Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning

Autores
Morales Garcia, John Armando; Orduña, Eduardo Agustín; Rehtanz, C.
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
One of most important elements of Electric Power Systems (EPS) is the transmission line (TL), which is permanently under adverse conditions especially lightning strokes. At the moment, those phenomena have been the root cause of short circuits and the most important cause of mal-operation of transmission line protection relays. Thus, this paper develops the classification of lightning transient signals with and without fault. Multi-resolution analysis (MRA) is used to analyze those signals considering five mother wavelets and different decomposition levels of three phase voltages. In this manner, Spectral Energy and Machine Learning as Artificial Neural Network, K-Nearest Neighbors and Support Vector Machine are employed to classify those signals. On the other hand, the developed work in this paper analyzes most important parameters of lightning strokes, which are essentials in producing conditions with and without fault. Results show that the methodology presents an acceptable performance. © 2013 Elsevier Ltd. All rights reserved.
Fil: Morales Garcia, John Armando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
Fil: Orduña, Eduardo Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
Fil: Rehtanz, C.. Universität Dortmund; Alemania
Materia
BACK-FLASHOVER
DECOMPOSITION LEVEL
FLASHOVER
LIGHTNING STROKE
MACHINE LEARNING
MULTI-RESOLUTION 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:11336/124808
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/124808
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Classification of lightning stroke on transmission line using multi-resolution analysis and machine learningMorales Garcia, John ArmandoOrduña, Eduardo AgustínRehtanz, C.BACK-FLASHOVERDECOMPOSITION LEVELFLASHOVERLIGHTNING STROKEMACHINE LEARNINGMULTI-RESOLUTION ANALYSIShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2One of most important elements of Electric Power Systems (EPS) is the transmission line (TL), which is permanently under adverse conditions especially lightning strokes. At the moment, those phenomena have been the root cause of short circuits and the most important cause of mal-operation of transmission line protection relays. Thus, this paper develops the classification of lightning transient signals with and without fault. Multi-resolution analysis (MRA) is used to analyze those signals considering five mother wavelets and different decomposition levels of three phase voltages. In this manner, Spectral Energy and Machine Learning as Artificial Neural Network, K-Nearest Neighbors and Support Vector Machine are employed to classify those signals. On the other hand, the developed work in this paper analyzes most important parameters of lightning strokes, which are essentials in producing conditions with and without fault. Results show that the methodology presents an acceptable performance. © 2013 Elsevier Ltd. All rights reserved.Fil: Morales Garcia, John Armando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Orduña, Eduardo Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Rehtanz, C.. Universität Dortmund; AlemaniaElsevier2014-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/124808Morales Garcia, John Armando; Orduña, Eduardo Agustín; Rehtanz, C.; Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning; Elsevier; International Journal of Electrical Power & Energy Systems; 58; 6-2014; 19-310142-0615CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijepes.2013.12.017info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0142061513005425info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:11:13Zoai:ri.conicet.gov.ar:11336/124808instacron: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:34982025-09-29 10:11:13.923CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning
title Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning
spellingShingle Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning
Morales Garcia, John Armando
BACK-FLASHOVER
DECOMPOSITION LEVEL
FLASHOVER
LIGHTNING STROKE
MACHINE LEARNING
MULTI-RESOLUTION ANALYSIS
title_short Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning
title_full Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning
title_fullStr Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning
title_full_unstemmed Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning
title_sort Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning
dc.creator.none.fl_str_mv Morales Garcia, John Armando
Orduña, Eduardo Agustín
Rehtanz, C.
author Morales Garcia, John Armando
author_facet Morales Garcia, John Armando
Orduña, Eduardo Agustín
Rehtanz, C.
author_role author
author2 Orduña, Eduardo Agustín
Rehtanz, C.
author2_role author
author
dc.subject.none.fl_str_mv BACK-FLASHOVER
DECOMPOSITION LEVEL
FLASHOVER
LIGHTNING STROKE
MACHINE LEARNING
MULTI-RESOLUTION ANALYSIS
topic BACK-FLASHOVER
DECOMPOSITION LEVEL
FLASHOVER
LIGHTNING STROKE
MACHINE LEARNING
MULTI-RESOLUTION 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 One of most important elements of Electric Power Systems (EPS) is the transmission line (TL), which is permanently under adverse conditions especially lightning strokes. At the moment, those phenomena have been the root cause of short circuits and the most important cause of mal-operation of transmission line protection relays. Thus, this paper develops the classification of lightning transient signals with and without fault. Multi-resolution analysis (MRA) is used to analyze those signals considering five mother wavelets and different decomposition levels of three phase voltages. In this manner, Spectral Energy and Machine Learning as Artificial Neural Network, K-Nearest Neighbors and Support Vector Machine are employed to classify those signals. On the other hand, the developed work in this paper analyzes most important parameters of lightning strokes, which are essentials in producing conditions with and without fault. Results show that the methodology presents an acceptable performance. © 2013 Elsevier Ltd. All rights reserved.
Fil: Morales Garcia, John Armando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
Fil: Orduña, Eduardo Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina
Fil: Rehtanz, C.. Universität Dortmund; Alemania
description One of most important elements of Electric Power Systems (EPS) is the transmission line (TL), which is permanently under adverse conditions especially lightning strokes. At the moment, those phenomena have been the root cause of short circuits and the most important cause of mal-operation of transmission line protection relays. Thus, this paper develops the classification of lightning transient signals with and without fault. Multi-resolution analysis (MRA) is used to analyze those signals considering five mother wavelets and different decomposition levels of three phase voltages. In this manner, Spectral Energy and Machine Learning as Artificial Neural Network, K-Nearest Neighbors and Support Vector Machine are employed to classify those signals. On the other hand, the developed work in this paper analyzes most important parameters of lightning strokes, which are essentials in producing conditions with and without fault. Results show that the methodology presents an acceptable performance. © 2013 Elsevier Ltd. All rights reserved.
publishDate 2014
dc.date.none.fl_str_mv 2014-06
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/11336/124808
Morales Garcia, John Armando; Orduña, Eduardo Agustín; Rehtanz, C.; Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning; Elsevier; International Journal of Electrical Power & Energy Systems; 58; 6-2014; 19-31
0142-0615
CONICET Digital
CONICET
url http://hdl.handle.net/11336/124808
identifier_str_mv Morales Garcia, John Armando; Orduña, Eduardo Agustín; Rehtanz, C.; Classification of lightning stroke on transmission line using multi-resolution analysis and machine learning; Elsevier; International Journal of Electrical Power & Energy Systems; 58; 6-2014; 19-31
0142-0615
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijepes.2013.12.017
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0142061513005425
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 Elsevier
publisher.none.fl_str_mv Elsevier
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
_version_ 1844614009399017472
score 13.070432

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