Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations

Wind farms? power-generation efficiency is constrained by the high system complexity. A novel deep reinforcement learning (RL)-based wind farm control scheme is proposed to handle this challenge and achieve power generation optimization. A reward regularization (RR) module is designed to estimate wind turbines? normalized power outputs under different yaw settings and uncertain wind conditions, which brings strong robustness and adaptability to the proposed control scheme. The RR module is then combined with the deep deterministic policy gradient algorithm to evaluate the optimal yaw settings for all the wind turbines within the farm. The proposed wind farm control scheme is data-driven and model-free, which addresses the limitations of current approaches, including reliance on accurate analytical/parametric models and lack of adaptability to uncertain wind conditions. In addition, a novel composite learning-based controller for each turbine is designed to achieve closed-loop yaw tracking, which can guarantee the exponential convergence of tracking errors in the presence of uncertainties of yaw actuators. The whole control system can be pre-trained offline and fine-tuned online, providing an easy-to-apply solution with enhanced generality and flexibility for wind farms. High-fidelity simulations with SOWFA (simulator for offshore wind farm applications) and Tensorflow show that the proposed scheme can significantly improve the wind farm?s power generation by exploiting a sparse data set without requiring any wake model.

Automated summary

This study proposes a wind farm control scheme based on deep reinforcement learning, which uses a reward regularization module and composite learning controller to optimize power generation and yaw tracking. The scheme demonstrates robustness and adaptability in handling uncertain wind conditions.