面向数字孪生电网设施虚实映射的边缘网络资源分配方法

胡平; 韩璟琳; 陈志永; 李想; 连浩然; 邢志坤

doi:10.19725/j.cnki.1007-2322.2023.0367

欢迎来到能源电力期刊网！

English Version 读者登录期刊登录

您当前的位置：

首页 >

文章列表页 >

面向数字孪生电网设施虚实映射的边缘网络资源分配方法

智能电网 | 更新时间：2025-10-22

- 面向数字孪生电网设施虚实映射的边缘网络资源分配方法
- An Edge Network Resource Allocation Method for Virtual-real Mapping for Digital Twin Power Grid Facilities
- 现代电力 2025年42卷第5期页码：878-890
- 作者机构：
  
  1. 国网河北省电力有限公司,河北省,石家庄市,050000
  2. 国网河北省电力有限公司经济技术研究院,河北省,石家庄市,050000
  3. 华北电力大学电气与电子工程学院, 北京市昌平区,102206
  4. 国网河北省电力有限公司雄安新区供电公司, 河北省雄安新区,071700
- 作者简介：
  
  [ "胡平(1968)，男，硕士，正高级工程师，通信作者，研究方向为电力系统及其自动化，E-mail：lx2429109351@163.com" ]
  [ "韩璟琳(1989)，男，硕士，工程师，研究方向为电力系统及其自动化，E-mail：jinglinoliver@163.com" ]
  [ "陈志永(1971)，男，学士，高级工程师，研究方向为电力系统及其自动化，E-mail：18207750059@163.com" ]
  [ "李想(1999)，女，博士研究生，研究方向为电力系统及其自动化，E-mail：2429109351@qq.com" ]
  [ "连浩然(1992)，男，硕士，工程师，研究方向为电力系统及其自动化，E-mail：1317359485@qq.com" ]
  [ "邢志坤(1983)，男，硕士，高级工程师，研究方向为电力系统及其自动化，E-mail：1913330860@qq.com" ]
- 基金信息：
  
  国家电网总部科技项目(面向清洁能源消纳的区域能源互联网建设关键技术研究及试点应用(5100-202113564A-0-5-SF))。
- DOI：10.19725/j.cnki.1007-2322.2023.0367
  中图分类号： 5
- 纸质出版：2025
- 稿件说明：
移动端阅览
胡平, 韩璟琳, 陈志永, 等. 面向数字孪生电网设施虚实映射的边缘网络资源分配方法[J]. 现代电力, 2025,42(5):878-890.

HU Ping, HAN Jinglin, CHEN Zhiyong, et al. An Edge Network Resource Allocation Method for Virtual-real Mapping for Digital Twin Power Grid Facilities[J]. 2025, 42(5): 878-890.
胡平, 韩璟琳, 陈志永, 等. 面向数字孪生电网设施虚实映射的边缘网络资源分配方法[J]. 现代电力, 2025,42(5):878-890. DOI： 10.19725/j.cnki.1007-2322.2023.0367.

HU Ping, HAN Jinglin, CHEN Zhiyong, et al. An Edge Network Resource Allocation Method for Virtual-real Mapping for Digital Twin Power Grid Facilities[J]. 2025, 42(5): 878-890. DOI： 10.19725/j.cnki.1007-2322.2023.0367.

摘要

数字孪生电网通过虚实映射实现物理电网的数字化，但目前电网设施的虚实映射过程面临静态模型重建精度低和动态响应实时性差等问题。传统人工建立静态模型效率低，且难度高，不适用于复杂电网的模型构建，传统云计算模式也无法满足模型重建和响应过程中大量随机传输业务的差异化需求。因此，形成了面向数字孪生电网设施虚实映射的边缘网络资源分配方法。首先，提出一种基于Shi-Tomasi和SURF融合算法的双阈值近邻比特征匹配方法，并进一步融合随机抽样一致性（random sample consensus，RANSAC）算法提高相机位姿估计准确度，且提高电网设施三维模型视觉效果以及重建准确度。在此基础上，考虑模型重建和动态响应过程中不同业务的传输需求，构建云-边-端数据交互架构，设计面向不同传输时延以及准确率需求的效用函数，并提出子信道及功率资源分配方法，实现通信资源的按需分配。所提算法与Shen算法和信道平均分配算法相比，在数据传输时效性方面分别提高9.03%和31.30%，同时能够保证数据传输的准确性，有效支撑电网设施准确高效的虚实映射过程。

