Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFS |
Texto Completo: | http://ri.ufs.br/jspui/handle/riufs/16894 |
Resumo: | Transmission lines transport the energy produced in power plants to distribution centers. In these lines, insulators perform the role of segregating regions of different electrical potential, while accomplishing the mechanical function of supporting the cables. Due to the nature of their function, insulators are exposed to electrical and mechanical stress throughout their life span, in addition to withstanding the wear caused by the environment through solar radiation, moisture, pollution and other weather conditions. Regarding polymeric high voltage insulators, which are the kind of insulator most widely used in the current scenario, these stresses lead to a decrease in their surface hydrophobicity, allowing moisture to accumulate on the insulator, giving rise to a leakage current and increasing the probability of a flashover to occur. In this sense, the present work presents a methodology for evaluating and classifying the surface hydrophobicity of polymeric high voltage insulators based on the method proposed by the Swedish Transmission Research Institute (STRI). The classification is performed automatically, using a Multilayer Perceptron artificial neural network, based on digital image processing, using spatial frequency information. A method for segmenting hydrophobicity images produced under unbalanced lighting conditions and with low contrast is also proposed. Furthermore, an image database containing 1200 hydrophobic surface samples in various stages of degradation was created. Images of an insulating column collected in the environment of a 500 kV substation were also used to validate the proposed method. The results obtained were compared with two other methods in the literature and it could be seen that the methodology developed was able to successfully segment and classify surface hydrophobicity images, obtaining a success rate above 78% for the database used. |
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Macedo, Matheus SantosFerreira, Tarso Vilela2022-12-13T17:37:04Z2022-12-13T17:37:04Z2022-08-29MACEDO, Matheus Santos. Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA. 2022. 123 f. Dissertação (Mestrado em Engenharia Elétrica) - Universidade Federal de Sergipe, São Cristóvão, 2022.http://ri.ufs.br/jspui/handle/riufs/16894Transmission lines transport the energy produced in power plants to distribution centers. In these lines, insulators perform the role of segregating regions of different electrical potential, while accomplishing the mechanical function of supporting the cables. Due to the nature of their function, insulators are exposed to electrical and mechanical stress throughout their life span, in addition to withstanding the wear caused by the environment through solar radiation, moisture, pollution and other weather conditions. Regarding polymeric high voltage insulators, which are the kind of insulator most widely used in the current scenario, these stresses lead to a decrease in their surface hydrophobicity, allowing moisture to accumulate on the insulator, giving rise to a leakage current and increasing the probability of a flashover to occur. In this sense, the present work presents a methodology for evaluating and classifying the surface hydrophobicity of polymeric high voltage insulators based on the method proposed by the Swedish Transmission Research Institute (STRI). The classification is performed automatically, using a Multilayer Perceptron artificial neural network, based on digital image processing, using spatial frequency information. A method for segmenting hydrophobicity images produced under unbalanced lighting conditions and with low contrast is also proposed. Furthermore, an image database containing 1200 hydrophobic surface samples in various stages of degradation was created. Images of an insulating column collected in the environment of a 500 kV substation were also used to validate the proposed method. The results obtained were compared with two other methods in the literature and it could be seen that the methodology developed was able to successfully segment and classify surface hydrophobicity images, obtaining a success rate above 78% for the database used.As linhas de transmissão realizam o transporte da energia produzida nas centrais geradoras até os centros de distribuição. Nessas linhas, os isoladores