Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 36, Issue 8, Pages 11352-11357Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2009.03.022
Keywords
Fault diagnosis; Support vector machine; Genetic algorithm; Power transformer
Ask authors/readers for more resources
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, non-linearity 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. (C) 2009 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available