  The increasing sophistication of wind turbine design and control generates a
need for high-quality data. Therefore, the relatively limited set of measured
wind data may be extended with computer-generated surrogate data, e.g. to make
reliable statistical studies of energy production and mechanical loads. This
paper presents a data-driven, statistical model for the generation of realistic
surrogate time series that is based on the phase-randomised Fourier transform.
The proposed model simulates an ergodic, pseudo-random process that makes use
of an iterative rank-reordering procedure to yield synthetic time series that
possess the power spectral density of the target data and concurrently
converges to the probability distribution of the target data with an arbitrary,
user-defined precision. A comparison with two established data-driven modelling
techniques for generating surrogate wind speeds is presented. The proposed
model is tested under the same input conditions given in the test cases of the
selected models, and its performance is investigated in terms of the agreement
with the target statistical descriptors. Simulation results show that the
proposed model can reproduce with high fidelity the statistical descriptors of
the input datasets and is able to capture the nonstationary diurnal and
seasonal variations of the wind speed.
