| Texto completo | |
| Autor(es): |
Leite, Jonatas Boas
[1]
;
Sanches Mantovani, Jose Roberto
[1]
;
Dokic, Tatjana
[2]
;
Yan, Qin
[2]
;
Chen, Po-Chen
[2]
;
Kezunovic, Mladen
[2]
Número total de Autores: 6
|
| Afiliação do(s) autor(es): | [1] Sao Paulo State Univ FEIS, Elect Engn Dept, BR-15385000 Ilha Solteira - Brazil
[2] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 - USA
Número total de Afiliações: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IEEE Transactions on Power Systems; v. 34, n. 6, p. 4249-4257, NOV 2019. |
| Citações Web of Science: | 0 |
| 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. (AU) | |
| Processo FAPESP: | 15/17757-2 - Análise de risco usando dados meteorológicos para operação do sistema elétrico |
| Beneficiário: | Jonatas Boas Leite |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado |