一种基于逐次变分模态分解的谐波检测方法

A harmonic detection method based on successive variational mode decomposition

传统谐波检测算法受噪声影响导致检测精度低,并且边界处容易出现畸变。对此,本文基于逐次变分模态分解,提出一种结合小波降噪和特征波形匹配延拓的谐波检测方法。首先,通过构造的自适应小波阈值函数对信号进行平滑降噪,剔除不良数据对分解结果的干扰;其次,利用特征波形匹配延拓法对信号边缘进行延拓后再裁剪,遏制边界效应带来的波形端点处畸变;最后,使用逐次变分模态分解对谐波信号进行检测,提取稳态谐波的幅频信息以及定位暂态谐波的起止时刻。仿真实验表明,本文提出的方法有效降低了噪声的干扰,并减轻边界效应造成的波形畸变。在电弧炉实例信号仿真中,幅值平均误差和频率平均误差分别为0.545%和0.146%。

The traditional harmonic detection algorithm is affected by noise,resulting in low detection accuracy and easy distortion at the boundary.In this paper,based on successive variational mode decomposition,a harmonic detection method combining wavelet denoising and characteristic waveform matching extension is proposed.Firstly,the adaptive wavelet threshold function is used to smooth the signal noise and eliminate the interference of bad data to the decomposition results.Secondly,the characteristic waveform matching extension method is used to extend the edge of the signal and then cut it to curb the distortion at the end of the waveform caused by the boundary effect.Finally,the harmonic signal is detected by successive variational mode decomposition,the amplitude-frequency information of steady-state harmonics is extracted,and the start-stop time of transient harmonics is located.Simulation results show that the proposed method can effectively reduce the noise interference and reduce the waveform distortion caused by the boundary effect.In electric arc furnace example signal simulation,the average amplitude error and the average frequency error are 0.545%and 0.146%respectively.