A Multilayer Resilience Assessment of Power Distribution Systems with Reliability Models, Service Restoration, and Dynamic Bayesian Networks

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de Minas Gerais (UFMG)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorBessani, Michel-
Autor(es): dc.creatorCaetano, Henrique O.-
Autor(es): dc.creatorLuiz Desuó, N.-
Autor(es): dc.creatorFogliatto, Matheus S. S.-
Autor(es): dc.creatorMaciel, Carlos D.-
Data de aceite: dc.date.accessioned2025-08-21T19:10:54Z-
Data de disponibilização: dc.date.available2025-08-21T19:10:54Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-031-67754-0_7-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309444-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309444-
Descrição: dc.descriptionElectrical energy is fundamental for contemporary society since failures directly impact other critical infrastructures such as water and gas distribution, hospitals, or banking services. Consequently, resilience, which is the capability of a system to handle high-impact low probability events, is a crucial aspect of such systems. Efficient resilience assessment methods are essential to achieving high-performance, resilient energy systems. This chapter introduces a multilayer method to address several factors of power distribution systems’ resilience. Reliability regressions model the failures’ instant and duration given a weather scenario, a dynamic Bayesian network (DBN) models how probabilities of failure propagate on the system’s physical connections, and a service restoration through switching operations, and field crew routing is obtained through an optimization algorithm for a given set of failures. Information related to these factors has the potential to be structured in a layered manner for a better understanding of the dynamic interaction among different information like weather, routes, power grid, and historical events logs. The ability to model these relationships enables the inference of the system resilience for different inputs during analysis. Resilience can also be inferred by considering the uncertainties associated with these layers due to DBN’s nature. A case study is presented to show the efficacy of this procedure. The findings showed its ability to evaluate the resilience of power distribution systems in the face of uncertainty and the considered aspects for different weather scenarios.-
Descrição: dc.descriptionDepartment of Electrical Engineering Universidade Federal de Minas Gerais (UFMG)-
Descrição: dc.descriptionDepartment of Electrical and Computer Engineering University of São Paulo (USP)-
Descrição: dc.descriptionDepartment of Electrical Engineering São Paulo State University (Unesp)-
Descrição: dc.descriptionDepartment of Electrical Engineering São Paulo State University (Unesp)-
Formato: dc.format201-237-
Idioma: dc.languageen-
Relação: dc.relationPower Systems-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDynamic bayesian networks-
Palavras-chave: dc.subjectPower distribution systems-
Palavras-chave: dc.subjectReliability-
Palavras-chave: dc.subjectResilience assessment-
Palavras-chave: dc.subjectService restoration-
Título: dc.titleA Multilayer Resilience Assessment of Power Distribution Systems with Reliability Models, Service Restoration, and Dynamic Bayesian Networks-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.