Stage Recognition of Surface Discharge in Oil-Impregnated Paper Based on Convolutional Neural Network

The stage recognition of surface discharge in oil-impregnated paper is of great significance to the safe and reliable operating of power transformer and the construction of digital twin system. Therefore, a convolutional neural network (CNN) method is proposed in this paper to identify the development stages of surface discharge. Firstly, the surface discharge experiment of oil-paper insulation was carried out by step-up voltage method. Then, according to the difference of phase-resolved partial discharge (PRPD), the surface discharge process could be divided into various stages. Finally, CNN was adopted to identify different stages of surface discharge, compared with support vector machine (SVM) and back propagation neural network (BPNN) based on 24-dimensional feature extraction. The results show that after 100 iterations, the recognition accuracy of CNN on surface discharge stage reaches 99.17%, while the overall accuracy of SVM and BPNN is only 91.04% and 87.29%, respectively. The CNN model proposed in this paper can automatically and effectively identify the PRPD patterns of different stages of surface discharge, with a higher recognition accuracy than the traditional methods such as SVM and BPNN.