Determination of Stochastic Wind Speed Model Parameters Using Allan Deviation Approach

摘要

This work offers a method of modeling the spatial wind speed variations using stochastic noise modeling. In particular, the Allan deviation approach is utilized in order to analyze the stochastic properties of a wind speed signal over time. Wind speed measurements from a weather station are used to quantify the error parameters for different stochastic models such as random walk and Gauss-Markov. The determined error parameters from the different stochastic models were used to simulate wind speed signals over time with realistic random errors for use within flight simulators and wind estimation algorithms. The simulation results revealed that a Gauss-Markov noise model more accurately represents wind speed signals over the random walk model. Also, the Gauss-Markov model performs better when using a more conservative fit to the Allan deviation data.