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According to the Department of Energy of the USA, today’s electrical distribution system is 97.97% reliable. However, power\udoutages and interruptions still impact many people. Many power outages are caused by animals coming into contact with the\udconductive elements of the electrical substations. This can be prevented by covering the conductive electrical objects with insulating\udmaterials. The design of these custom-built insulating covers requires a 3D as-built plan of the substation. This research aims to\uddevelop automated methods to create such a 3D as-built plan using terrestrial LiDAR data for which objects first need to be\udrecognized in the LiDAR point clouds. This paper reports on the application of a new algorithm for the segmentation of planar\udsurfaces found at electrical substations. The proposed approach is a region growing method that aggregates points based on their\udproximity to each other and their neighbourhood dispersion direction. PCA (principal components analysis) is also employed to\udsegment planar surfaces in the electrical substation. In this research two different laser scanners, Leica HDS 6100 and Faro Focus3D,\udwere utilized to scan an electrical substation in Airdrie, a city located in north of Calgary, Canada. In this research, three subsets\udincorporating one subset of Leica dataset with approximately 1.7 million points and two subsets of the Faro dataset with 587 and 79\udthousand points were utilized. The performance of our proposed method is compared with the performance of PCA by performing\udcheck point analysis and investigation of computational speed. Both methods managed to detect a great proportion of planar points\ud(about 70%). However, the proposed method slightly outperformed PCA. 95% of the points that were segmented by both methods as\udplanar points did actually lie on a planar surface. This exhibits the high ability of both methods to identify planar points. The results\udalso indicate that the computational speed of our method is superior to that of PCA by 50%. It is concluded that our proposed\udmethod achieves better results with higher computational speed than PCA in the segmentation of planar surfaces

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Segmentation of planar surfaces in LiDAR point clouds of an electrical substation by exploring the structure of points neighbourhood