Cooperative Wind Farm Optimization Using Policy Search Reinforcement Learning

This paper introduces a policy-search-based reinforcement learning algorithm aimed at generating optimal set-points of wind turbines in wind farms. The proposed approach addresses the problem of multivariable optimization in systems where the objective function is unknown or difficult to model. The algorithm is a model-free framework and relies solely on measured performance of the system. Namely, it does not require gradient information of the objective function or an explicit model of the aerodynamic interaction between wind turbines. The proposed scheme utilizes stochastic policy perturbations to explore the search space and update the policy parameters directly based on the observed reward signal. In this way, the algorithm progressively drives the control variables toward optimal operating conditions. The proposed policy-search reinforcement learning framework is analyzed to establish its connection with gradient-free optimization methods. The proposed method is applied to wind farm power optimization, where multiple turbine control variables must be adjusted in the presence of wake interactions cooperatively. The performance of the proposed approach is evaluated through extensive simulations under both steady-state and time-varying wind conditions. The proposed algorithm is compared with an extremum-seeking control method that was previously suggested for the same problem. The results demonstrate that the proposed approach is able to effectively maximize power production in wind farms while maintaining a simple and model-free optimization structure.