Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TLA.2010.5538398 http://hdl.handle.net/11449/9885 |
Resumo: | This work presents the application of a multiobjective evolutionary algorithm (MOEA) for optimal power flow (OPF) solution. The OPF is modeled as a constrained nonlinear optimization problem, non-convex of large-scale, with continuous and discrete variables. The violated inequality constraints are treated as objective function of the problem. This strategy allows attending the physical and operational restrictions without compromise the quality of the found solutions. The developed MOEA is based on the theory of Pareto and employs a diversity-preserving mechanism to overcome the premature convergence of algorithm and local optimal solutions. Fuzzy set theory is employed to extract the best compromises of the Pareto set. Results for the IEEE-30, RTS-96 and IEEE-354 test systems are presents to validate the efficiency of proposed model and solution technique. |
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Repositório Institucional da UNESP |
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Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow ProblemMultiobjective Evolutionary AlgorithmOptimal Power FlowMultiobjective OptimizationThis work presents the application of a multiobjective evolutionary algorithm (MOEA) for optimal power flow (OPF) solution. The OPF is modeled as a constrained nonlinear optimization problem, non-convex of large-scale, with continuous and discrete variables. The violated inequality constraints are treated as objective function of the problem. This strategy allows attending the physical and operational restrictions without compromise the quality of the found solutions. The developed MOEA is based on the theory of Pareto and employs a diversity-preserving mechanism to overcome the premature convergence of algorithm and local optimal solutions. Fuzzy set theory is employed to extract the best compromises of the Pareto set. Results for the IEEE-30, RTS-96 and IEEE-354 test systems are presents to validate the efficiency of proposed model and solution technique.Univ Fed Mato Grosso UFMS, Depto Engn Eletr, Campo Grande, MS, BrazilUNESP, Depto Engn Eletr, Ilha Solteira, SP, BrazilUNESP, Depto Engn Eletr, Ilha Solteira, SP, BrazilInstitute of Electrical and Electronics Engineers (IEEE)Universidade Federal de Mato Grosso do Sul (UFMS)Universidade Estadual Paulista (Unesp)Amorim, E. A.Hashimoto, S. H. M.Lima, F. G. M.Mantovani, J. R. S. [UNESP]2014-05-20T13:29:21Z2014-05-20T13:29:21Z2010-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article236-244application/pdfhttp://dx.doi.org/10.1109/TLA.2010.5538398IEEE Latin America Transactions. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc, v. 8, n. 3, p. 236-244, 2010.1548-0992http://hdl.handle.net/11449/988510.1109/TLA.2010.5538398WOS:000283584700006WOS000283584700006.pdf0614021283361265Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporIEEE Latin America Transactions0.5020,253info:eu-repo/semantics/openAccess2023-11-29T06:12:07Zoai:repositorio.unesp.br:11449/9885Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-11-29T06:12:07Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem |
title |
Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem |
spellingShingle |
Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem Amorim, E. A. Multiobjective Evolutionary Algorithm Optimal Power Flow Multiobjective Optimization |
title_short |
Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem |
title_full |
Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem |
title_fullStr |
Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem |
title_full_unstemmed |
Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem |
title_sort |
Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem |
author |
Amorim, E. A. |
author_facet |
Amorim, E. A. Hashimoto, S. H. M. Lima, F. G. M. Mantovani, J. R. S. [UNESP] |
author_role |
author |
author2 |
Hashimoto, S. H. M. Lima, F. G. M. Mantovani, J. R. S. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Mato Grosso do Sul (UFMS) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Amorim, E. A. Hashimoto, S. H. M. Lima, F. G. M. Mantovani, J. R. S. [UNESP] |
dc.subject.por.fl_str_mv |
Multiobjective Evolutionary Algorithm Optimal Power Flow Multiobjective Optimization |
topic |
Multiobjective Evolutionary Algorithm Optimal Power Flow Multiobjective Optimization |
description |
This work presents the application of a multiobjective evolutionary algorithm (MOEA) for optimal power flow (OPF) solution. The OPF is modeled as a constrained nonlinear optimization problem, non-convex of large-scale, with continuous and discrete variables. The violated inequality constraints are treated as objective function of the problem. This strategy allows attending the physical and operational restrictions without compromise the quality of the found solutions. The developed MOEA is based on the theory of Pareto and employs a diversity-preserving mechanism to overcome the premature convergence of algorithm and local optimal solutions. Fuzzy set theory is employed to extract the best compromises of the Pareto set. Results for the IEEE-30, RTS-96 and IEEE-354 test systems are presents to validate the efficiency of proposed model and solution technique. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-06-01 2014-05-20T13:29:21Z 2014-05-20T13:29:21Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/TLA.2010.5538398 IEEE Latin America Transactions. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc, v. 8, n. 3, p. 236-244, 2010. 1548-0992 http://hdl.handle.net/11449/9885 10.1109/TLA.2010.5538398 WOS:000283584700006 WOS000283584700006.pdf 0614021283361265 |
url |
http://dx.doi.org/10.1109/TLA.2010.5538398 http://hdl.handle.net/11449/9885 |
identifier_str_mv |
IEEE Latin America Transactions. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc, v. 8, n. 3, p. 236-244, 2010. 1548-0992 10.1109/TLA.2010.5538398 WOS:000283584700006 WOS000283584700006.pdf 0614021283361265 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
IEEE Latin America Transactions 0.502 0,253 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
236-244 application/pdf |
dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers (IEEE) |
publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers (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_ |
1799965106665160704 |