一种基于复小波变换提取PD信号的分块自适应复阈值算法

An Algorithm of Complex Self-Adaptive Block Threshold for Extracting PD Signal

基于复小波系数之间的相关性以及块阈值算法能充分利用周围小波系数的特点,创建一种用于复小波变换对PD信号的去噪的分块自适应复阈值算法,并与点自适应复阈值算法和分块自适应实阈值算法的去噪效果进行了对比。利用变化趋势参数(VTP)和波形相似性参数(NCC),并结合去噪前后的信噪比(SNR)对仿真PD信号的去噪效果进行综合评价。最后,将分块自适应复阈值技术用于对实验室实测的模拟GIS超高频PD信号的去噪。结果表明,采用分块自适应复阈值算法的复小波变换抑制白噪声效果更好,在保证高信噪比(SNR)或噪声抑制比(NRR)的情况下,对信号的畸变也更小。

According to the correlation of complex wavelet coefficients and the characteristic of block threshold making the best of the surrounding wavelet coefficients, an algorithm of complex self-adaptive block threshold is put forward. The de-noising effect of complex self-adaptive block threshold is better than the one of complex self-adaptive dot threshold and self-adaptive block threshold At the same time, the de-noising effect of the emulation PD signals is synthetically evaluated by Variational Trend Parameter （VTP）, NCC and SNR. Finally, complex self-adaptive block threshold is used to suppress the white noises which are buried in ultrahigh frequency PD signals in simulated GIS of lab. The results indicate that this threshold can suppress white noise effectively with small distortion and high SNR or NNR.