Identification of singular points in wind speed data
Lin Ye · Power System Protection and Control · 2011
The authentic and reliable wind speeds data can effectively improve the prediction accuracy of wind power.This paper proposes a novel approach to detect the singularity in wind speed data based on the method of wavelet transform modulus maxima.The method is a combination of Lipschitz α and threshold value determination.First,the original data of wind speeds are decomposed by wavelet to find the local modulus maxima.The suspected singular points can be detected by the threshold because the modulus maxima of singular points are higher than normal signals in general.Then,the transmission points of each local modulus maxima are found to draw a modulus maxima line,so the Lipschitz α which corresponds to a modulus maxima line can determine the point of singularity modulus maxima.When Lipschitz α1,the suspected points can be located.Finally,the auto regression moving average(ARMA)method is used to correct the singularity of wind speeds.Case studies are carried out based on the measured data in Miyun County of Beijing.Results show that the positions of singularity are accurately located and those values are effectively corrected,so the method proposed in this paper can be used in practice.