Journal cover Journal topic
Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
https://doi.org/10.5194/wes-2017-30
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research articles
25 Jul 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Wind Energy Science (WES).
Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads
Peter Graf, Katherine Dykes, Rick Damiani, Jason Jonkman, and Paul Veers National Renewable Energy Laboratory, Golden, CO, 80401, USA
Abstract. Wind turbine extreme loads estimation is especially difficult because turbulent inflow drives nonlinear turbine physics and control strategies, so there can be huge differences in turbine response to essentially equivalent environmental conditions. The two main current approaches, extrapolation and Monte Carlo sampling, are both unsatisfying: extrapolation-based methods are dangerous because by definition they make predictions outside the range of available data, but Monte Carlo methods converge too slowly to routinely reach the desired 50-year return period estimates. Thus a search for a better method is warranted. Here we introduce an adaptive stratified importance sampling approach that allows for treating the choice of environmental conditions at which to run simulations as a stochastic optimization problem that minimizes the variance of unbiased estimates of extreme loads. Furthermore, the framework, built on the traditional bin-based approach used in extrapolation methods, provides a close connection between sampling and extrapolation, and thus allows the solution of the stochastic optimization (i.e., the optimal distribution of simulations in different wind speed bins) to guide and recalibrate the extrapolation. Results show that indeed this is a promising approach, as the variance of both the Monte Carlo and extrapolation estimates are reduced quickly by the adaptive procedure. We conclude, however, that due to the extreme response variability of turbine loads to the same environmental conditions, our method and any similar method quickly reaches its fundamental limits, and that therefore our efforts going forward are best spent elucidating the underlying causes of the response variability.

Citation: Graf, P., Dykes, K., Damiani, R., Jonkman, J., and Veers, P.: Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads, Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2017-30, in review, 2017.
Peter Graf et al.
Peter Graf et al.
Peter Graf et al.

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Short summary
Current approaches to wind turbine extreme loads estimation are insufficient to routinely and reliably make required estimates over 50 year return periods. Our work hybridizes the two main approaches and casts the problem as stochastic optimization. However, the extreme variability of turbine response implies even an optimal sampling strategy needs unrealistic computing resources. We conclude therefore that further improvement requires better understanding of the underlying causes of loads.
Current approaches to wind turbine extreme loads estimation are insufficient to routinely and...
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