【摘要】 为了有效去除变压器局部放电超声信号中的白噪声干扰，提高后续局部放电模式识别及定位的准确性，提出了一种基于改进小波阈值和自适应噪声完全集合经验模态分解（complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN）的变压器局部放电超声波信号去噪方法。首先，通过对放电信号进行CEEMDAN分解得到一系列由高频到低频的本征模函数IMF(intrinsic mode function)；然后，利用多尺度排列熵（multi-scale permutation entropy,MPE）算法计算各阶IMF分量的排列熵PE(permutation entropy)，根据各IMF的排列熵值确定信号的去噪阈值与有效阈值。对高于去噪阈值的IMF分量采用改进小波阈值法进行去噪处理，对低于有效阈值的IMF分量视为基线漂移进行剔除。最后，通过重构去噪分量与剩余分量来获得去噪后的超声波信号。仿真和实验结果均表明，文中所提出的去噪算法大大提高了信号的信噪比，并保留了原始超声波信号中的有效信息，对提高后续利用超声波信号进行局部放电模式识别及定位的精确性具有重要意义。更多还原

【Abstract】 For removing effectively the white noise interference in the partial discharge ultrasonic signals of transformer and improving the accuracy of subsequent partial discharge pattern recognition and location,a kind of denoising method of partial discharge ultrasonic signal of transformer based on improved wavelet threshold and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)is proposed. Firstly,a series of intrinsic mode functions(IMF)from high frequency to low frequency are obtained by CEEMDAN decomposition of discharge signal. Then,a multi-scale permutation entropy(MPE)algorithm is used to calculate the permutation entropy(PE)of each IMF component,and the denoising threshold and effective threshold of the signal are determined in accordance with the permutation entropy of each IMF. IMF components above the denoising threshold are denoised by the improved wavelet threshold method and IMF components below the effective threshold are regarded as baseline drift. Finally,the de-noised ultrasonic signal is obtained by reconstructing the de-noised component and the residual component. Both simulation and experimental results show that the denoising algorithm proposed in this paper improves greatly the signal-to-noise ratio of the signal,and retains the effective information of the original ultrasonic signal,which is of great significance to improve the accuracy of partial discharge pattern recognition and location using ultrasonic signals.更多还原