Yaw misalignment, measured as the difference between the wind direction and the nacelle position of a wind turbine, has consequences on the power output, the safety and the lifetime of the turbine and its wind park as a whole. We use reinforcement learning to develop a yaw control agent to minimise yaw misalignment and optimally reallocate yaw resources, prioritising high-speed segments, while keeping yaw usage low. To achieve this, we carefully crafted and tested the reward metric to trade-off yaw usage versus yaw alignment (as proportional to power production), and created a novel simulator (environment) based on real-world wind logs obtained from a REpower MM82 2MW turbine. The resulting algorithm decreased the yaw misalignment by 5.5% and 11.2% on two simulations of 2.7 hours each, compared to the conventional active yaw control algorithm. The average net energy gain obtained was 0.31% and 0.33% respectively, compared to the traditional yaw control algorithm. On a single 2MW turbine, this amounts to a 1.5k-2.5k euros annual gain, which sums up to very significant profits over an entire wind park.

翻译：偏航误差定义为风向与风力涡轮机机舱位置之间的偏差，会影响风力涡轮机及其整个风电场的功率输出、安全性和寿命。我们利用强化学习开发了一个偏航控制智能体，以最小化偏航误差并优化偏航资源分配，优先处理高速段，同时保持较低的偏航使用频率。为实现这一目标，我们精心设计并测试了奖励指标，以权衡偏航使用与偏航对齐（与发电量成比例），并基于从REpower MM82 2MW涡轮机获取的真实风速日志，创建了一个新型模拟器（环境）。与传统的主动偏航控制算法相比，该算法在两个分别为2.7小时的模拟测试中，将偏航误差分别降低了5.5%和11.2%。与传统偏航控制算法相比，平均净能量增益分别达到0.31%和0.33%。对于单台2MW涡轮机，这相当于每年1.5k-2.5k欧元的收益，累计下来，整个风电场将获得非常可观的利润。