Deep Reinforcement Learning for Active Wake Control

Abstract

**Read paper on the following link:** https://ifaamas.org/Proceedings/aamas2022/pdfs/p944.pdf **Abstract:** Wind farms suffer from the so-called wake effects: when some of the turbines are located in the wakes of other turbines, their power output is substantially reduced. These losses can be partially mitigated via actively changing the yaw from the individually optimal direction. Many of the existing wake control techniques have two major limitations: they use simplified wake models to optimize the control strategy, and they assume that the atmospheric conditions remain stable. In this paper, we address these limitations by applying reinforcement learning (RL) to the active wake control problem. RL forgoes the wake model entirely and learns the optimal control strategy based on the observed atmospheric conditions and the reward signal, in this case the power output of the farm. It also accounts for random transitions in the observations, such as turbulent fluctuations in the wind. To evaluate the benefits of RL for active wake control, we implement a simulator based on the state-of-the-art FLORIS model in the OpenAI gym format, making it readily available to the RL community. Next, we propose three different state-action representations of the active wake control problem and investigate their effect on the performance of RL-based wake control. Finally, we compare RL to a state-of-the-art wake control strategy based on FLORIS and show that RL is less sensitive to changes in unobservable data.