Selection Method of Feature Derived from Dissolved Gas in Oil for Transformers Fault Diagnosis

被引：0
作者：
Bai X. [1 ]
Zang Y. [1 ]
Ge L. [2 ]
Li C. [1 ]
Li J. [3 ]
Yuan X. [1 ]
机构：
[1] College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao
[2] Key Laboratory of Smart Grid, Ministry of Education, Tianjin University, Tianjin
[3] College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao
来源：
Gaodianya Jishu/High Voltage Engineering | 2023年 / 49卷 / 09期
基金：
中国国家自然科学基金;
关键词：
cloud transformation; fault diagnosis; feature selection; gas dissolved in oil; transformer;
D O I：
10.13336/j.1003-6520.hve.20220670
中图分类号：
TM [电工技术];
学科分类号：
0808 ;
摘要：
The fault features based on dissolved gas analysis (DGA) is the important prior information for transformer fault diagnosis, and the quality of features directly affect the diagnostic effect. At present, there are many fault features based on DGA gas, whereas, the types are relatively single and the diagnostic effect is limited. To realize transformer fault diagnosis with higher accuracy, the cloud feature method is proposed to enrich the existing ratio feature set. To adapt the cloud transformation of high-dimensional cloud feature, the neural network of self-organized cloud concept extraction (SOCCE) is proposed to extract cloud concepts, so as to deeply mine the associated information between multi DGA gases and improve the diagnostic capability of intelligent algorithm. Finally, the optimal DGA hybrid feature set is selected through the feature optimization strategy in which ranking is followed by optimizing. Through the comparative diagnosis based on the IEC TC10 database, it can be seen that the optimal hybrid new features can achieve the fault diagnosis accuracy of 92.4%, which is an improvement of 13.2%~30.8% compared with the diagnosis accuracy of the traditional features. In addition, the new features show the strong ability in field application and multi diagnostic models. © 2023 Science Press. All rights reserved.
引用
收藏
页码：3864 / 3875
页数：11
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