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Measurement of combined gap in whole process of transmission lines’ live working based on 3D laser point cloud

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Transmission lines’ live working is one of an effective means to ensure the reliable operation of transmission lines. In order to solve the unsafe problems existing in the implementation of traditional live working, the paper uses ground-based lidar to collect point cloud data. A tile based on the pyramid data structure is proposed to complete the storage and calling of point cloud data. The improved bidirectional filtering algorithm is used to distinguish surface features quickly and obtain a 3D model of the site. Considering the characteristics of live working, the speed of data reading and querying, the nearest point search algorithm based on octree is used to acquire a real- time calculation of the safe distance of each point in the planned path, and the safety of the operation mode is obtained by comparing with the value specified in the regulation, and assist in making decisions of the operation plan. In the paper, the simulation of the actual working condition is carried out by taking the “the electric lifting device ascending” as an example. The experimental results show that the established three-dimensional model can meet the whole process control of the operation, and has achieved practical effect.
Rocznik
Strony
737--753
Opis fizyczny
Bibliogr. 28 poz., fig., tab.
Twórcy
autor
  • School of Automation & Electrical Engineering, Lanzhou Jiaotong University Gansu, China
autor
  • School of Automation & Electrical Engineering, Lanzhou Jiaotong University Gansu, China
  • Key Laboratory of Opto-Electronic Technology and Intelligent Control Ministry of Education Lanzhou Jiaotong University Gansu, China
  • The UHV Company of State Grid Gansu Electric Power Company Gansu, China
autor
  • The UHV Company of State Grid Gansu Electric Power Company Gansu, China
autor
  • The UHV Company of State Grid Gansu Electric Power Company Gansu, China
autor
  • The UHV Company of State Grid Gansu Electric Power Company Gansu, China
Bibliografia
  • [1] Li R.H., Analysis of the hazard rate and accident rate of live work, Power Safety Technology, vol. 19, no. 2, pp. 41–43 (2017).
  • [2] Tan Y.D., Wang X.X., Introduction to the way of entering the equipotential for live work, Power Grid Technology, no. 6, pp. 30–33 (2021).
  • [3] Zhang X.Q., Guo W.P., Innovation and practice of digital team construction in power grid enterprises, Innovation World Weekly, no. 4, pp. 70–80 (2021).
  • [4] Shi L., Guo T., Research on segmentation and security detection of power line laser point cloud, Laser Technology, vol. 43, no. 3, pp. 341–346 (2019).
  • [5] Wu Z.R., Fan L.M., Rapid detection method for hidden danger of transmission line tree barrier based on airborne laser point cloud, Applied Laser, vol. 43, no. 3, pp. 128–134 (2022), DOI: 10.14128/j.cnki.al.20224203.128.
  • [6] Xu L.G., Shi L., A transmission line tower tilt detection algorithm based on laser point cloud, Laser Technology, vol. 46, no. 3, pp. 390–396 (2022).
  • [7] Wang J., Zhang J., Research on 3D laser scanning technology based on point cloud data acquisition, 2014 International Conference on Audio, Language and Image Processing, Shanghai, China, pp. 631–634 (2014).
  • [8] Xiong Y.Y., Qiao J.G., Zhang W.J., Fisheye image and ground lidar point cloud registration based on line features, Bulletin of Surveying and Mapping, no. 7, pp. 74–80 (2021), DOI: 10.13474/j.cnki.11-2246.2021.0212.
  • [9] Tao S.B., Liang C., Sparse voxel pyramid neighborhood construction and classification of LiDAR point cloud, Journal of Image and Graphics, vol. 26, no. 11, pp. 2703–2712 (2021).
  • [10] Hui L., Yang H., Pyramid Point Cloud Transformer for Large-Scale Place Recognition, 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Shanghai, China, pp. 6078–6087 (2021).
  • [11] Zeng X.Z., Li C.R., Research on Superimposition Pyramid Index Applied to Massive Points Cloud Fast Display, Remote Sensing Technology and Application, vol. 30, no. 3, pp. 534–539 (2015).
