一种基于S_VMD与Sdr_SampEn的局部放电信号去噪方法

交联聚乙烯(XLPE)电缆作为“双碳”目标中电力传输的重要工具，在使用一定年限后绝缘性能会下降，局部放电(Partial Discharge, PD)检测作为评估XLPE电缆绝缘状态的重要手段已广泛应用。针对PD信号中存在的各类噪声问题，提出了一种基于Spearman变分模态分解(Spearman Variational Mode Decomposition, S_VMD)与空间相关递归样本熵(Spatial Dependence Recurrence Sample Entropy, Sdr_SampEn)的局部放电信号去噪方法。首先通过S_VMD将信号分解为K个最优本征模态分量(Intrinsic Mode Function，IMF)，然后通过计算各IMF的Sdr_SampEn值来判定其是噪声主导分量还是PD主导分量；再对分类后的IMF分别采取改进小波阈值去噪和Savitzky-Golay (SG)滤波去噪，最后进行重构得到去噪后的PD信号。利用该方法对仿真与实测PD信号进行去噪处理，并与自适应变分模态分解(Adaptive VMD, AVMD)等去噪算法进行对比分析，结果表明该方法能有效抑制PD信号中的噪声，具有一定的工程价值。

A denoising method for a partial discharge signal based on S_VMD and Sdr_SampEn

XLPE cables, an important tool for power transmission in the carbon peaking and carbon neutrality goals, suffer from a decline in insulation performance after a number of years. Partial discharge (PD) detection has been widely used as an important means to evaluate the insulation status of XLPE cables. To solve the problem of various types of noise in PD signals, a PD signal denoising method based on Spearman variational mode decomposition (S_VMD) and spatial dependence recurrence sample entropy (Sdr_SampEn) is proposed. First, the signal is decomposed into K optimal intrinsic mode functions (IMF) through S_VMD. Secondly, the Sdr_SampEn value of each IMF is calculated to determine whether the IMF is the noised-dominated IMF or the PD-dominated IMF. After classification, the IMF will be denoised by improved wavelet threshold denoising and Savitzky-Golay (SG) filter denoising. Finally, the processed IMF will reconstructed, and the noise-reduced signal is acquired. This method is used to denoise the simulated and lab-measured PD signals, and it is further compared with many denoising algorithms such as adaptive variational mode decomposition (AVMD), etc. The results prove that the proposed method can effectively remove various types of noise, and thus shows engineering value. This work is supported by the National Natural Science Foundation of China (No. U1404522).