云边智能: 电力系统运行控制的边缘计算方法及其应用现状与展望

白昱阳; 黄彦浩; 陈思远; 张俊; 李柏青; 王飞跃

doi:10.16383/j.aas.2020.y000001

云边智能: 电力系统运行控制的边缘计算方法及其应用现状与展望

doi: 10.16383/j.aas.2020.y000001

白昱阳^1,,
黄彦浩^2,,
陈思远^1,,
张俊^{1, 3,},
李柏青^2,,
王飞跃^3,

1.
武汉大学电气与自动化学院武汉 430072
2.
中国电力科学研究院有限公司北京 100192
3.
中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190

基金项目: 国家电网公司科技项目(XT71-19-032)资助

详细信息

作者简介:
白昱阳：武汉大学电气与自动化学院硕士研究生. 2019 年获得武汉大学电气工程学院学士学位. 主要研究方向为边缘计算在电力系统运行和控制中的应用. E-mail: baiyuyang@whu.edu.cn

黄彦浩：中国电力科学研究院有限公司博士, 高级工程师. 主要研究方向为电力系统仿真分析. E-mail: hyhao@epri.sgcc.com.cn

陈思远：武汉大学电气与自动化学院博士研究生. 2018 年获得武汉大学电气工程学院硕士学位. 主要研究方向为边缘计算在电力系统运行和控制中的应用. E-mail: wddqcsy@whu.edu.cn

张俊：武汉大学电气与自动化学院教授. 2003 年和 2005 年分别获得华中科技大学电子信息与通信工程系学士与硕士学位. 2008 年获得亚利桑那州立大学电气工程博士学位. 主要研究方向为智能系统, 人工智能, 知识自动化, 及其在智能电力和能源系统中的应用. 本文通信作者. E-mail: jun.zhang@qaii.ac.cn

李柏青：中国电力科学研究院有限公司教授级高级工程师, 主要研究方向为电力系统分析与运行控制技术.E-mail: libq@epri.sgcc.com.cn

王飞跃：中国科学院自动化研究所复杂系统管理与控制国家重点实验室主任, 国防科技大学军事计算实验与平行系统技术研究中心主任, 中国科学院大学中国经济与社会安全研究中心主任, 青岛智能产业技术研究院院长. 主要研究方向为平行系统的方法与应用, 社会计算, 平行智能以及知识自动化. E-mail: feiyue.wang@ia.ac.cn

计量
- 文章访问数: 3625
- HTML全文浏览量: 1736
- PDF下载量: 1086
- 被引次数: 0
出版历程
- 收稿日期: 2019-11-08
- 录用日期: 2019-12-01
- 网络出版日期: 2020-03-30
- 刊出日期: 2020-03-30

Cloud-edge Intelligence: Status Quo and Future Prospective of Edge Computing Approaches and Applications in Power System Operation and Control

1.
School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072
2.
China Electric Power Research Institute, Beijing 100192
3.
State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190

Funds: Supported by Science and Technology Project of State Grid Corporation of China (XT71-19-032)

摘要

摘要: 本文分析了当前我国电力系统的运行与控制面临的挑战, 对边缘计算的发展背景和关键技术进行了介绍, 阐述了云边协同和边边协同的功能与特征, 并对边缘协同技术下的边缘智能技术进行了探讨. 结合电力系统的层级式构架, 讨论了在电网部署边缘计算层的方法, 提出利用云边协同、边边协同、边缘智能等技术解决电力系统面临的实时性高、数据周期短、任务复杂等难题, 在减轻边缘节点与云中心通信压力的同时, 提高业务服务质量, 保障边缘节点的数据隐私. 通过对边缘计算在“源 − 网 − 荷”各环节的应用前景进行分析与讨论, 阐述了边缘计算在电网中的可行性与实用性. 最后, 对边缘计算的应用范式与方案进行了总结, 并对其在未来电力系统中的发展方向进行了展望.
- 边缘计算 /
- 云计算 /
- 云边协同技术 /
- 边缘智能 /
- 电力系统运行控制
Abstract: In this paper, the current challenges faced by China′ s power grid are analyzed, and the corresponding developmental background and key techniques of edge computing are introduced, including the functionalities and features of cloud-edge coordination and edge-edge coordination. Then, edge intelligence resulted from edge coordination is discussed. Considering the hierarchical architecture of power grids, the deployment of edge computing layer for power grid operation and control is illustrated in details. Through reducing communication data volume among edge nodes and the cloud center, the edge computing architecture and corresponding coordination mechanism aims to improve real-time performance of complex grid tasks, bring distributed intelligence to the system, while protecting data privacy of the edge nodes. Finally, the application paradigms of edge computing are summarized, and its future developmental directions in the power system operation and control are prospected.
- Edge computing /
- cloud computing /
- cloud-edge coordination /
- edge intelligence /
- power system operation and control

