An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/ecsa-7-08244 http://hdl.handle.net/11449/247575 |
Resumo: | Under normal operation, insulation systems of high voltage electrical devices, like power transformers, are constantly subjected to multiple types of stresses (electrical, thermal, mechanical, environmental, etc.), which can lead to a degradation of the machine insulation. One of the main indicators of the dielectric degradation process is the presence of partial discharges (PD). Although it starts due to operational stresses, PD can cause a progressive insulation deterioration, since it is characterized by localized current pulses that emit heat, UV radiation, acoustic, and electromagnetic waves. In this sense, acoustic emission (AE) transducers are wildly applied in PD detection. The goal is to reduce maintenance costs by predictive actions and avoid total failures. Becasue of the progressive deterioration, the assessment of the PD evolution is crucial for improving the maintenance planning and ensure the operation of the transformer. Based on this issue, this article presents a new wavelet -based analysis in order to characterize the PD evolution. Three levels of failures were carried out in a transformer and the acoustic signals captured by a lead zirconate titanate piezoelectric transducer were processed by discrete wavelet transform. The experimental results revealed that the energy of the approximation levels increasing with the failure evolution. More specifically, levels 4, 6, 7, and 10 presented a linear fit to characterize the phenomena, enhancing the applicability of the proposed approach to transformer monitoring. |
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An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensornon-destructive methodspartial discharge evolutionpiezoelectric sensorswavelet trasformUnder normal operation, insulation systems of high voltage electrical devices, like power transformers, are constantly subjected to multiple types of stresses (electrical, thermal, mechanical, environmental, etc.), which can lead to a degradation of the machine insulation. One of the main indicators of the dielectric degradation process is the presence of partial discharges (PD). Although it starts due to operational stresses, PD can cause a progressive insulation deterioration, since it is characterized by localized current pulses that emit heat, UV radiation, acoustic, and electromagnetic waves. In this sense, acoustic emission (AE) transducers are wildly applied in PD detection. The goal is to reduce maintenance costs by predictive actions and avoid total failures. Becasue of the progressive deterioration, the assessment of the PD evolution is crucial for improving the maintenance planning and ensure the operation of the transformer. Based on this issue, this article presents a new wavelet -based analysis in order to characterize the PD evolution. Three levels of failures were carried out in a transformer and the acoustic signals captured by a lead zirconate titanate piezoelectric transducer were processed by discrete wavelet transform. The experimental results revealed that the energy of the approximation levels increasing with the failure evolution. More specifically, levels 4, 6, 7, and 10 presented a linear fit to characterize the phenomena, enhancing the applicability of the proposed approach to transformer monitoring.São Paulo State University (UNESP) School of Engineering Bauru Department of Electrical Engineering, SPIEEE Women in Engineering São Paulo State University (UNESP), SPDepartment of Electrical Engineering Universidad Técnica Federico Santa María, Av. Vicuña Mackenna 3939São Paulo State University (UNESP) School of Engineering Bauru Department of Electrical Engineering, SPIEEE Women in Engineering São Paulo State University (UNESP), SPUniversidade Estadual Paulista (UNESP)Universidad Técnica Federico Santa MaríaSantos, Vitor Vecina dos [UNESP]Castro, Bruno Albuquerque de [UNESP]Binotto, Amanda [UNESP]Rey, Jorge Alfredo ArdilaLucas, Guilherme Beraldi [UNESP]Andreoli, André Luiz [UNESP]2023-07-29T13:19:49Z2023-07-29T13:19:49Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/ecsa-7-08244Engineering Proceedings, v. 2, n. 1, 2020.2673-4591http://hdl.handle.net/11449/24757510.3390/ecsa-7-082442-s2.0-85098510991Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEngineering Proceedingsinfo:eu-repo/semantics/openAccess2024-06-28T13:34:24Zoai:repositorio.unesp.br:11449/247575Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:38:16.411590Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor |
title |
An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor |
spellingShingle |
An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor Santos, Vitor Vecina dos [UNESP] non-destructive methods partial discharge evolution piezoelectric sensors wavelet trasform |
title_short |
An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor |
title_full |
An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor |
title_fullStr |
An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor |
title_full_unstemmed |
An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor |
title_sort |
An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor |
author |
Santos, Vitor Vecina dos [UNESP] |
author_facet |
Santos, Vitor Vecina dos [UNESP] Castro, Bruno Albuquerque de [UNESP] Binotto, Amanda [UNESP] Rey, Jorge Alfredo Ardila Lucas, Guilherme Beraldi [UNESP] Andreoli, André Luiz [UNESP] |
author_role |
author |
author2 |
Castro, Bruno Albuquerque de [UNESP] Binotto, Amanda [UNESP] Rey, Jorge Alfredo Ardila Lucas, Guilherme Beraldi [UNESP] Andreoli, André Luiz [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Universidad Técnica Federico Santa María |
dc.contributor.author.fl_str_mv |
Santos, Vitor Vecina dos [UNESP] Castro, Bruno Albuquerque de [UNESP] Binotto, Amanda [UNESP] Rey, Jorge Alfredo Ardila Lucas, Guilherme Beraldi [UNESP] Andreoli, André Luiz [UNESP] |
dc.subject.por.fl_str_mv |
non-destructive methods partial discharge evolution piezoelectric sensors wavelet trasform |
topic |
non-destructive methods partial discharge evolution piezoelectric sensors wavelet trasform |
description |
Under normal operation, insulation systems of high voltage electrical devices, like power transformers, are constantly subjected to multiple types of stresses (electrical, thermal, mechanical, environmental, etc.), which can lead to a degradation of the machine insulation. One of the main indicators of the dielectric degradation process is the presence of partial discharges (PD). Although it starts due to operational stresses, PD can cause a progressive insulation deterioration, since it is characterized by localized current pulses that emit heat, UV radiation, acoustic, and electromagnetic waves. In this sense, acoustic emission (AE) transducers are wildly applied in PD detection. The goal is to reduce maintenance costs by predictive actions and avoid total failures. Becasue of the progressive deterioration, the assessment of the PD evolution is crucial for improving the maintenance planning and ensure the operation of the transformer. Based on this issue, this article presents a new wavelet -based analysis in order to characterize the PD evolution. Three levels of failures were carried out in a transformer and the acoustic signals captured by a lead zirconate titanate piezoelectric transducer were processed by discrete wavelet transform. The experimental results revealed that the energy of the approximation levels increasing with the failure evolution. More specifically, levels 4, 6, 7, and 10 presented a linear fit to characterize the phenomena, enhancing the applicability of the proposed approach to transformer monitoring. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 2023-07-29T13:19:49Z 2023-07-29T13:19:49Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3390/ecsa-7-08244 Engineering Proceedings, v. 2, n. 1, 2020. 2673-4591 http://hdl.handle.net/11449/247575 10.3390/ecsa-7-08244 2-s2.0-85098510991 |
url |
http://dx.doi.org/10.3390/ecsa-7-08244 http://hdl.handle.net/11449/247575 |
identifier_str_mv |
Engineering Proceedings, v. 2, n. 1, 2020. 2673-4591 10.3390/ecsa-7-08244 2-s2.0-85098510991 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Engineering Proceedings |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129343181815808 |