Separation characteristics between time domain and frequency domain of wireless power communication signal in wind farm

Abstract

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.

A High-Resolution Dyadic Transform for Non-Stationary Signal Analysis

“…In this section, the authors present the Te-transform and its properties. Equation ( 11) and Equation (12) show the short-time Fourier transform (STFT) and the dyadic Wavelet transform (DWT), respectively, for an 𝑓 (𝑡) ∈ 𝐿² (ℝ) [1][2][3]18,[22][23][24].…”

“…In [12] results are presented on the separation characteristics between the time domain and the frequency domain for a signal from a wind farm. These authors in their study used the discrete Wavelet transform to analyze the time domain and the Welch-Bartlett periodogram to analyze the frequency domain.…”