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Article

An Expert-Driven Probabilistic Assessment of the Safety and Security of Offshore Wind Farms

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German Aerospace Center (DLR), Institute for the Protection of Terrestrial Infrastructures, Rathausallee 12, 53757 Sankt Augustin, Germany
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Fraunhofer Institute for High-Speed-Dynamics, Ernst-Mach-Institut, EMI, Am Klingelberg 1, 79588 Efringen-Kirchen, Germany
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German Aerospace Center (DLR), Institute for the Protection of Maritime Infrastructures, Fischkai 1, 27572 Bremerhaven, Germany
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FICHTNER GmbH & Co. KG, Sarweystrasse 3, 70191 Stuttgart, Germany
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German Aerospace Center (DLR), Institute for Communications and Navigation, Kalkhorstweg 53, 17235 Neustrelitz, Germany
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Ing. J. Mielniczek, Safety Engineer (Freelance), Hedwig-Augustin-Str. 27, 25348 Glückstadt, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Enrico Zio, Piero Baraldi and Roozbeh Razavi-Far
Energies 2021, 14(17), 5465; https://doi.org/10.3390/en14175465
Received: 18 June 2021 / Revised: 23 August 2021 / Accepted: 25 August 2021 / Published: 2 September 2021
(This article belongs to the Special Issue Special Issue of ESREL2020 PSAM15)
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. View Full-Text
Keywords: offshore wind farms; safety; security; Bayesian network offshore wind farms; safety; security; Bayesian network
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MDPI and ACS Style

Ramírez-Agudelo, O.H.; Köpke, C.; Guillouet, Y.; Schäfer-Frey, J.; Engler, E.; Mielniczek, J.; Sill Torres, F. An Expert-Driven Probabilistic Assessment of the Safety and Security of Offshore Wind Farms. Energies 2021, 14, 5465. https://doi.org/10.3390/en14175465

AMA Style

Ramírez-Agudelo OH, Köpke C, Guillouet Y, Schäfer-Frey J, Engler E, Mielniczek J, Sill Torres F. An Expert-Driven Probabilistic Assessment of the Safety and Security of Offshore Wind Farms. Energies. 2021; 14(17):5465. https://doi.org/10.3390/en14175465

Chicago/Turabian Style

Ramírez-Agudelo, Oscar H., Corinna Köpke, Yann Guillouet, Jan Schäfer-Frey, Evelin Engler, Jennifer Mielniczek, and Frank Sill Torres. 2021. "An Expert-Driven Probabilistic Assessment of the Safety and Security of Offshore Wind Farms" Energies 14, no. 17: 5465. https://doi.org/10.3390/en14175465

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