Calculation and Verification of Wind-Induced Deflection Risk for Transmission Lines Based on High-Precision Point Cloud Data

Wind-induced deflection has become a leading cause of faults in transmission lines under extreme weather conditions. This paper proposes a computational method for accurately assessing wind-induced deflection risk by leveraging high-precision point cloud data. First, the residual clustering algorithm was applied to extract the point clouds of individual power lines from the high-precision dataset, and a corresponding catenary equation was formulated. Next, tower contours, type identification, and structural parameters were extracted through an integrated approach combining the Alpha Shapes algorithm with the PointNet++ deep learning network. Subsequently, a wind-induced deflection calculation and evaluation model based on the finite element method was rapidly constructed, and its accuracy was validated through a dedicated wind-induced deflection test platform. Moreover, a Python-ANSYS co-simulation platform was developed to enable parametric modeling and automated solving of finite element models, substantially enhancing computational efficiency. Finally, by analyzing a wind-induced deflection fault case of a 500 kV transmission line, the effectiveness of the proposed point cloud-based wind-induced deflection assessment methodology was confirmed.