Exploration of Transmission Line Fault Prediction Methods Based on Deep Learning

Faults in transmission lines can have significant impacts on the power system, making accurate prediction of such faults particularly crucial. This paper aims to explore how to effectively integrate existing transmission line monitoring systems with advanced deep learning technologies to develop fault prediction methods. This research adopted a convolutional neural network based on the MobileNet architecture, utilizing techniques such as depthwise separable convolutions, Transformer attention mechanisms, and multi-scale feature extraction. Additionally, transfer learning and domain adaptation techniques were employed during the training process to enhance the model's generalization capabilities. On the validation dataset, the proposed algorithm achieved shorter detection times and higher accuracy rates, outperforming both VGG16 and traditional convolutional neural networks. This also demonstrated the robustness of the model across various scenarios.