论文标题
通过机器学习技术控制风力涡轮
Control of a Wind-Turbine via Machine Learning techniques
论文作者
论文摘要
本文介绍了两个无模型控制器，用于风力涡轮扭矩和音高控制。这些控制器基于强化学习（RL）和贝叶斯优化（BO），并且与在线性化模型上设计的经典方法相反，不依赖于风涡轮动力学的任何数学模型。使用刀片元件动量理论计算空气动力扭矩和刀片载荷的刀片元件动量理论，根据数值环境中的比例综合衍生（PID）调节器对无模型控制器进行了基准测试。结果表明，无模型方法可以增加功率收获，同时减少风力涡轮机负载。
This article presents two model-free controllers for wind-turbine torque and pitch control. These controllers are based on reinforcement learning (RL) and Bayesian optimization (BO) and do not rely on any mathematical model of the wind-turbine dynamics, in contrast to classical approaches designed on linearized models. The model-free controllers were benchmarked against a proportional-integral-derivative (PID) regulator in a numerical environment using Blade Element Momentum theory for computing the aerodynamic torque and the blade loads. The results showed that the model-free approaches could increase power harvesting while reducing wind turbine loads.
