Detection and Recognition for Fault Insulator Based on Deep Learning

Insulators are exerting an important influence in transmission lines. The timely detection of insulator explosion defects is an important guarantee for the safe operation of power systems. This paper puts forward an algorithm that detect the insulators' self-detonation defect based on deep learning. First, a Faster R-CNN target detection network is used to quickly classify and locate the insulators on the transmission lines. Then, a semantic segmentation to the located insulators is carried out by constructing a full convolutions neural network. In the end, the finished insulator image is input into the classification network to judge whether the insulator is burst. The experimental results show that the accuracy of insulator fault explosion recognition based on deep learning reaches more than 99%, and the intelligent design effectively improves the efficiency of the power transmission system.