Partial Discharge Pattern Recognition Based on a Multifrequency F–P Sensing Array, AOK Time–Frequency Representation, and Deep Learning

Recognizing the patterns of partial discharge (PD) is the key to assess the hazard level of PD. This article proposes an oil-immersed transformer PD pattern recognition method based on a multifrequency fiber-optic Fabry–Perot (F–P) ultrasound sensing array and deep learning (DL). Three F–P ultrasonic probes with different resonance bands were used to form a sensing array for collecting ultrasonic signals excited by PD. Five types of PD models were prepared, including metal tip (MT) discharge in oil, PD in the air cavity (AC), and surface discharge (SD) on the pressboard, in addition to SD after pressboard blocking or after pressboard plus transformer winding blocking, to study the influence of ultrasonic attenuation on recognition accuracy. The collected PD ultrasound signal time-frequency matrix was obtained using adaptive optimal-kernel time–frequency representation to reinforce the differences in PD patterns. Then, based on the features of the PD ultrasonic signals, the signal time and frequency band for analysis were determined. The intercepted time–frequency matrix of the signals from the three F–P probes was formed into a (3, 300, 375) tensor. A modified ResNet-18 net was used for PD pattern recognition, which achieved a 98% recognition accuracy. The probabilities given by the softmax function were used to study the confidence of the model’s predictions of signals belonging to known and unknown types.