Application of edge-cloud collaborative intelligence technologies in power grids

Qing HAN; Kunlun GAO; Ting ZHAO; Jiangqi CHEN; Xinyu YANG; Shusen YANG

doi:10.11959/j.issn.2096-3750.2021.00204

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Application of edge-cloud collaborative intelligence technologies in power grids

Topic: Edge Intelligence and Fog Computing in IoT | 更新时间：2024-06-05

- Application of edge-cloud collaborative intelligence technologies in power grids
- Chinese Journal on Internet of Things Vol. 5, Issue 1, Pages: 62-71(2021)
- 作者机构：
  
  1. 西安交通大学，陕西西安 710049
  2. 全球能源互联网研究院有限公司，北京 102209
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2021.00204
  CLC： TM862
- Published：30 March 2021，
  
  Published Online：2021-03，
- 稿件说明：
移动端阅览
QING HAN, KUNLUN GAO, TING ZHAO, et al. Application of edge-cloud collaborative intelligence technologies in power grids. [J]. Chinese journal on internet of things, 2021, 5(1): 62-71.
DOI：

QING HAN, KUNLUN GAO, TING ZHAO, et al. Application of edge-cloud collaborative intelligence technologies in power grids. [J]. Chinese journal on internet of things, 2021, 5(1): 62-71. DOI： 10.11959/j.issn.2096-3750.2021.00204.

摘要

随着电力物联网规模的不断扩大和部署在电力系统各环节的设备数量的快速增加，海量边缘设备所产生的数据呈指数级爆炸增长。海量边缘数据的高效、快速和安全处理与分析给传统的云计算智能技术带来极大挑战，而边云协同智能技术因节省带宽、减少时延、保护数据隐私等优点具有深度助力电力领域发展的巨大潜力。首先，对边云协同智能的概念和研究现状进行了介绍，阐述了边云协同智能的特征和优势，并对其赋能电力领域进行了适用性探讨。然后，结合电力系统的建设需求，讨论了面向电力场景的边云协同智能关键技术，接着针对电力领域的两个典型场景，分别给出了基于边云协同智能技术的解决方案，并搭建仿真实验进行效果验证。最后，对全文进行了总结并对下一步的研究方向进行了简要的展望。

Abstract

With the continuous development of the Internet of things on electricity (IoTE) and large-scale deployment of intelligent edge devices

an explosively increasing amount of data are being generated at the network edge.The efficient

fast and secure processing and analysis of the massive edge located data brings great challenges for the traditional cloud computing-based intelligence technologies.Instead

edge-cloud collaborative intelligence (ECCI) technologies can significantly outperform the cloud computing-based intelligence in terms of the network bandwidth saving

delay reduction and privacy protection

and therefore have shown a great potential in boosting the development of power grids.To investigate the application of ECCI in power grids

the concept and research progress of ECCI were firstly introduced.The characteristics and advantages of ECCI were summarized and its applicability in the power grids were discussed.Secondly

the key technologies of ECCI applications for power grids were discussed and the solutions based on ECCI technologies for two typical scenes were proposed respectively.Finally

a brief discussion of future work was given.

关键词

智能电网电力物联网人工智能边缘计算边云协同智能

Keywords

smart gridInternet of things on electricityartificial intelligenceedge computingedge-cloud collaborative intelligence

references

张聪, 樊小毅, 刘晓腾 ,等. 边缘计算使能智慧电网[J]. 大数据, 2019,5(2): 64-78.

ZHANG C, FAN X Y, LIU X T ,et al. Edge computing enabled smart grid[J]. Big Data Research, 2019,5(2): 64-78.

中国电机工程学会电力信息化专业委员会. 中国电力大数据发展白皮书[M]. 北京: 中国电力出版社, 2013: 10-15.

Informatization Committee of the CSEE. White paper of electric power big data of China[M]. Beijing: China Electric Power Press, 2013: 10-15.

王继业, 郭经红, 曹军威 ,等. 能源互联网信息通信关键技术综述[J]. 智能电网, 2015,3(6): 473-485.

WANG J Y, GUO J H, CAO J W ,et al. Review on information and communication key technologies of energy Internet[J]. Smart Grid, 2015,3(6): 473-485.

国家电网有限公司. 泛在电力物联网建设大纲[EB]. 2019.

State Grid Corporation of China. Construction outline of ubiquitous power Internet of things[EB]. 2019.

张在琛 . 泛在电力物联网关键支撑技术[J]. 电力工程技术, 2019,38(6): 1.

ZHANG Z C . Key supporting technologies for ubiquitous electricity Internet of things[J]. Electric Power Engineering Technology, 2019,38(6): 1.

