A Bayesian network model for the probabilistic safety assessment of offshore wind decommissioning. Issue 1 (February 2023)

• Record Type: Journal Article
• Authors: Shafiee, Mahmood; Adedipe, Tosin
• Is Part Of: Wind engineering. Volume 47:Issue 1(2023)
• Page Start: 104
• Page End: 125
• Publication Date: 2023-02
• Subjects: Bayesian network -- offshore wind -- decommissioning -- safety assessment -- lifting operations -- risk-influencing factors -- influence diagrams; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X221122569
• Languages: English
• ISSNs: 0309-524X

Abstract

With increasing the number of wind turbines approaching the end of their service life, it has become crucial for businesses to understand and assess safety and security issues related to the decommissioning phase of wind farm asset lifecycle. This paper aims to develop, for the first time, a Bayesian Network (BN) model for the safety assessment of offshore wind farm decommissioning operations. The most critical safety incidents are identified and their corresponding risk-influencing factors (RIF) are determined. The impacts of human errors as well as procedural and mechanical/electrical failures on the safety and efficiency of decommissioning operations are thoroughly analysed. The findings of the study revealed that the most critical RIFs during offshore wind decommissioning operations include: visibility, crew fatigue, number of personnel per operation, proper safety procedures, crane integrity, number of lifts available in the wind farm, inspection frequency, as well as equipment design.