Anomaly Detection and Fault Diagnosis of Power Distribution Line Point Cloud Data Based on Deep Learning

Early and accurate fault diagnosis in power distribution systems is essential to ensure stable electricity delivery and prevent outages. This study presents a deep learning-based anomaly detection framework that analyzes 3D LiDAR point cloud data to identify structural defects in power distribution lines. Leveraging advancements in deep learning and 3D sensing, a hybrid architecture combining PointNet++ and 3D Convolutional Neural Networks (3D CNN) is proposed. The system processes point clouds from the TS40K dataset, comprising high-resolution, annotated scans of power infrastructure, and uses a feature fusion strategy to integrate fine-grained local geometry from PointNet++ with global volumetric features from 3D CNN. Implemented in Python, the method achieves a 94.7% accuracy in fault diagnosis, outperforming standalone models. It robustly detects anomalies such as sagging wires, leaning poles, and broken insulators, maintaining precision, recall, and F1-scores above 90%, even under noisy and sparse conditions. Visualization of detected faults on 3D models confirms its precise localization capability, supporting real-time monitoring and maintenance planning in smart grids. By integrating complementary deep learning techniques, this approach offers a scalable, accurate, and automated solution for anomaly detection and fault diagnosis in power distribution systems. Future work will focus on multi-sensor fusion and semi-supervised learning to reduce dependence on labeled data and broaden applicability to other infrastructure use cases.