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

Leif Erik Andersson, Bart Doekemeijer, Daan Van Der Hoek, Jan Willem Van Wingerden, Lars Imsland

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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.

Original languageEnglish
Article number022043
Number of pages10
JournalJournal of Physics: Conference Series
Volume1618
Issue number2
DOIs
Publication statusPublished - 2020
EventScience of Making Torque from Wind 2020, TORQUE 2020 - Online, Virtual, Online, Netherlands
Duration: 28 Sep 20202 Oct 2020

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