desempenham o papel de segregar regiões de potencial elétrico diferente, enquanto realizam a função mecânica de suporte dos cabos. Devido à natureza da sua função, os isoladores estão expostos a esforços elétricos e mecânicos durante toda sua vida útil, além de suportarem a ação do meio ambiente através da irradiação solar, umidade, poluição e demais intempéries. No que tange aos isoladores de alta tensão do tipo polimérico, muito empregados no cenário atual, esses esforços levam à diminuição da sua hidrofobicidade de superfície, permitindo que a umidade se acumule sobre o isolador, facilitando o aumento da corrente de fuga e a probabilidade de ocorrência de uma descarga disruptiva (flashover). Nesse sentido, o presente trabalho apresenta uma metodologia para avaliação e classificação da hidrofobicidade de superfície de isoladores de alta tensão do tipo polimérico com base no método proposto pelo Swedish Transmission Research Institute (STRI). A classificação proposta é realizada de forma automática, utilizando uma rede neural artificial do tipo Perceptron de Múltiplas Camadas, com base no processamento digital de imagens, utilizando informações de frequência espacial. Um método para segmentação de imagens de hidrofobicidade produzidas sob iluminação não uniforme e com baixo contraste também é proposto. Ademais, uma base de imagens contendo 1200 amostras de superfície hidrofóbica em vários estágios de degradação foi criada. Imagens de uma coluna isolante coletadas no ambiente de uma subestação de 500 kV também foram utilizadas para validar o método. Os resultados obtidos foram comparados com outros dois métodos da literatura e pôde-se perceber que a metodologia desenvolvida foi capaz de segmentar e classificar com sucesso imagens de hidrofobicidade de superfície, obtendo uma taxa de acerto superior a 78% para a base de dados utilizada.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESSão CristóvãoporIsoladores e isolamentos elétricosProcessamento de imagensTécnicos digitaisRedes neurais (Computação)HidrofobicidadeIsoladores poliméricosProcessamento digital de imagensRede Neural ArtificialSwedish Transmission Research Institute (STRI)HydrophobicityPolymeric insulatorsDigital image processingArtificial neural networkENGENHARIAS::ENGENHARIA ELETRICAClassificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNAinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisPós-Graduação em Engenharia ElétricaUniversidade Federal de Sergipereponame:Repositório Institucional da UFSinstname:Universidade Federal de Sergipe (UFS)instacron:UFSinfo:eu-repo/semantics/openAccessORIGINALMATHEUS_SANTOS_MACEDO.pdfMATHEUS_SANTOS_MACEDO.pdfapplication/pdf4252711https://ri.ufs.br/jspui/bitstream/riufs/16894/2/MATHEUS_SANTOS_MACEDO.pdfce63f348ad3e55d7842465b0c6fec550MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81475https://ri.ufs.br/jspui/bitstream/riufs/16894/1/license.txt098cbbf65c2c15e1fb2e49c5d306a44cMD51TEXTMATHEUS_SANTOS_MACEDO.pdf.txtMATHEUS_SANTOS_MACEDO.pdf.txtExtracted texttext/plain217950https://ri.ufs.br/jspui/bitstream/riufs/16894/3/MATHEUS_SANTOS_MACEDO.pdf.txt3b00646fe7421a7746a694b6458c2259MD53THUMBNAILMATHEUS_SANTOS_MACEDO.pdf.jpgMATHEUS_SANTOS_MACEDO.pdf.jpgGenerated Thumbnailimage/jpeg1365https://ri.ufs.br/jspui/bitstream/riufs/16894/4/MATHEUS_SANTOS_MACEDO.pdf.jpgd3c8aa5d9df2d8f34a03d7a152015723MD54riufs/168942022-12-13 14:37:04.321oai:ufs.br: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Repositório InstitucionalPUBhttps://ri.ufs.br/oai/requestrepositorio@academico.ufs.bropendoar:2022-12-13T17:37:04Repositório Institucional da UFS - Universidade Federal de Sergipe (UFS)false |
dc.title.pt_BR.fl_str_mv |
Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA |
title |
Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA |
spellingShingle |
Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA Macedo, Matheus Santos Isoladores e isolamentos elétricos Processamento de imagens Técnicos digitais Redes neurais (Computação) Hidrofobicidade Isoladores poliméricos Processamento digital de imagens Rede Neural Artificial Swedish Transmission Research Institute (STRI) Hydrophobicity Polymeric insulators Digital image processing Artificial neural network ENGENHARIAS::ENGENHARIA ELETRICA |
title_short |
Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA |
title_full |
Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA |
title_fullStr |
Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA |
