Articles | Volume 6, issue 5
Wind Energ. Sci., 6, 1117–1142, 2021
Wind Energ. Sci., 6, 1117–1142, 2021
Research article
09 Sep 2021
Research article | 09 Sep 2021

Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics

Davide Conti et al.

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Cited articles

International Standard IEC61400-13: Wind turbines – Part 13: Measurement of mechanical loads, Standard, International Electrotechnical Commission (IEC), 2015. a
International Standard IEC61400-12-1: Wind energy generation systems – Part 12-1: Power performance measurements of electricity producing wind turbines, Standard, International Electrotechnical Commission (IEC), 2017. a
International Standard IEC61400-1: wind turbines – Part 1: design guidelines, Fourth; 2019, Standard, International Electrotechnical Commission (IEC), 2019. a, b, c, d, e, f, g, h, i, j
Ainslie, J. F.: Calculating the flowfield in the wke of wind turbines, J. Wind. Eng. Ind. Aerod., 27, 213–224, 1987. a
Aitken, M. L., Banta, R. M., Pichugina, Y. L., and Lundquist, J. K.: Quantifying wind turbine wake characteristics from scanning remote sensor data, J. Atmos. Ocean. Technol., 31, 765–787,, 2014. a
Short summary
We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected from the Scaled Wind Farm Technology (SWiFT) facility in Texas. The analysis includes the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric-stability conditions.