Predicting the benefit of wake steering on the annual energy production of a wind farm using large eddy simulations and Gaussian process regression. Issue 2 (September 2020)

• Record Type: Journal Article
• Authors: Hoek, Daan van der; Doekemeijer, Bart; Andersson, Leif Erik; Wingerden, Jan-Willem van
• Is Part Of: Journal of physics. Volume 1618:Issue 2(2020)
• Publication Date: 2020-09
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1618/2/022024
• Languages: English
• ISSNs: 1742-6588

Abstract

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.