Abstract: 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.