Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics

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Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorTexas A&M Univ-
Autor(es): dc.creatorLeite, Jonatas Boas [UNESP]-
Autor(es): dc.creatorSanches Mantovani, Jose Roberto [UNESP]-
Autor(es): dc.creatorDokic, Tatjana-
Autor(es): dc.creatorYan, Qin-
Autor(es): dc.creatorChen, Po-Chen-
Autor(es): dc.creatorKezunovic, Mladen-
Data de aceite: dc.date.accessioned2022-02-22T00:09:23Z-
Data de disponibilização: dc.date.available2022-02-22T00:09:23Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2019-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/TPWRS.2019.2913090-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/196418-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/196418-
Descrição: dc.descriptionA new predictive risk-based framework is proposed to increase power distribution network resiliency by improving operator understanding of the status of the grid. This paper expresses the risk assessment as the correlation between likelihood and impact. The likelihood is derived from the combination of Naive Bayes learning and Jenks natural breaks classifier. The analytics included in a geographic information system platform fuse together a massive amount of data from outage recordings and weather historical databases in just one semantic parameter known as failure probability. The financial impact is determined by a time-series-based formulation that supports spatiotemporal data from fault management events and customer interruption cost. Results offer prediction of hourly risk levels and monthly accumulated risk for each feeder section of a distribution network allowing for timely tracking of the operating condition.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionSao Paulo State Univ FEIS, Elect Engn Dept, BR-15385000 Ilha Solteira, Brazil-
Descrição: dc.descriptionTexas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA-
Descrição: dc.descriptionSao Paulo State Univ FEIS, Elect Engn Dept, BR-15385000 Ilha Solteira, Brazil-
Descrição: dc.descriptionFAPESP: 2015/17757-2-
Descrição: dc.descriptionCNPq: 305371/2012-6-
Formato: dc.format4249-4257-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-inst Electrical Electronics Engineers Inc-
Relação: dc.relationIeee Transactions On Power Systems-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectPower distribution system-
Palavras-chave: dc.subjectrisk assessment-
Palavras-chave: dc.subjectNaive Bayes learning-
Palavras-chave: dc.subjectfailure probability-
Palavras-chave: dc.subjecttime series-
Palavras-chave: dc.subjectinterruption cost-
Palavras-chave: dc.subjectgeographic information system (GIS)-
Título: dc.titleResiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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