Research on fault diagnosis method for transformer based on fuzzy genetic algorithm and artificial neural network

Abstract

Purpose

The purpose of this paper is to define a new method (grey relational analysis (GRA)) for extracting pattern samples of dissolved gases in power transformer oil, then a hybrid algorithm of the back‐propagation (BP) network and fuzzy genetic algorithm‐artificial neural network (FGA‐ANN) is used to power transformer fault diagnosis based on extracted pattern samples.

Design/methodology/approach

The existing manners (e.g. international electro technical commission triple‐ratio method), in practice, have certain faultiness due to the ambiguity of the inference and insufficient standard for judgment. So GRA method is chosen to solve a problem of optimal pattern samples data, then a hybrid algorithm of the BP network and FGA‐ANN is developed to optimize initial weights and to enable fast convergence of the BP network, and lastly, this algorithm is applied to the classification of dissolved gas analysis (DGA) data and power transformer fault diagnosis.

Findings

If possible, the results should be accompanied by significance. For comparative studies, the proposed scheme does not require the three ratio code and high diagnosis accuracy is obtained. In addition, useful information is provided for future fault trends and multiple faults analysis.

Research limitations/implications

Accessibility and availability of data are the main limitations which model will be applied.

Practical implications

This paper provides useful advice for power transformer fault diagnosis method based on DGA data.

Originality/value

The new method of optimal choice of options of pattern samples due to GRA. The paper is aimed at optimized samples data classified and abandons the traditional ratio method.

Citation

Peng, Z. and Song, B. (2010), "Research on fault diagnosis method for transformer based on fuzzy genetic algorithm and artificial neural network", Kybernetes, Vol. 39 No. 8, pp. 1235-1244. https://doi.org/10.1108/03684921011063510