Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system
Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Theref...
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| Format: | Article |
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Scientific and Technological Research Council of Turkey (TÜBİTAK)
2018
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| _version_ | 1825721790809243648 |
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| author | Gururajapathy, Sophi Shilpa Mokhlis, Hazlie Illias, Hazlee Azil |
| author_facet | Gururajapathy, Sophi Shilpa Mokhlis, Hazlie Illias, Hazlee Azil |
| author_sort | Gururajapathy, Sophi Shilpa |
| collection | UM |
| description | Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Therefore, in this work, a method using a support vector machine (SVM) to identify the fault type, faulty section, and distance considering the faulty phase is proposed. The proposed method uses voltage sag magnitude of the distribution system as the main feature for the SVM to identify faults. The fault type is classified using a directed acyclic graph SVM. The possible faulty sections are identified by estimating the fault resistance using support vector regression and matching the voltage sag data in the database with the actual voltage sag data. The most possible faulty sections are ranked using ranking analysis. The fault distance for the possible faulty sections is then identified using support vector regression analysis and its overfitting or underfitting issues are addressed by the proper selection of a regularization parameter. The feasibility of the proposed method was tested on an actual Malaysian distribution system. The results of faulty phase, fault type, faulty section, and fault distance are analyzed. The performance of the proposed method is compared with various other intelligent methods such as the artificial neural network, deep neural network, extreme learning machine, and kriging method. The test results indicate that the faulty phase and fault type yield 100% accurate results. All the faulty sections are identified and the proposed method obtains reliable fault location. |
| first_indexed | 2024-03-06T05:55:43Z |
| format | Article |
| id | um.eprints-21991 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:55:43Z |
| publishDate | 2018 |
| publisher | Scientific and Technological Research Council of Turkey (TÜBİTAK) |
| record_format | dspace |
| spelling | um.eprints-219912019-08-20T07:49:08Z http://eprints.um.edu.my/21991/ Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system Gururajapathy, Sophi Shilpa Mokhlis, Hazlie Illias, Hazlee Azil TK Electrical engineering. Electronics Nuclear engineering Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Therefore, in this work, a method using a support vector machine (SVM) to identify the fault type, faulty section, and distance considering the faulty phase is proposed. The proposed method uses voltage sag magnitude of the distribution system as the main feature for the SVM to identify faults. The fault type is classified using a directed acyclic graph SVM. The possible faulty sections are identified by estimating the fault resistance using support vector regression and matching the voltage sag data in the database with the actual voltage sag data. The most possible faulty sections are ranked using ranking analysis. The fault distance for the possible faulty sections is then identified using support vector regression analysis and its overfitting or underfitting issues are addressed by the proper selection of a regularization parameter. The feasibility of the proposed method was tested on an actual Malaysian distribution system. The results of faulty phase, fault type, faulty section, and fault distance are analyzed. The performance of the proposed method is compared with various other intelligent methods such as the artificial neural network, deep neural network, extreme learning machine, and kriging method. The test results indicate that the faulty phase and fault type yield 100% accurate results. All the faulty sections are identified and the proposed method obtains reliable fault location. Scientific and Technological Research Council of Turkey (TÜBİTAK) 2018 Article PeerReviewed Gururajapathy, Sophi Shilpa and Mokhlis, Hazlie and Illias, Hazlee Azil (2018) Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system. Turkish Journal of Electrical Engineering and Computer Sciences, 26 (6). pp. 3044-3056. ISSN 1300-0632, DOI https://doi.org/10.3906/elk-1711-194 <https://doi.org/10.3906/elk-1711-194>. https://doi.org/10.3906/elk-1711-194 doi:10.3906/elk-1711-194 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Gururajapathy, Sophi Shilpa Mokhlis, Hazlie Illias, Hazlee Azil Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system |
| title | Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system |
| title_full | Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system |
| title_fullStr | Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system |
| title_full_unstemmed | Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system |
| title_short | Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system |
| title_sort | classification and regression analysis using support vector machine for classifying and locating faults in a distribution system |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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