Cluster Analysis of Parameter of Stochastic Fourier Spectrum for Fluctuating Wind Speeds

Abstract:

The stochastic Fourier spectrum provides a physical based perspective for fluctuating wind simulation. To properly model the probability density function of the elemental variables, the basic physical meaning of the elemental variables is elaborated and the statistical distributions of them are identified from the measurements. It is revealed that the statistical distribution of the cutoff wave number displays an obvious bimodal pattern; the power spectrum density derived from the fitted distribution of the original identifications deviates from the Kaimal spectrum evidently in the low and middle frequency band. Besides, the identifications of the cutoff wave number are eminently characterized by a clustering phenomenon, based on which the kmeans cluster analysis is utilized to reveal the underlying structure of the data set. It is pointed that the cluster characteristic of the fluctuating wind speed sample is closely related to the above abnormal phenomenon. Finally, the distribution modeling for the cutoff wave number is conducted for the reasonably selected measurements based on the cluster results.