Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection

Author

Cho, Ming-Yuan ; Lee, Tsair-Fwu ; Kau, Shih-Wei ; Shieh, Chin-Shiuh ; Chou, Chao-Ji

Author_Institution

Dept. of Electr. Eng., Nat. Kaohsiung Univ. of Appl. Sci.

Volume

1

fYear

2006

fDate

Aug. 30 2006-Sept. 1 2006

Firstpage

26

Lastpage

30

Abstract

For the purpose of fault diagnosis of power transformers, a novel approach based on artificial neural network (ANN) and multi-layer support vector machine (SVM) is presented in the paper. The proposed approach is distinguished by features and kernel parameters selection using clonal selection algorithms (CSA). It is capable of filtering out irrelevant input features, leading to improve prediction accuracy. As revealed in the experimental results, the proposed approach outperforms previous ones in both classification accuracy and computational efficiency

Keywords

fault diagnosis; neural nets; power engineering computing; power transformers; support vector machines; ANN; SVM; artificial neural network; clonal selection algorithm; fault diagnosis; feature selection; kernel parameter selection; multilayer support vector machine; power transformer; Artificial neural networks; Dissolved gas analysis; Fault detection; Fault diagnosis; Kernel; Oil insulation; Power transformer insulation; Power transformers; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on

Conference_Location

Beijing

Print_ISBN

0-7695-2616-0

Type

conf

DOI

10.1109/ICICIC.2006.75

Filename

1691733