PROJECT TITLE :

Partial discharge signals separation using cumulative energy function and mathematical morphology gradient

ABSTRACT:

Partial discharge (PD) measurement and interpretation have become a robust tool for condition monitoring and failure risk assessment of high voltage power equipment insulation. The prevalence of multiple discharge sources affects interpretation accuracy. This paper presents a PD signal separation algorithm using cumulative energy (CE) operate parameters Clustering technique. The waveform of PD signals are acquired by digital detection instruments with high sampling rate. Cumulative energy functions in time domain (TCE) and frequency domain (FCE) are calculated from PD waveforms and their FFT spectrums, respectively. Mathematical morphology gradient (MMG) operation is applied to the TCE and FCE to explain their variation characteristics. The feature parameters including width, sharpness and gravity are extracted from CEs and MMGs in both time and frequency domain, and compose a six-dimension feature area. The improved density-based spatial clustering of applications with noise (IDBSCAN) clustering algorithm is adopted to find clusters within the feature area. The proposed separation algorithm is examined with mixed current impulse signals acquired from PD experiments on artificial multi-defect models and an on-site transformer. The separation results indicate that the proposed algorithm is effective for separating mixed PD signals initiated from multiple sources.