Intelligent overheating fault diagnosis for overhead transmission line using semantic segmentation

Abstract

The strain clamps and leading wires are important components that connect conductors on overhead transmission lines and conduct current. During operation, poor contact between these components can cause abnormal overheating, leading to electric failures and threatening power system reliability. Recently, the use of unmanned aerial vehicles equipped with infrared thermal imagers for strain clamp and leading wire maintenance has become increasingly popular. Deep learning-based image recognition shows promising prospects for intelligent fault diagnosis of overheating faults. A pre-treatment method is proposed based on dynamic histogram equalisation to enhance the contrast of infrared images. The DeepLab v3+ network, loss function, and existing networks with different backbones are compared. The DeepLab v3+ network with ResNet101 and convolutional block attention module added, and the Focal Loss function achieved the highest performance with an average pixel accuracy of 0.614, an average intersection over union (AIoU) of 0.567, an F1 score of 0.644, and a frequency weighted intersection over union of 0.594 on the test set. The optimised Atrous rates has increased the AIoU by 12.91%. Moreover, an intelligent diagnosis scheme for evaluating the defect state of the strain clamps and leading wires is proposed and which achieves a diagnostic accuracy of 91.0%.