Abstract：The current classification algorithms for partial discharge faults are mostly shallow learning algorithms, and the features extracted manually directly affect the classification results. In contrast to shallow learning algorithms, deep learning has a deeper architecture that automatically learns features from samples. Convolutional neural networks are typical deep learning algorithms. This thesis aims to study the application of convolutional neural network in partial discharge of switchgear, and proves that deep learning architecture can effectively improve the recognition rate. In this experiment, two kinds of audible signals are collected, which are normal and faulty. After extracting the above two types of sound signals, they are respectively classified into SVM model and convolutional neural network for classification. The experimental results show that the convolutional neural network improves the accuracy of voice recognition compared with the traditional SVM.
2019, Vol. 20 
