An Optimized Sensing Arrangement in Wind Field Reconstruction Using CFD and POD

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.

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