Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data

• URL: http://arxiv.org/abs/2003.13323v1
• Date: Mon, 30 Mar 2020 10:22:57 GMT
• Title: Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data
• Authors: Leif Erik Andersson, Bart Doekemeijer, Daan van der Hoek, Jan-Willem van Wingerden, Lars Imsland
• Abstract summary: This article investigates the optimization of yaw control inputs of a nine-turbine wind farm. The wind farm is simulated using the high-fidelity simulator SOWFA.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article investigates the optimization of yaw control inputs of a nine-turbine wind farm. The wind farm is simulated using the high-fidelity simulator SOWFA. The optimization is performed with a modifier adaptation scheme based on Gaussian processes. Modifier adaptation corrects for the mismatch between plant and model and helps to converge to the actual plan optimum. In the case study the modifier adaptation approach is compared with the Bayesian optimization approach. Moreover, the use of two different covariance functions in the Gaussian process regression is discussed. Practical recommendations concerning the data preparation and application of the approach are given. It is shown that both the modifier adaptation and the Bayesian optimization approach can improve the power production with overall smaller yaw misalignments in comparison to the Gaussian wake model.

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