An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning
This work addresses efficiency and profitability issues for wind energy operators, though it is incremental as it builds on existing control methods with specific improvements.
The paper tackled the problem of yaw misalignment in wind turbines, which affects power output and safety, by developing a reinforcement learning-based control algorithm that reduced yaw misalignment by 5.5% and 11.2% in simulations, leading to an average net energy gain of 0.31-0.33% and estimated annual gains of 1.5k-2.5k euros per turbine.
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.