Modelagem das curvas tempo x corrente de elos fusíveis do tipo expulsão por meio de redes neurais artificias
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/16569 |
Resumo: | Protection studies are essential to maintain the levels of energy supply in accordance with standards imposed by regulatory agencies. Currently, this type of study is carried out through computational tools. Therefore, a correct modeling of protection devices is essential. Among the devices used in power distribution systems protection, the most used ones are fuse cutouts. Fuse cutout consists of 3 components: base, fuse holder and fuse link. The fuse link is composed by 3 curves: Minimum Melting (MM), maximum melting and Total Clearing curve (TC). In this way, numerous works model the MM and TC curves of the fuse links through mathematical expressions. Due to the non-linear behavior of the curves, this task becomes complex. In order to overcome this adversity, this dissertation proposes the use of Artificial Neural Networks (ANNs). The results obtained are presented and a comparative analysis with other works is carried out. In addition to RNA, two mathematical functions were evaluated for modeling the TCC curves of the preferred “K” and “H” fuse links, with RNA being the technique that obtained the best results. The MATLAB software was used to develop the methods. To evaluate the models, the IEEE 34 Node test feeder was implemented in the DIgSILENT software. The system was modified for the insertion of fuse cutouts and through the Monte Carlo Method short circuits were applied at the end of each branch. In this way, the operating time of the fuse links was obtained. The operating times show that the proposed methodology provides a satisfactory and promising TCC model for implementation in programs dedicated to protection studies. |
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2019-05-16T13:50:47Z2019-05-16T13:50:47Z2018-08-06http://repositorio.ufsm.br/handle/1/16569Protection studies are essential to maintain the levels of energy supply in accordance with standards imposed by regulatory agencies. Currently, this type of study is carried out through computational tools. Therefore, a correct modeling of protection devices is essential. Among the devices used in power distribution systems protection, the most used ones are fuse cutouts. Fuse cutout consists of 3 components: base, fuse holder and fuse link. The fuse link is composed by 3 curves: Minimum Melting (MM), maximum melting and Total Clearing curve (TC). In this way, numerous works model the MM and TC curves of the fuse links through mathematical expressions. Due to the non-linear behavior of the curves, this task becomes complex. In order to overcome this adversity, this dissertation proposes the use of Artificial Neural Networks (ANNs). The results obtained are presented and a comparative analysis with other works is carried out. In addition to RNA, two mathematical functions were evaluated for modeling the TCC curves of the preferred “K” and “H” fuse links, with RNA being the technique that obtained the best results. The MATLAB software was used to develop the methods. To evaluate the models, the IEEE 34 Node test feeder was implemented in the DIgSILENT software. The system was modified for the insertion of fuse cutouts and through the Monte Carlo Method short circuits were applied at the end of each branch. In this way, the operating time of the fuse links was obtained. The operating times show that the proposed methodology provides a satisfactory and promising TCC model for implementation in programs dedicated to protection studies.Estudos de proteção são essenciais para garantir a segurança e manter os níveis de fornecimento de energia elétrica de acordo com as normas impostas pelas agências reguladoras. Atualmente, este tipo de estudo normalmente é realizado por meio de ferramentas computacionais. Logo, a correta modelagem dos dispositivos de proteção é essencial. Entre os dispositivos utilizados nos sistemas de distribuição, os mais utilizados são as chaves fusíveis. A chave fusível é constituída de 3 componentes: base, porta fusível e elo fusível. O elo fusível é composto por 3 curvas: Mínima Fusão (MF), Máxima Fusão e Máxima Interrupção (MI). Desta forma, inúmeros trabalhos modelam as curvas MF e MI do elo fusível através de expressões matemáticas. Devido ao comportamento não linear das curvas, esta tarefa torna-se complexa. Com o intuito de sobrepor tal adversidade, esta dissertação propõe a utilização de Redes Neurais Artificiais (RNAs). Os resultados obtidos são apresentados e uma análise comparativa com outros trabalhos é realizada. Além da RNA, duas funções matemáticas foram avaliadas na modelagem das curvas características dos elos fusíveis preferenciais do tipo “K” e “H”, sendo a RNA a técnica que obteve os melhores resultados. Utilizou-se para desenvolvimento dos métodos o software MATLAB. Para avaliação dos modelos, implementou-se o sistema IEEE 34 Nós no software DIgSILENT. O sistema foi modificado para a inserção de chaves fusíveis e através do Método de Monte Carlo aplicou-se curtos-circuitos no fim de cada ramal. Desta forma, obteve-se o tempo de operação dos elos fusíveis. Os tempos de operação mostram que a metodologia proposta fornece um modelo de curva tempo x corrente satisfatório e promissor para implementação em programas dedicados a estudos de proteção.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de TecnologiaPrograma de Pós-Graduação em Engenharia ElétricaUFSMBrasilEngenharia ElétricaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessProteção de redes de distribuição de energia elétricaElos fusíveisRede neural artificialPower distribution systems protectionFuse linksArtificial network neuralCNPQ::ENGENHARIAS::ENGENHARIA ELETRICAModelagem das curvas tempo x corrente de elos fusíveis do tipo expulsão por meio de redes neurais artificiasCurve fitting of expulsion fuse links using artificial neural networkinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisMorais, Adriano Peres dehttp://lattes.cnpq.br/2780595038162903Junior, Ghendy Cardosohttp://lattes.cnpq.br/6284386218725402Guarda, Fernando Guilherme Kaehlerhttp://lattes.cnpq.br/3425190645010192Marchesan, Gustavohttp://lattes.cnpq.br/4254867243649147http://lattes.cnpq.br/6556469824221064Costa, Guilherme Braga da3004000000076004249d7c0-5517-4293-93d0-a69d33cbdb1d19fe7410-87f4-4364-886e-67b2e61052779aa1e80d-3994-4a58-acc3-d7da8ef77d9c2b93caf1-3a67-4c78-b8f6-8dfce6de1d6f0697ee91-b245-4b63-bad9-fd26200fe829reponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGEE_2018_COSTA_GUILHERME.pdfDIS_PPGEE_2018_COSTA_GUILHERME.pdfDissertação de Mestradoapplication/pdf4290421http://repositorio.ufsm.br/bitstream/1/16569/1/DIS_PPGEE_2018_COSTA_GUILHERME.pdff5e8190ee19086b4cfd54bdc2d861e3dMD51LICENSElicense.txtlicense.txttext/plain; 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dc.title.por.fl_str_mv |
