Unique localization of faults in distribution systems by means of zones with SVM
This paper presents a new methodology for localizing faults in distribution systems by means of an artificial intelligence technique –Support Vector Machine– (SVM). This methodology divides the electrical system into different zones order to pinpoint the region where the fault exists with accuracy....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universidad de Antioquia
2013-12-01
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| Series: | Revista Facultad de Ingeniería Universidad de Antioquia |
| Subjects: | |
| Online Access: | https://revistas.udea.edu.co/index.php/ingenieria/article/view/17765 |
| _version_ | 1797861700186144768 |
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| author | Germán Morales-España Hermann Raúl Vargas-Torres René Barrera-Cárdenas |
| author_facet | Germán Morales-España Hermann Raúl Vargas-Torres René Barrera-Cárdenas |
| author_sort | Germán Morales-España |
| collection | DOAJ |
| description |
This paper presents a new methodology for localizing faults in distribution systems by means of an artificial intelligence technique –Support Vector Machine– (SVM). This methodology divides the electrical system into different zones order to pinpoint the region where the fault exists with accuracy. The advantage over classical distance methods is the unique estimation of the fault’s locus in branches systems. An example using a real system model shows that the proposed methodology is highly effective finding the fault localization. In such example load changes of ±40 % from nominal load are considered.
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| first_indexed | 2024-04-09T22:08:25Z |
| format | Article |
| id | doaj.art-88762b345a2b4056be698be23d9f5ed5 |
| institution | Directory Open Access Journal |
| issn | 0120-6230 2422-2844 |
| language | English |
| last_indexed | 2024-04-09T22:08:25Z |
| publishDate | 2013-12-01 |
| publisher | Universidad de Antioquia |
| record_format | Article |
| series | Revista Facultad de Ingeniería Universidad de Antioquia |
| spelling | doaj.art-88762b345a2b4056be698be23d9f5ed52023-03-23T12:33:54ZengUniversidad de AntioquiaRevista Facultad de Ingeniería Universidad de Antioquia0120-62302422-28442013-12-014710.17533/udea.redin.17765Unique localization of faults in distribution systems by means of zones with SVMGermán Morales-España0Hermann Raúl Vargas-Torres1René Barrera-Cárdenas2Universidad Industrial de SantanderUniversidad Industrial de SantanderUniversidad Industrial de Santander This paper presents a new methodology for localizing faults in distribution systems by means of an artificial intelligence technique –Support Vector Machine– (SVM). This methodology divides the electrical system into different zones order to pinpoint the region where the fault exists with accuracy. The advantage over classical distance methods is the unique estimation of the fault’s locus in branches systems. An example using a real system model shows that the proposed methodology is highly effective finding the fault localization. In such example load changes of ±40 % from nominal load are considered. https://revistas.udea.edu.co/index.php/ingenieria/article/view/17765Descriptorsartificial intelligencefault’s localizationmultiple estimationdistribution systemsSVM |
| spellingShingle | Germán Morales-España Hermann Raúl Vargas-Torres René Barrera-Cárdenas Unique localization of faults in distribution systems by means of zones with SVM Revista Facultad de Ingeniería Universidad de Antioquia Descriptors artificial intelligence fault’s localization multiple estimation distribution systems SVM |
| title | Unique localization of faults in distribution systems by means of zones with SVM |
| title_full | Unique localization of faults in distribution systems by means of zones with SVM |
| title_fullStr | Unique localization of faults in distribution systems by means of zones with SVM |
| title_full_unstemmed | Unique localization of faults in distribution systems by means of zones with SVM |
| title_short | Unique localization of faults in distribution systems by means of zones with SVM |
| title_sort | unique localization of faults in distribution systems by means of zones with svm |
| topic | Descriptors artificial intelligence fault’s localization multiple estimation distribution systems SVM |
| url | https://revistas.udea.edu.co/index.php/ingenieria/article/view/17765 |
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