Research on Power Line Fitting Based on Point Cloud and Risk Prediction of Power Corridor Vegetation Barrier

To prevent the risk of electric shock to individuals and structures, a necessary clearance space must be maintained around the conductors under high-voltage power lines. Due to the complex and unordered structure of point cloud data, detecting vegetation barriers and predicting risks to power lines using three-dimensional point cloud data pose challenges. This paper proposes a method based on point cloud power line fitting and risk prediction for power corridor vegetation barriers. Firstly, features of the power corridor are extracted, and point cloud objects such as power lines, towers, and trees are classified. Next, the power line point cloud is fitted for different wind deflection scenarios. Finally, a distance calculation method is used to measure the distance between the power lines and surrounding objects, which is compared against the safety distance. Regions with distances smaller than the safety distance are flagged for risk warnings. The proposed research method of power line fitting based on point cloud and risk prediction of power corridor vegetation barriers is experimentally validated using 16 kilometers of point cloud data covering five different power line grades. The point cloud classification accuracy reaches 94.35%, the average error of power line fitting is 0.0485m, and the average absolute error between predicted and actual distances is within 0.1m. These results demonstrate the promising prospects of the proposed method in predicting risks related to vegetation barriers in power corridors.