title_full_unstemmed |
Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA |
title_sort |
Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA |
author |
Macedo, Matheus Santos |
author_facet |
Macedo, Matheus Santos |
author_role |
author |
dc.contributor.author.fl_str_mv |
Macedo, Matheus Santos |
dc.contributor.advisor1.fl_str_mv |
Ferreira, Tarso Vilela |
contributor_str_mv |
Ferreira, Tarso Vilela |
dc.subject.por.fl_str_mv |
Isoladores e isolamentos elétricos Processamento de imagens Técnicos digitais Redes neurais (Computação) Hidrofobicidade Isoladores poliméricos Processamento digital de imagens Rede Neural Artificial |
topic |
Isoladores e isolamentos elétricos Processamento de imagens Técnicos digitais Redes neurais (Computação) Hidrofobicidade Isoladores poliméricos Processamento digital de imagens Rede Neural Artificial Swedish Transmission Research Institute (STRI) Hydrophobicity Polymeric insulators Digital image processing Artificial neural network ENGENHARIAS::ENGENHARIA ELETRICA |
dc.subject.eng.fl_str_mv |
Swedish Transmission Research Institute (STRI) Hydrophobicity Polymeric insulators Digital image processing Artificial neural network |
dc.subject.cnpq.fl_str_mv |
ENGENHARIAS::ENGENHARIA ELETRICA |
description |
Transmission lines transport the energy produced in power plants to distribution centers. In these lines, insulators perform the role of segregating regions of different electrical potential, while accomplishing the mechanical function of supporting the cables. Due to the nature of their function, insulators are exposed to electrical and mechanical stress throughout their life span, in addition to withstanding the wear caused by the environment through solar radiation, moisture, pollution and other weather conditions. Regarding polymeric high voltage insulators, which are the kind of insulator most widely used in the current scenario, these stresses lead to a decrease in their surface hydrophobicity, allowing moisture to accumulate on the insulator, giving rise to a leakage current and increasing the probability of a flashover to occur. In this sense, the present work presents a methodology for evaluating and classifying the surface hydrophobicity of polymeric high voltage insulators based on the method proposed by the Swedish Transmission Research Institute (STRI). The classification is performed automatically, using a Multilayer Perceptron artificial neural network, based on digital image processing, using spatial frequency information. A method for segmenting hydrophobicity images produced under unbalanced lighting conditions and with low contrast is also proposed. Furthermore, an image database containing 1200 hydrophobic surface samples in various stages of degradation was created. Images of an insulating column collected in the environment of a 500 kV substation were also used to validate the proposed method. The results obtained were compared with two other methods in the literature and it could be seen that the methodology developed was able to successfully segment and classify surface hydrophobicity images, obtaining a success rate above 78% for the database used. |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-12-13T17:37:04Z |
dc.date.available.fl_str_mv |
2022-12-13T17:37:04Z |
dc.date.issued.fl_str_mv |
2022-08-29 |
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.citation.fl_str_mv |
MACEDO, Matheus Santos. Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA. 2022. 123 f. Dissertação (Mestrado em Engenharia Elétrica) - Universidade Federal de Sergipe, São Cristóvão, 2022. |
dc.identifier.uri.fl_str_mv |
http://ri.ufs.br/jspui/handle/riufs/16894 |
identifier_str_mv |
MACEDO, Matheus Santos. Classificação da hidrofobicidade em isoladores elétricos utilizando análise de frequência e RNA. 2022. 123 f. Dissertação (Mestrado em Engenharia Elétrica) - Universidade Federal de Sergipe, São Cristóvão, 2022. |
url |
http://ri.ufs.br/jspui/handle/riufs/16894 |
dc.language.iso.fl_str_mv |
por |
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por |
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info:eu-repo/semantics/openAccess |
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openAccess |
dc.publisher.program.fl_str_mv |
Pós-Graduação em Engenharia Elétrica |
dc.publisher.initials.fl_str_mv |
Universidade Federal de Sergipe |
dc.source.none.fl_str_mv |
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