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. Wind turbine extreme load estimation is especially difficultbecause turbulent inflow drives nonlinear turbine physics and controlstrategies; thus there can be huge differences in turbine response toessentially equivalent environmental conditions. The two main currentapproaches, extrapolation and Monte Carlo sampling, are both unsatisfying:extrapolation-based methods are dangerous because by definition they makepredictions outside the range of available data, but Monte Carlo methodsconverge too slowly to routinely reach the desired 50-year return periodestimates. Thus a search for a better method is warranted. Here we introducean adaptive stratified importance sampling approach that allows for treatingthe choice of environmental conditions at which to run simulations as astochastic optimization problem that minimizes the variance of unbiasedestimates of extreme loads. Furthermore, the framework, built on thetraditional bin-based approach used in extrapolation methods, provides aclose connection between sampling and extrapolation, and thus allows thesolution of the stochastic optimization (i.e., the optimal distribution ofsimulations in different wind speed bins) to guide and recalibrate theextrapolation. Results show that indeed this is a promising approach, as thevariance of both the Monte Carlo and extrapolation estimates are reducedquickly by the adaptive procedure. We conclude, however, that due to theextreme response variability in turbine loads to the same environmentalconditions, our method and any similar method quickly reaches its fundamentallimits, and that therefore our efforts going forward are best spentelucidating the underlying causes of the response variability.

wind energy science

Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads