  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.
