一种基于新型小波阈值去噪预处理的EEMD谐波检测方法

为了提升经验模态分解（EMD）用于谐波检测的效果,用集合经验模态分解（EEMD）消除了EMD谐波检测的模态混叠问题。通过研究发现采样信号中的噪声会对EEMD的分解产生较大影响,提出了一种基于新型小波阈值去噪预处理的EEMD谐波检测方法。该方法首先采用变换小波系数精确选取小波阈值,然后采取软硬阈值相结合的方式,以消除随机噪声,再将去噪后的信号进行EEMD分解。经仿真分析,所提方法可以有效地消除随机噪声对谐波检测的影响,提高了EEMD谐波检测的精度与适用性。同时与原有EEMD算法相比,所提方法在分解速率上平均提高了大约3.8倍,有效分量与原始信号的相关度平均提升了22.5%。...

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Published in电力系统保护与控制 Vol. 44; no. 2; pp. 42 - 48
Main Author 孙曙光 庞毅 王景芹 张超 杜太行 于晗
Format Journal Article
LanguageChinese
Published 河北工业大学控制科学与工程学院,天津,300130%河北工业大学电气工程学院,天津,300130%陕西科技大学电气与信息工程学院,陕西西安,710021 2016
Subjects
小波系数
模态混叠
谐波检测
阈值
集合经验模态分解
mode mixing
wavelet coefficients
集合经验模态分解
阈值
小波系数
threshold
谐波检测
模态混叠
ensemble empirical mode decomposition
harmonic detection
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ISSN1674-3415

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Summary:为了提升经验模态分解（EMD）用于谐波检测的效果,用集合经验模态分解（EEMD）消除了EMD谐波检测的模态混叠问题。通过研究发现采样信号中的噪声会对EEMD的分解产生较大影响,提出了一种基于新型小波阈值去噪预处理的EEMD谐波检测方法。该方法首先采用变换小波系数精确选取小波阈值,然后采取软硬阈值相结合的方式,以消除随机噪声,再将去噪后的信号进行EEMD分解。经仿真分析,所提方法可以有效地消除随机噪声对谐波检测的影响,提高了EEMD谐波检测的精度与适用性。同时与原有EEMD算法相比,所提方法在分解速率上平均提高了大约3.8倍,有效分量与原始信号的相关度平均提升了22.5%。
Bibliography:In order to promote the effect of empirical mode decomposition（EMD） for the harmonic detection, this paper uses the ensemble empirical mode decomposition（EEMD） to eliminate the modal mixing of EMD harmonic detection. After studying, it is found that the noise in the sampling signal has great influence on the EEMD harmonic decomposition. This paper proposes an EEMD harmonic detection method based on a new wavelet threshold denoising pretreatment. This method adopts transforming wavelet coefficients to select threshold accurately, then uses the combination of hard and soft threshold and finally eliminates random noise. After simulation and analysis, this method can effectively eliminate the influence of random noise on harmonic detection and improve the accuracy and applicability of EEMD harmonic detection, and meanwhile, compared with the original EEMD algorithm, the decomposition rate increases about 3.8 times and the correlation between the effective components and the original signals increases by 22.5%.
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ISSN:1674-3415