Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding

This paper proposes a self-adaptive technique for partial discharge (PD) signal denoising with automatic threshold determination based on ensemble empirical mode decomposition (EEMD) and mathematical morphology. By introducing extra noise in the decomposition process, EEMD can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Through the kurtosis-based selection criterion, the IMFs embedded with PD impulses can be extracted for reconstruction. On the basis of mathematical morphology, an automatic morphological thresholding (AMT) technique is developed to form upper and lower thresholds for automatically eliminating the residual noise while maintaining the PD signals. The results on both simulated and real PD signals show that the above PD denoising technique is superior to wavelet transform (WT) and conventional EMD-based PD de-noising techniques.