Transmission Line Operation and Inspection Training Simulation Based on Multiple Time Scales and VR

doi:10.16182/j.issn1004731x.joss.21-0999

Abstract

Abstract:

Transmission line inspection is an important work to ensure the safe operation of the power grid. It involves the live-work, the power-outage-maintenance and the fault-diagnosis. Training using only 3D virtual technology cannot achieve realistic results. It is necessary to combine 3D virtual scenes with digital grids. To this end, using the collected monitoring data and multi-time-scale calculation models, a twin state digital power grid has been constructed for the live-work, a parallel state digital power grid has been constructed for the power-outage-maintenance, and corresponding analog devices have been added to the parallel digital power grid to complete the training of the fault-diagnosis.By switching between the twin sate and the parallel state digital power grids, the trainers can watch the actions and output wave of the equipment while performing the fault experiment operation, thereby improving the trainers' understanding and application level of knowledge.

Key words: transmission line operation and inspection, twin state, parallel state, three dimensional virtual technology, multiple time scale

CLC Number:

TP391.9

Jiawen Yan, Jijie Huang, Lie Zhou, Changjin Chen, Qiang Wu, Jintao Zhao. Transmission Line Operation and Inspection Training Simulation Based on Multiple Time Scales and VR[J]. Journal of System Simulation, 2022, 34(2): 212-220.

