Fatigue analysis for wind turbines is typically carried out in the time domain, using cycle counting techniques such as ASTM's Rainflow Cycle-Counting Algorithm. As an alternative, earlier workers investigated the feasibility of estimating wind turbine fatigue loads using spectral techniques such as Dirlik's method to estimate stress range probability distributions that are based on spectral moments of the load in question. The present paper re-examines this approach with a particular view to assessing its limitations and advantages in the context of modern, large-scale wind turbines and design methods. These relative advantages are considered in terms of accuracy, statistical reliability, and efficiency of calculation. Field data on loads from a utility-scale 1.5 MW turbine near Lamar, Colorado in the Colorado Green Wind Farm are analyzed here as a representative example. The results show that valuable and reliable information about tower loads can be obtained very efficiently. By contrast, the limitations of the Dirlik method are highlighted by poor results for edgewise blade loads.NOMENCLATURE f = frequency in Hertz m = material exponent for fatigue m n = n th spectral moment p(S) = stress range probability density function D = cumulative fatigue damage fraction EFL = constant-amplitude equivalent fatigue load K = material parameter for fatigue damage at failure N = number of stress cycles N F = number of stress cycles at failure P = peaks per second P s = power spectral density function for stress range, S S = stress range T = lifetime of structure Z = normalized stress range
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.
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