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.

利用强化学习开发了一种偏航控制代理来最小化偏航不一致并优化地重新分配偏航资源，重视高速段，同时保持偏航使用率低，从而显著提高风机的功率输出并提高整个风电场的收益。

基于强化学习的风力涡轮机偏航控制算法改进

An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement  Learning

The power output of a wind turbine depends on a variety of factors, including
wind speed at different heights, wind direction, temperature and turbine
properties. Wind speed and direction, in particular, have complex cycles and
fluctuate dramatically, leading to large uncertainties in wind power output.
This study uses variational mode decomposition (VMD) to decompose the wind
power series and Temporal fusion transformer (TFT) to forecast wind power for
the next 1h, 3h and 6h. The experimental results show that VMD outperforms
other decomposition algorithms and the TFT model outperforms other
decomposition models.

本研究使用 VMD 分解风力系列和 TFT 模型进行 1 小时、3 小时和 6 小时的预测，结果表明 VMD 优于其他分解算法，而 TFT 模型优于其他分解模型。