Open Access
Seasonality in synthetic average wind speed
(IDAE, 2024-08-11) Zivanovic, Miroslav; Runacres, Mark; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza
There is a growing demand for computer-generated realistic high-fidelity wind speed data for various applications in the wind industry. Such data should capture the non-stationary dynamics of real-world wind time series, as well as be consistent with the statistical descriptors - the probability density function and power spectral density - of the observed wind speed. However, complying with the statistical descriptors is not a guarantee that the seasonality will be correctly reproduced in synthetic data. The seasonality, characterized by the average diurnal and seasonal variations, is driven by the periodicities embedded in diurnal and annual harmonic series respectively. Those periodicities are determined by the long-term orbital forcing components, which establish the insolation for a given latitude and longitude. We show that average diurnal and seasonal variations can be visualized as the output of comb filters, whose fundamental frequencies match the diurnal and annual fundamental frequency respectively. The aforementioned theoretical findings are readily reproduced in synthetic wind speed, generated by a non-parametric data-driven statistical model, based on the phase-randomized Fourier transform. The model, tested on both 10-min and 1-min resolution real-world datasets, yields average non-stationarities in synthetic wind speed with the accuracy close to the computing precision.
Open Access
Data-driven generation of synthetic wind speeds: a comparative study
(Wiley, 2022) D'Ambrosio, Daniel; Schoukens, Johan; Troyer, T. de; Zivanovic, Miroslav; Runacres, Mark; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
The increasing sophistication of wind turbine design and control generates a need for high-quality wind data. The relatively limited set of available measured wind data may be extended with computer generated data, for example, to make reliable statistical studies of energy production and mechanical loads. Here, a data-driven model for the generation of surrogate wind speeds is compared with two state-of-the-art time series models that can capture the probability distribution and the autocorrelation of the target wind data. The proposed model, based on the phase-randomised Fourier transform, can generate wind speed time series that possess the power spectral density of the target data and converge to their generally non-Gaussian probability distribution with an arbitrary, user-defined precision. The model performance is benchmarked in terms of probability distribution, power spectral density, autocorrelation, and nonstationarities such as the diurnal and seasonal variations of the target data. Comparisons show that the proposed model can outperform the selected models in reproducing the statistical descriptors of the input datasets and is able to capture the nonstationary diurnal and seasonal variations of the wind speed.
Open Access
Synthetic wind speed generation for the simulation of realistic diurnal cycles
(IOP Publishing, 2020) D’Ambrosio, D.; Schoukens, Johan; Troyer, T. de; Zivanovic, Miroslav; Runacres, Mark; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Synthetic wind-speed generators can provide a detailed characterisation of the wind variability at different time scales. A keen interest in the availability of synthetic wind speeds has recently risen in wind power modelling applications. In particular, a proper simulation of the diurnal and annual variability of the wind speed is sought that can lead to a more efficient grid integration of this renewable source. This paper proposes a statistical model for generating synthetic wind speeds consistent with both the probability density function and the spectral density function of a measured wind-speed dataset and that simulates accurately its average diurnal variation. To test the proposed methodology, multiple synthetic time series are generated using three long-term wind-speed time series recorded at a meteorological site in the Netherlands. The accuracy in terms of the statistical descriptors of the generated time series and their average diurnal variation is assessed with respect to the target data. We show that the average diurnal cycles present in all the three measured time series are always reproduced accurately, and that the statistical descriptors of the target dataset are constantly matched with high accuracy. Possible advantages of the present approach in terms of power system modelling are discussed.

Zivanovic, Miroslav

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