windspectrum2timeseries#

[Eta, t] = windspectrum2timeseries(f, Sxx, fs, dispout)

Description#

Generate zero-mean wind velocity time series from a given spectrum

Add mean of the wind velocity to generated time series to get desired time series

Inputs#

f: Frequency (Hz)
Sxx: Power spectral density in ((m/s)^2/Hz)

Length of Sxx and f should be odd number
fs=2*max(f): Sampling frequency that data collected at in (Hz)
dispout=’no’: Define to display outputs or not (‘yes’: display, ‘no’: not display)

Outputs#

U: Wind velocity time series in (m/s)
t: Time in (s)

Examples#

import scientimate as sm
import numpy as np

N=2048+1 #Total number of points
fs=2 #Sampling frequency
df=fs/N #Frequency difference
f=np.arange(0.002,fs/2+df,df) #Frequency vector
Sxx=1.0**2*(6.868*100/10)/(1+10.32*f*100/10)**(5/3) #Calculate Spectrum
[U, t]=sm.windspectrum2timeseries(f, Sxx, fs, 'yes')

References#

Branlard, E. (2010). Generation of time series from a spectrum. Technical University Denmark. National Laboratory for Sustainable Energy.

Rose, S., & Apt, J. (2012). Generating wind time series as a hybrid of measured and simulated data. Wind Energy, 15(5), 699-715.

Shinozuka, M., & Jan, C. M. (1972). Digital simulation of random processes and its applications. Journal of sound and vibration, 25(1), 111-128.

Veers, P. (1984). Modeling stochastic wind loads on vertical axis wind turbines. In 25th Structures, Structural Dynamics and Materials Conference (p. 910).

windspectrum2timeseries

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