A methodology for transformer fault diagnosis based on the feature extraction from DGA data

被引:1
作者
Rao, Shaowei [1 ]
Yang, Shiyou [1 ]
Zou, Guoping [2 ]
机构
[1] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China
[2] China Jiliang Univ, Coll Mech & Elect Engn, Hangzhou, Peoples R China
关键词
Case-based reasoning; combinatorial optimization; fault diagnosis; feature extraction; transformer;
D O I
10.3233/JAE-220169
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A methodology to diagnose transformer faults based on dissolved gas analysis (DGA) is proposed. Since a fault is more sensitive to the ratios of gas contents, a general ratio feature extraction framework is proposed to generate a feature subset as the input of the diagnosis model. The feature subset is evaluated by the improved case-based reasoning (CBR) and optimized by using a proposed new algorithm called the k-optimal algorithm (k-OA). The comparison results between the k-OA and the genetic algorithm (GA) show that the k-OA is more efficient in solving such a combinatorial optimization problem. The obtained optimal feature subset is used to diagnose a public transformer fault dataset, and a 92.6% diagnosis accuracy is observed as compared to that of only 85.1% diagnosis accuracy by using the original features.
引用
收藏
页码:S313 / S320
页数:8
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