Transformer fault diagnosis based on euclidean clustering and support vector machines

Author

Zhu, Yong-li ; Zheng, Jian-bai ; Wang, Fang

Author_Institution

North China Electr. Power Univ., Baoding

Volume

4

fYear

2007

fDate

2-4 Nov. 2007

Firstpage

1453

Lastpage

1456

Abstract

A new method based on Euclidean clustering and support vector machines is presented and constructed in the paper. According to the Euclidean distances between the transformer´s state sorts, build the multi-classification model of support vector machines. The diagnosing experiments of different transformer testing scenarios show this method can avoid the blindness when building the multi-classifier, and can be used on transformer faults diagnosis commendably.

Keywords

fault diagnosis; power engineering computing; power transformer testing; support vector machines; Euclidean clustering; multi-classifier; support vector machines; transformer fault diagnosis; transformer testing scenarios; Dissolved gas analysis; Fault diagnosis; Notice of Violation; Pattern analysis; Pattern recognition; Power system reliability; Power transformers; Support vector machine classification; Support vector machines; Wavelet analysis; Euclidean clustering; Support Vector Machines; fault diagnosis; power transformer;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-1065-1

Electronic_ISBN

978-1-4244-1066-8

Type

conf

DOI

10.1109/ICWAPR.2007.4421678

Filename

4421678