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

Infrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions extreme weather conditions. There is growing concern among decision makers about the ability of farms withstand regain their performance when facing disruptions, in terms resilience-enhanced strategies. This paper proposes a probabilistic model calculate resilience disruptive In this study, considered be function reliability, maintainability, supportability, organizational resilience. The relationships between these variables can structured using Bayesian network models. use networks allows for analyzing different scenarios. Moreover, used quantify resilience, which demonstrated with case study farm Arctic Norway. results show that highly resilient under normal operating conditions, slightly degraded introduced calculation black swan A scenario an unknowable unknown affect system low probability very high consequences. analysis significantly addition, backward propagation illustrates percentage improvement required each factor order attain certain level