Wind turbine arrays can be viewed as large coupled networks, wherein wake effects limit the available power extraction of turbines downstream. In this paper, we incorporate wake steering and time dependent wind estimation models into a multiobjective wind farm control problem for improving power extraction. We further aim to mitigate the effects of turbulence and power spikes caused by wind passing through upstream turbines. We expand upon a previous heuristic method for the far-field wake problem and apply the algorithm on a model predictive control framework. Simulation results are given, demonstrating improved power output as compared to algorithms that do not incorporate wake steering or wind estimation models.

中文翻译：

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风力发电机组和农场的多目标模型预测控制设计

风力涡轮机阵列可以被视为大型耦合网络，其中尾流效应限制了下游涡轮机的可用功率提取。在本文中，我们将尾流转向和时间相关风估计模型结合到多目标风电场控制问题中，以提高功率提取。我们进一步的目标是减轻风通过上游涡轮机引起的湍流和功率尖峰的影响。我们扩展了先前针对远场尾流问题的启发式方法，并将该算法应用于模型预测控制框架。给出了仿真结果，证明了与不包含尾流转向或风估计模型的算法相比改进的功率输出。