The discrimination method as applied to a deteriorated porcelain insulator used in transmission lines on the basis of a convolution neural network

Based on the analysis of the principle and structure of a convolutional neural network (CNN) model used for in-depth learning, an intelligent discriminant diagnosis method for porcelain fuselage insulators in transmission lines is proposed. Firstly, the infrared image of a porcelain insulator is extracted, and then Lenet is used to optimize the network structure. Finally, the model of fixed parameters is formed by training. The model has high classification and judgment robustness and offers accuracy under different conditions such as: temperature, humidity, position of deterioration on the insulator, and thermal load, which allows weight-sharing in the CNN model under different environmental conditions. Based on the experimental data from an infrared heating experiment using a porcelain deteriorated insulator, this work uses the back-propagation gradient descent method to train the model, to form an intelligent detection model for deteriorated insulators. This method has the advantages of high accuracy and robustness, and represents a new method for intelligent detection of deteriorated insulators.