：
Existing transformer fault diagnosis methods based on dissolved gases analysis (DGA) fail to make full use of association relationship between characteristic gases in different kinds of faults. We proposed a transformer fault diagnosis method based on association characteristics between different gases. The maximal information coefficient (MIC) method was used for the quantitative characterization of the association relationship between characteristic gases, the receiver operating characteristic (ROC) curve was used to extract the association characteristics between characteristic gases and their distribution ranges, and the fuzzy inference system was established. Furthermore, in order to avoid long data-acquisition cycle in actual use, the Newton interpolation method was used to expand the size of data samples for diagnosis, which can help improve the effectiveness of characteristics. The accuracy of the method proposed on the time series data for test is 100%, which is much higher than that of the three-ratio method and David triangle method. The results illustrate that the proposed method makes full use of association relationship between characteristic gases, and can improve the performance of DGA based methods. Thus, a new kind of characteristics can be provided for transformer fault diagnosis method based on DGA. © 2019, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：386 / 392
页数：6
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