Reconfiguration of distribution systems to minimize losses using heuristic optimization: BPSO and DEEPSO methods

This article proposes the analysis and application of heuristic algorithms for the reconfi guration of distribution systems in order to reduce power losses in these systems. The algorithms used are based on the BPSO (Binary Particle Swarm Optimization) and DEEPSO (Diff erential Particle Swarm Optimiza...

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Fecha de publicación:
2019
Institución:
Universidad Católica de Pereira
Repositorio:
Repositorio Institucional - RIBUC
Idioma:
spa
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oai:repositorio.ucp.edu.co:10785/9764
Acceso en línea:
https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/409
http://hdl.handle.net/10785/9764
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openAccess
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Derechos de autor 2019 Entre Ciencia e Ingeniería
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oai_identifier_str oai:repositorio.ucp.edu.co:10785/9764
network_acronym_str RepoRIBUC
network_name_str Repositorio Institucional - RIBUC
repository_id_str
spelling Reconfiguration of distribution systems to minimize losses using heuristic optimization: BPSO and DEEPSO methodsReconfiguración de sistemas de distribución para minimizar pérdidas utilizando optimización heurística: Métodos BPSO y DEEPSOReconfiguração de sistemas de distribuição para minimizar perdas com otimização heurística: métodos BPSO e DEEPSOThis article proposes the analysis and application of heuristic algorithms for the reconfi guration of distribution systems in order to reduce power losses in these systems. The algorithms used are based on the BPSO (Binary Particle Swarm Optimization) and DEEPSO (Diff erential Particle Swarm Optimization) methods, these are intended to fi nd solutions close to the local optimum, with reduced processing times compared to analytical methods. Additionally, an example of application of each method to two radial distribution systems (distribution networks of 33 and 69 nodes) is shown to compare the results obtained in terms of the effi ciency and eff ectiveness of each algorithm.Este artículo presenta el análisis y aplicación de algoritmos heurísticos para la reconfi guración de sistemas de distribución con el objetivo de reducir las pérdidas de potencia en dichos sistemas. Los algoritmos utilizados se basan en los métodos BPSO (Binary Particle Swarm Optimization) y DEEPSO (Diff erential Evolutionary Particle Swarm Optimization), que tienen como propósito encontrar soluciones cercanas a los óptimos locales, con tiempos de procesamiento reducidos en comparación con métodos analíticos. Adicionalmente, se muestra un ejemplo de aplicación de cada método a dos sistemas de distribución radiales (redes de distribución de 33 y 69 nodos) para comparar los resultados obtenidos en cuanto a la efi ciencia y efi cacia de cada algoritmoEste artigo apresenta a análise e aplicação de algoritmos heurísticos para a reconfi guração de sistemas de distribuição com o objetivo de reduzir as perdas de potência em tais sistemas. Os algoritmos utilizados são baseados nos métodos BPSO (Binary Particle Swarm Optimization) e DEEPSO (Diff erential Evolutionary Particle Swarm Optimization), cujo objetivo é encontrar soluções próximas ao ótimo local, com tempos de processamento reduzidos em comparação com métodos analíticos. Além disso, mostra-se um exemplo de aplicação de cada método a dois sistemas de distribuição radiais (redes de distribuição de 33 e 69 nós) para comparar os resultados obtidos em relação à efi ciência e efi cácia de cada algoritmo.Universidad Católica de Pereira2022-06-01T19:08:28Z2022-06-01T19:08:28Z2019-06-17Artículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1application/pdfapplication/xmlhttps://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/40910.31908/19098367.3556http://hdl.handle.net/10785/9764Entre ciencia e ingeniería; Vol 11 No 22 (2017); 110-117Entre Ciencia e Ingeniería; Vol. 11 Núm. 22 (2017); 110-117Entre ciencia e ingeniería; v. 11 n. 22 (2017); 110-1172539-41691909-8367spahttps://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/409/415https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/409/1142Derechos de autor 2019 Entre Ciencia e Ingenieríahttps://creativecommons.org/licenses/by-nc/4.0/deed.es_EShttps://creativecommons.org/licenses/by-nc/4.0/deed.es_ESinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Acosta Piedrahita, Jorge LuisAlarcón Roa, Angel EstefanoRivera Rodriguez, Sergiooai:repositorio.ucp.edu.co:10785/97642025-01-28T00:01:06Z