HTML全文

图 1 边缘智能架构

Fig. 1 The architecture of edge intelligence

下载: 全尺寸图片幻灯片

图 2 云边协同和边边联邦协同的联合训练框架

Fig. 2 Joint training framework for cloud-edge collaboration and edge-edge federation collaboration

下载: 全尺寸图片幻灯片

图 3 边缘节点在电力系统中的部署示意图

Fig. 3 Schematic diagram of edge node deployment in power system

下载: 全尺寸图片幻灯片

图 4 边缘计算平台在电力系统中的应用框架

Fig. 4 Application framework of edge computing platform in power system

下载: 全尺寸图片幻灯片

图 5 变电站综自系统结构图

Fig. 5 Structure of integrated automation system of substation

下载: 全尺寸图片幻灯片

图 6 边缘计算在变电站中的数据交互示意图

Fig. 6 Data interaction diagram of edge computing in substation

下载: 全尺寸图片幻灯片

图 7 负荷建模边缘计算构架图

Fig. 7 The architecture diagram of edge computing for load modelling

下载: 全尺寸图片幻灯片

参考文献(107)

[1]	张杨. 云环境下的关联规则增量更新算法及其应用[硕士学位论文], 北京化工大学, 中国, 2016 Zhang Yang. Association Rule Incremental Updating Algorithm and Its Application in Cloud [Master thesis], Beijing University of Chemical Technology, China, 2016
[2]	郑亚军. 基于MapReduce的关联规则增量更新算法研究及应用[硕士学位论文], 合肥工业大学, 中国, 2014 Zheng Ya-Jun. Association Rules Incremental Updating Research and Application Based on MapReduce [Master thesis], Hefei University of Technology, China, 2014
[3]	3 Mao Y Y, You C S, Zhang J, Huang K B, Letaief K B. A survey on mobile edge computing: the communication perspective. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2322−2358
[4]	4 Wang Y, Tao X F, Zhang X F, Zhang P, Hou Y T. Cooperative task offloading in three-tier mobile computing networks: an ADMM framework. IEEE Transactions on Vehicular Technology, 2019, 68(3): 2763−2776 doi: 10.1109/TVT.2019.2892176
[5]	岳连忠. 智能技术在电力系统自动化中的应用. 价值工程, 2019, 38(34): 222−223 5 Yue Lian-Zhong. Application of intelligent technology in power system automation. Value Engineering, 2019, 38(34): 222−223
[6]	饶宇飞, 崔惟, 刘巍, 方舟, 孟高军. 一种基于主动解列算法的电力系统稳定控制策略. 电气自动化, 2019, 41(5): 73−76 doi: 10.3969/j.issn.1000－3886.2019.05.022 6 Rao Yu-Fei, Cui Wei, Liu Wei, Fang Zhou, Meng Gao-Jun. A stability control strategy for power systems based on active splitting algorithm. Electrical Automation, 2019, 41(5): 73−76 doi: 10.3969/j.issn.1000－3886.2019.05.022
[7]	谢季平, 张文, 杨浩. 考虑直流调制的交直流系统中长期电压稳定协调控制. 电力系统自动化, 2019, 43(22): 76−83 doi: 10.7500/AEPS20181205001 7 Xie Ji-Ping, Zhang Wen, Yang Hao. Coordinated control for middle-long-term voltage stability of AC/DC power system considering dc modulation. Automation of Electric Power Systems, 2019, 43(22): 76−83 doi: 10.7500/AEPS20181205001
[8]	易维. 考虑励磁限幅和发电机调速器的电力系统暂态稳定控制[硕士学位论文], 山东大学, 中国, 2019 Yi Wei. Transient Stability Control of Power System Considering Excitation Saturation and Turbine Governor [Master thesis], Shandong University, China, 2019
[9]	刘俊磊, 钱峰, 伍双喜, 杨银国, 孙百哲. 基于支持向量回归的直流受端电网动态无功需求在线评估. 电力系统保护与控制, 2019, 47(13): 37−45 doi: 10.7667/PSPC20191306 9 Liu Jun-Lei, Qian Feng, Wu Shuang-Xi, Yang Yin-Guo, Sun Bai-Zhe. Online evaluation of dynamic reactive power demand of DC receiving power grid based on SVR. Power System Protection and Control, 2019, 47(13): 37−45 doi: 10.7667/PSPC20191306
[10]	颜湘武, 徐韵. 考虑网络动态重构含多异质可再生分布式电源参与调控的配电网多时空尺度无功优化. 电工技术学报, 2019, 34(20): 4358−4372 10 Yan Xiang-Wu, Xu Yun. Multiple time and space scale reactive power optimization for distribution network with multi-heterogeneous RDG participating in regulation and considering network dynamic reconfiguration. Transactions of China Electrotechnical Society, 2019, 34(20): 4358−4372
[11]	颜湘武, 徐韵, 李若瑾, 金永盛, 李铁. 基于模型预测控制含可再生分布式电源参与调控的配电网多时间尺度无功动态优化. 电工技术学报, 2019, 34(10): 2022−2037 11 Yan Xiang-Wu, Xu Yun, Li Ruo-Jin, Jin Yong-Sheng, Li Tie. Multi-time scale reactive power optimization of distribution grid based on model predictive control and including RDG regulation. Transactions of China Electrotechnical Society, 2019, 34(10): 2022−2037
