Failure Probability Metric by Machine Learning for Online Risk Assessment in Distribution Networks

Detalhes bibliográficos
Autor(a) principal: Leite, Jonatas Boas [UNESP]
Data de Publicação: 2017
Outros Autores: Sanches Mantovani, Jose Roberto [UNESP], Dokic, Tatjana, Yan, Qin, Chen, Po-Chen, Kezunovic, Mladen, IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/185108
Resumo: The risk assessment approach is useful for monitoring and supervisory control because it provides distribution operator with the capability to quantify the tradeoff between reliability and economic performance. The risk assessment determines the likelihood of something going wrong in a distribution network through the failure probability metric. To deal with the massive variety of information required in the calculation of failure probability we propose a data mining approach. The proposed approach incorporates weather, asset and outage information for characterizing the risk in a distribution network section via GIS platform.
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spelling Failure Probability Metric by Machine Learning for Online Risk Assessment in Distribution Networkselectricity supply industryfailure probabilitygeographic information systemspower distributionrisk analysisThe risk assessment approach is useful for monitoring and supervisory control because it provides distribution operator with the capability to quantify the tradeoff between reliability and economic performance. The risk assessment determines the likelihood of something going wrong in a distribution network through the failure probability metric. To deal with the massive variety of information required in the calculation of failure probability we propose a data mining approach. The proposed approach incorporates weather, asset and outage information for characterizing the risk in a distribution network section via GIS platform.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Sao Paulo State Univ UNESP, Dep Elect Engn, Ilha Solteira, BrazilTexas A&M Univ, Dep Elect & Comp Engn, College Stn, TX 77843 USASao Paulo State Univ UNESP, Dep Elect Engn, Ilha Solteira, BrazilFAPESP: 2015/17757-2FAPESP: 2015/21972-6CNPq: 305371/2012-6IeeeUniversidade Estadual Paulista (Unesp)Texas A&M UnivLeite, Jonatas Boas [UNESP]Sanches Mantovani, Jose Roberto [UNESP]Dokic, TatjanaYan, QinChen, Po-ChenKezunovic, MladenIEEE2019-10-04T12:32:43Z2019-10-04T12:32:43Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject62017 Ieee Pes Innovative Smart Grid Technologies Conference - Latin America (isgt Latin America). New York: Ieee, 6 p., 2017.http://hdl.handle.net/11449/185108WOS:000451380200003Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2017 Ieee Pes Innovative Smart Grid Technologies Conference - Latin America (isgt Latin America)info:eu-repo/semantics/openAccess2021-10-23T19:02:20Zoai:repositorio.unesp.br:11449/185108Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T19:02:20Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Failure Probability Metric by Machine Learning for Online Risk Assessment in Distribution Networks
title Failure Probability Metric by Machine Learning for Online Risk Assessment in Distribution Networks
spellingShingle Failure Probability Metric by Machine Learning for Online Risk Assessment in Distribution Networks
Leite, Jonatas Boas [UNESP]
electricity supply industry
failure probability
geographic information systems
power distribution
risk analysis
title_short Failure Probability Metric by Machine Learning for Online Risk Assessment in Distribution Networks
title_full Failure Probability Metric by Machine Learning for Online Risk Assessment in Distribution Networks
title_fullStr Failure Probability Metric by Machine Learning for Online Risk Assessment in Distribution Networks
title_full_unstemmed Failure Probability Metric by Machine Learning for Online Risk Assessment in Distribution Networks
title_sort Failure Probability Metric by Machine Learning for Online Risk Assessment in Distribution Networks
author Leite, Jonatas Boas [UNESP]
author_facet Leite, Jonatas Boas [UNESP]
Sanches Mantovani, Jose Roberto [UNESP]
Dokic, Tatjana
Yan, Qin
Chen, Po-Chen
Kezunovic, Mladen
IEEE
author_role author
author2 Sanches Mantovani, Jose Roberto [UNESP]
Dokic, Tatjana
Yan, Qin
Chen, Po-Chen
Kezunovic, Mladen
IEEE
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Texas A&M Univ
dc.contributor.author.fl_str_mv Leite, Jonatas Boas [UNESP]
Sanches Mantovani, Jose Roberto [UNESP]
Dokic, Tatjana
Yan, Qin
Chen, Po-Chen
Kezunovic, Mladen
IEEE
dc.subject.por.fl_str_mv electricity supply industry
failure probability
geographic information systems
power distribution
risk analysis
topic electricity supply industry
failure probability
geographic information systems
power distribution
risk analysis
description The risk assessment approach is useful for monitoring and supervisory control because it provides distribution operator with the capability to quantify the tradeoff between reliability and economic performance. The risk assessment determines the likelihood of something going wrong in a distribution network through the failure probability metric. To deal with the massive variety of information required in the calculation of failure probability we propose a data mining approach. The proposed approach incorporates weather, asset and outage information for characterizing the risk in a distribution network section via GIS platform.
publishDate 2017
dc.date.none.fl_str_mv 2017-01-01
2019-10-04T12:32:43Z
2019-10-04T12:32:43Z
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 2017 Ieee Pes Innovative Smart Grid Technologies Conference - Latin America (isgt Latin America). New York: Ieee, 6 p., 2017.
http://hdl.handle.net/11449/185108
WOS:000451380200003
identifier_str_mv 2017 Ieee Pes Innovative Smart Grid Technologies Conference - Latin America (isgt Latin America). New York: Ieee, 6 p., 2017.
WOS:000451380200003
url http://hdl.handle.net/11449/185108
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2017 Ieee Pes Innovative Smart Grid Technologies Conference - Latin America (isgt Latin America)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 6
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
dc.source.none.fl_str_mv Web of Science
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
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