Coordinated tuning of the parameters of PI, PSS and POD controllers using a Specialized Chu-Beasley's Genetic Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.epsr.2016.04.019 http://hdl.handle.net/11449/165311 |
Resumo: | This paper presents a Specialized Chu-Beasley's Genetic Algorithm (SCBGA) to perform coordinated tuning of the parameters of proportional-integral and supplementary damping controllers (power system stabilizers and interline power flow controller-power oscillation damping) in-multi-machine electric power systems. The objective is to insert additional damping to low frequency electromechanical oscillations. The current sensitivity model was used to represent the system, therefore all of its devices and components were modeled by current injection. A novel current injection model for the interline power flow controller is presented and a static analysis is considered to validate it. The New England test system - consisting of 10 generators, 39 buses, and 46 transmission lines, divided into two areas with both local and inter-area oscillation modes - was used for the simulations. The SCBGA was compared to other five algorithms: a Random Search, a Local Search, a Simulated Annealing, a Genetic Algorithm, and a Particle Swarm Optimization method, in terms of performance for the tuning of the parameters of the controllers. The results demonstrated that the SCBGA was more efficient than these other techniques. In addition, the obtained solutions proved to be robust when variation of the loads was considered. (C) 2016 Elsevier B.V. All rights reserved. |
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Coordinated tuning of the parameters of PI, PSS and POD controllers using a Specialized Chu-Beasley's Genetic AlgorithmCurrent sensitivity modelInterline power flow controllerPower system stabilizersPower oscillation dampingSpecialized Chu-Beasley's Genetic AlgorithmThis paper presents a Specialized Chu-Beasley's Genetic Algorithm (SCBGA) to perform coordinated tuning of the parameters of proportional-integral and supplementary damping controllers (power system stabilizers and interline power flow controller-power oscillation damping) in-multi-machine electric power systems. The objective is to insert additional damping to low frequency electromechanical oscillations. The current sensitivity model was used to represent the system, therefore all of its devices and components were modeled by current injection. A novel current injection model for the interline power flow controller is presented and a static analysis is considered to validate it. The New England test system - consisting of 10 generators, 39 buses, and 46 transmission lines, divided into two areas with both local and inter-area oscillation modes - was used for the simulations. The SCBGA was compared to other five algorithms: a Random Search, a Local Search, a Simulated Annealing, a Genetic Algorithm, and a Particle Swarm Optimization method, in terms of performance for the tuning of the parameters of the controllers. The results demonstrated that the SCBGA was more efficient than these other techniques. In addition, the obtained solutions proved to be robust when variation of the loads was considered. (C) 2016 Elsevier B.V. All rights reserved.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Inst Fed Goias, Rua Maria Vieira Cunha 775, BR-75804714 Jatai, Go, BrazilUniv Estadual Paulista, Av Brasil 56,POB 31, BR-15385000 Ilha Solteira, SP, BrazilUniv Estadual Paulista, Av Brasil 56,POB 31, BR-15385000 Ilha Solteira, SP, BrazilCNPq: 141084/2016-2FAPESP: 2014/23741-9Elsevier B.V.Inst Fed GoiasUniversidade Estadual Paulista (Unesp)Fortes, Elenilson de VargasAraujo, Percival Bueno de [UNESP]Macedo, Leonardo H. [UNESP]2018-11-27T21:22:00Z2018-11-27T21:22:00Z2016-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article708-721application/pdfhttp://dx.doi.org/10.1016/j.epsr.2016.04.019Electric Power Systems Research. Lausanne: Elsevier Science Sa, v. 140, p. 708-721, 2016.0378-7796http://hdl.handle.net/11449/16531110.1016/j.epsr.2016.04.019WOS:000383527300074WOS000383527300074.pdf81326306032194510000-0002-2491-6254Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengElectric Power Systems Research1,048info:eu-repo/semantics/openAccess2023-10-15T06:03:33Zoai:repositorio.unesp.br:11449/165311Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-10-15T06:03:33Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Coordinated tuning of the parameters of PI, PSS and POD controllers using a Specialized Chu-Beasley's Genetic Algorithm |
