Representação esparsa utilizando Wavelets e variação total aplicados ao processamento de sinais de descargas parciais

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Paulo Vitor do Carmo Batista
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Minas Gerais
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://hdl.handle.net/1843/RAOA-BC2HJH
Resumo: Partial discharges are transient electrical discharges in the form of short pulses that occur inside insulation systems. In order to verify the existence of partial discharges, signal processing techniques are developed and used to enable interventions and scheduled maintenance in equipment, thus avoiding major financial losses. Several are the existing signal processing techniques that allow denoising, however, because they have specific characteristics, partial discharge signals are better processed when using the Wavelet Transform. Such a transform allows, among other characteristics, the decomposition of the signal into components localized in time (signal translation) and in the scale (signal dilation/contraction), which favors the representation of strictly localized signals. Specifically, in a variation of the Wavelet Transform known as Stationary Wavelet Transform, it is possible to reconstruct a signal from its circularly shifted versions by obtaining an overcomplete dictionary. However, by using an overcomplete dictionary, an indeterminate system is obtained, allowing infinite solutions. In order to find the best solution (least reconstruction error) among existing ones, it is necessary to apply an optimization method. This work presents the method known as Wavelet Total Variation, which based on the algorithm Split Variable Augmented Lagrangian Shrinkage Algorithm, aiming to eliminate noise in signals of partial discharges. The method is applied to signals of partial discharges measured in laboratory and generated by numerical models containing noises of harmonic, gaussian and impulsive type. The obtained results show that the method allows expressive levels of attenuation of the three types of noise investigated and produces little degradation in the partial discharges. The method is analyzed against another method in the literature and presents better quantitative results when comparing the resulting errors between original signals and the obtained signals.
id UFMG_358ca90c55b6eae4e87cf746f41d1b85
oai_identifier_str oai:repositorio.ufmg.br:1843/RAOA-BC2HJH
network_acronym_str UFMG
network_name_str Repositório Institucional da UFMG
repository_id_str
spelling Representação esparsa utilizando Wavelets e variação total aplicados ao processamento de sinais de descargas parciaisEngenharia elétricaDescargas elétricasWavelets (Matemática)Processamento de sinaisWaveletsDescargas parciaisVariação totalProcessamento de sinaisOtimizaçãoPartial discharges are transient electrical discharges in the form of short pulses that occur inside insulation systems. In order to verify the existence of partial discharges, signal processing techniques are developed and used to enable interventions and scheduled maintenance in equipment, thus avoiding major financial losses. Several are the existing signal processing techniques that allow denoising, however, because they have specific characteristics, partial discharge signals are better processed when using the Wavelet Transform. Such a transform allows, among other characteristics, the decomposition of the signal into components localized in time (signal translation) and in the scale (signal dilation/contraction), which favors the representation of strictly localized signals. Specifically, in a variation of the Wavelet Transform known as Stationary Wavelet Transform, it is possible to reconstruct a signal from its circularly shifted versions by obtaining an overcomplete dictionary. However, by using an overcomplete dictionary, an indeterminate system is obtained, allowing infinite solutions. In order to find the best solution (least reconstruction error) among existing ones, it is necessary to apply an optimization method. This work presents the method known as Wavelet Total Variation, which based on the algorithm Split Variable Augmented Lagrangian Shrinkage Algorithm, aiming to eliminate noise in signals of partial discharges. The method is applied to signals of partial discharges measured in laboratory and generated by numerical models containing noises of harmonic, gaussian and impulsive type. The obtained results show that the method allows expressive levels of attenuation of the three types of noise investigated and produces little degradation in the partial discharges. The method is analyzed against another method in the literature and presents better quantitative results when comparing the resulting errors between original signals and the obtained signals.Universidade Federal de Minas Gerais2019-08-09T22:00:17Z2025-09-09T00:52:52Z2019-08-09T22:00:17Z2018-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/1843/RAOA-BC2HJHPaulo Vitor do Carmo Batistainfo:eu-repo/semantics/openAccessporreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMG2025-09-09T00:52:52Zoai:repositorio.ufmg.br:1843/RAOA-BC2HJHRepositório InstitucionalPUBhttps://repositorio.ufmg.br/oairepositorio@ufmg.bropendoar:2025-09-09T00:52:52Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.none.fl_str_mv Representação esparsa utilizando Wavelets e variação total aplicados ao processamento de sinais de descargas parciais
