Analyzing the variability of wind power output through the power spectral density

The variability of wind power output is analyzed using the power spectral density (PSD) that is estimated through the modified covariance method, one of the parametric PSD estimation techniques. This method can estimate the PSD accurately under the assumption that the wind power follows the Autoregressive process. The seasonal trends are removed using the Multiple signal classification to satisfy this assumption before estimating the PSD. Phase angles of the wind power are analyzed and classified into deterministic and stochastic terms. The estimated PSD is modeled through piecewise affine functions, and trends of affine functions are encapsulated in multiple linear regression models. As the variability of wind power decreases, the slope of the third affine function also decreases. Furthermore, the changes of the third slope are a function of total capacity, number of wind farms, standard deviation, and mean. Therefore, the PSD of future wind power can be estimated from multiple linear regression models, and the variability of wind power can be quantified through the estimated PSD.