Wavefront Detection and Event Segmentation Method for Partial Discharge Signal Analysis

Monitoring the degradation of insulation systems in high-voltage equipment relies on identifying physical, chemical, or electrical phenomena occurring during operation, with partial discharge (PD) activity recognized as a primary indicator of insulation system deterioration. This study introduces a novel methodology for denoising, wavefront identification, and transient segmentation of PD signals collected in laboratory and substation settings using a printed monopole antenna (PMA) operating at ultra-high-frequency (UHF). The proposed approach employs a shift-invariant wavelet denoising technique, where thresholds are estimated using the empirical Bayes method at each decomposition level of the wavelet transform. The denoised signal is subsequently processed through linear zero-phase filtering and post-processing steps to determine the start and end points of the PD event and to mitigate distortion effects introduced by denoising and other disturbances. The proposed methodology enables accurate identification of the first wavefront occurrence and segmentation of the PD event, offering enhanced precision for future studies on PD localization and classification. Experimental results showed substantial improvements in signal-to-noise ratio (SNR), high cross-correlation (above 0.9) between the original and denoised signals, and a significant reduction in normalized mean squared error (NMSE), confirming the robustness of the method under low-SNR conditions.