刘俊勇, 潘力, 何迈 . 能源物联网及其关键技术[J]. 物联网学报, 2020,4(4): 9-16.

LIU J Y, PAN L, HE M . Internet of energy things and its key technologies[J]. Chinese Journal on Internet of Things, 2020,4(4): 9-16.

龚钢军, 罗安琴, 陈志敏 ,等. 基于边缘计算的主动配电网信息物理系统[J]. 电网技术, 2018,42(10): 3128-3135.

GONG G J, LUO A Q, CHEN Z M ,et al. Cyber physical system of active distribution network based on edge computing[J]. Power System Technology, 2018,42(10): 3128-3135.

刘思放, 邓春宇, 张国宾 ,等. 面向居民智能用电的边缘计算协同架构研究[J]. 电力建设, 2018,39(11): 60-68.

LIU S F, DENG C Y, ZHANG G B ,et al. Research on collaborative architecture for edge computing of residential intelligent usage of electricity[J]. Electric Power Construction, 2018,39(11): 60-68.

吴大鹏, 张普宁, 王汝言 . “端—边—云”协同的智慧物联网[J]. 物联网学报, 2018,2(3): 21-28.

WU D P, ZHANG P N, WANG R Y . Smart Internet of things aided by“terminal-edge-cloud” cooperation[J]. Chinese Journal on Internet of Things, 2018,2(3): 21-28.

徐恩庆, 董恩然 . 探析云边协同的九大应用场景[J]. 通信世界, 2019(21): 42-43.

XU E Q, DONG E R . Analysis of nine application scenarios of cloud-edge collaboration[J]. Communications World, 2019(21): 42-43.

徐恩庆, 董恩然 . 云计算与边缘计算协同发展的探索与实践[J]. 通信世界, 2019(9): 46-47.

XU E Q, DONG E R . Exploration and practice of coordinated development of cloud computing and edge computing[J]. Communications World, 2019(9): 46-47.

张星洲, 鲁思迪, 施巍松 . 边缘智能中的协同计算技术研究[J]. 人工智能, 2019(5): 55-67.

ZHANG X Z, LU S D, SHI W S . Research on collaborative computing technology in edge intelligence[J]. AI-View, 2019(5): 55-67.

施巍松, 张星洲, 王一帆 ,等. 边缘计算:现状与展望[J]. 计算机研究与发展, 2019,56(1): 69-89.

SHI W S, ZHANG X Z, WANG Y F ,et al. Edge computing:state-of-the-art and future directions[J]. Journal of Computer Research and Development, 2019,56(1): 69-89.

LECUN Y, BENGIO Y, HINTON G . Deep learning[J]. Nature, 2015,521(7553): 436-444.

IEC. Edge intelligence (white paper)[EB]. 2018.

ZHOU Z, CHEN X, LI E ,et al. Edge intelligence:paving the last mile of artificial intelligence with edge computing[J]. Proceedings of the IEEE, 2019,107(8): 1738-1762.

ZHANG X Z, WANG Y F, LU S D ,et al. OpenEI:an open framework for edge intelligence[C]// Proceedings of 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). Piscataway:IEEE Press, 2019: 1840-1851.

DENG S G, ZHAO H L, FANG W J ,et al. Edge intelligence:the confluence of edge computing and artificial intelligence[J]. IEEE Internet of Things Journal, 2020,7(8): 7457-7469.

STOICA I, SONG D, POPA R A ,et al. A Berkeley view of systems challenges for AI[J]. arXiv:1712.05855, 2017.

Microsoft Azure. Azure IoT edge[EB]. 2019.

Cloud IoT edge:deliver Google AI capabilities at the edge[EB]. 2019.

KONECNY J, MCMAHAN H B, YU F X ,et al. Federated learning:strategies for improving communication efficiency[J]. arXiv:1610.05492, 2016

Amazon Web Services. AWS IoT greengrass[EB]. 2019.

PANETTA K. 5 trends emerge in the gartner hype cycle for emerging technologies,2018[EB]. 2018.

工业互联网产业联盟. 工业互联网平台白皮书[EB]. 2018.

Alliance of Industrial Internet. White paper of industrial Internet platform[EB]. 2018.

华为云. 智能边缘平台[EB]. 2019.

Huawei Cloud. Intelligent EdgeFabric[EB]. 2019.

XIONG Y, SUN Y, XING L ,et al. Extend cloud to edge with KubeEdge[C]// Proceedings of2018 IEEE/ACM Symposium on Edge Computing (SEC). Piscataway:IEEE Press, 2018: 373-377.

