针对局部放电(partial discharge,PD)信号易埋没在噪声中难以提取的问题,提出一种基于奇异值分解(singular value decomposition,SVD)和双树复小波变换(dual-tree complex wavelet transform,DTCWT)的降噪方法.该方法首先使用基于K-means聚类算法的奇异值分解抑制窄带干扰,然后对重构信号进行双树复小波多尺度变换,利用改进阈值函数对实部树、虚部树产生的每层小波系数和最高层的尺度系数分别进行处理,最后经过双树复小波逆变换得到去噪后的PD信号.仿真和实验结果表明:该方法能有效抑制白噪声和窄带干扰,去噪后的信号波形相似度高.
Partial discharge signal extraction method based on SVD-DTCWT
In order to solve the problem that partial discharge(PD)signals are easily drowned in noise and difficult to extract,a new partial discharge noise de-noising algorithm based on dual-tree complex wavelet transform(DTCWT)and singular value decomposition(SVD)is proposed.In this method,SVD based on K-means clustering algorithm is used to select singular value reconstruction to suppress narrowband interference.Then,the reconstructed signal is multi-scalely transformed by DTCWT.The improved threshold function(ITF)is used to process the wavelet coefficients of each layer and the scale coefficients of the top layer generated by the real part tree and the imaginary part tree respectively.Finally,the de-noised local amplifier signal is obtained by inverse dual-tree complex wavelet transform.The analysis results of simulated and measured signals show that the method can effectively suppress white noise and narrowband interference,and the signal waveform similarity after de-noising is higher.
partial dischargedual-tree complex wavelet transformimproved threshold functionsingular value decomposition
NETL
NSTL
万方数据
维普
