Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition

Autores
González Arispe, Jimmy Cesar; Mombello, Enrique Esteban
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The detection of failures within power transformers is considered an important issue since these components are of critical importance for power system reliability; moreover, their replacement cost is extremely high. In monitoring the transformer condition along its useful life, frequency-response analysis (FRA) has gained great interest due to its sensitivity to failures in the windings and the iron core. These failures can be detected by evaluating transfer function changes by means of statistical and mathematical indices and classified according the frequency band in which these changes take place. However, this procedure involves evaluation inaccuracies due to disturbances or minor changes during FRA measurements. The new methodology is based on the decomposition of the original responses in several levels of decomposition (filtering) using the discrete wavelet transform, and the subsequent comparison using smooth versions of the responses. Fault detection is further supported with statistical indices calculated using the frequency band where abnormal differences appear. This procedure gives more robustness to the method and reduces the possible influence of disturbances during measurement in the diagnosis result. The methodology has been tested using different failure cases and two of them are used for validation purposes in this paper.
Fil: González Arispe, Jimmy Cesar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina. Deutscher Akademischer Austauschdiest ; Alemania
Fil: Mombello, Enrique Esteban. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Transformer Diagnosis
Discrete Wavelet Transform (Dwt)
Frequency-Response Analysis (Fra)
Power Transformers
Mathematical Analysis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/33061
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Detection of Failures Within Transformers by FRA Using Multiresolution DecompositionGonzález Arispe, Jimmy CesarMombello, Enrique EstebanTransformer DiagnosisDiscrete Wavelet Transform (Dwt)Frequency-Response Analysis (Fra)Power TransformersMathematical Analysishttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2The detection of failures within power transformers is considered an important issue since these components are of critical importance for power system reliability; moreover, their replacement cost is extremely high. In monitoring the transformer condition along its useful life, frequency-response analysis (FRA) has gained great interest due to its sensitivity to failures in the windings and the iron core. These failures can be detected by evaluating transfer function changes by means of statistical and mathematical indices and classified according the frequency band in which these changes take place. However, this procedure involves evaluation inaccuracies due to disturbances or minor changes during FRA measurements. The new methodology is based on the decomposition of the original responses in several levels of decomposition (filtering) using the discrete wavelet transform, and the subsequent comparison using smooth versions of the responses. Fault detection is further supported with statistical indices calculated using the frequency band where abnormal differences appear. This procedure gives more robustness to the method and reduces the possible influence of disturbances during measurement in the diagnosis result. The methodology has been tested using different failure cases and two of them are used for validation purposes in this paper.Fil: González Arispe, Jimmy Cesar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina. Deutscher Akademischer Austauschdiest ; AlemaniaFil: Mombello, Enrique Esteban. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaInstitute of Electrical and Electronics Engineers2014-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/33061González Arispe, Jimmy Cesar; Mombello, Enrique Esteban; Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition; Institute of Electrical and Electronics Engineers; IEEE Transactions on Power Delivery; 29; 3; 2-2014; 1127-11370885-89771937-4208CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1109/TPWRD.2014.2306674info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/6763102/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:21:42Zoai:ri.conicet.gov.ar:11336/33061instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:21:42.428CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition
title Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition
spellingShingle Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition
González Arispe, Jimmy Cesar
Transformer Diagnosis
Discrete Wavelet Transform (Dwt)
Frequency-Response Analysis (Fra)
Power Transformers
Mathematical Analysis
title_short Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition
title_full Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition
title_fullStr Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition
title_full_unstemmed Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition
title_sort Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition
dc.creator.none.fl_str_mv González Arispe, Jimmy Cesar
Mombello, Enrique Esteban
author González Arispe, Jimmy Cesar
author_facet González Arispe, Jimmy Cesar
Mombello, Enrique Esteban
author_role author
author2 Mombello, Enrique Esteban
author2_role author
dc.subject.none.fl_str_mv Transformer Diagnosis
Discrete Wavelet Transform (Dwt)
Frequency-Response Analysis (Fra)
Power Transformers
Mathematical Analysis
topic Transformer Diagnosis
Discrete Wavelet Transform (Dwt)
Frequency-Response Analysis (Fra)
Power Transformers
Mathematical Analysis
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The detection of failures within power transformers is considered an important issue since these components are of critical importance for power system reliability; moreover, their replacement cost is extremely high. In monitoring the transformer condition along its useful life, frequency-response analysis (FRA) has gained great interest due to its sensitivity to failures in the windings and the iron core. These failures can be detected by evaluating transfer function changes by means of statistical and mathematical indices and classified according the frequency band in which these changes take place. However, this procedure involves evaluation inaccuracies due to disturbances or minor changes during FRA measurements. The new methodology is based on the decomposition of the original responses in several levels of decomposition (filtering) using the discrete wavelet transform, and the subsequent comparison using smooth versions of the responses. Fault detection is further supported with statistical indices calculated using the frequency band where abnormal differences appear. This procedure gives more robustness to the method and reduces the possible influence of disturbances during measurement in the diagnosis result. The methodology has been tested using different failure cases and two of them are used for validation purposes in this paper.
Fil: González Arispe, Jimmy Cesar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina. Deutscher Akademischer Austauschdiest ; Alemania
Fil: Mombello, Enrique Esteban. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description The detection of failures within power transformers is considered an important issue since these components are of critical importance for power system reliability; moreover, their replacement cost is extremely high. In monitoring the transformer condition along its useful life, frequency-response analysis (FRA) has gained great interest due to its sensitivity to failures in the windings and the iron core. These failures can be detected by evaluating transfer function changes by means of statistical and mathematical indices and classified according the frequency band in which these changes take place. However, this procedure involves evaluation inaccuracies due to disturbances or minor changes during FRA measurements. The new methodology is based on the decomposition of the original responses in several levels of decomposition (filtering) using the discrete wavelet transform, and the subsequent comparison using smooth versions of the responses. Fault detection is further supported with statistical indices calculated using the frequency band where abnormal differences appear. This procedure gives more robustness to the method and reduces the possible influence of disturbances during measurement in the diagnosis result. The methodology has been tested using different failure cases and two of them are used for validation purposes in this paper.
publishDate 2014
dc.date.none.fl_str_mv 2014-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/33061
González Arispe, Jimmy Cesar; Mombello, Enrique Esteban; Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition; Institute of Electrical and Electronics Engineers; IEEE Transactions on Power Delivery; 29; 3; 2-2014; 1127-1137
0885-8977
1937-4208
CONICET Digital
CONICET
url http://hdl.handle.net/11336/33061
identifier_str_mv González Arispe, Jimmy Cesar; Mombello, Enrique Esteban; Detection of Failures Within Transformers by FRA Using Multiresolution Decomposition; Institute of Electrical and Electronics Engineers; IEEE Transactions on Power Delivery; 29; 3; 2-2014; 1127-1137
0885-8977
1937-4208
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/TPWRD.2014.2306674
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/6763102/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.48226

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