Self-adaptive partial discharge denoising based on variational mode decomposition and wavelet packet transform

For the problem of strong noisy interference in the data detection of the partial discharge (PD) signals, this paper proposes a self-adaptive technique for PD signal denoising with automatic thresholding determination based on variational mode decomposition (VMD) and wavelet packet transform (WPT). Firstly, VMD can decompose noisy signal into several modes, which are compactly around a center pulsation respectively. Then to extract modes which contain PD information for reconstruction based on the correlation-based selection scheme. Finally, an automatic wavelet packet thresholding technique is developed for eliminating the residual noises. Denoising experiments are based on both simulated and real PD signals. With comparative study on conventional EMD-based PD denoising techniques, the results show that the proposed method perform better with respect to several performance indexes.