边缘计算产业联盟,工业互联网产业联盟边缘计算与云计算协同白皮书(2018年)[R]. 2018.

Edge Computing Consortium,Alliance of Industrial Internet White paper of edge computing and cloud computing (2018)[R]. 2018.

YI S H, HAO Z J, ZHANG Q Y ,et al. LAVEA:latency-aware video analytics on edge computing platform[C]// Proceedings of the Second ACM/IEEE Symposium on Edge Computing. New York:ACM Press, 2017: 1-13.

LI Y, GAO W . MUVR:supporting multi-user mobile virtual reality with resource constrained edge cloud[C]// Proceedings of 2018 IEEE/ACM Symposium on Edge Computing (SEC). Piscataway:IEEE Press, 2018: 1-16.

HA K, CHEN Z, HU W L ,et al. Towards wearable cognitive assistance[C]// Proceedings of2014 Annual International Conference on Mobile Systems,Applications,and Services. New York:ACM Press, 2014: 68-81.

CAO J, XU L Y, ABDALLAH R ,et al. EdgeOS_H:a home operating system for Internet of everything[C]// Proceedings of2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). Piscataway:IEEE Press, 2017: 1756-1764.

华先胜, 黄建强, 沈旭 ,等. 城市大脑:云边协同城市视觉计算[J]. 人工智能, 2019(5): 77-91.

HUA X S, HUANG J Q, SHEN X ,et al. Urban brain:cloud-edge based collaborative urban visual computing[J]. AI-View, 2019(5): 77-91.

FU J S, LIU Y, CHAO H C ,et al. Secure data storage and searching for industrial IoT by integrating fog computing and cloud computing[J]. IEEE Transactions on Industrial Informatics, 2018,14(10): 4519-4528.

ZWOLENSKI M, WEATHERILL L . The digital universe:rich data and the increasing value of the Internet of things[J]. Australian Journal of Telecommunications and the Digital Economy, 2014,2(3): 47.

ANANTHANARAYANAN G, BAHL P, BODÍK P ,et al. Real-time video analytics:the killer app for edge computing[J]. Computer, 2017,50(10): 58-67.

HAN S, MAO H Z, DALLY W . Deep compression:compressing deep neural network with pruning,trained quantization and Huffman coding[C]// Proceedings of2016 International Conference on Learning Representations(ICLR).[S.l.:s.n.], 2016: 2-4.

HE Y H, ZHANG X Y, SUN J . Channel pruning for accelerating very deep neural networks[C]// Proceedings ofIEEE International Conference on Computer Vision(ICCV). Piscataway:IEEE Press, 2017: 1389-1397.

TEERAPITTAYANON S, MCDANEL B, KUNG H T . Distributed deep neural networks over the cloud,the edge and end devices[C]// Proceedings ofIEEE 37th International Conference on Distributed Computing Systems (ICDCS). Piscataway:IEEE Press, 2017: 328-339.

LI E, ZHOU Z, CHEN X . Edge intelligence:on-demand deep learning model co-inference with device-edge synergy[C]// Proceedings of ACM SIGCOMM 2018 Workshop on Mobile Edge Communications. New York:ACM Press, 2018: 31-36.

张佳乐, 赵彦超, 陈兵 ,等. 边缘计算数据安全与隐私保护研究综述[J]. 通信学报, 2018,39(3): 1-21.

ZHANG J L, ZHAO Y C, CHEN B ,et al. Survey on data security and privacy-preserving for the research of edge computing[J]. Journal on Communications, 2018,39(3): 1-21.

王丰, 文红, 陈松林 ,等. 边缘计算下移动智能终端隐私数据的保护方法[J]. 网络空间安全, 2018,9(2): 47-50.

WANG F, WEN H, CHEN S L ,et al. Privacy data protection method for mobile intelligent terminal based on edge computing[J]. Cyberspace Security, 2018,9(2): 47-50.

SMITH V, CHIANG C K, SANJABI M ,et al. Federated multi-task learning[C]// Proceedings of Advances in Neural Information Processing Systems (NIPS).[S.l.:s.n.], 2017: 4424-4434.

LIU Y, CHEN T, YANG Q . Secure federated transfer learning[J]. arXiv:1812.03337, 2018

WANG S B, YANG S S, ZHAO C . SurveilEdge:real-time video query based on collaborative cloud-edge deep learning[C]// Proceedings of IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2020: 2519-2528.

HAN Q, YANG S S, REN X B ,et al. OL4EL:online learning for edge-cloud collaborative learning on heterogeneous edges with resource constraints[J]. IEEE Communications Magazine, 2020,58(5): 49-55.

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