Fault diagnosis for distribution networks using enhanced support vector machine classifier with classical multidimensional scaling
Joint Authors
Source
Issue
Vol. 13, Issue 3 (30 Sep. 2017), pp.415-428, 14 p.
Publisher
Publication Date
2017-09-30
Country of Publication
Algeria
No. of Pages
14
Main Subjects
Abstract EN
In this paper, a new fault diagnosis techniques based on time domain reflectometry (TDR) method with pseudo-random binary sequence (PRBS) stimulus and support vector machine (SVM) classifier has been investigated to recognize the different types of fault in the radial distribution feeders.
This novel technique has considered the amplitude of reflected signals and the peaks of cross-correlation (CCR) between the reflected and incident wave for generating fault current dataset for SVM.
Furthermore, this multi-layer enhanced SVM classifier is combined with classical multidimensional scaling (CMDS) feature extraction algorithm and kernel parameter optimization to increase training speed and improve overall classification accuracy.
The proposed technique has been tested on a radial distribution feeder to identify ten different types of fault considering 12 input features generated by using Simulink software and MATLAB Toolbox.
The success rate of SVM classifier is over 95 % which demonstrates the effectiveness and the high accuracy of proposed method.
American Psychological Association (APA)
Cho, Ming-Yuan& Thom, Hoang Thi. 2017. Fault diagnosis for distribution networks using enhanced support vector machine classifier with classical multidimensional scaling. Journal of Electrical Systems،Vol. 13, no. 3, pp.415-428.
https://search.emarefa.net/detail/BIM-785539
Modern Language Association (MLA)
Cho, Ming-Yuan& Thom, Hoang Thi. Fault diagnosis for distribution networks using enhanced support vector machine classifier with classical multidimensional scaling. Journal of Electrical Systems Vol. 13, no. 3 (2017), pp.415-428.
https://search.emarefa.net/detail/BIM-785539
American Medical Association (AMA)
Cho, Ming-Yuan& Thom, Hoang Thi. Fault diagnosis for distribution networks using enhanced support vector machine classifier with classical multidimensional scaling. Journal of Electrical Systems. 2017. Vol. 13, no. 3, pp.415-428.
https://search.emarefa.net/detail/BIM-785539
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 427-428
Record ID
BIM-785539