ULTRASONIC DETECTION METHOD AND SYSTEM FOR PARTIAL DISCHARGE OF POWER DISTRIBUTION NETWORK

The present disclosure relates to an ultrasonic detection method and system for partial discharge of a power distribution network based on deep learning. The method includes: training a neural network model; converting an ultrasonic signal of a partial discharge defect of to-be-detected power distribution network equipment into Mel Frequency Cepstral data; inputting the Mel Frequency Cepstral data into a periodic neural network layer for learning to obtain a first feature; inputting an image of the partial discharge defect of the to-be-detected power distribution network equipment into a convolution neural network layer for learning to obtain a second feature; linearly splicing the first feature and the second feature to obtain a third feature; and inputting the third feature into a multilayer fully-connected layer to obtain a detection result of the to-be-detected power distribution network equipment. Compared with the existing manual detection method, the detection method and system provided by the present disclosure are more efficient and accurate.