Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data
This work addresses wind farm efficiency optimization for renewable energy applications, presenting an incremental improvement in control methods.
The study optimized yaw control for a nine-turbine wind farm using high-fidelity simulations and Gaussian process-based modifier adaptation, showing that both modifier adaptation and Bayesian optimization improved power production with reduced yaw misalignments compared to a Gaussian wake model.
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.