Genetic Algorithm Application in Distribution System Reconfiguration

Detalhes bibliográficos
Autor(a) principal: Mahdavi, Meisam [UNESP]
Data de Publicação: 2021
Outros Autores: Siano, Pierluigi, Alhelou, Hassan Haes, Padmanaban, Sanjeevikumar
Tipo de documento: Capítulo de livro
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1002/9781119599593.ch19
http://hdl.handle.net/11449/247154
Resumo: This chapter describes genetic algorithm (GA) in detail and presents several examples to show its efficiency and effectiveness in solving the problem of distribution network reconfiguration. GA includes three basic operators (selection or reproduction, crossover, and mutation) that conduct chromosomes into the best fitness. The proposed GA-based distribution system reconfiguration (DSR) model is applied to several test systems using decimal codification of a branch (DCGA), improved DCGA (IDCGA), and efficient DCGA (EDCGA), and the results are presented in comparison with other GA methods. Evaluation of simulation results show that IDCGA and EDCGA solve the DSR problem in small-sized distribution networks more accurately and faster than DCGA and other genetic algorithms, in which EDCGA is the fastest method for solving DSR. It is concluded that EDCGA is the best method for studying the reconfiguration of radial distribution systems because of its high accuracy and low computational time.
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spelling Genetic Algorithm Application in Distribution System ReconfigurationThis chapter describes genetic algorithm (GA) in detail and presents several examples to show its efficiency and effectiveness in solving the problem of distribution network reconfiguration. GA includes three basic operators (selection or reproduction, crossover, and mutation) that conduct chromosomes into the best fitness. The proposed GA-based distribution system reconfiguration (DSR) model is applied to several test systems using decimal codification of a branch (DCGA), improved DCGA (IDCGA), and efficient DCGA (EDCGA), and the results are presented in comparison with other GA methods. Evaluation of simulation results show that IDCGA and EDCGA solve the DSR problem in small-sized distribution networks more accurately and faster than DCGA and other genetic algorithms, in which EDCGA is the fastest method for solving DSR. It is concluded that EDCGA is the best method for studying the reconfiguration of radial distribution systems because of its high accuracy and low computational time.Department of Electrical Engineering São Paulo State University, SPDepartment of Management and Innovation Systems University of SalernoDepartment of Electrical Power Engineering Tishreen University, LattakiaCTIF Global Capsule (CGC) Laboratory Department of Business Development and Technology Aarhus UniversityDepartment of Electrical Engineering São Paulo State University, SPUniversidade Estadual Paulista (UNESP)University of SalernoTishreen UniversityAarhus UniversityMahdavi, Meisam [UNESP]Siano, PierluigiAlhelou, Hassan HaesPadmanaban, Sanjeevikumar2023-07-29T13:07:51Z2023-07-29T13:07:51Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookPart479-516http://dx.doi.org/10.1002/9781119599593.ch19Active Electrical Distribution Network: A Smart Approach, p. 479-516.http://hdl.handle.net/11449/24715410.1002/9781119599593.ch192-s2.0-85152343011Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengActive Electrical Distribution Network: A Smart Approachinfo:eu-repo/semantics/openAccess2023-07-29T13:07:51Zoai:repositorio.unesp.br:11449/247154Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-07-29T13:07:51Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Genetic Algorithm Application in Distribution System Reconfiguration
title Genetic Algorithm Application in Distribution System Reconfiguration
spellingShingle Genetic Algorithm Application in Distribution System Reconfiguration
Mahdavi, Meisam [UNESP]
title_short Genetic Algorithm Application in Distribution System Reconfiguration
title_full Genetic Algorithm Application in Distribution System Reconfiguration
title_fullStr Genetic Algorithm Application in Distribution System Reconfiguration
title_full_unstemmed Genetic Algorithm Application in Distribution System Reconfiguration
title_sort Genetic Algorithm Application in Distribution System Reconfiguration
author Mahdavi, Meisam [UNESP]
author_facet Mahdavi, Meisam [UNESP]
Siano, Pierluigi
Alhelou, Hassan Haes
Padmanaban, Sanjeevikumar
author_role author
author2 Siano, Pierluigi
Alhelou, Hassan Haes
Padmanaban, Sanjeevikumar
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
University of Salerno
Tishreen University
Aarhus University
dc.contributor.author.fl_str_mv Mahdavi, Meisam [UNESP]
Siano, Pierluigi
Alhelou, Hassan Haes
Padmanaban, Sanjeevikumar
description This chapter describes genetic algorithm (GA) in detail and presents several examples to show its efficiency and effectiveness in solving the problem of distribution network reconfiguration. GA includes three basic operators (selection or reproduction, crossover, and mutation) that conduct chromosomes into the best fitness. The proposed GA-based distribution system reconfiguration (DSR) model is applied to several test systems using decimal codification of a branch (DCGA), improved DCGA (IDCGA), and efficient DCGA (EDCGA), and the results are presented in comparison with other GA methods. Evaluation of simulation results show that IDCGA and EDCGA solve the DSR problem in small-sized distribution networks more accurately and faster than DCGA and other genetic algorithms, in which EDCGA is the fastest method for solving DSR. It is concluded that EDCGA is the best method for studying the reconfiguration of radial distribution systems because of its high accuracy and low computational time.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01
2023-07-29T13:07:51Z
2023-07-29T13:07:51Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1002/9781119599593.ch19
Active Electrical Distribution Network: A Smart Approach, p. 479-516.
http://hdl.handle.net/11449/247154
10.1002/9781119599593.ch19
2-s2.0-85152343011
url http://dx.doi.org/10.1002/9781119599593.ch19
http://hdl.handle.net/11449/247154
identifier_str_mv Active Electrical Distribution Network: A Smart Approach, p. 479-516.
10.1002/9781119599593.ch19
2-s2.0-85152343011
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Active Electrical Distribution Network: A Smart Approach
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 479-516
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_ 1803046212092297216

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