Fault diagnosis method for power transformer based on ant colony -SVM classifier

Author

Wu, Niu ; Liangfa, Xu ; Sanguo, Hu

Author_Institution

Dept. of Found., First Aeronaut. Inst. of Air Force, Xinyang, China

Volume

1

fYear

2010

fDate

26-28 Feb. 2010

Firstpage

629

Lastpage

631

Abstract

Failure of power transformer is very complex, so that it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on ant colony-support vector machine (AC-SVM) approach is presented for fault diagnosis of power transformer. The AC-SVM selects kernel function parameter and soft margin constant C penalty parameter of support vector machine (SVM) classifier. The performance of the AC-SVM system proposed in this study is evaluated by cases in China. The test results show that this AC-SVM model is effective to detect failure of power transformer.

Keywords

cooperative systems; fault diagnosis; fault location; optimisation; pattern classification; power engineering computing; power transformers; support vector machines; ant colony SVM classifier; ant colony-support vector machine classifier; failure detection; fault diagnosis method; intelligent diagnostic method; power transformer; Artificial neural networks; Fault diagnosis; Kernel; Machine intelligence; Mathematical model; Power system modeling; Power transformers; Risk management; Support vector machine classification; Support vector machines; ant colony-support vector machine; fault diagnosis; kernel function parameter; transformer;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-5585-0

Electronic_ISBN

978-1-4244-5586-7

Type

conf

DOI

10.1109/ICCAE.2010.5451326

Filename

5451326