An Improved AlexNet for Power Edge Transmission Line Anomaly Detection

Since most outdoor transmission line equipment suffers from harsh disasters, they are prone to wire breakage, tower collapse and insulator flashover. When anomaly occurs, too much time is required for the State Grid Corporation to fix it manually. To reduce the inspection burden, many methods have been presented in the past to diagnose and locate anomaly. In this paper, we propose an improved AlexNet model for anomaly detection. In the aspect of feature extraction, the proposed model extracts the characteristics of transmission line equipment through a deep convolutional neural network (DCNN). In the aspect of the recognition algorithm, by referring to the advantages of the traditional machine learning method and incorporating the advantages of the support vector machine (SVM), an SVM classification method incorporating deep learning is proposed. Finally, the improved AlexNet model and SVM classification method are used to classify images of various types of power equipment. The results show that the proposed methods can be effectively applied to the image recognition of various types of power equipment, and they greatly improve the recognition rate of power equipment images, which has great potential for future real-time transmission line monitoring platform design.