Feature extraction and pattern recognition of multi-source PD signals
Partial discharge signals from industrial models of stator winding bars were used to compose multisource PD patterns, which were represented by 3-D charts. The tabulated data were calculated and served as characteristic features. Pattern recognition was performed using a back-propagation (BP) network. Successful classification of multi-source PD signals showed that tabulated data were sensitive and stable to reflect different PD patterns. The suitability and robustness of the BP network for classification was further verified by analyzing the untrained patterns' recognition.