Figures/Tables 6

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

References 22

1	Andrés Ayala Carcia, Israel Galván Bobadilla, Gustavo Arroyo Figueroa, et al. Virtual Reality Training System for Main-tenance and Operation of High-voltage Overhead Power Lines[J].Virtual Reality(S1359-4338),2016,20(1) : 27-40.
2	陈彬, 邱晓刚, 王亦平. 智能化的平行实验方法[J].系统仿真学报, 2017, 29(9): 2064-2072.
	Chen Bin, Qiu Xiaogang, Wang Yiping.Intelligent ACP Based Experimental Approach[J].Journal of System Simulation, 2017, 29(9): 2064-2072.
3	杨林瑶, 陈思远, 王晓, 等. 数字孪生与平行系统: 发展现状、对比及展望[J].自动化学报, 2019, 45(11): 2001-2031.
	Yang Linyao, Chen Siyuan, Wang Xiao, et al. Digital Twins and Parallel Systems: State of the Art, Comparisons and Prospect[J]. Acta Automatica Sinica, 2019, 45(11): 2001-2031.
4	陶飞, 马昕, 胡天亮, 等. 数字孪生标准体系[J]. 计算机集成制造系统, 2019, 25(10): 2405-2418.
	Tao Fei, Ma Xin, Hu Tianliang, et al. Research on Digital Twin Standard System[J].Computer Integrated Manufacturing Systems, 2019, 25(10): 2405-2418.
5	陶飞, 张贺, 戚庆林, 等. 数字孪生模型构建理论及应用[J]. 计算机集成制造系统, 2021, 27(1): 1-15.
	Tao Fei, Zhang He, Qi Qinglin, et al. Theory of Digital Twin Modeling and its Application[J]. Computer Integrated Manufacturing Systems, 2021, 27(1): 1-15.
6	张霖. 关于数字孪生的冷思考及其背后的建模和仿真技术[J]. 系统仿真学报, 2020, 32(4): 3-12.
	Zhang Lin. Cold Thinking about Digital Twin and its Modeling and Simulation Technology[J]. Journal of System Simulation, 2020, 32(4): 3-12.
7	刘大同, 郭凯, 王本宽, 等. 数字孪生技术综述与展望[J]. 仪器仪表学报, 2018, 39(11) : 1-10.
	Liu Datong, Guo Kai, Wang Benkuan. Summary and Perspective Survey on Digital Twin Technology[J].Chinese Journal of Scientific Instrument, 2018, 39(11) : 1-10.
8	贺兴, 艾芊, 朱天怡, 等. 数字孪生在电力系统应用中的机遇和挑战[J]. 电网技术, 2020, 44(6): 2009-2019.
	He Xing, Ai Qian, Zhu Tianyi, et al. Opportunities and Challenges of the Digital Twin in Power System Applications[J]. Power Systems Technology, 2020, 44 (6): 2009-2019.
9	王成山, 董博, 于浩, 等.智慧城市综合能源系统数字孪生技术及应用[J].中国电机工程学报, 2021, 41(5): 1597-1605.
	Wang Chengshan, Dong Bo, Yu Hao, et al. Digital Twin Technology and Its Application in the Integrated Energy System of Smart City[J]. Proceedings of the CSEE, 2021, 41(5):1597-1605.
10	孙荣富, 王隆扬, 王玉林, 等. 基于数字孪生的光伏发电功率超短期预测[J].电网技术, 2020, 44(6): 1258-1264.
	Sun Rongfu, Wang Longyang, WANG Yulin, et al. Ultra-short-term Prediction of Photovoltaic Power Generation Based on Digital Twins[J]. Power System Technology, 2020, 44(6): 1258-1264.
11	Pargmann H, Euhausen D, Faber R. Intelligent Big Data Processing for Wind Farm Monitoring and Analysis based on Cloud-technologies and Digital Twins: A Quantitative Approach[C]//in Proc. IEEE 3rd Int. Conf. Cloud Comput. Big Data Anal. (ICCCBDA), Apr. 2018: 233-237.
12	Sivalingam K, Sepulveda M, Spring M, et al. A Review and Methodology Development for Remaining Useful Life Prediction of Offshore Fifixed and Flfloating Wind Turbine Power Converter with Digital Twin Technology Perspective[C]// in Proc. 2nd Int. Conf. Green Energy Appl. (ICGEA), Mar. 2018: 197-204.
13	Brosinsky C, Westermann D, Krebs R. Recent and Prospective Developments in Power System Control Centers: Adapting the Digital Twin Technology for Application in Power System Control Centers[C]// in Proc. IEEE Int. Energy Conf. (ENERGYCON), Jun. 2018: 1-6.
14	Jain P, Poon J, Singh J P, et al. A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems[J]. IEEE Trans. Power Electron(S0885-8993), 2020, 35(1): 940-956.
15	程乐峰, 余涛, 张孝顺, 等. 信息–物理–社会融合的智慧能源调度机器人及其知识自动化: 框架、技术与挑战[J]. 中国电机工程学报, 2018, 48(1): 25-40.
	Cheng Lefeng, Yu Tao, Zhang Xiaoshun, et al. Cyber-physical-social Systems Based Smart Energy Robotic Dispatcher and Its Knowledge Automation: Framework, Techniques and Challenges[J]. Proceedings of the CSEE, 2018, 48(1): 25-40.
16	施博辰, 赵争鸣, 朱义诚, 等. 电力电子混杂系统多时间尺度离散状态事件驱动仿真方法[J]. 中国电机工程学报, 2021, 41(9): 2980-2989.
	Shi Bochen, Zhao Zhengming, Zhu Yicheng, et al. Discrete-state Event-driven Simulation Approach for Multi-time-scale Power Electronic Hybrid System[J]. Proceedings of the CSEE, 2021, 41(9): 2980-2989.
17	卓助航, 杨俊杰, 胡丰晔, 等. 基于XGBoost短期预测控制的微电网多时间尺度调度[J]. 计算机应用与软件, 2021, 38(5): 54-60.
	Zhuo Zhuhang, Yang Junjie, Hu Fengye, et al. Multi-Time Scale Scheduling of Microgrid Based on Xgboost Short-Term Predictive Control[J]. Computer Applications and Software, 2021, 38(5): 54-60.
18	王庆平, 陈超英, 刘秀玲, 等. 耦合双回线路任意点故障的仿真[J]. 电力系统自动化, 2001(15): 34-38.
	Wang Qingping, Chen Chaoying, Liu Xiuling, et al. Simulations of Random Faults on Coupled Parallel Transmission Lines[J]. Automation of Electric Power Systems, 2001 (15): 34-38.
19	夏天华, 马骏超, 黄弘扬, 等. 基于RTDS硬件在环测试的SVG控制器参数辨识[J]. 电力系统保护与控制, 2020, 48(13): 110-116.
	Xia Tianhua, Ma Junchao, Huang Hongyang, et al. Parameter Identification for SVG Controller Based on RTDS Hardware-in-the-loop Test[J]. Power System Protection and Control, 2020, 48(13): 110-116.
20	杨维, 石德乾, 岳育强, 等. 基于RT-LAB+QNX的CAN驱动程序设计[J].测控技术, 2014, 33(1): 118-121.
	Yang Wei, Shi Deqian, Yue Yuqiang, et al. Design of CAN Driver based on RT-LAB+QNX[J]. Measurement & Control Technology, 2014, 33(1): 118-121.
21	张德刚, 王达达, 李泽河, 等. 基于计算机协同工作的高层体系结构组播扩展[J].计算机技术与发展, 2018, 28(6): 188-192.
	Zhang Degang, Wang Dada, Li Zehe, et al. High-level Architecture Multicast Extension Based on Computer Cooperative Work[J].Computer Technology and Development, 2018, 28(6): 188-192.
22	魏子衿, 肖丽. 基于区域分解的并行动态LOD构建算法[J]. 计算机工程与应用, 2018, 54(6): 168-177.
	Wei Zijin, Xiao Li. Parallel Dynamic Level-of-detail Construct Algorithm based on Domain Decomposition[J]. Computer Engineering and Applications, 2018, 54(6): 168-177.

