Fault location in an unbalanced distribution system using support vector classification and regression analysis
Support vector machine (SVM) is a novel machine for data analysis and has advantageous characteristic of good generalization. Because of this characteristic, SVM is used in this work for fault classification and diagnosis in distribution systems. This work proposes an effective fault location method...
| Main Authors: | , , , , |
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| Format: | Article |
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Institute of Electrical Engineers of Japan
2017
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| _version_ | 1825721791000084480 |
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| author | Gururajapathy, Sophi Shilpa Mokhlis, Hazlie Illias, Hazlee Azil Bakar, Ab Halim Abu Awalin, Lilik Jamilatul |
| author_facet | Gururajapathy, Sophi Shilpa Mokhlis, Hazlie Illias, Hazlee Azil Bakar, Ab Halim Abu Awalin, Lilik Jamilatul |
| author_sort | Gururajapathy, Sophi Shilpa |
| collection | UM |
| description | Support vector machine (SVM) is a novel machine for data analysis and has advantageous characteristic of good generalization. Because of this characteristic, SVM is used in this work for fault classification and diagnosis in distribution systems. This work proposes an effective fault location method using SVM to identify the fault type, faulty section, and fault distance. The classification and regression analysis of the SVM are performed to locate a fault. The proposed method utilizes the voltage sag magnitude and angle measured at the primary substation of a distribution system. First, the fault type is identified using one- versus-one concept of support vector classification. The next step identifies the faulty section by calculating fault resistance, finding possible faulty sections and ranking the possible sections. Finally, the fault distance is identified using support vector regression analysis. The performance of the proposed method is tested using SaskPower distribution system from Canada having 20 line sections. Test cases are carried out under various fault scenarios considering the fault type and fault resistance. The results of fault distance are compared for different kernel functions, and the most accurate kernel is chosen. Test results show that the proposed method obtains reliable fault location. |
| first_indexed | 2024-03-06T05:55:43Z |
| format | Article |
| id | um.eprints-21992 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:55:43Z |
| publishDate | 2017 |
| publisher | Institute of Electrical Engineers of Japan |
| record_format | dspace |
| spelling | um.eprints-219922019-08-20T07:54:59Z http://eprints.um.edu.my/21992/ Fault location in an unbalanced distribution system using support vector classification and regression analysis Gururajapathy, Sophi Shilpa Mokhlis, Hazlie Illias, Hazlee Azil Bakar, Ab Halim Abu Awalin, Lilik Jamilatul TK Electrical engineering. Electronics Nuclear engineering Support vector machine (SVM) is a novel machine for data analysis and has advantageous characteristic of good generalization. Because of this characteristic, SVM is used in this work for fault classification and diagnosis in distribution systems. This work proposes an effective fault location method using SVM to identify the fault type, faulty section, and fault distance. The classification and regression analysis of the SVM are performed to locate a fault. The proposed method utilizes the voltage sag magnitude and angle measured at the primary substation of a distribution system. First, the fault type is identified using one- versus-one concept of support vector classification. The next step identifies the faulty section by calculating fault resistance, finding possible faulty sections and ranking the possible sections. Finally, the fault distance is identified using support vector regression analysis. The performance of the proposed method is tested using SaskPower distribution system from Canada having 20 line sections. Test cases are carried out under various fault scenarios considering the fault type and fault resistance. The results of fault distance are compared for different kernel functions, and the most accurate kernel is chosen. Test results show that the proposed method obtains reliable fault location. Institute of Electrical Engineers of Japan 2017 Article PeerReviewed Gururajapathy, Sophi Shilpa and Mokhlis, Hazlie and Illias, Hazlee Azil and Bakar, Ab Halim Abu and Awalin, Lilik Jamilatul (2017) Fault location in an unbalanced distribution system using support vector classification and regression analysis. IEEJ Transactions on Electrical and Electronic Engineering, 13 (2). pp. 237-245. ISSN 1931-4973, DOI https://doi.org/10.1002/tee.22519 <https://doi.org/10.1002/tee.22519>. https://doi.org/10.1002/tee.22519 doi:10.1002/tee.22519 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Gururajapathy, Sophi Shilpa Mokhlis, Hazlie Illias, Hazlee Azil Bakar, Ab Halim Abu Awalin, Lilik Jamilatul Fault location in an unbalanced distribution system using support vector classification and regression analysis |
| title | Fault location in an unbalanced distribution system using support vector classification and regression analysis |
| title_full | Fault location in an unbalanced distribution system using support vector classification and regression analysis |
| title_fullStr | Fault location in an unbalanced distribution system using support vector classification and regression analysis |
| title_full_unstemmed | Fault location in an unbalanced distribution system using support vector classification and regression analysis |
| title_short | Fault location in an unbalanced distribution system using support vector classification and regression analysis |
| title_sort | fault location in an unbalanced distribution system using support vector classification and regression analysis |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT gururajapathysophishilpa faultlocationinanunbalanceddistributionsystemusingsupportvectorclassificationandregressionanalysis AT mokhlishazlie faultlocationinanunbalanceddistributionsystemusingsupportvectorclassificationandregressionanalysis AT illiashazleeazil faultlocationinanunbalanceddistributionsystemusingsupportvectorclassificationandregressionanalysis AT bakarabhalimabu faultlocationinanunbalanceddistributionsystemusingsupportvectorclassificationandregressionanalysis AT awalinlilikjamilatul faultlocationinanunbalanceddistributionsystemusingsupportvectorclassificationandregressionanalysis |