  • [12] Wang H.P., Zhang C.S., Applicability analysis of thinning algorithm for point cloud data of transmission line corridors, Science of Surveying and Mapping, vol. 45, no. 9, pp. 152–158 (2020), DOI:10.16251/j.cnki.1009-2307.2020.09.023.
  • [13] Prio M.H, Patel S., Implementation of Dynamic Radius Outlier Removal (DROR) Algorithm on LiDAR Point Cloud Data with Arbitrary White Noise Addition, 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Helsinki, Finland, pp. 1–7 (2022), DOI: 10.1109/VTC2022-Spring54318.2022.9860643.
  • [14] Munir N., Awrangjeb M., Extraction of Forest Power lines From LiDAR point cloud Data, 2021 Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, Australia, pp. 1–6 (2021), DOI: 10.1109/DICTA52665.2021.9647062.
  • [15] Hao G., Hu X.F., A preprocessing method for 3D laser scanning point cloud data, Science of Surveying and Mapping, vol. 39, no. 7, pp. 90–93 (2014), DOI: 10.16251/j.cnki.1009-2307.2014.07.017.
  • [16] Itakura K., Miyatani S., Estimating Tree Structural Parameters via Automatic Tree Segmentation from LiDAR Point Cloud Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 555–564 (2022), DOI: 10.1109/JSTARS.2021.3135491.
  • [17] Ma D.L., Wang X.K., A point cloud data classification method based on height difference, Bulletin of Surveying and Mapping, vol. 8, no. 6, pp. 46–49 (2018), DOI: 10.13474/j.cnki.11-2246.2018.0174.
  • [18] Zhao J.H., Dou X.T., A method for 3D point cloud classification based on segmentation results, Science of Surveying and Mapping, vol. 47, no. 3, pp. 85–95 (2022), DOI: 10.16251/j.cnki.1009-2307.2022.03.012.
  • [19] Zhou W., Peng C.C., LIDAR point cloud texture feature extraction method, Journal of National University of Defence Technology, vol. 41, no. 2, pp. 124–131 (2019).
  • [20] Zhou R.Q., Xu Z.H., An airborne laser point cloud classification method for high-voltage power transmission corridors, Science of Surveying and Mapping, vol. 44, no. 3, pp. 22–27 (2019), DOI:10.16251/j.cnki.1009-2307.2019.03.004.
  • [21] Jia Y., Design of nearest neighbor search for dynamic interaction points, 2021 2nd International Conference on Big Data and Informatization Education (ICBDIE), Hangzhou, China, pp. 389–393 (2021).
  • [22] Bai Y., Tang W., Real-time and high-precision ranging method for large dynamic range of imaging lidar, Infrared and Laser Engineering, vol. 49, no. S2, pp. 1–6 (2020).
  • [23] Wang Y. et al., Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks, 2021. IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, pp. 5035–5041(2021), DOI: 10.1109/ICRA48506.2021.9561736.
  • [24] Wang Y.J., Lian T.F., Point cloud registration method based on octree and KD tree index, Surveying and Mapping Engineering, vol. 26, no. 8, pp. 35–40 (2017), DOI: 10.19349/j.cnki.issn1006-7949.2017.08.008.
  • [25] Wen Y.W., Deng C.Y., Application of 3D laser scanner in actual measurement of electric power engineering, Bulletin of Surveying and Mapping, no. 10, pp. 163–167 (2021).
  • [26] Zhang Q.S., Wang L.N., Simulation and analysis method of gap discharge characteristics of live work combination, High Voltage Technology, vol. 44, no. 4, pp. 1293–1301 (2018), DOI: 10.13336/j.1003-6520.hve.20180329032.
  • [27] Wang L.N., Hu Y., Discharge mechanism of combined gaps in live operation of UHV transmission lines, High Voltage Technology, vol. 37, no. 5, pp. 1224–1230 (2011), DOI: 10.13336/j.1003-6520.hve.2011.05.023.
  • [28] State Grid Corporation of China, Q/GDW1799.2, State Grid Corporation Electric Power Safety Work Regulations (Line Part) [S], Beijing (2013).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-74f0d880-fd45-4a77-b9e9-2490f0cdea28
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