Abstract
ABSTRACTIn 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.
Highlights
The power transformer is the core equipment in power transmission and distribution, and the safe and reliable operation of power transformer has an important impact on the power grid and national economy
There were fault diagnosis methods based on grey theory (Li, Sun, Chen, Zhou, & Du, 2003), Bayesian classifiers (Wang, Zhang, Jin, & Guo, 2018) and the expert system (Shi, Shi, Mu, Li, & Liu, 2014), but most of them had the disadvantages of over fitting, long training time and low diagnostic accuracy
An improved fault diagnosis method is proposed based on fuzzy clustering and the flower pollination algorithm
Summary
The power transformer is the core equipment in power transmission and distribution, and the safe and reliable operation of power transformer has an important impact on the power grid and national economy. Dissolved gas analysis (DGA) technology is one of the most convenient and effective methods for fault diagnosis of oil-immersed transformers It can diagnose the latent fault which may cause serious damage accurately and reliably. Ma (2008) proposed a method based on the genetic algorithm and WNN, but the method had the disadvantages of a relatively complicated network structure, long training time and low accuracy. There were fault diagnosis methods based on grey theory (Li, Sun, Chen, Zhou, & Du, 2003), Bayesian classifiers (Wang, Zhang, Jin, & Guo, 2018) and the expert system (Shi, Shi, Mu, Li, & Liu, 2014), but most of them had the disadvantages of over fitting, long training time and low diagnostic accuracy. The theory and simulation results prove that the proposed method has global optimization and it has a high rate of accuracy for transformer fault diagnosis
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