Multimodal fusion of deep neural networks for transmission line flashover classification

Abstract

ABSTRACT The power system plays a vital role in modern society. However, the occurrence of transmission line flashover faults seriously threatens the safety and stability of the power system. In this paper, a deep neural network flashover classification model based on multimodal attention aggregation is proposed. By using the model, the problems of insufficient feature extraction capability of the unimodal flashover data model and easy loss of key feature information in the process of audiovisual multimodal flashover data alignment are solved. Experiments on the arc- flashover fault dataset show that the flashover fault detection model proposed in this paper can be used stably and effectively for flashover fault detection.