[1]刘 昊,张景超,毛万登,等.智慧换流站云边协同数据交互方法[J].郑州大学学报(工学版),2022,43(05):104-110.[doi:10.13705/j.issn.1671-6833.2022.05.017]
　LIU Hao,ZHANG Jingchao,MAO Wandeng,et al.Cloud Edge Collaboration Data Interaction Method of Intelligent Converter Station[J].Journal of Zhengzhou University (Engineering Science),2022,43(05):104-110.[doi:10.13705/j.issn.1671-6833.2022.05.017]
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智慧换流站云边协同数据交互方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
43
期数:
2022年05期
页码:
104-110
栏目:
出版日期:
2022-08-22

文章信息/Info

Title:
Cloud Edge Collaboration Data Interaction Method of Intelligent Converter Station
作者:
刘 昊1 ;  张景超2 ;  毛万登 1;  马士棋3 ;  姜 欣3 ;  金 阳3
1.国网河南省电力公司电力科学研究院;2.国网河南省电力公司;3.郑州大学电气工程学院;

Author(s):
LIU Hao1;  ZHANG Jingchao2;  MAO Wandeng1;  MA Shiqi3;  JIANG Xin3;  JIN Yang3
1.Electric Power Research Institute of State Grid Henan Electric Power Co., Ltd., Zhengzhou 450052, China; 
2.State Grid Henan Electric Power Co., Ltd., Zhengzhou 450052, China; 
3.School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
关键词:
Keywords:
power Internet of Things;  intelligent converter station;  edge Internet of Things agent;  cloud-edge collaboration;  data interaction
分类号:
TN919. 1
DOI:
10.13705/j.issn.1671-6833.2022.05.017
文献标志码:
A
摘要:
智慧换流站作为泛在电力物联网建设中的关键一环,在建设过程中面临数据共享不充分、未能充分发掘数据价值等问题,为此,面向智慧换流站建设提出了一种基于云边协同的数据交互方法。 首先,以边缘计算与容器化技术为基础,以时延最短为目标,结合基于权值算法的本地通用服务器计算资源分配策略,提出一种边缘物联代理模型。 在此基础上,以时延最短为目标,提出基于任务规模大小的边缘计算任务卸载至云端的任务卸载策略,确定云边协同策略。 最后以河南省内某换流站监测系统为例,油色谱数据经站内边缘物联代理预处理后,上传至远程云平台,通过本地数据预处理与远程数据分析与挖掘,最终开发出掌上油色 谱微应用,数据传输的完整率与准确率均为 100%,数据传输时延控制在 150 ms左右,并且上下浮动在 15 ms 以内,达到实际工程应用的要求,验证了所提方案的准确性,使得专业人员能够及时准确地把握站内变压器运行情况,提高了换流站运行的稳定性与安全性,提升了数据价值。
Abstract:
As a key in the construction of power Internet of Things, intelligent converter station. faces the problem of insufficient data sharing and fails to fully explore the value of data. Therefore, based on cloud-edge collaboration, this study proposed a data interaction method for the construction of intelligent converter station. Firstly, based on edge computing and container, an edge Iot agent model was proposed, which aimed at the shortest time delay. It used the local universal server computing resource allocation strategy based on weight algorithm. On this basis, the cloud-side collaboration strategy was determined to minimize the time delay. It used the unloading strategy of edge computing tasks to cloud based on task size. Finally, taking the converter station monitoring system in Henan province as an example, the oil chromatographic instrument APP is eventually developed through the local data pretreatment and remote data analysis and mining. The data is firstly pretreated by the station edge Iot agent and then uploaded to the remote cloud platform. The integrity of the data transmission rate and accuracy were 100%. The data transmission delay was controlled in 150 ms with the float up and down in 15 ms. The performance meets the requirements of practical engineering application and verifies the accuracy of the proposed scheme. The method proposed in this paper enables professionals to timely and accurately grasp the transformer operation situation in the station, also improves the stability and safety of the converter station operation, and enhances the data value.

参考文献/References:

[1] YAN Z, ZENG L, XI C, et al. Study of architecture of power internet of things[C]∥IET International Conference on Communication Technology and Application. London: IET, 2012:718-722. 

[2] MOON J, CHO S, KUM S, et al. Cloud-edge collaboration framework for IoT data analytics[ C]∥2018 International Conference on Information and Communication Technology Convergence ( ICTC ) . Piscataway: IEEE, 2018:162-171.
[3] PALLIS G, VAKALI A. Insight and perspectives for content delivery networks[ J] . Communications of the acm, 2006, 49(1) : 101-106.

更新日期/Last Update: 2022-08-23