dc.title.none.fl_str_mv Reconfiguration of distribution systems to minimize losses using heuristic optimization: BPSO and DEEPSO methods
Reconfiguración de sistemas de distribución para minimizar pérdidas utilizando optimización heurística: Métodos BPSO y DEEPSO
Reconfiguração de sistemas de distribuição para minimizar perdas com otimização heurística: métodos BPSO e DEEPSO
title Reconfiguration of distribution systems to minimize losses using heuristic optimization: BPSO and DEEPSO methods
spellingShingle Reconfiguration of distribution systems to minimize losses using heuristic optimization: BPSO and DEEPSO methods
title_short Reconfiguration of distribution systems to minimize losses using heuristic optimization: BPSO and DEEPSO methods
title_full Reconfiguration of distribution systems to minimize losses using heuristic optimization: BPSO and DEEPSO methods
title_fullStr Reconfiguration of distribution systems to minimize losses using heuristic optimization: BPSO and DEEPSO methods
title_full_unstemmed Reconfiguration of distribution systems to minimize losses using heuristic optimization: BPSO and DEEPSO methods
title_sort Reconfiguration of distribution systems to minimize losses using heuristic optimization: BPSO and DEEPSO methods
description This article proposes the analysis and application of heuristic algorithms for the reconfi guration of distribution systems in order to reduce power losses in these systems. The algorithms used are based on the BPSO (Binary Particle Swarm Optimization) and DEEPSO (Diff erential Particle Swarm Optimization) methods, these are intended to fi nd solutions close to the local optimum, with reduced processing times compared to analytical methods. Additionally, an example of application of each method to two radial distribution systems (distribution networks of 33 and 69 nodes) is shown to compare the results obtained in terms of the effi ciency and eff ectiveness of each algorithm.
publishDate 2019
dc.date.none.fl_str_mv 2019-06-17
2022-06-01T19:08:28Z
2022-06-01T19:08:28Z
dc.type.none.fl_str_mv Artículo de revista
http://purl.org/coar/resource_type/c_6501
http://purl.org/coar/version/c_970fb48d4fbd8a85
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
status_str publishedVersion
dc.identifier.none.fl_str_mv https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/409
10.31908/19098367.3556
http://hdl.handle.net/10785/9764
url https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/409
http://hdl.handle.net/10785/9764
identifier_str_mv 10.31908/19098367.3556
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/409/415
https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/409/1142
dc.rights.none.fl_str_mv Derechos de autor 2019 Entre Ciencia e Ingeniería
https://creativecommons.org/licenses/by-nc/4.0/deed.es_ES
https://creativecommons.org/licenses/by-nc/4.0/deed.es_ES
info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Derechos de autor 2019 Entre Ciencia e Ingeniería
https://creativecommons.org/licenses/by-nc/4.0/deed.es_ES
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/xml
dc.publisher.none.fl_str_mv Universidad Católica de Pereira
publisher.none.fl_str_mv Universidad Católica de Pereira
dc.source.none.fl_str_mv Entre ciencia e ingeniería; Vol 11 No 22 (2017); 110-117
Entre Ciencia e Ingeniería; Vol. 11 Núm. 22 (2017); 110-117
Entre ciencia e ingeniería; v. 11 n. 22 (2017); 110-117
2539-4169
1909-8367
institution Universidad Católica de Pereira
repository.name.fl_str_mv
repository.mail.fl_str_mv
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