Partial discharge (PD) acoustic signal detection is one of the effective means to assess the insulation status of power transformers. In actual monitoring, white noise is likely to cause strong interference to the partial discharge acoustic signal of the transformer, which seriously affects the discharge fault identification and monitoring results. In order to suppress the interference of white noise in partial discharge detection, this paper proposes an adaptive partial discharge based on the combination of variational mode decomposition (VMD) and principal component analysis (PCA) based on improved Spearman correlation coefficient. The white noise suppression method is analyzed for the separation and denoising of partial discharge acoustic signals in the environment of -10 ∼ 10 dB. Firstly, the Spearman correlation coefficient is used to determine the optimal number of decomposing modes of VMD. Then the decomposed modal components are adaptively reduced and reconstructed by principal component analysis to remove redundant clutter interference and reduce the influence of human error. Finally, through the simulation signal and actual discharge pulse acoustic signal are tested for denoising. The results show that SVMD-PCA can suppress the interference of white noise in partial discharge acoustic signals and extract clean discharge pulse signal characteristics, the method has enhanced anti-noise performance and can effectively suppress white noise interference.