[12]	刘英明. 基于物联网技术的实训室智能管理系统设计. 电子技术与软件工程, 2019, (20): 44−45 12 Liu Ying-Ming. Design of intelligent management system for training room based on internet of things technology. Electronic Technology & Software Engineering, 2019, (20): 44−45
[13]	余洋, 朱少敏, 卞超轶. 基于知识图谱的泛在电力物联网安全可视化技术. 电信科学, 2019, 35(11): 132−139 13 Yu Yang, Zhu Shao-Min, Bian Chao-Yi. Knowledge graph based ubiquitous power IoT security visualization technology. Telecommunications Science, 2019, 35(11): 132−139
[14]	张在琛. 泛在电力物联网关键支撑技术. 电力工程技术, 2019, 38(6): 1 14 Zhang Zai-Chen. Key supporting technologies for ubiquitous electricity internet of things. Electric Power Engineering Technology, 2019, 38(6): 1
[15]	曾鸣, 王雨晴, 闫彤, 兰梦心, 董厚琦, 张晓春, 等. 基于泛在电力物联网平台的可再生能源政策评估系统设计与模型研究. 电网技术, 2019, 43(12): 4263−4273 15 Zeng Ming, Wang Yu-Qing, Yan Tong, Lan Meng-Xin, Dong Hou-Qi, Zhang Xiao-Chun, et al. System design and model research of renewable energy policy evaluation system based on platform of ubiquitous power internet of things. Power System Technology, 2019, 43(12): 4263−4273
[16]	王毅, 陈启鑫, 张宁, 冯成, 滕飞, 孙铭阳, 等. 5G通信与泛在电力物联网的融合: 应用分析与研究展望. 电网技术, 2019, 43(5): 1575−1585 16 Wang Yi, Chen Qi-Xin, Zhang Ning, Feng Cheng, Teng Fei, Sun Ming-Yang, et al. Fusion of the 5G communication and the ubiquitous electric internet of things: application analysis and research prospects. Power System Technology, 2019, 43(5): 1575−1585
[17]	江秀臣, 刘亚东, 傅晓飞, 徐鹏, 王劭菁, 盛戈皞. 输配电设备泛在电力物联网建设思路与发展趋势. 高电压技术, 2019, 45(5): 1345−1351 17 Jiang Xiu-Chen, Liu Ya-Dong, Fu Xiao-Fei, Xu Peng, Wang Shao-Qing, Sheng Ge-Hao. Construction ideas and development trends of transmission and distribution equipment of the ubiquitous power internet of things. High Voltage Engineering, 2019, 45(5): 1345−1351
[18]	张晓华, 刘道伟, 李柏青, 冯长有. 智能全景系统概念及其在现代电网中的应用体系. 中国电机工程学报, 2019, 39(10): 2885−2894 18 Zhang Xiao-Hua, Liu Dao-Wei, Li Bai-Qing, Feng Chang-You. The concept of intelligent panoramic system and its application system in modern power grid. Proceedings of the CSEE, 2019, 39(10): 2885−2894
[19]	19 Chen L, Deng Z, Xu X. Two-Stage Dynamic Reactive Power Dispatch Strategy in Distribution Network Considering the Reactive Power Regulation of Distributed Generations. IEEE Transactions on Power Systems, 2018, 34(2): 1021−1032
[20]	刘宇博. 基于云计算的人工智能训练平台的研究与设计[硕士学位论文], 北京邮电大学, 中国, 2019 Liu Yu-Bo. Research and Design of Artificial Intelligence Training Platform Based on Cloud Computing [Master thesis]. Beijing University of Posts and Telecommunications, China, 2019
[21]	21 Wang S, Zhang X, Zhang Y, Wang L, Yang J W, Wang W B. A survey on mobile edge networks: Convergence of computing, caching and communications. IEEE Access, 2017, 5: 6757−6779 doi: 10.1109/ACCESS.2017.2685434
[22]	22 Nikoobakht A, Mardaneh M, Aghaei J, et al. Flexible power system operation accommodating uncertain wind power generation using transmission topology control: an improved linearised AC SCUC model. IET Generation, Transmission & Distribution, 2017, 11(1): 142−153
[23]	张星洲, 鲁思迪, 施巍松. 边缘智能中的协同计算技术研究. 人工智能, 2019, (5): 55−67 23 Zhang Xing-Zhou, Lu Si-Di, Shi Wei-Song. Research on collaborative computing technology in edge intelligence. AI-View, 2019, (5): 55−67
[24]	Wang F Y. Shadow Systems: a New Concept for Nested and Embedded Cosimulation for Intelligent Systems. Tucson, Arizona, USA, 1994
[25]	25 Wang F Y, Kim H M. Implementing adaptive fuzzy logic controllers with neural networks: a design paradigm. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 1995, 3(2): 165−180
