In a real wind farm, complex airflow conditions result in complexities of wind speed and direction, with possibly significant intermittency and fluctuations. This problem can be alleviated if the wind speed distribution over a wind farm is known in advance. In this article, a new method is proposed for real-time wind field reconstruction for large areas, based on the idea of a “virtual time”, i.e., a time span needed for an object to travel across a certain distance. The distribution of wind speed and direction can be acquired prior to its occurrence in the wind farm with refined spatial resolutions. A procedure is also developed to stabilize the solution process, and this stabilization leads to an optimal allocation of the wind speed sensors; this allocation is necessary for the efficient use of a limited number of sensors. The reconstruction algorithm has been substantially studied, and a mathematical quantity was correlated to the reconstruction error. This correlation enables us to obtain good reconstruction results by using the greedy algorithm we proposed in this study. Simulation and experimental results demonstrated the strong feasibility of successful reconstructions by our proposed algorithm. Moreover, the sensor optimization scheme not only reduces the error significantly but also improves the efficiency of sensor applications; this improvement should apply to a wide range of conditions.

中文翻译：

---

使用CFD和POD的风场重建中的优化传感布置

在实际的风电场中，复杂的气流条件会导致风速和风向的复杂性，并可能导致明显的间歇性和波动性。如果预先知道风电场上的风速分布，则可以减轻该问题。在本文中，基于“虚拟时间”（即对象跨越一定距离行进所需的时间跨度）的思想，提出了一种用于大面积实时风场重建的新方法。风速和风向的分布可以在风电场出现之前以精确的空间分辨率获取。还开发了一种程序来稳定求解过程，这种稳定导致了风速传感器的最佳分配。这种分配对于有效使用有限数量的传感器是必需的。已经对重构算法进行了大量研究，并且将数学量与重构误差相关联。这种相关性使我们能够通过使用本研究中提出的贪婪算法来获得良好的重建结果。仿真和实验结果证明了我们提出的算法成功重建的强大可行性。此外，传感器优化方案不仅大大减少了误差，而且提高了传感器应用效率。这种改进应适用于各种条件。仿真和实验结果证明了我们提出的算法成功重建的强大可行性。此外，传感器优化方案不仅大大减少了误差，而且提高了传感器应用效率。这种改进应适用于各种条件。仿真和实验结果证明了我们提出的算法成功重建的强大可行性。此外，传感器优化方案不仅大大减少了误差，而且提高了传感器应用效率。这种改进应适用于各种条件。