Long-term extreme response of an offshore turbine: How accurate are contour-based estimates?. (January 2022)

• Record Type: Journal Article
• Authors: Haselsteiner, Andreas F.; Frieling, Malte; Mackay, Ed; Sander, Aljoscha; Thoben, Klaus-Dieter
• Is Part Of: Renewable energy. Volume 181(2022)
• Page Start: 945
• Page End: 965
• Publication Date: 2022-01
• Subjects: Offshore wind turbine -- Environmental contour method -- Reliability -- Structural design -- Multivariate extremes; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2021.09.077
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: According to design standards, offshore wind turbines need to withstand environmental loads with a return period of 50 years. This work compares the extreme response along the 50-year environmental contour with the true 50-year wind turbine response. It was found that the environmental contour method that is currently described in the IEC design standard for offshore wind turbines can strongly under-predict the 50-year return value of response variables whose annual maxima typically occur during power production. The bias in the contour-based estimate of the 50-year response can be attributed to three sources: (1) the method used to construct the contour; (2) neglecting serial correlation in environmental conditions; and (3) neglecting the short-term variability in the response. In our analysis the 50-year maximum mudline overturning moment was underestimated by 4–8% by the contour-based approach that is currently recommended, whereas the bending moment at 10 m water depth was underestimated by 25–28%. This underestimation was mainly due to ignoring the short-term variability in the response. The bias associated with contour construction, an effect much discussed in recent publications, was of much smaller magnitude. Highlights: Long-term extreme response of an offshore wind turbine was estimated. Estimates from contour methods were compared to true response. Sources of bias of the contour-based estimates were quantified. Bias is due to contour construction, serial correlation and short-term variability. Applying contour method from IEC's standard can underestimate the extreme response.