Predicting the benefit of wake steering on the annual energy production of a wind farm using large eddy simulations and Gaussian process regression
D.C. Van Der Hoek (TU Delft - Team Jan-Willem van Wingerden)
Bart M. Doekemeijer (TU Delft - Team Jan-Willem van Wingerden)
Erik Andersson (Norwegian University of Science and Technology (NTNU))
Jan Willem Van Wingerden (TU Delft - Team Jan-Willem van Wingerden)
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Abstract
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.
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