Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems
This paper presents a hybrid alternative to obtain a low computational cost strategy used to adjust the parameters of a Support Vector Machine based fault locator. The proposed strategy to determine the best parameters is based on the Chu Beasley Genetic Algorithm. The fault locator is tested in th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universidad de Antioquia
2013-03-01
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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/14788 |
| _version_ | 1797861608640217088 |
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| author | Jaime Gutiérrez Gallego Juan Mora Flórez Sandra Pérez Londoño |
| author_facet | Jaime Gutiérrez Gallego Juan Mora Flórez Sandra Pérez Londoño |
| author_sort | Jaime Gutiérrez Gallego |
| collection | DOAJ |
| description |
This paper presents a hybrid alternative to obtain a low computational cost strategy used to adjust the parameters of a Support Vector Machine based fault locator. The proposed strategy to determine the best parameters is based on the Chu Beasley Genetic Algorithm. The fault locator is tested in the IEEE 34 bus feeder, using a database of 2,180 registers of single phase, phase to phase, double phase to ground and three phase faults, obtained from simulation in ATP and Matlab. As results, the best alternatives for all of these four types of faults give an average cross validation error of 0.3%.
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| first_indexed | 2024-04-09T22:06:59Z |
| format | Article |
| id | doaj.art-37bad4241831453891d93013f2de25a6 |
| institution | Directory Open Access Journal |
| issn | 0120-6230 2422-2844 |
| language | English |
| last_indexed | 2024-04-09T22:06:59Z |
| publishDate | 2013-03-01 |
| publisher | Universidad de Antioquia |
| record_format | Article |
| series | Revista Facultad de Ingeniería Universidad de Antioquia |
| spelling | doaj.art-37bad4241831453891d93013f2de25a62023-03-23T12:35:33ZengUniversidad de AntioquiaRevista Facultad de Ingeniería Universidad de Antioquia0120-62302422-28442013-03-0153Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systemsJaime Gutiérrez Gallego0Juan Mora Flórez1Sandra Pérez Londoño2Technological University of PereiraTechnological University of PereiraTechnological University of Pereira This paper presents a hybrid alternative to obtain a low computational cost strategy used to adjust the parameters of a Support Vector Machine based fault locator. The proposed strategy to determine the best parameters is based on the Chu Beasley Genetic Algorithm. The fault locator is tested in the IEEE 34 bus feeder, using a database of 2,180 registers of single phase, phase to phase, double phase to ground and three phase faults, obtained from simulation in ATP and Matlab. As results, the best alternatives for all of these four types of faults give an average cross validation error of 0.3%. https://revistas.udea.edu.co/index.php/ingenieria/article/view/14788classificationfault locationgenetic algorithmspower distribution systems and support vector machines |
| spellingShingle | Jaime Gutiérrez Gallego Juan Mora Flórez Sandra Pérez Londoño Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems Revista Facultad de Ingeniería Universidad de Antioquia classification fault location genetic algorithms power distribution systems and support vector machines |
| title | Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems |
| title_full | Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems |
| title_fullStr | Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems |
| title_full_unstemmed | Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems |
| title_short | Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems |
| title_sort | strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems |
| topic | classification fault location genetic algorithms power distribution systems and support vector machines |
| url | https://revistas.udea.edu.co/index.php/ingenieria/article/view/14788 |
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