Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TPWRS.2019.2913090 http://hdl.handle.net/11449/196418 |
Resumo: | A 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. |
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Repositório Institucional da UNESP |
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Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk AnalyticsPower distribution systemrisk assessmentNaive Bayes learningfailure probabilitytime seriesinterruption costgeographic information system (GIS)A 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.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 FEIS, Elect Engn Dept, BR-15385000 Ilha Solteira, BrazilTexas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USASao Paulo State Univ FEIS, Elect Engn Dept, BR-15385000 Ilha Solteira, BrazilFAPESP: 2015/17757-2CNPq: 305371/2012-6Ieee-inst Electrical Electronics Engineers IncUniversidade Estadual Paulista (Unesp)Texas A&M UnivLeite, Jonatas Boas [UNESP]Sanches Mantovani, Jose Roberto [UNESP]Dokic, TatjanaYan, QinChen, Po-ChenKezunovic, Mladen2020-12-10T19:44:17Z2020-12-10T19:44:17Z2019-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article4249-4257http://dx.doi.org/10.1109/TPWRS.2019.2913090Ieee Transactions On Power Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 34, n. 6, p. 4249-4257, 2019.0885-8950http://hdl.handle.net/11449/19641810.1109/TPWRS.2019.2913090WOS:000503069700010Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Transactions On Power Systemsinfo:eu-repo/semantics/openAccess2021-10-23T02:54:29Zoai:repositorio.unesp.br:11449/196418Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T02:54:29Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics |
title |
Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics |
spellingShingle |
Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics Leite, Jonatas Boas [UNESP] Power distribution system risk assessment Naive Bayes learning failure probability time series interruption cost geographic information system (GIS) |
title_short |
Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics |
title_full |
Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics |
title_fullStr |
Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics |
title_full_unstemmed |
Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics |
title_sort |
Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics |
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 |
author_role |
author |
author2 |
Sanches Mantovani, Jose Roberto [UNESP] Dokic, Tatjana Yan, Qin Chen, Po-Chen Kezunovic, Mladen |
author2_role |
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 |
dc.subject.por.fl_str_mv |
Power distribution system risk assessment Naive Bayes learning failure probability time series interruption cost geographic information system (GIS) |
topic |
Power distribution system risk assessment Naive Bayes learning failure probability time series interruption cost geographic information system (GIS) |
description |
A 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. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-11-01 2020-12-10T19:44:17Z 2020-12-10T19:44:17Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/TPWRS.2019.2913090 Ieee Transactions On Power Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 34, n. 6, p. 4249-4257, 2019. 0885-8950 http://hdl.handle.net/11449/196418 10.1109/TPWRS.2019.2913090 WOS:000503069700010 |
url |
http://dx.doi.org/10.1109/TPWRS.2019.2913090 http://hdl.handle.net/11449/196418 |
identifier_str_mv |
Ieee Transactions On Power Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 34, n. 6, p. 4249-4257, 2019. 0885-8950 10.1109/TPWRS.2019.2913090 WOS:000503069700010 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ieee Transactions On Power Systems |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
4249-4257 |
dc.publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
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 |
|
_version_ |
1803649655031988224 |