Representação esparsa utilizando Wavelets e variação total aplicados ao processamento de sinais de descargas parciais
| Ano de defesa: | 2018 |
|---|---|
| Autor(a) principal: | |
| Orientador(a): | |
| Banca de defesa: | |
| 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 |