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.
Highlights
Utilizing wind energy to its complete potential has been the goal of energy development in all countries worldwide, especially in China
4 Conclusions The power spectrum characteristics of the active power of wind power are an important manifestation of the wind power fluctuation characteristics
The instantaneous power spectrum characteristics are important for real-time scheduling and optimal control of new energy power systems
Summary
Utilizing wind energy to its complete potential has been the goal of energy development in all countries worldwide, especially in China. In [14], a large number of actual wind speed data was used for the range of wind speed fluctuations in wind farms to correct the quantitative characterization model to develop and improve it as per the IEC standard. In [19], the quantitative characterization model of the intermittency of wind speed based on abrupt changes in the duty cycle from the intermittent nature of atmospheric turbulence was developed. This method can be applied to the intermittent quantitative characterization of wind power [20]. Many scholars are analyzing the error [22–24]
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