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, Rubén [UNESP]
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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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. © 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
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