Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TIM.2021.3121488 http://hdl.handle.net/11449/222760 |
Resumo: | The insulation systems of equipment, cables, and electrical machines subjected to high voltage are continuously exposed to multiple aging mechanisms of electrical, mechanical, thermal, and environmental type. Normally, a failure in the material under these conditions does not occur immediately, but over time, different degradation processes emerge and evolve, progressively deteriorating the insulation, until breakdown occurs, and consequently, the asset finishes its operation with a catastrophic failure. In this context, partial discharge (PD) measurement can be considered as one of the best indicators when diagnosing the status of much electrical equipment in operation. However, the simultaneous presence of noise sources or multiple PD sources can generate important difficulties in identifying the type or types of sources measured. These practical limitations can be solved if, prior to the identification, a separation process is carried out, which allows classifying the different sources acting over the equipment being monitored. Once the sources separation is executed, the subsequent identification and diagnosis process can be carried out more easily. In this article, we present a novel separation technique based on the temporal and spectral behavior of the PD signals. For this technique, the separation of sources is carried out through three different parameters. Two of them are based on the peakedness of PD signals and electrical noise, and the third parameter is associated with their spectral content. Based on these parameters, a 3-D separation map is established, representing in clusters each source captured by the sensors. |
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Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the SignalsClusteringelectrical noisekurtosismonopole antennapartial discharge (PD)separation mapskewnessThe insulation systems of equipment, cables, and electrical machines subjected to high voltage are continuously exposed to multiple aging mechanisms of electrical, mechanical, thermal, and environmental type. Normally, a failure in the material under these conditions does not occur immediately, but over time, different degradation processes emerge and evolve, progressively deteriorating the insulation, until breakdown occurs, and consequently, the asset finishes its operation with a catastrophic failure. In this context, partial discharge (PD) measurement can be considered as one of the best indicators when diagnosing the status of much electrical equipment in operation. However, the simultaneous presence of noise sources or multiple PD sources can generate important difficulties in identifying the type or types of sources measured. These practical limitations can be solved if, prior to the identification, a separation process is carried out, which allows classifying the different sources acting over the equipment being monitored. Once the sources separation is executed, the subsequent identification and diagnosis process can be carried out more easily. In this article, we present a novel separation technique based on the temporal and spectral behavior of the PD signals. For this technique, the separation of sources is carried out through three different parameters. Two of them are based on the peakedness of PD signals and electrical noise, and the third parameter is associated with their spectral content. Based on these parameters, a 3-D separation map is established, representing in clusters each source captured by the sensors.Department of Electrical Engineering Universidad Técnica Federico Santa MaríaSchool of Electrical and Electronics Engineering SASTRA Deemed University, Tamil NaduDepartment of Electrical Engineering School of Engineering São Paulo State University (UNESP)Department of Electrical Engineering School of Engineering São Paulo State University (UNESP)Universidad Técnica Federico Santa MaríaSASTRA Deemed UniversityUniversidade Estadual Paulista (UNESP)Ardila-Rey, Jorge AlfredoSchurch, RogerPoblete, Nicolas MedinaGovindarajan, SuganyaMunoz, OsvaldoDe Castro, Bruno Albuquerque [UNESP]2022-04-28T19:46:34Z2022-04-28T19:46:34Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1109/TIM.2021.3121488IEEE Transactions on Instrumentation and Measurement, v. 70.1557-96620018-9456http://hdl.handle.net/11449/22276010.1109/TIM.2021.31214882-s2.0-85118237229Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Transactions on Instrumentation and Measurementinfo:eu-repo/semantics/openAccess2022-04-28T19:46:34Zoai:repositorio.unesp.br:11449/222760Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:46:34Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals |
title |
Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals |
spellingShingle |
Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals Ardila-Rey, Jorge Alfredo Clustering electrical noise kurtosis monopole antenna partial discharge (PD) separation map skewness |
title_short |
Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals |
title_full |
Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals |
title_fullStr |
Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals |
title_full_unstemmed |
Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals |
title_sort |
Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals |
author |
Ardila-Rey, Jorge Alfredo |
author_facet |
Ardila-Rey, Jorge Alfredo Schurch, Roger Poblete, Nicolas Medina Govindarajan, Suganya Munoz, Osvaldo De Castro, Bruno Albuquerque [UNESP] |
author_role |
author |
author2 |
Schurch, Roger Poblete, Nicolas Medina Govindarajan, Suganya Munoz, Osvaldo De Castro, Bruno Albuquerque [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidad Técnica Federico Santa María SASTRA Deemed University Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Ardila-Rey, Jorge Alfredo Schurch, Roger Poblete, Nicolas Medina Govindarajan, Suganya Munoz, Osvaldo De Castro, Bruno Albuquerque [UNESP] |
dc.subject.por.fl_str_mv |
Clustering electrical noise kurtosis monopole antenna partial discharge (PD) separation map skewness |
topic |
Clustering electrical noise kurtosis monopole antenna partial discharge (PD) separation map skewness |
description |
The insulation systems of equipment, cables, and electrical machines subjected to high voltage are continuously exposed to multiple aging mechanisms of electrical, mechanical, thermal, and environmental type. Normally, a failure in the material under these conditions does not occur immediately, but over time, different degradation processes emerge and evolve, progressively deteriorating the insulation, until breakdown occurs, and consequently, the asset finishes its operation with a catastrophic failure. In this context, partial discharge (PD) measurement can be considered as one of the best indicators when diagnosing the status of much electrical equipment in operation. However, the simultaneous presence of noise sources or multiple PD sources can generate important difficulties in identifying the type or types of sources measured. These practical limitations can be solved if, prior to the identification, a separation process is carried out, which allows classifying the different sources acting over the equipment being monitored. Once the sources separation is executed, the subsequent identification and diagnosis process can be carried out more easily. In this article, we present a novel separation technique based on the temporal and spectral behavior of the PD signals. For this technique, the separation of sources is carried out through three different parameters. Two of them are based on the peakedness of PD signals and electrical noise, and the third parameter is associated with their spectral content. Based on these parameters, a 3-D separation map is established, representing in clusters each source captured by the sensors. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-04-28T19:46:34Z 2022-04-28T19:46:34Z |
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/10.1109/TIM.2021.3121488 IEEE Transactions on Instrumentation and Measurement, v. 70. 1557-9662 0018-9456 http://hdl.handle.net/11449/222760 10.1109/TIM.2021.3121488 2-s2.0-85118237229 |
url |
http://dx.doi.org/10.1109/TIM.2021.3121488 http://hdl.handle.net/11449/222760 |
identifier_str_mv |
IEEE Transactions on Instrumentation and Measurement, v. 70. 1557-9662 0018-9456 10.1109/TIM.2021.3121488 2-s2.0-85118237229 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IEEE Transactions on Instrumentation and Measurement |
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_ |
1803649741063454720 |