针对大型风力发电机机组中常见的脉动湍流、风机尾流与涡流等湍流信号, 研究了利用自然梯度下降的独立分量分析方法的湍流频谱分离效果, 以区分中心风速与湍流信号, 提高风机机组的综合工作效率。首先分析了风机组中常见湍流信号的后向散射与频谱分布特点, 然后依据这些特点设计了对应的独立分量分析模型。在仿真结果符合要求的基础上, 进行了双目激光雷达天线的风速采集与实际分离效果检测。实验结果表明, 在大气折射率结构常数C2n≤10－14同时广义大气常数α≥4的通常情况下, 利用双目信号能够分离出一个湍流中心和一个中心风速。对1 s内两个谱峰的波动范围进行统计, 获得(2.59±0.05) MHz的中心风速以及(1.22±0.19) MHz的湍流中心估计, 且二者的平均信噪比分别为25.93 dB和31.01 dB, 能够在获得稳定的中心风速估计的同时得到一个较为稳定的湍流中心估计。

Research on spectrum separation in a turbulent environment using an Independent Component Analysis (ICA) algorithm of natural gradient descent is carried out to investigate the turbulence generated by heat or blades in giant wind turbines. When the main wind is distinguished from a mixed signal with turbulence, the efficiency of wind turbines in a wind field increases. Firstly, the backscattering characteristics and spectrum distribution of turbulence signals are analyzed. Then, the ICA model is designed according to these characteristics. As the simulation results meeting the requirements, a wind speed detection of outdoor binocular lidar antenna and the wind spectrum separation effect of the detection are carried out. The results show that under the condition with refractive index structure constant C2n≤10－14 and generalized atmospheric constant α≥4, the turbulence center and main wind speed can be separated using binocular signals. Statistical analysis of the fluctuation range of the two peaks in 1 s shows the estimated central wind speed of (2.59±0.05) MHz and estimated turbulence center of (1.22±0.19) MHz. The average signal-to-noise ratio of the two peaks is 25.93 dB and 31.01 dB, respectively, which meet the requirements for obtaining stable center wind speed and relatively stable turbulence center estimates.