Abstract

The digital twin power grid digitizes the physical power grid through virtual-real mapping. The current virtual real mapping process of power grid facilities

however

faces challenges in terms of limited accuracy in reconstructing static models and poor real-time dynamic response. The traditional manual establishment of static models is inefficient and challenging

making it unsuitable for construction of model in complex power grid. The traditional cloud computing model fails to meet the diverse demands of numerous randomly transmission services during model reconstruction and response processes. Therefore

in this paper we propose a new edge network resource allocation method for virtual-real mapping of digital twin power grid facilities. Firstly

a feature matching method of double threshold nearest neighbor ratio is proposed based on the fusion of Shi-Tomasi and SURF algorithms. Additionally

the random sample consensus (RANSAC) algorithm is further integrated to enhance the accuracy of camera pose estimation

thereby improving the accuracy of the three-dimensional reconstruction model of power grid facilities. The aforementioned foundation serves as the basis for designing utility functions for different transmission delay and accuracy requirements

while also presents a method for subchannel and power resource allocation so as to achieve on-demand allocation of communication resources. In addition

a cloud-edge-end data interaction architecture is built to support the above process. Compared with Shen algorithm and channel average allocation algorithm

our algorithm can improve data transmission timeliness by 9.03% and 31.30% respectively

while ensuring data transmission accuracy. Moreover

the algorithm effectively facilitates the accurate and efficient virtual-real mapping process of power grid facilities.

关键词

Keywords

references

SHENG Gehao, QIAN Yong, LUO Lingen, et al. Key technologies and development trends of digital power equipment for new type power system[J]. High Voltage Engineering, 2023, 49(05): 1765−1778(in Chinese).

盛戈皞, 钱勇, 罗林根, 等. 面向新型电力系统的数字化电力设备关键技术及其发展趋势[J]. 高电压技术, 2023, 49(05): 1765−1778.

BAI Hao, ZHOU Changcheng, YUAN Zhiyong, et al. Prospect and thinking of digital power grid based on digital twin[J]. Southern Power System Technology, 2020, 14(08): 18−24+40(in Chinese).

白浩, 周长城, 袁智勇, 等. 基于数字孪生的数字电网展望和思考[J]. 南方电网技术, 2020, 14(08): 18−24+40.

SHAO Tianrui, SHANG Tao, LI Xianxu. Security analysis of digital twin of power systems[J]. Electric Power Information and Communication Technology, 2022, 20(08): 60−65(in Chinese).

邵天睿, 尚涛, 李显旭. 数字孪生电网安全分析[J]. 电力信息与通信技术, 2022, 20(08): 60−65.

LI Peng, XI Wei, CAI Tiantian, et al. Concept, architecture and key technologies of digital power grids[J]. Proceedings of the CSEE, 2022, 42(14): 5002−5017(in Chinese).

李鹏, 习伟, 蔡田田, 等. 数字电网的理念、架构与关键技术[J]. 中国电机工程学报, 2022, 42(14): 5002−5017.

GUO Jia, XIE Jinghai, SUN Mi, et al. A lightweight construction method for substation digital twin[J]. Electrical Measurement Instrumentation, 2025, 62(4): 130−140(in Chinese).

郭嘉, 谢景海, 孙密, 等. 一种变电站数字孪生体的轻量化构建方法[J]. 电测与仪表, 2025, 62(4): 130−140.

WANG Xin, WANG Lin, YU Yun, et al. Survey on characteristics, architecture and applications of digital twin power grid[J]. Journal of Electronics Information Technology, 2022, 44(11): 3721−3733(in Chinese).

王鑫, 王霖, 余芸, 等. 数字孪生电网的特性、架构及应用综述[J]. 电子与信息学报, 2022, 44(11): 3721−3733.

WANG Shuo, XUAN Yingying, ZHANG Ji, et al. The real scene modeling technology application in the power grid digital twinning system[J]. Electric Power Survey Design, 2023(02): 78−82(in Chinese).

王朔, 轩莹莹, 张骥, 等. 实景建模技术在电网数字孪生系统中的应用分析[J]. 电力勘测设计, 2023(02): 78−82.

QIAO Yiya, XI Xiaohuan, NIE Sheng, et al. Power pylon reconstruction from airborne LiDAR data based on component segmentation and model matching[J]. Remote sensing, 2022, 14(19): 4905-4911.

GUO Jianlong, FENG Weixia, HAO Tengfei, et al. Denoising of a multi-station point cloud and 3D modeling accuracy for substation equipment based on statistical outlier removal[C]//2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). Wuhan, China: IEEE, 2020: 2793-2797.

MORARJEE B. Using multiple view geometry for transmission tower reconstruction[D]. Cape Town: University of Cape Town, 2016.

CHEN Yingda, ZENG Qunsheng, CHEN Bin, et al. Research on visual 3D reconstruction and semantic segmentation technology for power grid substation[J]. Power Systems and Big Data, 2022, 25(10): 45−52(in Chinese).

陈英达, 曾群生, 陈彬, 等. 面向电网变电站的视觉三维重建及语义分割技术研究[J]. 电力大数据, 2022, 25(10): 45−52.

YANG Zihan. A 3D real-scene modeling method for substation based on tilt photography[J]. Electronic Test, 2022, 36(09): 126−128(in Chinese).

