Fault Diagnosis and Localization of Transmission Lines Based on R-Net Algorithm Optimized by Feature Pyramid Network

Abstract

Timely fault diagnosis and localization of transmission lines is crucial for ensuring the reliable operation of increasingly complex power systems. This study proposes an optimized R-Net algorithm based on a feature pyramid network (FPN) and densely connected convolutional network (D-Net) for transmission line fault diagnosis and localization. The R-Net network is enhanced by reshaping the anchor points using an improved K-means algorithm and incorporating an FPN for multi-scale feature extraction. The backbone network is further optimized using D-Net to strengthen inter-layer connections and improve feature reuse. Experimental results demonstrate that the optimized R-Net achieves an overall average accuracy of 0.64, outperforming the original network by 1.30%. The accuracy improvement is particularly significant for ground wire defects (2.40%). The D-Net-based R-Net, despite having fewer parameters, maintains high accuracy (0.6502). Compared to other object detection algorithms, such as YOLO-v3 and Faster R-CNN, the optimized R-Net exhibits superior performance in terms of mean average precision (15.58% and 2.45% higher, respectively) and parameter efficiency (17M vs. 38M and 81M). Considering both performance and speed, the optimized R-Net achieves a processing rate of 10.5 frames per second. This study provides an efficient and accurate tool for transmission line fault diagnosis and localization, with significant practical implications for power system operation and maintenance.