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在进行电气设备局部放电(partial discharge，PD)在线监测中，当多个随机周期性窄带干扰的频率位于传感器监测频段内部时，会严重影响监测的可信性和准确性，然而目前缺乏有效抑制此类干扰的方法。为此，利用谐波小波具有严格盒形频谱特性的优点，提出一种基于最优离散谐波小波包变换的PD去噪新方法，将不同频率窄带干扰的能量分别集中在单一的子带内，用分解后子带香农熵比值的大小来确定包含各窄带干扰的子带，只要将对应的小波系数置零后重构就能得到去除窄带干扰后的PD信号，克服了离散小波包变换子带间存在频谱泄漏的缺点，实现了对PD监测信号的自适应优化分解。通过对仿真和实测PD信号频带范围内含有的混频随机窄带干扰进行去噪处理，并与离散小波包变换去噪结果进行对比分析后表明，最优离散谐波小波包变换对PD信号去噪后的能量损失和波形畸变较小，有利于后续对PD信号的模式识别，可以解决干扰频率位于监测频段内难于抑制的难题。“,”During the electrical equipment partial discharge (PD) online monitoring, when the frequencies of the multiple random periodic narrow-band noise lie in the monitor band of the sensor, it will seriously affect the credibility and accuracy of monitoring, but it’s lack of good method to suppress this kind of noise. Therefore, this dissertation took advantage of the strict box-shaped spectral characteristic of harmonic wavelet and proposed a new PD de-noising method based on the optimal discrete harmonic wavelet packet transform (DHWPT). The energy of the narrow-band noise with different frequencies was concentrated in a single sub-band respectively and the sub-band containing narrow-band noise was identified according to the value of the sub-band Shannon entropy ratio. After the corresponding wavelet coefficients set to zero and reconstruction, the de-noised PD signal was obtained. The sub-band spectral leakage of discrete wavelet packet transform(DWPT) was overcome and the adaptive optimization decomposition of PD monitoring signals was achieved. Through de-noising processing of the random narrow-band noise with mixed frequencies that lied in the frequency band of the simulated and measured PD signal, and compared with the result of DWPT method, it shows that the energy loss and waveform distortion of the PD signal is smaller after the optimal DHWPT de-noising. This is beneficial to the subsequent pattern recognition of the PD signal, and the de-noising problem when the noise frequencies lie in the monitoring band is solved.