Fault identification in an unbalanced distribution system using support vector machine
Joint Authors
Gururajapathy, Sophi Shilpa
Mokhlis, Hazlie
Illias, HazleeAzil
Abu Bakr, Abu Halim
Awalin, Lilik Jamilatul
Source
Issue
Vol. 12, Issue 4 (31 Dec. 2016), pp.786-800, 15 p.
Publisher
Publication Date
2016-12-31
Country of Publication
Algeria
No. of Pages
15
Main Subjects
Abstract EN
Fast and effective fault location in distribution system is important to improve the power system reliability.
Most of the researches rarely mention about effective fault location consisting of faulted phase, fault type, faulty section and fault distance identification.
This work presents a method using support vector machine to identify the faulted phase, fault type, faulty section and distance at the same time.
Support vector classification and regression analysis are performed to locate fault.
The method uses the voltage sag data during fault condition measured at the primary substation.
The faulted phase and the fault type are identified using three-dimensional support vector classification.
The possible faulty sections are identified by matching voltage sag at fault condition to the voltage sag in database and the possible sections are ranked using shortest distance principle.
The fault distance for the possible faulty sections isthen identified using support vector regression analysis.
The performance of the proposed method was tested on an unbalanced distribution system from SaskPower, Canada.
The results show that the accuracy of the proposed method is satisfactory.
American Psychological Association (APA)
Gururajapathy, Sophi Shilpa& Mokhlis, Hazlie& Illias, HazleeAzil& Abu Bakr, Abu Halim& Awalin, Lilik Jamilatul. 2016. Fault identification in an unbalanced distribution system using support vector machine. Journal of Electrical Systems،Vol. 12, no. 4, pp.786-800.
https://search.emarefa.net/detail/BIM-739656
Modern Language Association (MLA)
Gururajapathy, Sophi Shilpa…[et al.]. Fault identification in an unbalanced distribution system using support vector machine. Journal of Electrical Systems Vol. 12, no. 4 (2016), pp.786-800.
https://search.emarefa.net/detail/BIM-739656
American Medical Association (AMA)
Gururajapathy, Sophi Shilpa& Mokhlis, Hazlie& Illias, HazleeAzil& Abu Bakr, Abu Halim& Awalin, Lilik Jamilatul. Fault identification in an unbalanced distribution system using support vector machine. Journal of Electrical Systems. 2016. Vol. 12, no. 4, pp.786-800.
https://search.emarefa.net/detail/BIM-739656
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 800
Record ID
BIM-739656