A Hybrid Method Based on PSO and FCM for Transformer Fault Diagnosis Using Dissolved Gas

被引：2
|
作者：
Guo, Xinchen [1 ]
Song, Qiong [1 ]
Zhang, Fuwei [2 ]
机构：
[1] Northeast Dianli Univ, Coll Sci, Jilin 132012, Peoples R China
[2] Changchun Univ Sci & Technol, Changchun 130022, Peoples R China
来源：
ADVANCED RESEARCH ON INFORMATION SCIENCE, AUTOMATION AND MATERIAL SYSTEM, PTS 1-6 | 2011年 / 219-220卷
关键词：
Fuzzy C-means; Particle Swarm Optimization; Transformer Fault Diagnosis;
D O I：
10.4028/www.scientific.net/AMR.219-220.375
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The fault diagnosis for power transformer plays an important role in improving the safety and reliability for an electrical network. Dissolved gas analysis (DGA) is a basic method to diagnose the fault of power transformer. Considering the disadvantages in DGA using fuzzy c-means (FCM) clustering algorithm, a hybrid method based on particle swarm optimization (PSO) to solve the FCM model is presented. In the new algorithm, the PSO's search space is the vector space after straightening the membership matrix in the FCM. With the results of experiments on real DGA data, it shows our approach can improve the clustering performance for the transformer fault diagnosis.
引用
收藏
页码：375 / +
页数：2
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