Probabilistic Methods for Predicting Wind Turbine Design Loads

Further study of probabilistic methods for predicting extreme wind turbine loading was performed on two large-scale wind turbine models with stall and pitch regulation. Long-term exceedance probability distributions were calculated using maxima extracted from time series simulations of in-plane and out-of-plane blade loads. It was discovered that using a threshold on the selection of maxima increased the accuracy of the fitted distribution in following the trends of the largest extreme values for a given wind condition. The optimal threshold value for in-plane and out-of-plane blade loads was found to be the mean value plus 1.4 times the standard deviation of the original time series for the quantity of interest. When fitting a distribution to a given data set, the higher-order moments were found to have the greatest amount of uncertainty and also the largest influence on the extrapolated long-term load's. This uncertainty was reduced by using large data sets, smoothing of the moments between wind conditions and parametrically modeling moments of the distribution. A deterministic turbulence model using the 90th percentile level of the conditional turbulence distribution given mean wind speed was used to greatly simplify the calculation of the long-term probability distribution. Predicted extreme loads using this simplified distribution were equal to or more conservative than the loads predicted by the full integration method.