Statistical Extrapolation Methods for Estimating Wind Turbine Extreme Loads

With the introduction of the third edition of the International Electrotechnical Commission (IEC) Standard 61400-1, designers of wind turbines are now explicitly required, in one of the prescribed load cases, to use statistical extrapolation techniques to determine nominal design loads. In this study, we use field data from a utility-scale $1.5MW$ turbine sited in Lamar, Colorado to compare the performance of several alternative techniques for statistical extrapolation of rotor and tower loads—these include the method of global maxima, the peak-over-threshold method, and a four-moment process model approach. Using each of these three options, $50-year$ return loads are estimated for the selected wind turbine. We conclude that the peak-over-threshold method is the superior approach, and we examine important details intrinsic to this method, including selection of the level of the threshold to be employed, the parametric distribution used in fitting, and the assumption of statistical independence between successive peaks. While we are primarily interested in the prediction of extreme loads, we are also interested in assessing the uncertainty in our predictions as a function of the amount of data used. Towards this end, we first obtain estimates of extreme loads associated with target reliability levels by making use of all of the data available, and then we obtain similar estimates using only subsets of the data. From these separate estimates, conclusions are made regarding what constitutes a sufficient amount of data upon which to base a statistical extrapolation. While this study makes use of field data in addressing statistical load extrapolation issues, the findings should also be useful in simulation-based attempts at deriving wind turbine design load levels where similar questions regarding extrapolation techniques, distribution choices, and amount of data needed are just as relevant.