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Machine learning approach for tenaga nasional berhad (tnb) overhead powerline and electricity pole inventory using mobile laser scanning data

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dc.contributor.authorMohd Rapheal M.S.A.en_US
dc.contributor.authorFarhana A.en_US
dc.contributor.authorMohd Salleh M.R.en_US
dc.contributor.authorAbd Rahman M.Z.en_US
dc.contributor.authorMajid Z.en_US
dc.contributor.authorMusliman I.A.en_US
dc.contributor.authorAbdullah A.F.en_US
dc.contributor.authorAbd Latif Z.en_US
dc.contributor.authorid57929782800en_US
dc.contributor.authorid57930161900en_US
dc.contributor.authorid57008087300en_US
dc.contributor.authorid56757045000en_US
dc.contributor.authorid9247739400en_US
dc.contributor.authorid55780272000en_US
dc.contributor.authorid55142706500en_US
dc.contributor.authorid57216335420en_US
dc.date.accessioned2023-05-29T09:38:32Z
dc.date.available2023-05-29T09:38:32Z
dc.date.issued2022
dc.descriptionDecision trees; Laser applications; Learning algorithms; Machine learning; Mean square error; Scanning; Classifieds; Electricity assets; Electricity pole; Laser scanner data; Laserscanners; Machine-learning; Mobile laser scanner; Overhead powerline; Point-clouds; Power lines; Polesen_US
dc.description.abstractElectricity assets recognition and inventory is a fundamental task in the geospatial-based electrical power distribution management. In Malaysia, Tenaga Nasional Berhad (TNB) aims to complete their assets inventory throughout the country by 2022. Previous research has shown that a method for assets detection especially for TNB is still at an early stage, which mainly relied on manual extraction of the assets from different data sources including mobile laser scanner (MLS). This research aims at evaluating a geospatial method based on machine learning to classify the TNB assets using high density MLS data. The MLS data was collected using Riegl VMQ-1 HA scanner and supported by the base station and control points for point cloud registration purpose. In the first stage the point clouds were classified into ground and non-ground objects. The non-ground points were further classified into different landcover types i.e. vegetation, building, and other classes. The points classified as other classes were used for overhead powerline and electricity poles classification using random forest-based Machine Learning (ML) approach in LiDAR 360 software. Based on the classified point clouds, detailed characteristics of electricity poles (i.e. number of poles, height, diameter and inclination from ground) and overhead powerlines (number of cable segments) were estimated. This information was validated using field collected reference data. The results show that the detection accuracy for electricity poles and overhead power line are 65% and 63% respectively. The estimation of length, diameter and height of the spun pole from point clouds has produced Root Mean Square Error (RMSE) value of 0.081cm, 0.263 cm and 0.372 cm respectively. Meanwhile for the concrete pole, the length, diameter and height has been successfully estimated with the value of RMSE of 0.034 cm, 0.029 cm and 0.331 cm respectively. The length of overhead powerline was estimated with 59.02 cm RMSE. In conclusion, the MLS data had show promising results for a semi-automatic detection and characterization of TNB overhead powerlines and poles in the sub-urban area. Such outcome can be used to support the inventory and maintenance process of the TNB assets. � 2022 International Society for Photogrammetry and Remote Sensing. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.5194/isprs-archives-XLVI-4-W3-2021-239-2022
dc.identifier.epage246
dc.identifier.issue4/W3-2021
dc.identifier.scopus2-s2.0-85139923614
dc.identifier.spage239
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85139923614&doi=10.5194%2fisprs-archives-XLVI-4-W3-2021-239-2022&partnerID=40&md5=5e7829b2b474f240d4c5a6b900bc9b6c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27000
dc.identifier.volume46
dc.publisherInternational Society for Photogrammetry and Remote Sensingen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
dc.titleMachine learning approach for tenaga nasional berhad (tnb) overhead powerline and electricity pole inventory using mobile laser scanning dataen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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