风速数据奇异点辨识研究  

Identification of singular points in wind speed data

作　　者：李丽[1] 叶林[1] 

机构地区：[1]中国农业大学信息与电气工程学院,北京100083

出　　处：《电力系统保护与控制》2011年第21期92-97,共6页Power System Protection and Control

基　　金：国家自然科学基金研究项目(51077126;51174290);北京市自然科学基金项目(3113029);教育部新世纪优秀人才支持计划(NCET-08-0543);教育部科学技术重点研究项目(109017)资助~~

摘　　要：保证风速数据的真实性与可靠性,可以有效地提高风电功率预测精度。针对风速信号中包含奇异点,采用基于小波模极大值的方法进行辨识。该方法将阈值判定与李氏指数相结合,首先,求出小波分解后细节系数的局部极值点,由于奇异点的高频分量很大,因此利用阈值对奇异点的位置进行初步判定;然后,寻找各尺度局部极值点的传播点并绘制模极大值线,从而求得李氏指数α,当李氏指数α<1时,判定该点为奇异点;最后利用自回归滑动平均法ARMA(p,q)对奇异点进行修正。研究实例表明,所采用的基于小波模极大值的奇异点辨识方法,能够准确的判断出信号的奇异性以及发生的时刻,并且能够有效地修正奇异点的值,从而保证风速数据的可靠性,具有一定的实际应用价值。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 ofLipschitz 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 α 〈1, 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.

关 键 词：模极大值 阈值 LIPSCHITZ 自回归滑动平均法 风速:风电功率预测 

分 类 号：TM614[电气工程—电力系统及自动化]