Fault identification in an unbalanced distribution system using support vector machine
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 u...
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| Format: | Article |
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Engineering and Scientific Research Groups
2016
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| author | Gururajapathy, S.S. Mokhlis, Hazlie Illias, Hazlee Azil Bakar, Ab Halim Abu Awalin, L.J. |
| author_facet | Gururajapathy, S.S. Mokhlis, Hazlie Illias, Hazlee Azil Bakar, Ab Halim Abu Awalin, L.J. |
| author_sort | Gururajapathy, S.S. |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:43:48Z |
| format | Article |
| id | um.eprints-17883 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:43:48Z |
| publishDate | 2016 |
| publisher | Engineering and Scientific Research Groups |
| record_format | dspace |
| spelling | um.eprints-178832019-10-10T02:36:12Z http://eprints.um.edu.my/17883/ Fault identification in an unbalanced distribution system using support vector machine Gururajapathy, S.S. Mokhlis, Hazlie Illias, Hazlee Azil Bakar, Ab Halim Abu Awalin, L.J. TK Electrical engineering. Electronics Nuclear engineering 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. Engineering and Scientific Research Groups 2016 Article PeerReviewed Gururajapathy, S.S. and Mokhlis, Hazlie and Illias, Hazlee Azil and Bakar, Ab Halim Abu and Awalin, L.J. (2016) Fault identification in an unbalanced distribution system using support vector machine. Journal of Electrical Systems, 12 (4). pp. 786-800. ISSN 1112-5209, http://journal.esrgroups.org/jes/papers/12_4_11.pdf |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Gururajapathy, S.S. Mokhlis, Hazlie Illias, Hazlee Azil Bakar, Ab Halim Abu Awalin, L.J. Fault identification in an unbalanced distribution system using support vector machine |
| title | Fault identification in an unbalanced distribution system using support vector machine |
| title_full | Fault identification in an unbalanced distribution system using support vector machine |
| title_fullStr | Fault identification in an unbalanced distribution system using support vector machine |
| title_full_unstemmed | Fault identification in an unbalanced distribution system using support vector machine |
| title_short | Fault identification in an unbalanced distribution system using support vector machine |
| title_sort | fault identification in an unbalanced distribution system using support vector machine |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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