Biased Random-Key Genetic Algorithm Applied to the Optimal Reconfiguration of Radial Distribution Systems

Detalhes bibliográficos
Autor(a) principal: Vargas, Rommel [UNESP]
Data de Publicação: 2018
Outros Autores: Romero, Ruben [UNESP], Franco, John F. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/TDC-LA.2018.8511718
http://hdl.handle.net/11449/189908
Resumo: The optimal reconfiguration of radial Electrical Distribution Systems (EDSs) is a classical optimization problem that deals with the operation of the system and is of great interest to the electricity sector. Although there is a large number of approaches in the specialized literature to solve this problem, the solution of the reconfiguration problem for large-scale EDSs is still difficult. This paper proposes a method to solve the reconfiguration problem of EDSs that is based on the specialized metaheuristic Biased Random-Key Genetic Algorithm, which showed excellent performance on the solution of complex problems in operational research. Tests carried out using a wellknown EDS demonstrate the efficiency of the proposed method.
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spelling Biased Random-Key Genetic Algorithm Applied to the Optimal Reconfiguration of Radial Distribution SystemsBiased random-key genetic algorithmelectrical distribution systemspower lossesreconfigurationThe optimal reconfiguration of radial Electrical Distribution Systems (EDSs) is a classical optimization problem that deals with the operation of the system and is of great interest to the electricity sector. Although there is a large number of approaches in the specialized literature to solve this problem, the solution of the reconfiguration problem for large-scale EDSs is still difficult. This paper proposes a method to solve the reconfiguration problem of EDSs that is based on the specialized metaheuristic Biased Random-Key Genetic Algorithm, which showed excellent performance on the solution of complex problems in operational research. Tests carried out using a wellknown EDS demonstrate the efficiency of the proposed method.Department of Electrical Engineering São Paulo State University (UNESP)School of Energy Engineering São Paulo State University (UNESP)Department of Electrical Engineering São Paulo State University (UNESP)School of Energy Engineering São Paulo State University (UNESP)Universidade Estadual Paulista (Unesp)Vargas, Rommel [UNESP]Romero, Ruben [UNESP]Franco, John F. [UNESP]2019-10-06T16:56:08Z2019-10-06T16:56:08Z2018-10-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/TDC-LA.2018.8511718Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018.http://hdl.handle.net/11449/18990810.1109/TDC-LA.2018.85117182-s2.0-85057015390Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018info:eu-repo/semantics/openAccess2024-07-04T19:11:28Zoai:repositorio.unesp.br:11449/189908Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:37:52.445318Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Biased Random-Key Genetic Algorithm Applied to the Optimal Reconfiguration of Radial Distribution Systems
title Biased Random-Key Genetic Algorithm Applied to the Optimal Reconfiguration of Radial Distribution Systems
spellingShingle Biased Random-Key Genetic Algorithm Applied to the Optimal Reconfiguration of Radial Distribution Systems
Vargas, Rommel [UNESP]
Biased random-key genetic algorithm
electrical distribution systems
power losses
reconfiguration
title_short Biased Random-Key Genetic Algorithm Applied to the Optimal Reconfiguration of Radial Distribution Systems
title_full Biased Random-Key Genetic Algorithm Applied to the Optimal Reconfiguration of Radial Distribution Systems
title_fullStr Biased Random-Key Genetic Algorithm Applied to the Optimal Reconfiguration of Radial Distribution Systems
title_full_unstemmed Biased Random-Key Genetic Algorithm Applied to the Optimal Reconfiguration of Radial Distribution Systems
title_sort Biased Random-Key Genetic Algorithm Applied to the Optimal Reconfiguration of Radial Distribution Systems
author Vargas, Rommel [UNESP]
author_facet Vargas, Rommel [UNESP]
Romero, Ruben [UNESP]
Franco, John F. [UNESP]
author_role author
author2 Romero, Ruben [UNESP]
Franco, John F. [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Vargas, Rommel [UNESP]
Romero, Ruben [UNESP]
Franco, John F. [UNESP]
dc.subject.por.fl_str_mv Biased random-key genetic algorithm
electrical distribution systems
power losses
reconfiguration
topic Biased random-key genetic algorithm
electrical distribution systems
power losses
reconfiguration
description The optimal reconfiguration of radial Electrical Distribution Systems (EDSs) is a classical optimization problem that deals with the operation of the system and is of great interest to the electricity sector. Although there is a large number of approaches in the specialized literature to solve this problem, the solution of the reconfiguration problem for large-scale EDSs is still difficult. This paper proposes a method to solve the reconfiguration problem of EDSs that is based on the specialized metaheuristic Biased Random-Key Genetic Algorithm, which showed excellent performance on the solution of complex problems in operational research. Tests carried out using a wellknown EDS demonstrate the efficiency of the proposed method.
publishDate 2018
dc.date.none.fl_str_mv 2018-10-26
2019-10-06T16:56:08Z
2019-10-06T16:56:08Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/TDC-LA.2018.8511718
Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018.
http://hdl.handle.net/11449/189908
10.1109/TDC-LA.2018.8511718
2-s2.0-85057015390
url http://dx.doi.org/10.1109/TDC-LA.2018.8511718
http://hdl.handle.net/11449/189908
identifier_str_mv Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018.
10.1109/TDC-LA.2018.8511718
2-s2.0-85057015390
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808128680786919424

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