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An expert-driven probabilistic assessment of the safety and security of offshore wind farms

 
: Ramírez-Agudelo, Oscar Hernán; Köpke, Corinna; Guillouet, Yann; Schäfer-Frey, Jan; Engler, Evelin; Mielniczek, Jennifer; Sill Torres, Frank

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Fulltext ()

Energies 14 (2021), No.17, Art. 5465, 18 pp.
ISSN: 1996-1073
English
Journal Article, Electronic Publication
Fraunhofer EMI ()
offshore wind farms; safety; security; Bayesian network

Abstract
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.

: http://publica.fraunhofer.de/documents/N-642214.html