[26]	王飞跃, 黄小池. 基于网络的智能家居系统现状和发展趋势. 家用电器科技, 2001, (6): 56−61 26 Wang Fei-Yue, Huang Xiao-Chi. The status quo and future prospective of internet-based intelligent residential systems. Science and Technology of Household Electric Appliance, 2001, (6): 56−61
[27]	王飞跃, 王成红. 基于网络控制的若干基本问题的思考和分析. 自动化学报, 2002, 28(S1): 171−176 27 Wang Fei-Yue, Wang Cheng-Hong. On some basic issues in network-based direct control systems. Acta Automatica Sinica, 2002, 28(S1): 171−176
[28]	王飞跃. 连通环境下联网系统的智能控制与管理问题. 模式识别与人工智能, 2004, 17(1): 1−6 doi: 10.3969/j.issn.1003-6059.2004.01.001 28 Wang Fei-Yue. Intelligent control and management for networked systems in a connected world. PR & AI, 2004, 17(1): 1−6 doi: 10.3969/j.issn.1003-6059.2004.01.001
[29]	29 Wang F Y. Agent-based control for networked traffic management systems. IEEE Intelligent System, 2005, 20(5): 92−96 doi: 10.1109/MIS.2005.80
[30]	Wang F Y, Liu D R. Networked Control Systems: Theory and Applications. London: Springer, 2008
[31]	徐晶. 物联网技术在智能电网监测中的应用分析. 计算机产品与流通, 2019, (12): 81 31 Xu Jing. Application analysis of Internet of things technology in smart grid monitoring. Computer Products and Circulation, 2019, (12): 81
[32]	杨子元, 许晓斌, 李欣, 赵一萌. 基于智能感知技术的用电事件识别方法研究. 物联网学报[Online], available: http://kns. cnki.net/kcms/detail/10.1491.TP.20191226.1020.006.html, December 27, 2019 Yang Zi-Yuan, Xu Xiao-Bin, Li Xin, Zhao Yi-Meng. Research on appliance event detection method based on intelligent perception technology. Chinese Journal on Internet of Things [Online], available: http://kns.cnki.net/kcms/detail/10.1491.TP.20191226.1020.006.html, December 27, 2019
[33]	时薇, 陈湘国, 魏忠诚, 李鹏, 赵继军. 基于NB-IoT的智能家居环境感知及安防报警系统. 电脑知识与技术, 2019, 15(29): 194−197, 207 33 Shi Wei, Chen Xiang-Guo, Wei Zhong-Cheng, Li Peng, Zhao Ji-Jun. Smart home environment awareness and security alarm system based on NB-IoT. Computer Knowledge and Technology, 2019, 15(29): 194−197, 207
[34]	张立, 汪卓越, 王春东, 马云飞, 向朝参. 物联网终端智能识别系统设计与实现. 重庆邮电大学学报(自然科学版), 2019, 31(4): 443−450 34 Zhang Li, Wang Zhuo-Yue, Wang Chun-Dong, Ma Yun-Fei, Xiang Chao-Can. Design and implementation of intelligent identification system for IoT terminals. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2019, 31(4): 443−450
[35]	齐俊鹏, 田梦凡, 马锐. 面向物联网的无限射频识别技术的应用及发展. 科学技术与工程, 2019, 19(29): 1−10 doi: 10.3969/j.issn.1671-1815.2019.29.001 35 Qi Jun-Peng, Tian Meng-Fan, Ma Rui. Application and development of radio frequency identification technology for internet of things. Science Technology and Engineering, 2019, 19(29): 1−10 doi: 10.3969/j.issn.1671-1815.2019.29.001
[36]	张娟萍. 人脸识别与对比技术在物联网智能交通中的应用. 电子技术与软件工程, 2019, (16): 166−167 36 Zhang Juan-Ping. Application of face recognition and contrast technology in intelligent transportation of internet of things. Electronic Technology & Software Engineering, 2019, (16): 166−167
[37]	罗小青, 胡荣, 洪胜华, 熊婷. 物联网智能感知数字图像自适应增强方法仿真. 计算机仿真, 2018, 35(12): 271−275 37 Luo Xiao-Qing, Hu Rong, Hong Sheng-Hua, Xiong-Ting. Simulation of Intelligent Sensing Digital Image Adaptive Enhancement for Internet of Things. Computer Simulation, 2018, 35(12): 271−275
[38]	顾广安. 基于物联网的化工园区封闭化智慧管理平台研究. 信息通信技术, 2019, 13(5): 52−57 doi: 10.3969/j.issn.1674-1285.2019.05.010 38 Gu Guang-An. Research on closed intelligent management platform of chemical industry park based on internet of things. Information and Communications Technologies, 2019, 13(5): 52−57 doi: 10.3969/j.issn.1674-1285.2019.05.010
[39]	何雪, 黄超禹. 基于物联网的现代农业生态园区智能管理系统. 机械与电子, 2019, 37(9): 47−50 doi: 10.3969/j.issn.1001-2257.2019.09.011 39 He Xue, Huang Chao-Yu. Intelligent management system of modern agricultural eco-park based on internet of things. Machinery & Electronics, 2019, 37(9): 47−50 doi: 10.3969/j.issn.1001-2257.2019.09.011
