An Enhanced Hankel Matrix based Singular Value Decomposition Method for Removing Noise from Partial Discharge Signals

Partial Discharges (PD) measurement has long been used as a test to evaluate insulation condition in electrical equipment. Different types of noise, such as white noise, random noise, and discrete spectral interference couples with the PD signal during on line and/or onsite PD measurements. Because of these interferences PD source separation becomes troublesome process. In this present work, combination of Hankel Matrix based Enhanced Singular Value Decomposition (E-HSVD) is proposed to remove the noise from PD signals. An adaptive spectral kurtosis is employed to select the optimal singular component obtained by applying E-HSVD to the PD signal. The proposed technique is applied on the PD signals using simulated and PD signal measured at online onsite to examine its performance under different noisy environments. The evaluation metrics results confirm that E-HSVD has significant improvements in performance compared to existing state of the art PD denoising techniques.