Fault diagnosis of transformer based on fuzzy clustering and the optimized wavelet neural network

• Published in: Systems science & control engineering Vol. 6; no. 3; pp. 359 - 363
• Main Authors: Teng, Wenhui, Fan, Shuxian, Gong, Zheng, Jiang, Wen, Gong, Maofa
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 21.09.2018; Taylor & Francis Group
• Subjects: fault diagnosis; flower pollination algorithm; fuzzy clustering; Transformers; wavelet neural network
• ISSN: 2164-2583; 2164-2583
• DOI: 10.1080/21642583.2018.1564891

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.