[40]	徐永杰. 物联网技术在智能仓库管理系统中的应用. 信息记录材料, 2019, 20(9): 232−233 40 Xu Yong-Jie. Application of Internet of things technology in intelligent warehouse management system. Information Recording Materials, 2019, 20(9): 232−233
[41]	Amarasinghe G, de Assunçao M D, Harwood A, Karunasekera S. A data stream processing optimisation framework for edge computing applications. In: Proceedings of the 21st IEEE International Symposium on Real-Time Distributed Computing. Singapore, Singapore: IEEE, 2018. 91−98
[42]	42 Gu L, Zeng D Z, Guo S, Barnawi A, Xiang Y. Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Transactions on Emerging Topics in Computing, 2017, 5(1): 108−119 doi: 10.1109/TETC.2015.2508382
[43]	43 Liu C, Cao Y, Luo Y, Chen G L, Vokkarane V, Ma Y S, et al. A new deep learning-based food recognition system for dietary assessment on an edge computing service infrastructure. IEEE Transactions on Services Computing, 2018, 11(2): 249−261 doi: 10.1109/TSC.2017.2662008
[44]	44 Sodhro A H, Pirbhulal S, de Albuquerque V H C. Artificial intelligence-driven mechanism for edge computing-based industrial applications. IEEE Transactions on Industrial Informatics, 2019, 15(7): 4235−4243 doi: 10.1109/TII.2019.2902878
[45]	45 Li Y H Z, Dong Z Y, Sha K W, Jiang C F, Wan J, Wang Y. TMO: time domain outsourcing attribute-based encryption scheme for data acquisition in edge computing. IEEE Access, 2019, 7: 40240−40257 doi: 10.1109/ACCESS.2019.2907319
[46]	张聪, 樊小毅, 刘晓腾, 庞海天, 孙立峰, 刘江川. 边缘计算使能智慧电网. 大数据, 2019, 5(2): 64−78 46 Zhang Cong, Fan Xiao-Yi, Liu Xiao-Teng, Pang Hai-Tian, Sun Li-Feng, Liu Jiang-Chuan. Edge computing enabled smart grid. Big Data Research, 2019, 5(2): 64−78
[47]	龚钢军, 罗安琴, 陈志敏, 栾敬钊, 安晓楠, 王雪蓓, 等. 基于边缘计算的主动配电网信息物理系统. 电网技术, 2018, 42(10): 3128−3135 47 Gong Gang-Jun, Luo An-Qin, Chen Zhi-Min, Luan Jing-Zhao, An Xiao-Nan, Wang Xue-Bei, et al. Cyber physical system of active distribution network based on edge computing. Power System Technology, 2018, 42(10): 3128−3135
[48]	张佳乐, 赵彦超, 陈兵, 胡峰, 朱琨. 边缘计算数据安全与隐私保护研究综述. 通信学报, 2018, 39(3): 1−21 doi: 10.11959/j.issn.1000-436x.2018037 48 Zhang Jia-Le, Zhao Yan-Chao, Chen Bing, Hu Feng, Zhu Kun. Survey on data security and privacy-preserving for the research of edge computing. Journal on Communications, 2018, 39(3): 1−21 doi: 10.11959/j.issn.1000-436x.2018037
[49]	49 Song C, Zhang M, Zhan Y Y, Wang D S, Guan L Y, Liu W, et al. Hierarchical edge cloud enabling network slicing for 5G optical fronthaul. Journal of Optical Communications and Networking, 2019, 11(4): B60−B70 doi: 10.1364/JOCN.11.000B60
[50]	50 Xu X L, Li D M, Dai Z H, Li S C, Chen X N. A heuristic offloading method for deep learning edge services in 5G networks. IEEE Access, 2019, 7: 67734−67744 doi: 10.1109/ACCESS.2019.2918585
[51]	51 Maier M, Ebrahimzadeh A. Towards immersive tactile Internet experiences: low-latency FiWi enhanced mobile networks with edge intelligence. Journal of Optical Communications and Networking, 2019, 11(4): B10−B25 doi: 10.1364/JOCN.11.000B10
[52]	52 Fu J S, Liu Y, Chao H C, Bhargava B K, Zhang Z J. Secure data storage and searching for industrial IoT by integrating fog computing and cloud computing. IEEE Transactions on Industrial Informatics, 2018, 14(10): 4519−4528 doi: 10.1109/TII.2018.2793350
[53]	53 Sharma S K, Wang X B. Live data analytics with collaborative edge and cloud processing in wireless IoT networks. IEEE Access, 2017, 5: 4621−4635 doi: 10.1109/ACCESS.2017.2682640
