Emprego de redes neurais artificiais para predição de temperatura de topo de óleo e perda de vida útil em transformadores de potência
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional Manancial UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/22470 |
Resumo: | The precise temperature prediction in power transformers allows a better use of its nominal capacity, extending the equipment's useful life and strategic planning based on the expected future operating conditions. The proposal of new models that present a good predictive capacity is, therefore, of great interest to those responsible for power transformers. The present work presents a method of developing Artificial Neural Networks (ANNs), justifying the parameters chosen based on the thermal behavior of power transformers, for prediction of top-oil temperature using the NARX neural network model, not yet used for temperature prediction of transformers. All data sets used for training and testing the predictive ability of ANNs are real monitoring data from five elevating transformers in a hydroelectric plant. Tests of prediction ability were performed for all transformers, combining trained networks from one of the transformers and applied to the inputs of others, addressing in which situations the best and worst performances occurred. Afterwards, the methods for calculating the loss of life of transformers proposed by standards are presented and a comparison is made between the one calculated from the monitoring data and from the temperature values provided by the neural network. In order to validate the prediction capacity for expected future scenarios, six fictitious scenarios of long duration are proposed and then their useful life is estimated. All the results obtained are satisfactory, with errors below 4%, or 2 °C on absolute values, most of the periods in which the tests were carried out, capable of proving the predictive capacity of the ANNs developed using the method presented not only in its application for temperature monitoring, but also from the perspective of loss of life. |
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2021-10-19T19:08:14Z2021-10-19T19:08:14Z2020-09-24http://repositorio.ufsm.br/handle/1/22470The precise temperature prediction in power transformers allows a better use of its nominal capacity, extending the equipment's useful life and strategic planning based on the expected future operating conditions. The proposal of new models that present a good predictive capacity is, therefore, of great interest to those responsible for power transformers. The present work presents a method of developing Artificial Neural Networks (ANNs), justifying the parameters chosen based on the thermal behavior of power transformers, for prediction of top-oil temperature using the NARX neural network model, not yet used for temperature prediction of transformers. All data sets used for training and testing the predictive ability of ANNs are real monitoring data from five elevating transformers in a hydroelectric plant. Tests of prediction ability were performed for all transformers, combining trained networks from one of the transformers and applied to the inputs of others, addressing in which situations the best and worst performances occurred. Afterwards, the methods for calculating the loss of life of transformers proposed by standards are presented and a comparison is made between the one calculated from the monitoring data and from the temperature values provided by the neural network. In order to validate the prediction capacity for expected future scenarios, six fictitious scenarios of long duration are proposed and then their useful life is estimated. All the results obtained are satisfactory, with errors below 4%, or 2 °C on absolute values, most of the periods in which the tests were carried out, capable of proving the predictive capacity of the ANNs developed using the method presented not only in its application for temperature monitoring, but also from the perspective of loss of life.A correta predição de temperatura em transformadores de potência possibilita um melhor aproveitamento de sua capacidade nominal, prolongamento da vida útil do equipamento e planejamento estratégico com base nas condições de operação futuras esperadas. A proposição de novos modelos que apresentem uma boa capacidade preditiva é, portanto, de grande interesse aos responsáveis pelos transformadores de potência. O presente trabalho apresenta um método de desenvolvimento de Redes Neurais Artificiais (RNAs), justificando os parâmetros escolhidos com base no comportamento térmico de transformadores, para a predição de temperatura de topo de óleo utilizando o modelo de rede neural NARX, ainda não utilizado para predição de temperatura em transformadores. Todos os conjuntos de dados utilizados para treinamento e testes da capacidade preditiva das RNAs são dados reais de monitoramento, provenientes de cinco transformadores elevadores de uma usina hidrelétrica. Foram realizados testes da capacidade de predição para todos transformadores, combinando redes treinadas de um dos transformadores e aplicadas às entradas de outro, abordando em que situações ocorreram os melhores e piores desempenhos. Após, são apresentados os métodos de cálculo de perda de vida útil de transformadores propostos por normas e realiza-se uma comparação entre o calculado a partir dos dados de monitoramento e a partir dos valores de temperatura fornecidos pela rede neural. Com o intuito de validar a capacidade de predição para cenários futuros esperados, são propostos seis cenários fictícios de longo período de duração e então a vida útil para estes é estimada. Todos os resultados obtidos são satisfatórios, com erros abaixo de 4%, ou 2 °C em valores absolutos, grande parte dos períodos em que foram realizados os testes, capazes de comprovar a capacidade preditiva das RNAs desenvolvidas utilizando o método apresentado não apenas em sua aplicação para monitoramento de temperatura, mas também sob a perspectiva de perda de vida útil.porUniversidade 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/openAccessRedes neurais artificiaisNARXPredição de temperaturaTemperatura de topo de óleoTransformadores de potênciaTemperatura de ponto mais quentePerda de vida útilArtificial neural networksTemperature predictionTop-Oil temperaturePower transformersHot-spot temperatureLoss of lifeCNPQ::ENGENHARIAS::ENGENHARIA ELETRICAEmprego