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

Oscar Hernán Ramírez-Agudelo, Corinna Köpke, Yann Guillouet, Jan Schäfer-Frey, Evelin Engler, Jennifer Mielniczek and Frank Sill Torres
Additional contact information
Oscar Hernán Ramírez-Agudelo: German Aerospace Center (DLR), Institute for the Protection of Terrestrial Infrastructures, Rathausallee 12, 53757 Sankt Augustin, Germany
Corinna Köpke: Fraunhofer Institute for High-Speed-Dynamics, Ernst-Mach-Institut, EMI, Am Klingelberg 1, 79588 Efringen-Kirchen, Germany
Yann Guillouet: German Aerospace Center (DLR), Institute for the Protection of Maritime Infrastructures, Fischkai 1, 27572 Bremerhaven, Germany
Jan Schäfer-Frey: FICHTNER GmbH & Co. KG, Sarweystrasse 3, 70191 Stuttgart, Germany
Evelin Engler: German Aerospace Center (DLR), Institute for Communications and Navigation, Kalkhorstweg 53, 17235 Neustrelitz, Germany
Jennifer Mielniczek: Ing. J. Mielniczek, Safety Engineer (Freelance), Hedwig-Augustin-Str. 27, 25348 Glückstadt, Germany
Frank Sill Torres: German Aerospace Center (DLR), Institute for the Protection of Maritime Infrastructures, Fischkai 1, 27572 Bremerhaven, Germany

Energies, 2021, vol. 14, issue 17, 1-18

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.

Keywords: offshore wind farms; safety; security; Bayesian network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/17/5465/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/17/5465/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:17:p:5465-:d:627632

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().