[54]	何秀丽, 任智源, 史晨华, 丛犁. 面向医疗大数据的云雾网络及其分布式计算方案. 西安交通大学学报, 2016, 50(10): 71−77 doi: 10.7652/xjtuxb201610011 54 He Xiu-Li, Ren Zhi-Yuan, Shi Chen-Hua, Cong Li. A cloud and fog network architecture for medical big data and its distributed computing scheme. Journal of Xi′an Jiaotong University, 2016, 50(10): 71−77 doi: 10.7652/xjtuxb201610011
[55]	55 Cheng N, Lyu F, Quan W, Zhou C H, He H L, Shi W S, et al. Space/aerial-assisted computing offloading for IoT applications: a learning-based approach. IEEE Journal on Selected Areas in Communications, 2019, 37(5): 1117−1129 doi: 10.1109/JSAC.2019.2906789
[56]	Huang Z, Lin K J, Shih C S. Supporting edge intelligence in service-oriented smart IoT applications. In: Proceedings of the 2016 IEEE International Conference on Computer and Information Technology (CIT). IEEE, 2016: 492−499
[57]	57 Chien W C, Lai C F, Chao H C. Dynamic resource prediction and allocation in C-RAN with edge artificial intelligence. IEEE Transactions on Industrial Informatics, 2019, 15(7): 4306−4314 doi: 10.1109/TII.2019.2913169
[58]	58 Jaradat M, Jarrah M, Bousselham A, Jararweh Y, Al-Ayyoub M. The internet of energy: smart sensor networks and big data management for smart grid. Procedia Computer Science, 2015, 56: 592−597 doi: 10.1016/j.procs.2015.07.250
[59]	59 Botta A, De Donato W, Persico V, et al. Integration of cloud computing and internet of things: a survey. Future generation computer systems, 2016, 56: 684−700
[60]	60 Stergiou C, Psannis K E, Kim B G, et al. Secure integration of IoT and cloud computing. Future Generation Computer Systems, 2018, 78: 964−975
[61]	刘思放, 邓春宇, 张国宾, 祁兵, 李彬, 李德智, 等. 面向居民智能用电的边缘计算协同架构研究. 电力建设, 2018, 39(11): 60−68 doi: 10.3969/j.issn.1000-7229.2018.11.008 61 Liu Si-Fang, Deng Chun-Yu, Zhang Guo-Bin, Qi Bing, Li Bin, Li De-Zhi, et al. Research on collaborative architecture for edge computing of residential intelligent usage of electricity. Electric Power Construction, 2018, 39(11): 60−68 doi: 10.3969/j.issn.1000-7229.2018.11.008
[62]	刘慧媛, 肖繁, 张哲, 尹项根, 刘玮. 新能源电源接入不平衡配电网的短路计算方法. 电力系统自动化, 2019, 43(21): 177−186 doi: 10.7500/AEPS20181003005 62 Liu Hui-Yuan, Xiao Fan, Zhang Zhe, Yin Xiang-Gen, Liu Wei. Short-circuit calculation method for unbalanced distribution network with integration of renewable energy. Automation of Electric Power Systems, 2019, 43(21): 177−186 doi: 10.7500/AEPS20181003005
[63]	范卓艺. 新能源集中接入对电网保护影响分析及应对策略研究[硕士学位论文], 华中科技大学, 中国, 2019 Fan Zhuo-Yi. Research on the Impact of New Energy Centralized Access on Power Network Protection and the Countermeasure [Master thesis], Huazhong University of Science and Technology, China, 2019
[64]	鲁璐. 大规模新能源接入宁东电网对继电保护的影响研究[硕士学位论文], 华北电力大学, 中国, 2018 Lu Lu. Study on Influence of Large-scale New Energy Access to Ningdong Power Grid on Relay Protection [Master thesis], North China Electric Power University, China, 2018
[65]	张旭阳, 白云. 电力电子化电力系统暂态稳定性. 电子技术与软件工程, 2019, (21): 213−214 65 Zhang Xu-Yang, Bai Yun. Transient stability of power electronic power system. Electronic Technology and Software Engineering, 2019, (21): 213−214
[66]	胡家兵, 袁小明, 程时杰. 电力电子并网装备多尺度切换控制与电力电子化电力系统多尺度暂态问题. 中国电机工程学报, 2019, 39(18): 5457−5467 66 Hu Jia-Bing, Yuan Xiao-Ming, Cheng Sji-Jie. Multi-time scale transients in power-electronized power systems considering multi-time scale switching control schemes of power electronics apparatus. Proceedings of the CSEE, 2019, 39(18): 5457−5467
[67]	唐斌. 电力电子技术在电力系统中的应用. 电子技术与软件工程, 2019, (9): 226 67 Tang Bin. Application of power electronics technology in power system. Electronic Technology & Software Engineering, 2019, (9): 226
