Modeling Wind Speed Based on Fractional Ornstein-Uhlenbeck Process

Sergey Obukhov, Emad M. Ahmed, Denis Y. Davydov, Talal Alharbi, Ahmed Ibrahim and Ziad M. Ali
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Sergey Obukhov: Department of Electric Power Systems, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia
Emad M. Ahmed: Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Denis Y. Davydov: Department of Electric Power Systems, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia
Talal Alharbi: Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia
Ahmed Ibrahim: Department of Electric Power Systems, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia
Ziad M. Ali: College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia

Energies, 2021, vol. 14, issue 17, 1-15

Abstract: The primary task of the design and feasibility study for the use of wind power plants is to predict changes in wind speeds at the site of power system installation. The stochastic nature of the wind and spatio-temporal variability explains the high complexity of this problem, associated with finding the best mathematical modeling which satisfies the best solution for this problem. In the known discrete models based on Markov chains, the autoregressive-moving average does not allow variance in the time step, which does not allow their use for simulation of operating modes of wind turbines and wind energy systems. The article proposes and tests a SDE-based model for generating synthetic wind speed data using the stochastic differential equation of the fractional Ornstein-Uhlenbeck process with periodic function of long-run mean. The model allows generating wind speed trajectories with a given autocorrelation, required statistical distribution and provides the incorporation of daily and seasonal variations. Compared to the standard Ornstein-Uhlenbeck process driven by ordinary Brownian motion, the fractional model used in this study allows one to generate synthetic wind speed trajectories which autocorrelation function decays according to a power law that more closely matches the hourly autocorrelation of actual data. In order to demonstrate the capabilities of this model, a number of simulations were carried out using model parameters estimated from actual observation data of wind speed collected at 518 weather stations located throughout Russia.

Keywords: wind energy; wind speed model; stochastic differential equations; fractional Brownian motion; time-series modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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