Design of Fatigue Binning Methods From Hindcast Metocean Data for Offshore Wind Turbine Simulations

Abstract In the design cycle of a floating offshore wind turbine project, performing fatigue analysis to evaluate the damage equivalent loads over the design life on vital structural connections over the design life is crucial, as standardized in Design Load Case 1.2 and 6.4 by IEC. Employing a fatigue binning approach is essential in reducing the large number of numerical simulations needed for the accurate evaluation of the fatigue load. Three novel fatigue binning methodologies are designed to represent the environmental conditions for analysis of the long-term fatigue load on a floating offshore wind turbine. The first method is a probability constrained subset technique that categorizes the hindcast metocean data to reproduce the distribution of the environmental conditions. The second method leverages statistical sampling techniques to select statistically representative bins from the hindcast dataset. The third method selects short-term data out of the total hindcast dataset which mostly preserves the statistical characteristics of the environment conditions with empirical selection on daily data. All three methods are validated with long-term hindcast metocean data set. The created fatigue bins effectively represent the wind, wave, and current distribution as in the hindcast data. These methods substantially reduce the computational cost for the fatigue load analysis, offering an efficient solution for offshore wind energy projects.