Specialized Genetic Algorithm to Solve the Electrical Distribution System Expansion Planning

Detalhes bibliográficos
Autor(a) principal: Camargo, Vera
Data de Publicação: 2013
Outros Autores: Lavorato, Marina [UNESP], Romero, Ruben [UNESP], IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/197430
Resumo: A specialized genetic algorithm with a adaptation of Chu-Beasley algorithm is presented in this paper to solve the electrical distribution distribution system expansion planning (DSP) problem modeled by a mixed integer nonlinear programming problem. The specialized genetic algorithm proposed in this paper starting from a initial population where all elements have a radial topology found using a heuristic algorithm and after the selection and mutation operations must also go through a local improvement in order to make the proposed solution in a feasible solution, if necessary, with respect to operational constraints. The DSP problem presented in this paper consider the circuit construction/recondutoring for different types of conductors and the substation construct/reinforcement. To evaluate the quality of the proposed methodology were used three different test systems found in the literature, 23, 54 and 136 buses systems.
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spelling Specialized Genetic Algorithm to Solve the Electrical Distribution System Expansion PlanningDistribution network planninggenetic algorithmmixed integer nonlinear programmingpower systems optimizationA specialized genetic algorithm with a adaptation of Chu-Beasley algorithm is presented in this paper to solve the electrical distribution distribution system expansion planning (DSP) problem modeled by a mixed integer nonlinear programming problem. The specialized genetic algorithm proposed in this paper starting from a initial population where all elements have a radial topology found using a heuristic algorithm and after the selection and mutation operations must also go through a local improvement in order to make the proposed solution in a feasible solution, if necessary, with respect to operational constraints. The DSP problem presented in this paper consider the circuit construction/recondutoring for different types of conductors and the substation construct/reinforcement. To evaluate the quality of the proposed methodology were used three different test systems found in the literature, 23, 54 and 136 buses systems.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Mato Grosso State Univ, Dept Math Sinop, Sinop, MT, BrazilUniv Estadual Paulista, Fac Engn Ilha Solteira, Ilha Solteira, SP, BrazilUniv Estadual Paulista, Fac Engn Ilha Solteira, Ilha Solteira, SP, BrazilIeeeMato Grosso State UnivUniversidade Estadual Paulista (Unesp)Camargo, VeraLavorato, Marina [UNESP]Romero, Ruben [UNESP]IEEE2020-12-10T22:31:23Z2020-12-10T22:31:23Z2013-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject52013 Ieee Power And Energy Society General Meeting (pes). New York: Ieee, 5 p., 2013.1944-9925http://hdl.handle.net/11449/197430WOS:000331874302022Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2013 Ieee Power And Energy Society General Meeting (pes)info:eu-repo/semantics/openAccess2021-10-23T14:40:26Zoai:repositorio.unesp.br:11449/197430Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T14:40:26Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Specialized Genetic Algorithm to Solve the Electrical Distribution System Expansion Planning
title Specialized Genetic Algorithm to Solve the Electrical Distribution System Expansion Planning
spellingShingle Specialized Genetic Algorithm to Solve the Electrical Distribution System Expansion Planning
Camargo, Vera
Distribution network planning
genetic algorithm
mixed integer nonlinear programming
power systems optimization
title_short Specialized Genetic Algorithm to Solve the Electrical Distribution System Expansion Planning
title_full Specialized Genetic Algorithm to Solve the Electrical Distribution System Expansion Planning
title_fullStr Specialized Genetic Algorithm to Solve the Electrical Distribution System Expansion Planning
title_full_unstemmed Specialized Genetic Algorithm to Solve the Electrical Distribution System Expansion Planning
title_sort Specialized Genetic Algorithm to Solve the Electrical Distribution System Expansion Planning
author Camargo, Vera
author_facet Camargo, Vera
Lavorato, Marina [UNESP]
Romero, Ruben [UNESP]
IEEE
author_role author
author2 Lavorato, Marina [UNESP]
Romero, Ruben [UNESP]
IEEE
author2_role author
author
author
dc.contributor.none.fl_str_mv Mato Grosso State Univ
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Camargo, Vera
Lavorato, Marina [UNESP]
Romero, Ruben [UNESP]
IEEE
dc.subject.por.fl_str_mv Distribution network planning
genetic algorithm
mixed integer nonlinear programming
power systems optimization
topic Distribution network planning
genetic algorithm
mixed integer nonlinear programming
power systems optimization
description A specialized genetic algorithm with a adaptation of Chu-Beasley algorithm is presented in this paper to solve the electrical distribution distribution system expansion planning (DSP) problem modeled by a mixed integer nonlinear programming problem. The specialized genetic algorithm proposed in this paper starting from a initial population where all elements have a radial topology found using a heuristic algorithm and after the selection and mutation operations must also go through a local improvement in order to make the proposed solution in a feasible solution, if necessary, with respect to operational constraints. The DSP problem presented in this paper consider the circuit construction/recondutoring for different types of conductors and the substation construct/reinforcement. To evaluate the quality of the proposed methodology were used three different test systems found in the literature, 23, 54 and 136 buses systems.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01
2020-12-10T22:31:23Z
2020-12-10T22:31:23Z
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 2013 Ieee Power And Energy Society General Meeting (pes). New York: Ieee, 5 p., 2013.
1944-9925
http://hdl.handle.net/11449/197430
WOS:000331874302022
identifier_str_mv 2013 Ieee Power And Energy Society General Meeting (pes). New York: Ieee, 5 p., 2013.
1944-9925
WOS:000331874302022
url http://hdl.handle.net/11449/197430
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2013 Ieee Power And Energy Society General Meeting (pes)
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eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
dc.source.none.fl_str_mv Web of Science
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)
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