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

Abstract:

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.

• Periodical: Advanced Materials Research (Volumes 219-220)
• Pages: 375-378
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.219-220.375
• Online since: March 2011
• Authors: Xin Chen Guo, Qiong Song, Fu Wei Zhang
• Keywords: Fuzzy C-Means (FCM), Particle Swarm Optimization Algorithm (PSO), Transformer Fault Diagnosis

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