The effective recognition for partial discharge will provide a scientific reference basis for evaluating the insulation condition in power transformers. However

the recognition of partial discharge type often needs to extract the description feature artificially

which has poor adaptability. Aiming at the current problem

an intelligent recognition method based on convolutional neural networks is proposed. According to visual attention mechanism

partial discharge signal images are slipped out. After the processes of graying

bilinear interpolation

and normalizing

the images are taken as the input of the convolutional neural network. This method simulates the human brains to explain data

which can automatically learn features and identify patterns for discharge signals are collected directly. The experiments show that the recognition rate of four typical kinds of discharge type is above94%

which is superior to that of traditional methods significantly. Results demonstrate that the proposed method requires no complex feature extraction and has high accuracy and good robustness.