Substation Modelling and Network Security Monitoring Based on Scene Replication Technology

doi:10.11930/j.issn.1004-9649.202004053

Abstract

Abstract: In view of the intelligent demand of the SGCC for safety operation of power grid equipment, a real scene reproduction technology is proposed. A novel substation safety monitoring platform is constructed using deep network architecture displayed with global and local point cloud features. The internet of things (IOT) network directly uses the original point cloud as the input data of the network without the voxel or rendering operation. For power equipment objects, the local geometric dominant subspace of point cloud data is extracted to realize fast data processing, improve the data quality and estimate the point cloud normal vector. The repetition structures in the IOT safety scenario are automatically recognized to realize data augmentation and structure modelling. The proposed algorithm can minimize the fitting error between the model and the original point cloud, so that the three-dimensional model of the power equipment objects can be obtained quickly, and the intelligent understanding and high quality reconstruction of the large scene of transmission and transformation can be realized finally. On this basis, a substation IOT security monitoring platform is established, which can realize accurate modeling and simulation, visual analysis, intelligent management of substations, effectively enhancing the IOT safety monitoring capability of substations.

Key words: three-dimensional modeling, internet of things, point cloud, repetition detection, safety monitoring, substation reconstruction

WANG Xiaohu, Guo Guangxin, DONG Jiahan, WANG Lei, CAO Can. Substation Modelling and Network Security Monitoring Based on Scene Replication Technology[J]. Electric Power, 2021, 54(11): 221-228.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202004053

