An Efficient Voxel-Based Segmentation Algorithm Based on Hierarchical Clustering to Extract LIDAR Power Equipment Data in Transformer Substations

Abstract

Light detection and ranging (LIDAR) scanning is a common method of substation scene modeling that extracts point clouds of electrical equipment from the point cloud scene of a substation. The extraction effect is limited by uncertainty regarding the noise level, nonuniform point cloud density, and the computational complexity. In this paper, we propose a point cloud extraction solution for electrical equipment models. First, a statistical analysis of substation ground elevation is performed to obtain the point clouds of devices at the feature height and remove large numbers of redundant underground point clouds. Second, based on the statistically derived power equipment feature heights, the point cloud data are sliced according to the featured elevation intervals. Based on voxelization, the point cloud slices are then clustered using horizontal hierarchical clustering. The clustering results at different elevation intervals are then reclustered using vertical hierarchical clustering. Finally, we use filters combined with the DBSCAN algorithm to perform fine segmentation on the point cloud data. The results show that our slice clustering approach reduces the computational burden involved in point cloud processing, and the comprehensive clustering strategy ensures the accuracy of the clustering results.

Keywords

• Clustering methods
• point cloud segmentation
• smart grid
• substation modeling