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://dx.doi.org/10.1109/PESMG.2013.6672615 http://hdl.handle.net/11449/232223 |
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. © 2013 IEEE. |
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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. © 2013 IEEE.Department of Mathematics of Sinop Mato Grosso State University, Sinop - MTFaculty of Engineering of Ilha Solteira Paulista State University, Ilha Solteira SPFaculty of Engineering of Ilha Solteira Paulista State University, Ilha Solteira SPMato Grosso State UniversityUniversidade Estadual Paulista (UNESP)Camargo, VeraLavorato, Marina [UNESP]Romero, Rubén [UNESP]2022-04-29T09:23:53Z2022-04-29T09:23:53Z2013-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/PESMG.2013.6672615IEEE Power and Energy Society General Meeting.1944-99251944-9933http://hdl.handle.net/11449/23222310.1109/PESMG.2013.66726152-s2.0-84893174914Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Power and Energy Society General Meetinginfo:eu-repo/semantics/openAccess2024-07-04T19:07:14Zoai:repositorio.unesp.br:11449/232223Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T13:32:20.570829Repositório 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, Rubén [UNESP] |
author_role |
author |
author2 |
Lavorato, Marina [UNESP] Romero, Rubén [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Mato Grosso State University Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Camargo, Vera Lavorato, Marina [UNESP] Romero, Rubén [UNESP] |
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. © 2013 IEEE. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-12-01 2022-04-29T09:23:53Z 2022-04-29T09:23:53Z |
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/PESMG.2013.6672615 IEEE Power and Energy Society General Meeting. 1944-9925 1944-9933 http://hdl.handle.net/11449/232223 10.1109/PESMG.2013.6672615 2-s2.0-84893174914 |
url |
http://dx.doi.org/10.1109/PESMG.2013.6672615 http://hdl.handle.net/11449/232223 |
identifier_str_mv |
IEEE Power and Energy Society General Meeting. 1944-9925 1944-9933 10.1109/PESMG.2013.6672615 2-s2.0-84893174914 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IEEE Power and Energy Society General Meeting |
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_ |
1808128244240613376 |