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dc.contributor.authorMustafa, Albara
dc.contributor.authorBarabadi, Abbas
dc.date.accessioned2021-08-03T10:10:44Z
dc.date.available2021-08-03T10:10:44Z
dc.date.issued2021-07-22
dc.description.abstractInfrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions such as extreme weather conditions. There is growing concern among decision makers about the ability of wind farms to withstand and regain their performance when facing disruptions, in terms of resilience-enhanced strategies. This paper proposes a probabilistic model to calculate the resilience of wind farms facing disruptive weather conditions. In this study, the resilience of wind farms is considered to be a function of their reliability, maintainability, supportability, and organizational resilience. The relationships between these resilience variables can be structured using Bayesian network models. The use of Bayesian networks allows for analyzing different resilience scenarios. Moreover, Bayesian networks can be used to quantify resilience, which is demonstrated in this paper with a case study of a wind farm in Arctic Norway. The results of the case study show that the wind farm is highly resilient under normal operating conditions, and slightly degraded under Arctic operating conditions. Moreover, the case study introduced the calculation of wind farm resilience under Arctic black swan conditions. A black swan scenario is an unknowable unknown scenario that can affect a system with low probability and very high extreme consequences. The results of the analysis show that the resilience of the wind farm is significantly degraded when operating under Arctic black swan conditions. In addition, a backward propagation of the Bayesian network illustrates the percentage of improvement required in each resilience factor in order to attain a certain level of resilience of the wind farm under Arctic black swan conditions.en_US
dc.identifier.citationMustafa A, Barabadi A. Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks . Energies. 2021;14(15)en_US
dc.identifier.cristinIDFRIDAID 1922471
dc.identifier.doi10.3390/en14154439
dc.identifier.issn1996-1073
dc.identifier.urihttps://hdl.handle.net/10037/21897
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.ispartofMustafa, A. (2023). Risk and Resilience Assessment of Wind Farms Performance in Cold Climate Regions. (Doctoral thesis). <a href=https://hdl.handle.net/10037/28610>https://hdl.handle.net/10037/28610</a>.
dc.relation.journalEnergies
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Technology: 500::Electrotechnical disciplines: 540en_US
dc.subjectVDP::Teknologi: 500::Elektrotekniske fag: 540en_US
dc.titleResilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networksen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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