[68]	张美清. 含高比例电力电子化装备的弱送端系统动态相互作用分析研究[博士学位论文], 华中科技大学, 中国, 2018 Zhang Mei-Qing. Study of Dynamic Interaction Analysis in Weak Sending-End Power System with High Proportion Power Electronics Apparatus [Ph. D. dissertation], Huazhong University of Science and Technology, China, 2018
[69]	刘畅. 交直流混联电网场景下继电保护面临的新问题及对策[硕士学位论文], 华中科技大学, 中国, 2018 Liu Chang. New Problems and Solutions of Relay Protection under the Situation of AC/DC Hybrid Power Grid [Master thesis], Huazhong University of Science and Technology, China, 2018
[70]	樊晓春, 何浩. 智能电网背景下继电保护的关键问题及对策. 电子技术与软件工程, 2019, (15): 212−213 70 Fan Xiao-Chun, He Hao. Key issues and countermeasures of relay protection in the smart grid. Electronic Technology & Software Engineering, 2019, (15): 212−213
[71]	71 Mei H B, Wang K Z, Yang K. Multi-layer cloud-RAN with cooperative resource allocations for low-latency computing and communication services. IEEE Access, 2017, 5: 19023−19032 doi: 10.1109/ACCESS.2017.2752279
[72]	72 Lin B, Zhu F N, Zhang J S, Chen J Q, Chen X, Xiong N N, et al. A time-driven data placement strategy for a scientific workflow combining edge computing and cloud computing. IEEE Transactions on Industrial Informatics, 2019, 15(7): 4254−4265 doi: 10.1109/TII.2019.2905659
[73]	73 Rimal B P, Van D P, Maier M. Mobile-edge computing versus centralized cloud computing over a converged FiWi access network. IEEE Transactions on Network and Service Management, 2017, 14(3): 498−513 doi: 10.1109/TNSM.2017.2706085
[74]	74 Hao F, Park D S, Kang J, Min G. 2L-MC.3: a two-layer multi-community-cloud/cloudlet social collaborative paradigm for mobile edge computing. IEEE Internet of Things Journal, 2018, 6(3): 4764−4773
[75]	75 Shi W, Cao J, Zhang Q, et al. Edge computing: Vision and challenges. IEEE Internet of Things Journal, 2016, 3(5): 637−646
[76]	徐恩庆, 董恩然. 探析云边协同的九大应用场景. 通信世界, 2019, (21): 42−43 76 Xu En-Qing, Dong En-Ran. Analysis of nine application scenarios of cloud-edge collaboration. Communications World, 2019, (21): 42−43
[77]	徐恩庆, 董恩然. 云计算与边缘计算协同发展的探索与实践. 通信世界, 2019, (9): 46−47 77 Xu En-Qing, Dong En-Ran. Exploration and practice of coordinated development of cloud computing and edge computing. Communications World, 2019, (9): 46−47
[78]	华先胜, 黄建强, 沈旭, 付志航, 赵一儒, 黄镇, 等. 城市大脑: 云边协同城市视觉计算. 人工智能, 2019, (5): 77−91 78 Hua Xian-Sheng, Huang Jian-Qiang, Shen Xu, Fu Zhi-Hang, Zhao Yi-Ru, Huang Zhen, et al. Urban brain: cloud-edge based collaborative urban visual computing. AI-View, 2019, (5): 77−91
[79]	79 Ren J J, Wang H C, Hou T T, Zheng S, Tang C S. Federated learning-based computation offloading optimization in edge computing-supported internet of things. IEEE Access, 2019, 7: 69194−69201 doi: 10.1109/ACCESS.2019.2919736
[80]	于博文, 蒲凌君, 谢玉婷, 徐敬东, 张建忠. 移动边缘计算任务卸载和基站关联协同决策问题研究. 计算机研究与发展, 2018, 55(3): 537−550 doi: 10.7544/issn1000-1239.2018.20170714 80 Yu Bo-Wen, Pu Ling-Jun, Xie Yu-Ting, Xu Jing-Dong, Zhang Jian-Zhong. Joint task off loading and base station association in mobile edge computing. Journal of Computer Research and Development, 2018, 55(3): 537−550 doi: 10.7544/issn1000-1239.2018.20170714
[81]	81 Porambage P, Okwuibe J, Liyanage M, Ylianttila M, Taleb T. Survey on multi-access edge computing for internet of things realization. IEEE Communications Surveys & Tutorials, 2018, 20(4): 2961−2991
[82]	82 Long C C, Cao Y, Jiang T, Zhang Q. Edge computing framework for cooperative video processing in multimedia IoT systems. IEEE Transactions on Multimedia, 2018, 20(5): 1126−1139 doi: 10.1109/TMM.2017.2764330
[83]	齐彦丽, 周一青, 刘玲, 田霖, 石晶林. 融合移动边缘计算的未来5G移动通信网络. 计算机研究与发展, 2018, 55(3): 478−486 doi: 10.7544/issn1000-1239.2018.20170801 83 Qi Yan-Li, Zhou Yi-Qing, Liu Ling, Tian Lin, Shi Jing-Lin. MEC coordinated future 5G mobile wireless networks. Journal of Computer Research and Development, 2018, 55(3): 478−486 doi: 10.7544/issn1000-1239.2018.20170801
