Fault diagnosis of transformer based on fuzzy clustering and the optimized wavelet neural network. Issue 3 (21st September 2018)

• Record Type: Journal Article
• Authors: Teng, Wenhui; Fan, Shuxian; Gong, Zheng; Jiang, Wen; Gong, Maofa
• Is Part Of: Systems science & control engineering. Volume 6:Issue 3(2018)
• Page Start: 359
• Page End: 363
• Publication Date: 2018-09-21
• Subjects: Transformers -- fault diagnosis -- wavelet neural network -- fuzzy clustering -- flower pollination algorithm; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2018.1564891
• Languages: English
• ISSNs: 2164-2583

Abstract

ABSTRACT: In order to solve the disadvantages of the traditional wavelet neural network (WNN) algorithm applied in transformer fault diagnosis, such as uneven sample distribution of training samples and high diagnostic error rate and long training time, an improved fault diagnosis method is proposed based on fuzzy clustering and the flower pollination algorithm. Firstly, fuzzy clustering is applied to deal with transformer fault sample data so as to remove the bad data; secondly, the flower pollination algorithm is applied to obtain the optimal parameters of the WNN. The example analysis results show that WNN based on the flower pollination algorithm (FPA-WNN) has better convergence, lower diagnosis error rate and shorter training time compared with WNN based on the particle swarm algorithm (PWA-WNN) and it is more suitable for transformer fault diagnosis.