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Automated extraction of key points from three-dimensional (3D) point clouds in transmission corridors provides technical support for digital twin construction and risk management of the power grid. However, accurately and efficiently segmenting the point clouds of transmission corridors remains a challenging problem. Traditional segmentation methods for transmission corridors suffer from low accuracy and poor generalization ability, and the potential of deep learning in this field has been overlooked. Therefore, the PointNet++ deep learning model is employed as the backbone network for the segmentation of 3D point clouds in transmission corridors. Additionally, given the distinct distribution of key components, an end-to-end CA-PointNet++ architecture is proposed by integrating the Coordinate Attention (CA) module with PointNet++. This approach captures long-distance spatial contextual features and improves feature saliency for more precise segmentation. Furthermore, CA-PointNet++ is evaluated on a dataset of 3D point clouds collected by unmanned aerial vehicles (UAV) equipped with Light Detection and Ranging (LiDAR) for inspecting transmission corridors. The results show that CA-PointNet++ achieved 93.7% overall accuracy (OA) and 67.4% mean Intersection over Union (mIoU). Comparative studies with established deep learning models confirm that our proposed CA-PointNet++ exhibits high accuracy and strong generalization ability for point cloud segmentation tasks in transmission corridors.

With the upsurge of the digital twin power grid, it is very necessary to carry out a three-dimensional model and simulation analysis of the power grid system. Insulators are one of the important components in the power system, and the appropriate simplification of the simulation model is of great significance. In this paper, the finite element software is used to calculate the three-dimensional model, and the influence of the length of the conductor, the tower, the yoke plate, the grading ring, and the arrangement of the insulators on the axial electric field distribution (EFD) is analyzed. The results show that: the length of the conductor will affect the uniformity of the axial electric field; compared with the grading ring, the yoke plate will cause serious distortion of the electric field intensity curve; the integrity of the tower has different effects; the arrangement of the insulators will affect the magnitude of the electric field and even the shape of the curve. Therefore, in the simulation analysis of the axial electric field, the above factors should be considered when simplifying the insulator model, which will promote the development of relevant digital twin technology.

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