基于变分模态分解的风电场风速降噪研究
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摘要
风电预测是在现有数据基础上进行的,风电中采集到的风速数据夹杂着不同类型的噪声,为提高预测精度,需要进行降噪处理。传统的EMD(Empirical mode decomposition)降噪方法存在模态混叠现象,为了提高降噪效果,提出采用变分模态分解来进行降噪。变分模态分解是一种新的分解方法,具有良好的抗噪性,且不存在模态混叠现象。为深入研究该方法在降噪上的应用,对江苏某风电场的数据进行了降噪处理,对其分解重构,有效滤除了噪声,并将其降噪效果与EMD进行对比分析,验证了该方法的有效性。
Wind power forecast is based on the existing data,and the wind speed data in wind power are mixed with different types of noise.In order to improve the precision of prediction,noise reduction is needed.However,the traditional empirical mode decomposition noise reduction method has the phenomenon of mode mixing.To improve the effect of the noise reduction,a kind of noise reduction method based on variational mode decomposition is proposed.The variational mode decomposition is a new method which has good noise immunity and no mode mixing.To thoroughly research the application of variational mode decomposition in noise reduction,the wind speed data of a wind farm is used.The signals are decomposed and reconstructed through the variational mode decomposition so as to filter out the noise.The results are compared with the EMD for verifying the validity of the method.
Wind power forecast is based on the existing data,and the wind speed data in wind power are mixed with different types of noise.In order to improve the precision of prediction,noise reduction is needed.However,the traditional empirical mode decomposition noise reduction method has the phenomenon of mode mixing.To improve the effect of the noise reduction,a kind of noise reduction method based on variational mode decomposition is proposed.The variational mode decomposition is a new method which has good noise immunity and no mode mixing.To thoroughly research the application of variational mode decomposition in noise reduction,the wind speed data of a wind farm is used.The signals are decomposed and reconstructed through the variational mode decomposition so as to filter out the noise.The results are compared with the EMD for verifying the validity of the method.
引文
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[4]王文波,张晓东,汪祥莉.基于主成分分析的经验模态分解消噪方法[J].电子学报,2013,41(7):1425-1430.Wang Wenbo,Zhang Xiaodong,Wang Xiangli.Empirical Mode Decomposition De-noising Method Based on Principal Component Analysis[J].Acta Electronica Sinica,2013,41(7):1425-1430.
[5]曲从善,路廷镇,谭营.一种改进型经验模态分解及其在信号消噪中的应用[J].自动化学报,2010,36(1):67-73.Qu Congshan,Lu Tingzhen,Tan Ying.A Modified Empirical Mode Decomposition Method with Applications to Signal De-noising[J].Acta Automatica Sinica,2010,36(1):67-73.
[6]石敏,李影,王冰,等.基于变分模态分解的齿轮箱故障诊断[J].电力科学与工程,2016,32(1):23-26.Shi Min,Li Ying,Wang Bing,et al.Fault diagnosis of Gearbox Based on variational mode decomposition[J].Electric Power Science and Engineering,2016,32(1):23-26.
[7]赵磊,朱永利,高艳丰,等.基于变分模态分解和小波分析的变压器局部放电去噪研究[J].电测与仪表,2016,53(11):13-18.Zhao Lei,Zhu Yongli,Gao Yanfeng,et al.Research on denoising of transformer partial discharge based on variational mode decomposition and wavelet analysis[J].Electrical Measurement&Instrumentation,2016,53(11):13-18.
[8]唐贵基,王晓龙.参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J].西安交通大学学报,2015,49(5):73-81.Tang Guiji,Wang Xiaolong.Parameter Optjmized Variational Mode Decomposition Metbod with Application to Incipient Fault Diagnosis of Rolling Bearing[J].Journal of Xi’an Jiaotong University,2015,49(5):73-81.
[9]Konstantin Dragomiretskiy,Dominique Zosso.Variational mode decomposition[J].IEEE Transactions Signal Processing(S1053-587X),2014,62(3):531-544.
[10]Satish Mohanty,Karunesh Kumar Gupta,Kota SolomonRaju.Comparative Study between VMD and EMD in Bearing Fault Diagnosis[C].International Conference on Industrial and Information Systems(ICIIS),2014:1-6.
[11]MA Kabir,C Shahnaz.Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains[J].Biomedical Signal Processing&Control(S1746-8094),2012,7(5):481-489.
[12]Y Wang,R Markert.Detecting Rub-Impact Fault of Rotor System Based on Variational Mode Decomposition[M].Springer International Publishing,Berlin,2015.
[13]C S Lahmiri.Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains[J].Healthcare Technology Letters(S2053-3713),2014,1(3):104-109.