Fast online reinforcement learning control of small lift-driven vertical axis wind turbines with an active programmable four bar linkage mechanism
Fast online reinforcement learning control of small lift-driven vertical axis wind turbines with an active programmable four bar linkage mechanism
复制标题
DOI：
10.1016/j.energy.2022.125350
复制
发表时间：
2022-09
期刊：
影响因子：
9
通讯作者：
He Shen;À. Ruiz;Ni Li
中科院分区：
文献类型：
--
作者：
He Shen;À. Ruiz;Ni Li
由于不可预测的湍流-叶片相互作用和复杂的叶片干扰带来的挑战，在不断变化的风环境中保持小型升力驱动垂直轴风力涡轮机的高发电量尚未得到很好的研究。在此，提出了一种使用主动可编程四连杆机构的快速在线强化学习桨距控制，使得涡轮机能够快速适应风力变化并在运行中保持高功率输出。我们制定的俯仰机构使用的拖杆配置与可变的框架链接长度到一个优化问题，并进一步解决它的内点算法在很宽的叶尖速比。然后，设计了一种参数探索策略梯度强化学习方法，用于涡轮机自适应调整帧链路长度。由于该设计显著减少了描绘整个俯仰轨迹所需的参数数量，因此所提出的在线学习过程可以快速收敛，使其能够处理城市环境中的复杂风况。在许多文献中被忽视的瞬态行为也进行了研究。两个基准的比较表明，我们提出的系统具有上级的性能。
Maintaining high power generation for small lift-driven vertical axis wind turbines in a changing wind environment has not been well studied yet, due to the challenges inherited from the unpredictable turbulent flow-blade interaction and complex blade interferences. Herein, a fast online reinforcement learning pitch control using an active programmable four bar linkage mechanism is proposed, making it possible for turbines to quickly adapt to wind changes and maintain high power output in operation. We formulate the pitching mechanism using a drag-link configuration with a variable frame link length into an optimization problem and further solve it by the interior point algorithm under a wide range of tip speed ratios. Then, a parameter explorative policy gradient reinforcement learning method is designed for the turbine to adaptively tune the frame link length. Since the design significantly reduces the number of parameters needed to depict a whole pitch trajectory, the proposed online learning process can converge quickly, making it capable of handling complex wind conditions in an urban environment. The transient behavior overlooked in much of the literature is also studied. Comparisons to two benchmarks have demonstrated that our proposed system has a superior performance.