杨梓瀚. 一种基于倾斜摄影的变电站三维实景建模方法[J]. 电子测试, 2022, 36(09): 126−128.

TAO Fei, LIU Weiran, ZHANG Meng, et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25(01): 1−18(in Chinese).

陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统, 2019, 25(01): 1−18.

ZHANG Qiuyu, JIANG Yunfeng, ZHANG Changwen, et al. Research on load distribution algorithm for minimum service delay in new power system[J]. Electrical Measurement Instrumentation, 2022, 59(01): 1−9(in Chinese).

张秋雨, 蒋云峰, 张常稳, 等. 面向新型电力系统的最小服务延迟的负载分配算法研究[J]. 电测与仪表, 2022, 59(01): 1−9.

SUN Yukun, ZHANG Xing, LEI Bo. Study on intelligent computing aware route allocation policy in edge computing-aware networks[J]. Radio Communications Technology, 2022, 48(01): 60−67(in Chinese).

孙钰坤, 张兴, 雷波. 边缘算力网络中智能算力感知路由分配策略研究[J]. 无线电通信技术, 2022, 48(01): 60−67.

WANG Qing, JING Zhen, LI Congcong, et al. A novel algorithm of physical layer resource allocation for broadband power line carrier communication[J]. Electrical Measurement Instrumentation, 2021, 58(07): 124−129(in Chinese).

王清, 荆臻, 李琮琮, 等. 宽带电力线载波通信物理层资源分配新算法[J]. 电测与仪表, 2021, 58(07): 124−129.

WANG Haizhou. Research on resource allocation algorithm oriented to differentiated service requirements of the UAVswarm[D]. Beijing: Beijing University of Posts and Telecommunications, 2021(in Chinese).

王海洲. 面向差异化业务需求的无人机群带宽资源分配方法研究[D]. 北京: 北京邮电大学, 2021.

LU Yangguang. Research on UAV task scheduling strategy for edge computing in 5G enhanced network[D]. Beijing: Beijing Information Science and Technology University, 2022(in Chinese).

卢阳光. 5G增强网络中边缘计算无人机任务调度策略研究[D]. 北京: 北京信息科技大学, 2022.

ZHANG Zewei, LI Taoshen, XU Junzhi. Multi-user-oriented MEC task hierarchical processing offloading mechanism[J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(05):72-79 (in Chinese).

张泽维, 李陶深, 许钧智. 面向多用户的移动边缘计算任务分级处理卸载机制[J]. 北京邮电大学学报, 2023, 46(05):72-79.

FU Xiong, FANG Lei, WANG Junchang. Edge server placement for energy consumption and load balancing[J]. Computer Science, 2023, 50(S1): 631−635(in Chinese).

付雄, 房磊, 王俊昌. 面向能耗优化和负载均衡的边缘服务器放置研究[J]. 计算机科学, 2023, 50(S1): 631−635.

LIU Guicai, LAN Julong. Hybrid CCN caching scheme based on service classification and node partition[J]. Application Research of Computers, 2019, 36(04): 1181−1185(in Chinese).

刘贵财, 兰巨龙. CCN中基于业务分类和节点分区的混合缓存机制[J]. 计算机应用研究, 2019, 36(04): 1181−1185.

YU Hao, WANG Xiaowei, WANG Tao, et al. Differential resource allocation scheme for electric 5G network slices based on SDN and NFV[J]. Electrical Measurement Instrumentation, 2021, 58(09): 89−95(in Chinese).

于浩, 汪筱巍, 王韬, 等. 基于SDN与NFV的电力5G网络切片差异化资源分配方案[J]. 电测与仪表, 2021, 58(09): 89−95.

CHEN Mingzhe, YANG Zhaohui, SAAD W, et al, A joint learning and communications framework for federated learning over wireless networks[J] IEEE Transactions on Wireless Communications, 2021, 20(1): 269-283.

HAO Haojie, LI Zhaokui, FANG Jun, et al. Improved AKAZE feature extraction global SFM 3D reconstruction algorithm[J]. Journal of Geomatics Science and Technology, 2021, 38(05): 492−498(in Chinese).

郝豪杰, 李朝奎, 方军, 等. 改进的AKAZE特征提取的全局式SFM三维重建算法[J]. 测绘科学技术学报, 2021, 38(05): 492−498.

ZHANG Qingpeng, CAO Yu. Research on three dimensional reconstruction algorithm of weak textured objects in indoor scenes[J]. Laser Optoelectronics Progress, 2021, 58(08): 197−203(in Chinese).

张庆鹏, 曹宇. 室内场景下弱纹理物体三维重建算法的研究[J]. 激光与光电子学进展, 2021, 58(08): 197−203.

SHEN Zukang, ANDREWS J G, EVANS B L. Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints[J]. IEEE Press, 2005, 4(06): 2726−2737.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于运动恢复结构原理的电网设施三维静态重建方法