局部放电(PD)在线监测是高压电机状态监测的常用技术。然而现场的噪声干扰难以避免，最常见的噪声是白噪声和周期性窄带噪声。为此提出一种结合奇异值分解与小波变换（SVD-WT）的去噪方法，对原始PD信号进行SVD分解，通过计算奇异值序列的峭度值，自适应的选取需要重构奇异值实现周期性窄带噪声的去除；通过计算滑动窗内信号的方差值，确定PD信号的起始位置；对无PD发生的位置进行置零，得到去除噪声后的PD信号。通过对仿真和实测的PD信号进行去噪分析，与经验模态分解与小波变换(EMDWT)和自适应奇异值分解(ASVD)进行对比分析，仿真和实测的PD信号去噪结果表明，SVDWT方法具有优异的性能。

Partial discharge （PD） on-line monitoring is a common technology in condition of high-voltage motor monitoring. However, it is difficult to avoid noise interference on site. The most common noises are white noise and periodic narrowband noise. A new denoising method combining singular value decomposition and wavelet transform (SVD-WT) is proposed. The original signal is decomposed by SVD. Based on calculating the kurtosis value of the singular value sequence, the periodic narrowband noise is removed by adaptively selecting the singular value to be reconstructed. Then, the starting position of PD signal is determined by calculating the variance of the signal in the sliding window. Finally, the PD signal after denoising is obtained by zeroing the no PD location. The simulated and measured PD signals are denoised and compared with empirical mode decomposition and wavelet transform (EMD-WT) and adaptive singular value decomposition (ASVD). The results of simulated and measured PD signals show that the SVD-WT method has excellent performance.