Multi-parameter full probabilistic modeling of long-term joint wind-wave actions using multi-source data and applications to fatigue analysis of floating offshore wind turbines

In the fatigue assessment of offshore wind turbines, joint probabilistic models of long-term wind and wave parameters are usually required. In practice, annual met-ocean data typically exhibit non-stationarity due to the seasonal variations and extreme weather effects, and therefore cannot be considered as being from the same probability space. Thus, data separation and data segmentation should be performed. In this paper, the full probabilistic modeling of wind and wave parameters for a site in the South China Sea usually hit by typhoons is studied. For this purpose, the typhoon data is firstly separated from the normal wind data using multi-source data and physically based approach. Then, a modified Fisher's optimum partition method is proposed for the seasonal effects segmentation of the normal wind data. On this basis, the full probabilistic model of the environmental variables is developed using the C-vine copula method. The application of the full probabilistic model to the fatigue analysis of a floating offshore wind turbine (FOWT) is illustrated through an example. Numerical results indicate that the separation and segmentation of the long-term met-ocean data is quite significant to the full probabilistic modeling of the environmental variables, and the proposed methods can deal with this problem effectively.

Automated summary

This paper studies the joint probabilistic modeling of long-term wind and wave parameters in the fatigue assessment of offshore wind turbines. Data separation and segmentation are performed to develop a full probabilistic model for a site in the South China Sea. Numerical results indicate that the proposed methods can effectively deal with this problem.