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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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (25 Oct 2017) by Michael Muskulus
AR by Peter Graf on behalf of the Authors (07 Dec 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (22 Jan 2018) by Michael Muskulus
RR by Lars Einar S. Stieng (02 Feb 2018)
RR by Anonymous Referee #3 (27 Feb 2018)
RR by Nikolay Dimitrov (04 Mar 2018)
ED: Publish subject to minor revisions (review by editor) (16 Mar 2018) by Michael Muskulus
AR by Peter Graf on behalf of the Authors (11 Apr 2018)  Author's response    Manuscript
ED: Publish as is (28 Apr 2018) by Michael Muskulus
ED: Publish as is (29 Apr 2018) by Gerard J.W. van Bussel(Chief Editor)
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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.