Offshore wind farms (OWFs) are important infrastructure which provide an alternative and clean means of energy production worldwide. The offshore wind industry has been continuously growing. Over the years, however, it has become evident that OWFs are facing a variety of safety and security challenges. If not addressed, these issues may hinder their progress. Based on these safety and security goals and on a Bayesian network model, this work presents a methodological approach for structuring and organizing expert knowledge and turning it into a probabilistic model to assess the safety and security of OWFs. This graphical probabilistic model allowed us to create a high-level representation of the safety and security state of a generic OWF. By studying the interrelations between the different functions of the model, and by proposing different scenarios, we determined the impacts that a failing function may have on other functions in this complex system. Finally, this model helped us define the performance requirements of such infrastructure, which should be beneficial for optimizing operation and maintenance.

Complex interconnected systems have high demands on meaningful analysis of the impact of failures on the actual service provision. This includes the study of obvious and high probable events, but also failures that are difficult to anticipate, e.g. due to cascading effects or combined events. This work introduces a framework for failure analysis that enables the exhaustive identification of combined failures with the strongest impact on the functionality of a system. The framework consists of two principal elements: a method for capturing the propagation of failures in complex systems that are represented via function models, and algorithms for solving the identification problem, which is formulated as combinatorial optimization problem. The feasibility of the approach is verified at hand of a function model of an Offshore Wind Farm (OWF). Both algorithms are then applied to the model of an offshore wind farm in order to identify the failure combinations with the strongest impact on the functionality.

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