Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP

Detalhes bibliográficos
Autor(a) principal: Silveira, M. C G [UNESP]
Data de Publicação: 2003
Outros Autores: Lotufo, A. D P [UNESP], Minussi, C. R. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/PTC.2003.1304414
http://hdl.handle.net/11449/67496
Resumo: This work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.
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spelling Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAPAdaptive resonance theoryFuzzy ARTMAPNeural networkPower systemsTransient stability analysisElectric energy systemsElectrical power systemFuzzy ARTMAP architectureSynchronous machineFrequency stabilityFuzzy set theoryNeural networksQuality controlStandby power systemsSynchronous machineryTransient analysisPower qualityThis work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.UNESP, Ilha Solteira, SPUNESP, Ilha Solteira, SPUniversidade Estadual Paulista (Unesp)Silveira, M. C G [UNESP]Lotufo, A. D P [UNESP]Minussi, C. R. [UNESP]2014-05-27T11:20:56Z2014-05-27T11:20:56Z2003-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject339-345http://dx.doi.org/10.1109/PTC.2003.13044142003 IEEE Bologna PowerTech - Conference Proceedings, v. 3, p. 339-345.http://hdl.handle.net/11449/6749610.1109/PTC.2003.13044142-s2.0-848614962917166279400544764Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2003 IEEE Bologna PowerTech - Conference Proceedingsinfo:eu-repo/semantics/openAccess2021-10-23T04:16:02Zoai:repositorio.unesp.br:11449/67496Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T04:16:02Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP
title Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP
spellingShingle Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP
Silveira, M. C G [UNESP]
Adaptive resonance theory
Fuzzy ARTMAP
Neural network
Power systems
Transient stability analysis
Electric energy systems
Electrical power system
Fuzzy ARTMAP architecture
Synchronous machine
Frequency stability
Fuzzy set theory
Neural networks
Quality control
Standby power systems
Synchronous machinery
Transient analysis
Power quality
title_short Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP
title_full Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP
title_fullStr Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP
title_full_unstemmed Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP
title_sort Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP
author Silveira, M. C G [UNESP]
author_facet Silveira, M. C G [UNESP]
Lotufo, A. D P [UNESP]
Minussi, C. R. [UNESP]
author_role author
author2 Lotufo, A. D P [UNESP]
Minussi, C. R. [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Silveira, M. C G [UNESP]
Lotufo, A. D P [UNESP]
Minussi, C. R. [UNESP]
dc.subject.por.fl_str_mv Adaptive resonance theory
Fuzzy ARTMAP
Neural network
Power systems
Transient stability analysis
Electric energy systems
Electrical power system
Fuzzy ARTMAP architecture
Synchronous machine
Frequency stability
Fuzzy set theory
Neural networks
Quality control
Standby power systems
Synchronous machinery
Transient analysis
Power quality
topic Adaptive resonance theory
Fuzzy ARTMAP
Neural network
Power systems
Transient stability analysis
Electric energy systems
Electrical power system
Fuzzy ARTMAP architecture
Synchronous machine
Frequency stability
Fuzzy set theory
Neural networks
Quality control
Standby power systems
Synchronous machinery
Transient analysis
Power quality
description This work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.
publishDate 2003
dc.date.none.fl_str_mv 2003-12-01
2014-05-27T11:20:56Z
2014-05-27T11:20:56Z
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/PTC.2003.1304414
2003 IEEE Bologna PowerTech - Conference Proceedings, v. 3, p. 339-345.
http://hdl.handle.net/11449/67496
10.1109/PTC.2003.1304414
2-s2.0-84861496291
7166279400544764
url http://dx.doi.org/10.1109/PTC.2003.1304414
http://hdl.handle.net/11449/67496
identifier_str_mv 2003 IEEE Bologna PowerTech - Conference Proceedings, v. 3, p. 339-345.
10.1109/PTC.2003.1304414
2-s2.0-84861496291
7166279400544764
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2003 IEEE Bologna PowerTech - Conference Proceedings
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 339-345
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_ 1799965510162448384

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