Predicting the benefit of wake steering on the annual energy production of a wind farm using large eddy simulations and Gaussian process regression

Predicting the benefit of wake steering on the annual energy production of a wind farm using large eddy simulations and Gaussian process regression

van der Hoek, D.C. (TU Delft Team Jan-Willem van Wingerden)
Doekemeijer, B.M. (TU Delft Team Jan-Willem van Wingerden)
Andersson, Leif Erik (Norwegian University of Science and Technology (NTNU))
van Wingerden, J.W. (TU Delft Team Jan-Willem van Wingerden)

2020

In recent years, wake steering has been established as a promising method to increase the energy yield of a wind farm. Current practice in estimating the benefit of wake steering on the annual energy production (AEP) consists of evaluating the wind farm with simplified surrogate models, casting a large uncertainty on the estimated benefit. This paper presents a framework for determining the benefit of wake steering on the AEP, incorporating simulation results from a surrogate model and large eddy simulations in order to reduce the uncertainty. Furthermore, a time-varying wind direction is considered for a better representation of the ambient conditions at the real wind farm site. Gaussian process regression is used to combine the two data sets into a single improved model of the energy gain. This model estimates a 0.60% gain in AEP for the considered wind farm, which is a 76% increase compared to the estimate of the surrogate model.

http://resolver.tudelft.nl/uuid:209a8d2f-093b-4bf3-ad75-d7c4e5453246

https://doi.org/10.1088/1742-6596/1618/2/022024

1742-6588

Journal of Physics: Conference Series, 1618 (2)

Science of Making Torque from Wind 2020, TORQUE 2020, 2020-09-28 → 2020-10-02, Online, Virtual, Online, Netherlands

Institutional Repository

journal article

© 2020 D.C. van der Hoek, B.M. Doekemeijer, Leif Erik Andersson, J.W. van Wingerden