The partial discharge signal of power equipment is prone to interference from narrow band noise and white noise in the environment. In order to better preserve the characteristics of local discharge signal for fault diagnosis and prediction, a method of denoising transformer partial discharge signal based on compressed sensing reconstruction and variational mode decomposition is proposed. This method firstly uses the window function to suppress the frequency leakage of narrowband interference, and then separates and reconstructs narrowband signals to suppress narrowband noise by taking advantage of the difference in sparsity between narrowband interference and local emission signal and white noise in the frequency domain. Secondly, by improving variational mode decomposition method, different modes are classified and denoised according to the amount of local emission signal information contained in each mode. Finally restore the outgoing release signal. The denoising effect of this method is tested by simulation and actual signal, and the denoising effect is compared with that of singular value decomposition and variational mode decomposition. The results show that this method can effectively suppress the interference of partial discharge signal, and the waveform similarity coefficient is improved by about 2% compared with the traditional algorithm, and the waveform characteristics of partial discharge signal can be better preserved.