Abstract
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.
Highlights
Infrastructure systems in the Arctic are prone to disruptions in their operation caused mainly by the harsh weather conditions they face
Youn et al [16] proposed a theoretical definition of engineering resilience, which derives its generic formula from the system reliability and the three key attributes of prognostics and health management (PHM) efficiency, which are diagnostics, prognostics, Energies 2021, 14, 4439 and condition-based maintenance [17]
The maintainability of a Wind farms (WFs) can be expressed in terms of two factors, which reflect the ability to restore the functionality of the WF: the level of labor dexterity when carrying out the maintenance activities, and the accessibility to the WF; both factors are affected by the Arctic operating conditions
Summary
Infrastructure systems in the Arctic are prone to disruptions in their operation caused mainly by the harsh weather conditions they face. Wind farms (WFs) are among the infrastructure systems installed in the Arctic that are prone to disruptions resulting from the weather conditions in the region. This study is motivated by the observation that uncertainties emerging from the changing climate conditions might open up the possibility for unexpectedly harsh weather conditions, characterized as a black swan, to take place in regions such as the Arctic. The effects of such a scenario taking place, and affecting the operation of WFs in the Arctic, are not well addressed in the literature.
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