摘要
为了解决噪声干扰Prony算法提取谐波参数问题,提出了一种集成局部均值分解(ELMD)-奇异值分解(SVD)-Prony的谐波分析方法(ELMD-SVD-Prony)。首先采用ELMD分解含噪信号,对获得的一系列乘积函数(PF)采用K-L散度来确定含噪分量与有效分量之间的分界点,去除噪声分量并保留有效分量,对有效分量通过相空间重构Hankel矩阵,运用奇异值分解进行二次降噪并重构。最后将重构的信号与ELMD余项叠加得到去噪后的谐波信号,结合Prony算法检测谐波的频率、幅值与相位。仿真实验结果表明,该方法能有效降噪并提取谐波特征参数。
In order to solve the problem of extracting harmonic parameters by noise interference Prony algorithm,a harmonic analysis method based on ensemble local mean decomposition(ELMD),singular value decomposition(SVD)and Prony was proposed.Firstly,the noisy signal was decomposed by ELMD,and the demarcation point between the noisy component and the effective component was determined by Kullback-Leibler divergence for a series of product functions(PF).The noise component was removed and the effective component was retained.The Hankel matrix was reconstructed through the phase space for the effective component,and SVD was used for secondary noise reduction and reconstruction.Finally,the reconstructed signal was superimposed with the ELMD remainder to get the de-noised harmonic signal,which was combined with the Prony algorithm to detect the frequency,amplitude and phase of the harmonic.It shows that this method can effectively reduce noise and extract harmonic characteristic parameters through the simulation experiment results.
作者
刘士绮
王雅静
梅宇
张祥珂
施瑶
窦震海
LIU Shiqi;WANG Yajing;MEI Yu;ZHANG Xiangke;SHI Yao;DOU Zhenhai(School of Electrical and Electronic Engineering,Shandong University of Technology,Zibo 255049,Shandong,China)
出处
《电气传动》
2022年第13期48-55,共8页
Electric Drive
基金
山东省重点研发计划(2019GGX104025)。
关键词
集成局部均值分解
奇异值分解
PRONY算法
K-L散度
降噪
谐波检测
ensemble local mean decomposition(ELMD)
singular value decomposition(SVD)
Prony algorithm
Kullback-Leibler divergence
denoising
harmonic detection