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Article

Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks

Department of Technology and Safety, UiT the Arctic University of Norway, 6050 Tromsø, Norway
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Academic Editor: Enrico Zio
Energies 2021, 14(15), 4439; https://doi.org/10.3390/en14154439
Received: 16 June 2021 / Revised: 4 July 2021 / Accepted: 21 July 2021 / Published: 22 July 2021
(This article belongs to the Special Issue Critical Infrastructure Resilience Assessment and Management)
Infrastructure 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.
Keywords: wind farms; wind turbines; Arctic conditions; Arctic black swan; resilience; Bayesian network wind farms; wind turbines; Arctic conditions; Arctic black swan; resilience; Bayesian network
MDPI and ACS Style

Mustafa, A.M.; Barabadi, A. Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks. Energies 2021, 14, 4439. https://doi.org/10.3390/en14154439

AMA Style

Mustafa AM, Barabadi A. Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks. Energies. 2021; 14(15):4439. https://doi.org/10.3390/en14154439

Chicago/Turabian Style

Mustafa, Albara M., and Abbas Barabadi. 2021. "Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks" Energies 14, no. 15: 4439. https://doi.org/10.3390/en14154439

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