An approach based on neural networks for identification of fault sections in radial distribution systems

Detalhes bibliográficos
Autor(a) principal: Ziolkowski, Valmir
Data de Publicação: 2006
Outros Autores: Da Silva, Ivan Nunes, Flauzino, Rogerio Andrade [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ICIT.2006.372351
http://hdl.handle.net/11449/69237
Resumo: The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.
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spelling An approach based on neural networks for identification of fault sections in radial distribution systemsArtificial intelligenceAutomationClassification (of information)Computer networksElectric fault locationElectric load distributionElectric power systemsElectric power transmissionElectric toolsElectronic data interchangeFeedingAutomatic identificationIndustrial technologiesInternational conferencesNeural networksThe main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.University of São Paulo - USP Department of Electrical Engineering, CP 359, São Carlos, SPSão Paulo State University UNESP Department of Electrical Engineering, CP 473, Bauru, SPSão Paulo State University UNESP Department of Electrical Engineering, CP 473, Bauru, SPUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Ziolkowski, ValmirDa Silva, Ivan NunesFlauzino, Rogerio Andrade [UNESP]2014-05-27T11:22:02Z2014-05-27T11:22:02Z2006-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject25-30http://dx.doi.org/10.1109/ICIT.2006.372351Proceedings of the IEEE International Conference on Industrial Technology, p. 25-30.http://hdl.handle.net/11449/6923710.1109/ICIT.2006.3723512-s2.0-51349143502Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the IEEE International Conference on Industrial Technologyinfo:eu-repo/semantics/openAccess2024-06-28T13:34:36Zoai:repositorio.unesp.br:11449/69237Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:38:37.766021Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv An approach based on neural networks for identification of fault sections in radial distribution systems
title An approach based on neural networks for identification of fault sections in radial distribution systems
spellingShingle An approach based on neural networks for identification of fault sections in radial distribution systems
Ziolkowski, Valmir
Artificial intelligence
Automation
Classification (of information)
Computer networks
Electric fault location
Electric load distribution
Electric power systems
Electric power transmission
Electric tools
Electronic data interchange
Feeding
Automatic identification
Industrial technologies
International conferences
Neural networks
title_short An approach based on neural networks for identification of fault sections in radial distribution systems
title_full An approach based on neural networks for identification of fault sections in radial distribution systems
title_fullStr An approach based on neural networks for identification of fault sections in radial distribution systems
title_full_unstemmed An approach based on neural networks for identification of fault sections in radial distribution systems
title_sort An approach based on neural networks for identification of fault sections in radial distribution systems
author Ziolkowski, Valmir
author_facet Ziolkowski, Valmir
Da Silva, Ivan Nunes
Flauzino, Rogerio Andrade [UNESP]
author_role author
author2 Da Silva, Ivan Nunes
Flauzino, Rogerio Andrade [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Ziolkowski, Valmir
Da Silva, Ivan Nunes
Flauzino, Rogerio Andrade [UNESP]
dc.subject.por.fl_str_mv Artificial intelligence
Automation
Classification (of information)
Computer networks
Electric fault location
Electric load distribution
Electric power systems
Electric power transmission
Electric tools
Electronic data interchange
Feeding
Automatic identification
Industrial technologies
International conferences
Neural networks
topic Artificial intelligence
Automation
Classification (of information)
Computer networks
Electric fault location
Electric load distribution
Electric power systems
Electric power transmission
Electric tools
Electronic data interchange
Feeding
Automatic identification
Industrial technologies
International conferences
Neural networks
description The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.
publishDate 2006
dc.date.none.fl_str_mv 2006-12-01
2014-05-27T11:22:02Z
2014-05-27T11:22:02Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/ICIT.2006.372351
Proceedings of the IEEE International Conference on Industrial Technology, p. 25-30.
http://hdl.handle.net/11449/69237
10.1109/ICIT.2006.372351
2-s2.0-51349143502
url http://dx.doi.org/10.1109/ICIT.2006.372351
http://hdl.handle.net/11449/69237
identifier_str_mv Proceedings of the IEEE International Conference on Industrial Technology, p. 25-30.
10.1109/ICIT.2006.372351
2-s2.0-51349143502
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings of the IEEE International Conference on Industrial Technology
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 25-30
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_ 1808128958069211136

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