论文标题
通过自由涡流唤醒模型对风电场流量控制的伴随优化
Adjoint Optimisation for Wind Farm Flow Control with a Free-Vortex Wake Model
论文作者
论文摘要
风电场流量控制旨在通过减少涡轮机之间的空气动力唤醒相互作用来改善风力涡轮机的性能。基于动态的,基于物理的风电场流量对于探索控制策略,例如唤醒重定向和动态诱导控制至关重要。自由涡流方法可以提供一种计算有效的方法,以建模风力涡轮机唤醒动力学以进行控制优化。我们提出了2D和3D执行器盘的面向控制的自由涡流唤醒模型,以代表风力涡轮机唤醒。离散伴随方程的新型推导允许在经济模型预测性控制算法中进行基于梯度的优化的有效梯度评估。在两柏林案例研究中,给出了平均功率最大化的初始结果。使用大致周期性的2D模型发现了感应控制信号,并支持动态诱导控制的先前结果以刺激唤醒混合。 3D模型制定有效地模拟了偏航未对准的卷曲唤醒。在随着时间变化的风向下,优化找到了溶液,既表明唤醒转向,又表现出平稳的转变向贪婪的控制。带有梯度信息的自由涡流唤醒模型显示出有效优化的潜力,并提供了进一步探索动态风电场流量控制的有希望的方法。
Wind farm flow control aims to improve wind turbine performance by reducing aerodynamic wake interaction between turbines. Dynamic, physics-based models of wind farm flows have been essential for exploring control strategies such as wake redirection and dynamic induction control. Free vortex methods can provide a computationally efficient way to model wind turbine wake dynamics for control optimisation. We present a control-oriented free-vortex wake model of a 2D and 3D actuator disc to represent wind turbine wakes. The novel derivation of the discrete adjoint equations allows efficient gradient evaluation for gradient-based optimisation in an economic model-predictive control algorithm. Initial results are presented for mean power maximisation in a two-turbine case study. An induction control signal is found using the 2D model that is roughly periodic and supports previous results on dynamic induction control to stimulate wake mixing. The 3D model formulation effectively models a curled wake under yaw misalignment. Under time-varying wind direction, the optimisation finds solutions demonstrating both wake steering and a smooth transition to greedy control. The free-vortex wake model with gradient information shows potential for efficient optimisation and provides a promising way to further explore dynamic wind farm flow control.