Transformer Fault Diagnosis Based on Fuzzy Support Vector Machines

Abstract:

Article Preview

Due to lack of typical damage samples in the transformer fault diagnosis, a new fault diagnosis method based on fuzzy support vector machines (FFSVMs) was presented. According to the method, the five characteristic gases dissolved in transformer oil were extracted by the K-means clustering (KMC) method as feature vectors, which were input to fuzzy optimal multi-classified SVMs for training. Then the FSVMs diagnosis model was established to implement fault samples classification. Experiment showed that by adopting facture extraction with KMC, the diagnosis information was concentrated and the consuming in parameter determination was solved effectively. The presented method enabled to detect transformer faults with a high correct judgment rate, and can be used as an automation approach for diagnosis under condition of small samples.

Info:

Periodical:

Edited by:

Robin G. Qiu and Yongfeng Ju

Pages:

1102-1107

DOI:

10.4028/www.scientific.net/AMM.135-136.1102

Citation:

Y. Y. Liu et al., "Transformer Fault Diagnosis Based on Fuzzy Support Vector Machines", Applied Mechanics and Materials, Vols. 135-136, pp. 1102-1107, 2012

Online since:

October 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.