https://www.electricpower.com.cn/EN/Y2021/V54/I11/221

References

[1] 沙立成, 张岩, 张璇, 等. 电网多维异构数据融合三维可视化方法[J]. 自动化与仪器仪表, 2017(7): 201–203
SHA Lichen, ZHANG Yan, ZHANG Xuan, et al. 3D visualization method of grid multidimensional data fusion[J]. Automation and Instruments, 2017(7): 201–203
[2] 王鹏, 冯光, 韦延方, 等. 10 kV配电网的接地故障测试述评及真型实验场设计[J]. 电力系统保护与控制, 2020, 48(11): 178–187
WANG Peng, FENG Guang, WEI Yanfang, et al. Review of grounding fault testing and design of real experiment field for 10 kV distribution network[J]. Power System Protection and Control, 2020, 48(11): 178–187
[3] 陈强, 王建, 熊小伏, 等. 一种降雨诱发滑坡灾害下输电杆塔的监测与预警方法[J]. 电力系统保护与控制, 2020, 48(3): 147–155
CHEN Qiang, WANG Jian, XIONG Xiaofu, et al. Monitoring and early warning method for transmission tower under rainfall-induced landslide disaster[J]. Power System Protection and Control, 2020, 48(3): 147–155
[4] 李富鹏, 沈秋英, 王森, 等. 基于大数据和多因素组合分析的单元制配电网精细化负荷预测[J]. 智慧电力, 2020, 48(1): 55–62
LI Fupeng, SHEN Qiuying, WANG Sen, et al. Refined load forecasting method for unit distribution network based on big data and multiple factors[J]. Smart Power, 2020, 48(1): 55–62
[5] 李江. 城区10 kV电力配网三维应用系统的设计与实现[D]. 西安: 西安科技大学, 2012.
LI Jiang. Design and implementation of 10 k V city electric power distribution network 3D application system[D]. Xi'an: Xi'an University of Science and Technology, 2012.
[6] AWAN S, MUHAMAD M, KUSEVIC K, et al. Object class recognition in mobile urban lidar data using global shape descriptors[C]//2013 International Conference on 3D Vision, June 29-July 1, 2013. Seattle, WA, USA. IEEE, 2013: 350-357.
[7] 杨小青. 三维重建过程中点云数据精简与配准方法研究[D]. 太原: 中北大学, 2016.
YANG Xiaoqing. Algorithm research of point cloud data simplification and registration in three-dimensional reconstruction process[D]. Taiyuan: North University of China, 2016.
[8] GUAN H Y, YU Y T, LI J, et al. Extraction of power-transmission lines from vehicle-borne lidar data[J]. International Journal of Remote Sensing, 2016, 37(1): 229–247.
[9] 曲朝阳, 熊泽宇, 颜佳, 等. 基于空间分割的电力大数据三维全景可视化场景管理方法[J]. 华北电力大学学报(自然科学版), 2016, 43(2): 23–29
QU Zhaoyang, XIONG Zeyu, YAN Jia, et al. Scene management method of three-dimensional panoramic visualization of electric power big data based on space partition[J]. Journal of North China Electric Power University, 2016, 43(2): 23–29
[10] 杨成顺, 杨中亚, 黄宵宁. 基于资产类模型和空间信息模型的输电线路三维可视化平台[J]. 电测与仪表, 2015, 52(23): 122–128
YANG Chengshun, YANG Zhongya, HUANG Xiaoning. 3D visualization platform of transmission lines based on asset model and spatial information model[J]. Electrical Measurement and Instrumentation, 2015, 52(23): 122–128
[11] 曲朝阳, 陈帅, 杨帆, 等. 基于云计算技术的电力大数据预处理属性约简方法[J]. 电力系统自动化, 2014, 38(8): 67–71
QU Zhaoyang, CHEN Shuai, YANG Fan, et al. An attribute reducing method for electric power big data preprocessing based on cloud computing technology[J]. Automation of Electric Power Systems, 2014, 38(8): 67–71
[12] 于德明, 陈方东, 郭昕阳, 等. 输电线路直升机激光扫描三维成像技术的应用[J]. 高电压技术, 2011, 37(3): 711–716
YU Deming, CHEN fangdong, GUO Xinyang, et al. Application of helicopter laser scanning 3D imaging technology in transmission lines[J]. High Voltage Engineering, 2011, 37(3): 711–716
[13] YUAN J, BAE E, TAI X C, et al. A continuous max-flow approach to potts model[M]//Computer Vision – ECCV 2010. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010: 379-392.
[14] ISACK H, BOYKOV Y. Energy-based geometric multi-model fitting[J]. International Journal of Computer Vision, 2012, 97(2): 123–147.
[15] 赵杰, 燕必希. 自由曲面CAD模型点云数据生成方法研究[J]. 机械设计与制造, 2013(8): 136–138
ZHAO Jie, YAN Bixi. The study on the method of the generation of the point cloud data on the free surface of CAD model[J]. Machinery Design & Manufacture, 2013(8): 136–138
[16] 赵春林. 基于三维全景快速建模的变电站可视化的研究和应用[D]. 济南: 山东大学, 2013.
ZHAO Chunlin. Research and application of substation visualization based on 3D panorama and rapid modeling technology[D]. Jinan: Shandong University, 2013.
[17] LI H D. Two-view motion segmentation from linear programming relaxation[C]//2007 IEEE Conference on Computer Vision and Pattern Recognition, June 17-22, 2007. Minneapolis, MN, USA. IEEE, 2007: 1-8.
[18] 方文崇, 梁寿愚. 基于三维可视化的变电站智能辅助系统应用研究[J]. 信息技术, 2017, 41(4): 54–56, 60
FANG Wenchong, LIANG Shouyu. Study and application of intelligent assisted system in substationbased on 3D visualization[J]. Information Technology, 2017, 41(4): 54–56, 60
[19] 季知祥, 邓春宇. 面向电力大数据应用的专业化分析技术研究[J]. 供用电, 2017, 34(6): 32–37
JL Zhixiang, DENG Chunyu. Big data analytics technology research for power applications[J]. Distribution & Utilization, 2017, 34(6): 32–37
[20] 吴颖, 张新家, 茹芬. 基于四叉树分割的连续LOD漫游地形绘制[J]. 计算机技术与发展, 2011, 21(4): 5–8, 12
WU Ying, ZHANG Xinjia, RU Fen. Continuous LOD roaming terrain rendering based on quad-tree division[J]. Computer Technology and Development, 2011, 21(4): 5–8, 12
[21] OOMS J F, WANG D D, RAJANI R, et al. Computed tomography-derived 3D modeling to guide sizing and planning of transcatheter mitral valve interventions[J]. JACC: Cardiovascular Imaging, 2021, 14(8): 1644–1658.
[22] PAHL T, HAGEMEIER S, BISCHOFF J, et al. Rigorous 3D modeling of confocal microscopy on 2D surface topographies[J]. Measurement Science and Technology, 2021, 32(9): 094010.
[23] SILVERII F, PULVIRENTI F, MONTGOMERY-BROWN E K, et al. The 2011-2019 Long Valley Caldera inflation: new insights from separation of superimposed geodetic signals and 3D modeling[J]. Earth and Planetary Science Letters, 2021, 569: 117055.
[24] YUAN Z L, LI Y, TANG S J, et al. A survey on indoor 3D modeling and applications via RGB-D devices[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(6): 815–826.
[25] ZHOU T, LV L, LIU J, et al. Application of uav oblique photography in real scene 3d modeling[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021: 413–418.