The utilization of wind energy has attracted extensive attentions in the last few decades around the world, providing a sustainable and clean source to generate electricity. It is a common phenomenon of wake interference among wind turbines and hence the optimization of wind farm layout is of great importance to improve the wind turbine yields. More specifically, the accuracy of the three-dimensional wake model is critical to the optiamal design of a real wind farm layout considering the combinatorial effect of wind turbine interaction and topography. In this paper, a novel learning-based three-dimensional wake model is proposed and subsequently validated by comparison to the high-fidelity wake simulation results. Moreover, due to the fact that the inevitable deviation of actual wind scenario from the anticipated one can greatly jeopardize the wind farm optimization outcome, the inaccuracy of wind condition prediction using the existing meteorologic data with limited-time measurement is incorporated into the optimization study. Different scenarios including short-, medium-, and long-term wind data are studied specifically with the wind speed/direction prediction errors of $(\pm$ 0.25 m/s, $\pm$ 5.62 ${}^{\circ })$, $(\pm$ 0.08 m/s, $\pm$ 1.75 ${}^{\circ })$ and $(\pm$ 0.025 m/s, $\pm$ 0.56 ${}^{\circ })$, respectively. An advanced objective function which simultaneously maximizes the power output and minimizes the power variance is employed for the optimization study. Through comparison, it is found that the optimized wind farm layout yields over 210 kW more total power output on average than the existed wind farm layout, which verifies the effectiveness of the wind farm layout optimization tool. The results show that as the measurement time for predicting the wind condition gets longer, the total wind farm power output average increases while the error of power output prediction decreases. For the wind farm with 20 wind turbines installed, the individual power output is above 500 kW with an error of 90 kW under the short-term wind $(\pm$ 0.25 m/s, $\pm$ 5.62 ${}^{\circ })$, while it is above 530 kW with an error of 10 kW under the long-term wind $(\pm$ 0.025 m/s, $\pm$ 0.56 ${}^{\circ })$.

在过去的几十年里，风能的利用在世界范围内引起了广泛关注，它为发电提供了一种可持续和清洁的来源。风电机组间尾流干扰是普遍存在的现象，因此优化风电场布局对提高风电机组产量具有重要意义。更具体地说，考虑到风力涡轮机相互作用和地形的组合效应，三维尾流模型的准确性对于真实风电场布局的优化设计至关重要。在本文中，提出了一种新的基于学习的三维尾流模型，并随后通过与高保真尾流仿真结果的比较进行了验证。而且，由于实际风场与预期风场不可避免的偏差会极大地危及风电场优化结果，因此将利用现有限时测量的气象数据进行风况预测的不准确性纳入优化研究。包括短期、中期和长期风数据在内的不同场景被专门研究，风速/风向预测误差为$(\pm$ 0.25 m/s, $\pm$ 5.62 ${}^{\circ })$, $(\pm$ 0.08 米/秒， $\pm$ 1.75 ${}^{\circ })$ 和 $(\pm$ 0.025 米/秒， $\pm$ 0.56 ${}^{\circ })$， 分别。一个先进的目标函数同时最大化功率输出和最小化功率方差被用于优化研究。通过对比发现，优化后的风电场布局比现有风电场布局平均多输出210 kW以上的总功率，验证了风电场布局优化工具的有效性。结果表明，随着预测风况的测量时间变长，风电场总出力平均值增加，而出力预测误差减小。安装20台风电机组的风电场，单机出力500kW以上，短时风下误差90kW$(\pm$ 0.25 m/s, $\pm$ 5.62 ${}^{\circ })$, 而在530 kW以上，长期风下误差10 kW $(\pm$ 0.025 米/秒， $\pm$ 0.56 ${}^{\circ })$.