title |
Coordinated tuning of the parameters of PI, PSS and POD controllers using a Specialized Chu-Beasley's Genetic Algorithm |
spellingShingle |
Coordinated tuning of the parameters of PI, PSS and POD controllers using a Specialized Chu-Beasley's Genetic Algorithm Fortes, Elenilson de Vargas Current sensitivity model Interline power flow controller Power system stabilizers Power oscillation damping Specialized Chu-Beasley's Genetic Algorithm |
title_short |
Coordinated tuning of the parameters of PI, PSS and POD controllers using a Specialized Chu-Beasley's Genetic Algorithm |
title_full |
Coordinated tuning of the parameters of PI, PSS and POD controllers using a Specialized Chu-Beasley's Genetic Algorithm |
title_fullStr |
Coordinated tuning of the parameters of PI, PSS and POD controllers using a Specialized Chu-Beasley's Genetic Algorithm |
title_full_unstemmed |
Coordinated tuning of the parameters of PI, PSS and POD controllers using a Specialized Chu-Beasley's Genetic Algorithm |
title_sort |
Coordinated tuning of the parameters of PI, PSS and POD controllers using a Specialized Chu-Beasley's Genetic Algorithm |
author |
Fortes, Elenilson de Vargas |
author_facet |
Fortes, Elenilson de Vargas Araujo, Percival Bueno de [UNESP] Macedo, Leonardo H. [UNESP] |
author_role |
author |
author2 |
Araujo, Percival Bueno de [UNESP] Macedo, Leonardo H. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Inst Fed Goias Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Fortes, Elenilson de Vargas Araujo, Percival Bueno de [UNESP] Macedo, Leonardo H. [UNESP] |
dc.subject.por.fl_str_mv |
Current sensitivity model Interline power flow controller Power system stabilizers Power oscillation damping Specialized Chu-Beasley's Genetic Algorithm |
topic |
Current sensitivity model Interline power flow controller Power system stabilizers Power oscillation damping Specialized Chu-Beasley's Genetic Algorithm |
description |
This paper presents a Specialized Chu-Beasley's Genetic Algorithm (SCBGA) to perform coordinated tuning of the parameters of proportional-integral and supplementary damping controllers (power system stabilizers and interline power flow controller-power oscillation damping) in-multi-machine electric power systems. The objective is to insert additional damping to low frequency electromechanical oscillations. The current sensitivity model was used to represent the system, therefore all of its devices and components were modeled by current injection. A novel current injection model for the interline power flow controller is presented and a static analysis is considered to validate it. The New England test system - consisting of 10 generators, 39 buses, and 46 transmission lines, divided into two areas with both local and inter-area oscillation modes - was used for the simulations. The SCBGA was compared to other five algorithms: a Random Search, a Local Search, a Simulated Annealing, a Genetic Algorithm, and a Particle Swarm Optimization method, in terms of performance for the tuning of the parameters of the controllers. The results demonstrated that the SCBGA was more efficient than these other techniques. In addition, the obtained solutions proved to be robust when variation of the loads was considered. (C) 2016 Elsevier B.V. All rights reserved. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-11-01 2018-11-27T21:22:00Z 2018-11-27T21:22:00Z |
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/10.1016/j.epsr.2016.04.019 Electric Power Systems Research. Lausanne: Elsevier Science Sa, v. 140, p. 708-721, 2016. 0378-7796 http://hdl.handle.net/11449/165311 10.1016/j.epsr.2016.04.019 WOS:000383527300074 WOS000383527300074.pdf 8132630603219451 0000-0002-2491-6254 |
url |
http://dx.doi.org/10.1016/j.epsr.2016.04.019 http://hdl.handle.net/11449/165311 |
identifier_str_mv |
Electric Power Systems Research. Lausanne: Elsevier Science Sa, v. 140, p. 708-721, 2016. 0378-7796 10.1016/j.epsr.2016.04.019 WOS:000383527300074 WOS000383527300074.pdf 8132630603219451 0000-0002-2491-6254 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Electric Power Systems Research 1,048 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
708-721 application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier B.V. |
publisher.none.fl_str_mv |
Elsevier B.V. |
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_ |
1803045996204130304 |