Understanding the intrinsic characteristics of wind power is important for the safe and efficient parallel function of wind turbines in large-scale wind farms. Current research on the spectrum characteristics of wind power focuses on estimation of power spectral density, particularly the structural characteristics of Kolmogorov’s scaling law. In this study, the wavelet Mallat algorithm, which is different from the conventional Fourier transform, with compactly supported characteristics is used to extract the envelope of the signal and analyze the instantaneous spectral characteristics of wind power signals. Then, the theory for the change in the center frequency of the wind power is obtained. The results showed that within a certain range, the center frequency decreases as the wind power increases by using enough wind farm data. In addition, the center frequency remains unchanged when the wind power is sufficiently large. Together with the time domain characteristics of wind power fluctuation, we put forward the time-frequency separation characteristics of wind power and the corresponding physical parameter expressions, which corresponds to wind speed’s amplitude and frequency modulation characteristics. Lastly, the physical connotation of the time-frequency separation characteristics of wind power from the perspective of atmospheric turbulent energy transport mechanism and wind turbine energy transfer mechanism is established.

中文翻译：

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风电场无线电力通信信号时域与频域的分离特性

了解风力的内在特性对于大型风力发电场中风力涡轮机的安全高效并行功能至关重要。当前对风能频谱特征的研究集中在功率谱密度的估计上，尤其是柯尔莫哥洛夫定标定律的结构特征。在这项研究中，与传统的傅立叶变换不同的是，具有紧凑支持特性的小波Mallat算法被用于提取信号的包络并分析风电信号的瞬时频谱特性。然后，获得了用于改变风力中心频率的理论。结果表明，在一定范围内，通过使用足够的风电场数据，中心频率会随着风力的增加而降低。此外，当风力足够大时，中心频率保持不变。结合风电波动的时域特征，提出了风电的时频分离特性及相应的物理参数表达式，分别对应于风速的幅值和调频特性。最后，从大气湍流能量传输机制和风力涡轮机能量传递机制的角度，建立了风电时频分离特性的物理内涵。提出了风电的时频分离特性和相应的物理参数表达式，分别对应于风速的振幅和频率调制特性。最后，从大气湍流能量传输机制和风力涡轮机能量传递机制的角度，建立了风电时频分离特性的物理内涵。提出了风电的时频分离特性和相应的物理参数表达式，分别对应于风速的振幅和频率调制特性。最后，从大气湍流能量传输机制和风力涡轮机能量传递机制的角度，建立了风电时频分离特性的物理内涵。