A Practical Bayesian Optimization Approach for the Optimal Estimation of the Rotor Effective Wind Speed

Modern wind turbines require careful tuning of controller and estimator parameters. However, tuning requires expert control experience, and is therefore in practice often performed by a trial-and-error brute-force approach. The contribution of this work is twofold. Firstly, a framework for tuning the parameters for conventional control and estimator architectures with Bayesian optimization is proposed. Secondly, the proposed scheme is applied to the problem of tuning Kalman filter parameters for the estimation of the rotor effective wind speed. For accomplishing the beforementioned task, the Bayesian optimization machine learning algorithm uses entropy search as utility function. The NREL 5-MW reference wind turbine is used in high-fidelity simulation software to show the efficacy of the proposed methodology. The Bayesian optimized Kalman filter configuration, is shown to estimate the rotor effective wind speed with a root mean square error smaller than 5 %, with respect to the actual effective wind speed over all load cases.