title Representação esparsa utilizando Wavelets e variação total aplicados ao processamento de sinais de descargas parciais
spellingShingle Representação esparsa utilizando Wavelets e variação total aplicados ao processamento de sinais de descargas parciais
Paulo Vitor do Carmo Batista
Engenharia elétrica
Descargas elétricas
Wavelets (Matemática)
Processamento de sinais
Wavelets
Descargas parciais
Variação total
Processamento de sinais
Otimização
title_short Representação esparsa utilizando Wavelets e variação total aplicados ao processamento de sinais de descargas parciais
title_full Representação esparsa utilizando Wavelets e variação total aplicados ao processamento de sinais de descargas parciais
title_fullStr Representação esparsa utilizando Wavelets e variação total aplicados ao processamento de sinais de descargas parciais
title_full_unstemmed Representação esparsa utilizando Wavelets e variação total aplicados ao processamento de sinais de descargas parciais
title_sort Representação esparsa utilizando Wavelets e variação total aplicados ao processamento de sinais de descargas parciais
author Paulo Vitor do Carmo Batista
author_facet Paulo Vitor do Carmo Batista
author_role author
dc.contributor.author.fl_str_mv Paulo Vitor do Carmo Batista
dc.subject.por.fl_str_mv Engenharia elétrica
Descargas elétricas
Wavelets (Matemática)
Processamento de sinais
Wavelets
Descargas parciais
Variação total
Processamento de sinais
Otimização
topic Engenharia elétrica
Descargas elétricas
Wavelets (Matemática)
Processamento de sinais
Wavelets
Descargas parciais
Variação total
Processamento de sinais
Otimização
description Partial discharges are transient electrical discharges in the form of short pulses that occur inside insulation systems. In order to verify the existence of partial discharges, signal processing techniques are developed and used to enable interventions and scheduled maintenance in equipment, thus avoiding major financial losses. Several are the existing signal processing techniques that allow denoising, however, because they have specific characteristics, partial discharge signals are better processed when using the Wavelet Transform. Such a transform allows, among other characteristics, the decomposition of the signal into components localized in time (signal translation) and in the scale (signal dilation/contraction), which favors the representation of strictly localized signals. Specifically, in a variation of the Wavelet Transform known as Stationary Wavelet Transform, it is possible to reconstruct a signal from its circularly shifted versions by obtaining an overcomplete dictionary. However, by using an overcomplete dictionary, an indeterminate system is obtained, allowing infinite solutions. In order to find the best solution (least reconstruction error) among existing ones, it is necessary to apply an optimization method. This work presents the method known as Wavelet Total Variation, which based on the algorithm Split Variable Augmented Lagrangian Shrinkage Algorithm, aiming to eliminate noise in signals of partial discharges. The method is applied to signals of partial discharges measured in laboratory and generated by numerical models containing noises of harmonic, gaussian and impulsive type. The obtained results show that the method allows expressive levels of attenuation of the three types of noise investigated and produces little degradation in the partial discharges. The method is analyzed against another method in the literature and presents better quantitative results when comparing the resulting errors between original signals and the obtained signals.
publishDate 2018
dc.date.none.fl_str_mv 2018-11-01
2019-08-09T22:00:17Z
2019-08-09T22:00:17Z
2025-09-09T00:52:52Z
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 https://hdl.handle.net/1843/RAOA-BC2HJH
url https://hdl.handle.net/1843/RAOA-BC2HJH
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
repository.name.fl_str_mv Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)
repository.mail.fl_str_mv repositorio@ufmg.br
_version_ 1856414123363926016