Specialized Genetic Algorithm to Solve the Electrical Distribution System Expansion Planning
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , |
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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Repositório Institucional da UNESP |
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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) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
5 |
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) |
repository.mail.fl_str_mv |
|
_version_ |
1803047208069627904 |