[1]	Yanqiang Di, Ting Li, Shaochong Feng, Qiongyao Liu, Jianhong Lü, Zhijia Chen, Yang Zhang, Pengfei Cao. Parallel Simulation System of Equipment Precision Maintenance Based on Cloud-Edge-End Architecture [J]. Journal of System Simulation, 2022, 34(09): 1909-1919.
[2]	Minghao Li, Wenhao Bi, An Zhang, Wenxuan Sun. Unmanned Air Vehicles Launching Aircraft Combat System and Key Technologies for Penetrating Counterair [J]. Journal of System Simulation, 2022, 34(9): 1920-1932.
[3]	Yanfang Fu, Nan Zhang, Jianing Wei, Shaochun Qu, Ying Lu, Chang Liu. OPNET Based Simulation of Hybrid TDMA Protocol for Helicopters Datalink [J]. Journal of System Simulation, 2022, 34(9): 1933-1940.
[4]	Junren Luo, Wanpeng Zhang, Weilin Yuan, Zhenzhen Hu, Shaofei Chen, Jing Chen. Research on Opponent Modeling Framework for Multi-agent Game Confrontation [J]. Journal of System Simulation, 2022, 34(9): 1941-1955.
[5]	Li Zhang, Huizhen Zhang, Dong Liu, Yuxin Lu. Particle Swarm Algorithm for Solving Emergency Material Dispatch Considering Urgency [J]. Journal of System Simulation, 2022, 34(9): 1988-1998.
[6]	Qiming Qi, Ruigang Fu, Ping Wang, Min Wang, Hongqi Fan. Design of Optical Compound Eye Simulation Software for Small Aircraft Applications [J]. Journal of System Simulation, 2022, 34(9): 1999-2008.
[7]	Yiting Zhu, Yun Yan, Zhaocheng He. Mesoscopic Modeling and Simulation of Mixed Traffic Flow of Buses and Vehicles [J]. Journal of System Simulation, 2022, 34(9): 2019-2027.
[8]	Chengbing Li, Yunfei Li, Peng Wu. Study on Invulnerability of Urban Agglomeration Passenger Traffic Network Considering Time Characteristics [J]. Journal of System Simulation, 2022, 34(9): 2037-2045.
[9]	Junjie Sheng, Zhao Tang, Shaodi Dong, Shuyang Wu, Hao Liang. Architecture Design and Prototype Verification of Railway Vehicle Dynamics Cloud Platform [J]. Journal of System Simulation, 2022, 34(9): 2056-2064.
[10]	Lifeng Zhang, Yu Miao. A High Resolution Reconstruction Method of Temperature Distribution in Acoustic Tomography [J]. Journal of System Simulation, 2022, 34(9): 2065-2073.
[11]	Wen Zheng, Zhe Zhang, Jingyi Zhu. Simulation of the Market Exclusive Competition between Platforms [J]. Journal of System Simulation, 2022, 34(9): 2098-2106.
[12]	Weidong Jin, Shuli Zhang, Peng Tang, Man Zhang. Image Dehazing Network Based on Densely Connected Residual Block and Channel Pixel Attention [J]. Journal of System Simulation, 2022, 34(8): 1663-1673.
[13]	Huilin Zhang, Yujie Jin, Haima Yang. Sensorless Control of PMSM Based on an ANFIS Optimized Flux Sliding Mode Observer [J]. Journal of System Simulation, 2022, 34(8): 1682-1690.
[14]	Qimiao Xie, Shuaishuai Guo. Research on Passenger Ship Evacuation Simulation Based on Social Force Model [J]. Journal of System Simulation, 2022, 34(8): 1710-1724.
[15]	Wanjie Hu, Jianjun Dong, Rui Ren, Zhilong Chen. Layout Planning of Metro-based Underground Logistics System Network Considering Fuzzy Uncertainties [J]. Journal of System Simulation, 2022, 34(8): 1725-1740.