Neural Network Based GIS Local Discharge Spectrogram Recognition Study

With the wide application of the deep learning algorithm in the field of PD pattern recognition, it makes up for the disadvantages of the traditional method, at the same time, it can solve the problems of traditional machine learning methods such as poor performance and under-fitting when the training sample size is large.By training the convolutional neural network to recognize the PRPD maps, not only can we avoid the complex feature extraction of statistical data, but also can greatly improve the work efficiency and the accuracy of localization type recognition. For this reason, this paper is designed based on the comparative analysis of convolutional neural network and support vector machine. First, an experimental model of four typical partial discharge defects in GIS was designed and constructed, and experimental data were collected. The PRPD spectrogram training set is then augmented with data using conditional generative adversarial networks. Finally, the PRPD spectra of each type of defects are identified and classified using convolutional networks and conventional machine learning support vector machines, respectively, and analyzed for comparison.