Design of Power Transformer Fault Diagnosis Model Based on Support Vector Machine

Author

Liu, Tao ; Wang, Zhijie

Author_Institution

Dept. of Electron. & Inf. Eng., Suzhou Vocational Univ., Suzhou, China

fYear

2009

fDate

15-16 May 2009

Firstpage

137

Lastpage

140

Abstract

Support vector machines (SVM) is a machine-learning algorithm based on statistical learning theory. The method for power transformer fault diagnosis based on SVM is proposed in this paper. The principle and algorithm of this method are introduced. Through a finite learning sample the relation is established between the transformer fault signature and the quantity of its dissolved gas. A faults classifier is constructed by using the dissolved gas data of the fault transformer. The testing results show that this method can successfully be applied to the diagnosis of gear faults.

Keywords

fault diagnosis; power engineering computing; power transformers; statistical analysis; support vector machines; faults classifier; gear fault; machine-learning algorithm; power transformer fault diagnosis model; statistical learning theory; support vector machine; transformer fault signature; Fault diagnosis; Function approximation; Machine learning; Neural networks; Oil insulation; Power transformers; Roads; Statistical learning; Support vector machine classification; Support vector machines; fault diagnosis; model; power transformer; support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Ubiquitous Computing and Education, 2009 International Symposium on

Conference_Location

Chengdu

Print_ISBN

978-0-7695-3619-4

Type

conf

DOI

10.1109/IUCE.2009.59

Filename

5223286