Classification and separation of partial discharge ultra-high-frequency signals in a 252 kV gas insulated substation by using cumulative energy technique

Ultra-high-frequency (UHF) method is regarded as an effective approach for partial discharge (PD) detection in gas insulated substation (GIS). The feature parameters representing UHF signal characteristics can be extracted from its waveform, and applied to PD source classification and mixed signals separation. In this study, an UHF signal feature extraction algorithm based on cumulative energy (CE) technique is proposed. The CE functions in time domain (TCE) and frequency domain (FCE) are calculated from UHF waveforms and their fast Fourier transform spectrums, respectively. The mathematical morphology gradient (MMG) operation is applied to TCE and FCE to characterise their rising behavior. The feature parameters including width, area and sharpness are extracted from CEs and MMGs in both time and frequency domain. Moreover, the extracted features are applied to UHF signals classification and mixed signals separation. First, experiments on four kinds of typical defects in a 252 kV GIS are performed, and the classification performance of the extracted features is examined with the acquired signals. Second, by clustering the extracted features with fuzzy maximum-likelihood algorithm, mixed UHF signals originated from multiple PD sources are successfully separated. The results indicate that the proposed features are effective for representing UHF signal characteristics.