Modelagem das curvas tempo x corrente de elos fusíveis do tipo expulsão por meio de redes neurais artificias |
dc.title.alternative.eng.fl_str_mv |
Curve fitting of expulsion fuse links using artificial neural network |
title |
Modelagem das curvas tempo x corrente de elos fusíveis do tipo expulsão por meio de redes neurais artificias |
spellingShingle |
Modelagem das curvas tempo x corrente de elos fusíveis do tipo expulsão por meio de redes neurais artificias Costa, Guilherme Braga da Proteção de redes de distribuição de energia elétrica Elos fusíveis Rede neural artificial Power distribution systems protection Fuse links Artificial network neural CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
title_short |
Modelagem das curvas tempo x corrente de elos fusíveis do tipo expulsão por meio de redes neurais artificias |
title_full |
Modelagem das curvas tempo x corrente de elos fusíveis do tipo expulsão por meio de redes neurais artificias |
title_fullStr |
Modelagem das curvas tempo x corrente de elos fusíveis do tipo expulsão por meio de redes neurais artificias |
title_full_unstemmed |
Modelagem das curvas tempo x corrente de elos fusíveis do tipo expulsão por meio de redes neurais artificias |
title_sort |
Modelagem das curvas tempo x corrente de elos fusíveis do tipo expulsão por meio de redes neurais artificias |
author |
Costa, Guilherme Braga da |
author_facet |
Costa, Guilherme Braga da |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Morais, Adriano Peres de |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2780595038162903 |
dc.contributor.advisor-co1.fl_str_mv |
Junior, Ghendy Cardoso |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/6284386218725402 |
dc.contributor.referee1.fl_str_mv |
Guarda, Fernando Guilherme Kaehler |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/3425190645010192 |
dc.contributor.referee2.fl_str_mv |
Marchesan, Gustavo |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/4254867243649147 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/6556469824221064 |
dc.contributor.author.fl_str_mv |
Costa, Guilherme Braga da |
contributor_str_mv |
Morais, Adriano Peres de Junior, Ghendy Cardoso Guarda, Fernando Guilherme Kaehler Marchesan, Gustavo |
dc.subject.por.fl_str_mv |
Proteção de redes de distribuição de energia elétrica Elos fusíveis Rede neural artificial |
topic |
Proteção de redes de distribuição de energia elétrica Elos fusíveis Rede neural artificial Power distribution systems protection Fuse links Artificial network neural CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
dc.subject.eng.fl_str_mv |
Power distribution systems protection Fuse links Artificial network neural |
dc.subject.cnpq.fl_str_mv |
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
description |
Protection studies are essential to maintain the levels of energy supply in accordance with standards imposed by regulatory agencies. Currently, this type of study is carried out through computational tools. Therefore, a correct modeling of protection devices is essential. Among the devices used in power distribution systems protection, the most used ones are fuse cutouts. Fuse cutout consists of 3 components: base, fuse holder and fuse link. The fuse link is composed by 3 curves: Minimum Melting (MM), maximum melting and Total Clearing curve (TC). In this way, numerous works model the MM and TC curves of the fuse links through mathematical expressions. Due to the non-linear behavior of the curves, this task becomes complex. In order to overcome this adversity, this dissertation proposes the use of Artificial Neural Networks (ANNs). The results obtained are presented and a comparative analysis with other works is carried out. In addition to RNA, two mathematical functions were evaluated for modeling the TCC curves of the preferred “K” and “H” fuse links, with RNA being the technique that obtained the best results. The MATLAB software was used to develop the methods. To evaluate the models, the IEEE 34 Node test feeder was implemented in the DIgSILENT software. The system was modified for the insertion of fuse cutouts and through the Monte Carlo Method short circuits were applied at the end of each branch. In this way, the operating time of the fuse links was obtained. The operating times show that the proposed methodology provides a satisfactory and promising TCC model for implementation in programs dedicated to protection studies. |
publishDate |
2018 |
dc.date.issued.fl_str_mv |
2018-08-06 |
dc.date.accessioned.fl_str_mv |
2019-05-16T13:50:47Z |
dc.date.available.fl_str_mv |
2019-05-16T13:50:47Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/16569 |
url |
http://repositorio.ufsm.br/handle/1/16569 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
300400000007 |
dc.relation.confidence.fl_str_mv |
600 |
dc.relation.authority.fl_str_mv |
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dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Engenharia Elétrica |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Engenharia Elétrica |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
dc.source.none.fl_str_mv |
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