Effects of finite sampling on fatigue damage estimation of wind turbine components: A statistical study

Abstract

The variability of the wind turbine loads complicates fatigue assessment in the design phase, as performing simulations covering the entire lifetime is computationally expensive. The current work provides important information for assessing the uncertainty in fatigue damage estimation due to finite data. We study the sample size effect on mean, variance, and skewness of damage in each wind bin, identify the important wind bins, and study the uncertainty propagation from each wind bin to the lifetime damage using 3600 aeroelastic simulations and bootstrapping. To achieve less than 1% error in the damage estimation across all load channels in the current case study, at least 100 turbulence seeds are needed. Damage in different wind bins follows a lognormal distribution when using the conventional approach of six seeds. The provided insights and information allow the designer to achieve a specific level of accuracy for a given computational cost using strategic bin sampling.

Uncertainty quantification of wind turbine fatigue lifetime predictions through binning

“…where σ2 d i stands for the fatigue damage variance corresponding to the ith bin. By following this process, the resulting combined variance is naturally equal to the variance estimated directly from unbinned fatigue damage measurements for a given monitoring period, and, in contrast to other works [5], a zero covariance between bins does not have to be assumed.…”

“…high dimensional) cannot be implemented due to, for instance, SCADA parameters' unavailability. With a slightly different application focus, [5] and [6] are among the few studies that explicitly account for damage variability within each bin. Furthermore, some studies [1,2,3,5] rely on resampling methods to quantify confidence levels in the predicted lifetime mean value.…”

“…With a slightly different application focus, [5] and [6] are among the few studies that explicitly account for damage variability within each bin. Furthermore, some studies [1,2,3,5] rely on resampling methods to quantify confidence levels in the predicted lifetime mean value. By applying bootstrapping on already clustered data, some of these methods deviate from principled bootstrapping methods, which typically involve resampling from a sample representative of the entire population [7].…”