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dc.contributor.authorLeite, Jonatas Boas [UNESP]
dc.contributor.authorMantovani, Jose Roberto Sanches [UNESP]
dc.contributor.authorDokic, Tatjana
dc.contributor.authorYan, Qin
dc.contributor.authorChen, Po-Chen
dc.contributor.authorKezunovic, Mladen
dc.identifier.citation2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017, v. 2017-January, p. 1-6.
dc.description.abstractThe 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.en
dc.relation.ispartof2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017
dc.subjectElectricity supply industry
dc.subjectFailure probability
dc.subjectGeographic information systems
dc.subjectPower distribution
dc.subjectRisk analysis
dc.titleFailure probability metric by machine learning for online risk assessment in distribution networksen
dc.typeTrabalho apresentado em evento
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionTexas AandM University
dc.description.affiliationDep. of Electrical Engineering São Paulo State University-UNESP
dc.description.affiliationDep. of Electrical and Computer Engineering Texas AandM University
dc.description.affiliationUnespDep. of Electrical Engineering São Paulo State University-UNESP
dc.rights.accessRightsAcesso aberto
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