Articles | Volume 6, issue 3
Wind Energ. Sci., 6, 841–866, 2021
https://doi.org/10.5194/wes-6-841-2021
Wind Energ. Sci., 6, 841–866, 2021
https://doi.org/10.5194/wes-6-841-2021
Research article
07 Jun 2021
Research article | 07 Jun 2021

Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals

Davide Conti et al.

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Cited articles

IEC: International Standard IEC61400-13: Wind turbines – Part 13: Measurement of mechanical loads, Standard, IEC, 2015. a, b, c
IEC: International Standard IEC61400-1: wind turbines – Part 1: design guidelines, Fourth; 2019, Standard, IEC, 2019. a, b, c, d, e
Achen, C. H.: Interpreting and Using Regression, Sage Publications, Beverly Hills, https://doi.org/10.4135/9781412984560, 1982. a
Ainslie, J.: Calculating the flow field in the wake of wind turbines, J. Wind Eng. Ind. Aerod., 27, 213–224, https://doi.org/10.1016/0167-6105(88)90037-2, 1988. a
Ainslie, J. F.: Wake modelling and the prediction of turbulence properties, in: Proceedings of the Bwea Wind Energy Conference, british Wind Energy Association, 20–24 October 1986, Cambridge, 115–120, 1986. a
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Short summary
We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time series on the turbulence fields. The uncertainty in the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.