Fault diagnosis of power transformer based on support vector machine with genetic algorithm

Authors:
Sheng-wei Fei;Xiao-bin Zhang
Affiliations:
School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China;Guangxi Special Equipment Supervision and Inspection Institute, Nanning 530022, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 6
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Abstract

Diagnosis of potential faults concealed inside power transformers is the key of ensuring stable electrical power supply to consumers. Support vector machine (SVM) is a new machine learning method based on the statistical learning theory, which is a powerful tool for solving the problem with small sampling, nonlinearity and high dimension. The selection of SVM parameters has an important influence on the classification accuracy of SVM. However, it is very difficult to select appropriate SVM parameters. In this study, support vector machine with genetic algorithm (SVMG) is applied to fault diagnosis of a power transformer, in which genetic algorithm (GA) is used to select appropriate free parameters of SVM. The experimental data from several electric power companies in China are used to illustrate the performance of the proposed SVMG model. The experimental results indicate that the SVMG method can achieve higher diagnostic accuracy than IEC three ratios, normal SVM classifier and artificial neural network.