Paper Title:
Transformer Fault Diagnosis Based on Fuzzy Support Vector Machines
  Abstract

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
Chapter
Chapter 7: Mechanical & Automation
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, S. H. He, Y. F. Ju, C. D. Duan, "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
$32.00
Share

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

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

Authors: Yan Gao
Abstract:For poor accuracy of detection of the urban traffic network classification, the Support Vector Machine (SVM)is applied to classification of...
489
Authors: Wen Jie Wu, Da Gui Huang, Zheng Dong
Abstract:This paper describes a support vector machine(SVM) approach to improve the test validity and accuracy for Aero-engine fault diagnosis. A new...
811
Authors: Wei Niu, Guo Qing Wang, Zheng Jun Zhai, Juan Cheng
Abstract:The vibration signals of rotating machinery in operation consist of plenty of information about its running condition, and extraction and...
1982
Authors: Hang Cheng, Xi Li, Zheng Bo Qin, Chao Yong Huang
Chapter 5: Control and Detection Technology
Abstract:This paper has put forward one method, combining with theory of decision tree and method of voting, and established one kind of multi-failure...
1010
Authors: Bai Lin Liu, Li Xing Gao
Chapter 6: Measurement Techniques, Technologies and Equipment
Abstract:To solve the problem that large training samples and slow speed in diagnosing based on support vector classifier, a hybrid classification...
887