Advanced signal processing techniques for transformer condition assessment

Partial discharge (PD) measurement has been widely adopted for condition assessment of transformers. The major tasks include effective extraction of PD signals from measured signals, accurate representation of PD signals, explicit multiple PD source separation, and PD source classification. This paper applies empirical mode decomposition (EMD) and mathematical morphology (MM) for extracting PD signals from noise-corrupted measured signals and representing PD signals on a joint time-frequency (TF) map, which is used for separating multiple PD sources. A Support Vector Machine (SVM) algorithm is then adopted for classifying each PD source. Case studies are provided to demonstrate the applicability of the two techniques in analyzing PD signals obtained from online PD measurement of field transformer. Comparisons between the two techniques and conventional wavelet transform-based techniques are also provided in the paper.