de redes neurais artificiais para predição de temperatura de topo de óleo e perda de vida útil em transformadores de potênciaArtificial neural networks application for top oil temperature and loss of life prediction in power transformersinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisMarchesan, Tiago Bandeirahttp://lattes.cnpq.br/2318413245910780Bender, Vitor CristianoKnak Neto, Nelsonhttp://lattes.cnpq.br/0460548793328096Kaminski Júnior, Antônio Mário3004000000076006006006006000197cd95-ed8b-4ca0-89af-e654162d964dae8a34b9-2f47-4dcb-8244-5b3cd9c940a8d15da859-355f-42b3-ad14-ed04a355242bb5d0b951-8321-4e81-a1f4-ac0686eea93breponame:Repositório Institucional Manancial UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGEE_2020_KAMINSKI_JÚNIOR_ANTÔNIO.pdfDIS_PPGEE_2020_KAMINSKI_JÚNIOR_ANTÔNIO.pdfDissertação de Mestradoapplication/pdf5847421http://repositorio.ufsm.br/bitstream/1/22470/1/DIS_PPGEE_2020_KAMINSKI_J%c3%9aNIOR_ANT%c3%94NIO.pdf7a3ad393c0479b1316d61cf221e4d064MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Emprego de redes neurais artificiais para predição de temperatura de topo de óleo e perda de vida útil em transformadores de potência |
dc.title.alternative.eng.fl_str_mv |
Artificial neural networks application for top oil temperature and loss of life prediction in power transformers |
title |
Emprego de redes neurais artificiais para predição de temperatura de topo de óleo e perda de vida útil em transformadores de potência |
spellingShingle |
Emprego de redes neurais artificiais para predição de temperatura de topo de óleo e perda de vida útil em transformadores de potência Kaminski Júnior, Antônio Mário Redes neurais artificiais NARX Predição de temperatura Temperatura de topo de óleo Transformadores de potência Temperatura de ponto mais quente Perda de vida útil Artificial neural networks Temperature prediction Top-Oil temperature Power transformers Hot-spot temperature Loss of life CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
title_short |
Emprego de redes neurais artificiais para predição de temperatura de topo de óleo e perda de vida útil em transformadores de potência |
title_full |
Emprego de redes neurais artificiais para predição de temperatura de topo de óleo e perda de vida útil em transformadores de potência |
title_fullStr |
Emprego de redes neurais artificiais para predição de temperatura de topo de óleo e perda de vida útil em transformadores de potência |
title_full_unstemmed |
Emprego de redes neurais artificiais para predição de temperatura de topo de óleo e perda de vida útil em transformadores de potência |
title_sort |
Emprego de redes neurais artificiais para predição de temperatura de topo de óleo e perda de vida útil em transformadores de potência |
author |
Kaminski Júnior, Antônio Mário |
author_facet |
Kaminski Júnior, Antônio Mário |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Marchesan, Tiago Bandeira |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2318413245910780 |
dc.contributor.referee1.fl_str_mv |
Bender, Vitor Cristiano |
dc.contributor.referee2.fl_str_mv |
Knak Neto, Nelson |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/0460548793328096 |
dc.contributor.author.fl_str_mv |
Kaminski Júnior, Antônio Mário |
contributor_str_mv |
Marchesan, Tiago Bandeira Bender, Vitor Cristiano Knak Neto, Nelson |
dc.subject.por.fl_str_mv |
Redes neurais artificiais NARX Predição de temperatura Temperatura de topo de óleo Transformadores de potência Temperatura de ponto mais quente Perda de vida útil |
topic |
Redes neurais artificiais NARX Predição de temperatura Temperatura de topo de óleo Transformadores de potência Temperatura de ponto mais quente Perda de vida útil Artificial neural networks Temperature prediction Top-Oil temperature Power transformers Hot-spot temperature Loss of life CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
dc.subject.eng.fl_str_mv |
Artificial neural networks Temperature prediction Top-Oil temperature Power transformers Hot-spot temperature Loss of life |
dc.subject.cnpq.fl_str_mv |
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
description |
The precise temperature prediction in power transformers allows a better use of its nominal capacity, extending the equipment's useful life and strategic planning based on the expected future operating conditions. The proposal of new models that present a good predictive capacity is, therefore, of great interest to those responsible for power transformers. The present work presents a method of developing Artificial Neural Networks (ANNs), justifying the parameters chosen based on the thermal behavior of power transformers, for prediction of top-oil temperature using the NARX neural network model, not yet used for temperature prediction of transformers. All data sets used for training and testing the predictive ability of ANNs are real monitoring data from five elevating transformers in a hydroelectric plant. Tests of prediction ability were performed for all transformers, combining trained networks from one of the transformers and applied to the inputs of others, addressing in which situations the best and worst performances occurred. Afterwards, the methods for calculating the loss of life of transformers proposed by standards are presented and a comparison is made between the one calculated from the monitoring data and from the temperature values provided by the neural network. In order to validate the prediction capacity for expected future scenarios, six fictitious scenarios of long duration are proposed and then their useful life is estimated. All the results obtained are satisfactory, with errors below 4%, or 2 °C on absolute values, most of the periods in which the tests were carried out, capable of proving the predictive capacity of the ANNs developed using the method presented not only in its application for temperature monitoring, but also from the perspective of loss of life. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020-09-24 |
dc.date.accessioned.fl_str_mv |
2021-10-19T19:08:14Z |
dc.date.available.fl_str_mv |
2021-10-19T19:08:14Z |
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/1/22470 |
url |
http://repositorio.ufsm.br/handle/1/22470 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
300400000007 |
dc.relation.confidence.fl_str_mv |
600 600 600 600 600 |
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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