Volume 3, issue 2 | Copyright
Wind Energ. Sci., 3, 475-487, 2018
https://doi.org/10.5194/wes-3-475-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research articles 11 Jul 2018

Research articles | 11 Jul 2018

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 Peter Graf et al.
  • National Renewable Energy Laboratory, Golden, CO, 80401, USA

Abstract. Wind turbine extreme load estimation is especially difficult because turbulent inflow drives nonlinear turbine physics and control strategies; thus 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 in 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.

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Current approaches to wind turbine extreme load 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 in turbine response implies even an optimal sampling strategy needs unrealistic computing resources. We therefore conclude that further improvement requires better understanding of the underlying causes of loads.
Current approaches to wind turbine extreme load estimation are insufficient to routinely and...
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