Articles | Volume 3, issue 2
Wind Energ. Sci., 3, 475–487, 2018
https://doi.org/10.5194/wes-3-475-2018
Wind Energ. Sci., 3, 475–487, 2018
https://doi.org/10.5194/wes-3-475-2018
Research article
11 Jul 2018
Research article | 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 et al.

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Cited articles

Bos, R., Bierbooms, W., and van Bussel, G.: Importance sampling of severe wind gusts, in: 11th EAWE PhD Seminar on Wind Energy in Europe, 4 pp., 2015. a
Choe, Y., Pan, Q., and Byon, E.: Computationally Efficient Uncertainty Minimization in Wind Turbine Extreme Load Assessment, J. Sol. Energ. Engin., 138, 041012–041012–8, 2016. a
Dimitrov, N.: Comparative analysis of methods for modelling the short-term probability distribution of extreme wind turbine loads, Wind Energy, 19, 717–737, https://doi.org/10.1002/we.1861, 2016. a
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Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press, http://www.deeplearningbook.org (last access: 27 June 2017), 2016. a
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Short summary
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.