In order to achieve real-time instance segmentation and ranging of power transmission lines, a realtime power transmission lines point cloud segmentation (MPPS) method based on light detection and ranging (Li-DAR) mobile scanning is proposed. This method first designs a point cloud fast stitching enhancement algorithm that employs a Kalman filtering strategy with sliding spatial window for dynamic point cloud registration. Then, a 3D point cloud semantic classification network tailored for power transmission targets is designed. The network handles large spatial targets through uniform sampling and local feature aggregation (LFA). Finally, an overhead projection and a fast euclidean clustering algorithm are applied for power transmission line target segmentation and ranging. Experiments demonstrate that this method achieves a classification accuracy of 94.7% and an average intersection over union of 81.6% on a 3D point cloud dataset of power transmission lines obtained from LiDAR mobile scans, validating its ability to achieve real-time instance segmentation and distance measurement for power transmission targets.