Partial discharge (PD) is a hidden danger to the stable operation of the power grid, and it is necessary to carry out realtime and accurate distributed online monitoring of PD of cables and electrical equipment. In order to solve the problems of poor noise reduction effect, high consumption of arithmetic resources, slow noise reduction speed and poor adaptivity in traditional PD signal noise reduction algorithms, a noise reduction algorithm for PD signals based on the gray wolf algorithm optimized variational modal decomposition (GWOVMD) is proposed. The algorithm firstly uses GWO to adaptively select the VMD decomposition parameters k and α to obtain the decomposed modal components; then selects and reconstructs the modal components according to the minimum envelope entropy; finally uses the adaptive threshold wavelet function to process the decomposed and reconstructed PD signal, achieving fast and effective adaptive noise reduction of PD signal. In this paper, the theoretical PD signal and the measured PD signal are simulated and processed for noise reduction. The experimental results show that the proposed GWOVMD algorithm has significantly improved the noise reduction effect, arithmetic resource utilization and noise reduction speed, which can provide a useful reference for the optimal design of edge computing of partial discharge online monitoring system based on power IOT technology.