[84]	84 Ren J, Guo Y D, Zhang D Y, Liu Q Q, Zhang Y X. Distributed and efficient object detection in edge computing: challenges and solutions. IEEE Network, 2018, 32(6): 137−143 doi: 10.1109/MNET.2018.1700415
[85]	85 Li L Z, Ota K, Dong M X. Deep learning for smart industry: efficient manufacture inspection system with fog computing. IEEE Transactions on Industrial Informatics, 2018, 14(10): 4665−4673 doi: 10.1109/TII.2018.2842821
[86]	邓晓衡, 关培源, 万志文, 刘恩陆, 罗杰, 赵智慧, 等. 基于综合信任的边缘计算资源协同研究. 计算机研究与发展, 2018, 55(3): 449−477 doi: 10.7544/issn1000-1239.2018.20170800 86 Deng Xiao-Heng, Guan Pei-Yuan, Wan Zhi-Wen, Liu En-Lu, Luo Jie, Zhao Zhi-Hui, et al. Integrated trust based resource cooperation in edge computing. Journal of Computer Research and Development, 2018, 55(3): 449−477 doi: 10.7544/issn1000-1239.2018.20170800
[87]	87 Lin H, Chen Z H, Wang L S. Offloading for edge computing in low power wide area networks with energy harvesting. IEEE Access, 2019, 7: 78919−78929 doi: 10.1109/ACCESS.2019.2922399
[88]	88 Su X, Sperli G, Moscato V, Picariello A, Esposito C, Choi C. An edge intelligence empowered recommender system enabling cultural heritage applications. IEEE Transactions on Industrial Informatics, 2019, 15(7): 4266−4275 doi: 10.1109/TII.2019.2908056
[89]	89 Chen S L, Wen H, Wu J S, Lei W X, Hou W J, Liu W J, et al. Internet of things based smart grids supported by intelligent edge computing. IEEE Access, 2019, 7: 74089−74102 doi: 10.1109/ACCESS.2019.2920488
[90]	90 Zhang K, Zhu Y X, Leng S P, HE Y J, Maharjan S, Zhang Y. Deep learning empowered task offloading for mobile edge computing in urban informatics. IEEE Internet of Things Journal, 2019, 6(5): 7635−7647 doi: 10.1109/JIOT.2019.2903191
[91]	Zhou Z, Chen X, Li E, Zeng L K, Luo K, Zhang J S. Edge intelligence: paving the last mile of artificial intelligence with edge computing. arXiv: 1905.10083, 2019
[92]	陈伟, 陈庆奎. 面向深度学习训练任务的自适应任务分配方法. 小型微型计算机系统, 2019, 40(12): 2531−2537 92 Chen Wei, Chen Qing-Kui. Adaptive task assignment method for deep learning training. Journal of Chinese Computer Systems, 2019, 40(12): 2531−2537
[93]	Wang X F, Han Y W, Leung V C M, Niyato D, Yan X Q, Chen X. Convergence of edge computing and deep learning: a comprehensive survey. arXiv: 1907.08349, 2019
[94]	谭琨, 王雪, 杜培军. 结合深度学习和半监督学习的遥感影像分类进展. 中国图象图形学报, 2019, 24(11): 1823−1841 doi: 10.11834/jig.190348 94 Tan Kun, Wang Xue, Du Pei-Jun. Research progress of the remote sensing classification combining deep learning and semi-supervised learning. Journal of Image and Graphics, 2019, 24(11): 1823−1841 doi: 10.11834/jig.190348
[95]	王丰, 文红, 陈松林, 陈柳霏, 侯文静. 边缘计算下移动智能终端隐私数据的保护方法. 网络空间安全, 2018, 9(2): 47−50 doi: 10.3969/j.issn.1674-9456.2018.02.011 95 Wang Feng, Wen Hong, Chen Song-Lin, Chen Liu-Fei, Hou Wen-Jing. Privacy data protection method for mobile intelligent terminal based on edge computing. Information Security and Technology, 2018, 9(2): 47−50 doi: 10.3969/j.issn.1674-9456.2018.02.011
[96]	96 Meloni A, Pegoraro P A, Atzori L, et al. Cloud-based IoT solution for state estimation in smart grids: Exploiting virtualization and edge-intelligence technologies. Computer Networks, 2018, 130: 156−165
[97]	Konečný J, McMahan B, Yu F X, Richtárik P, Suresh A T, Bacon D. Federated learning: strategies for improving communication efficiency. arXiv: 1610.05492, 2016
[98]	98 Tao X Y, Ota K, Dong M X, Qi H, Li K Q. Performance guaranteed computation offloading for mobile-edge cloud computing. IEEE Wireless Communications Letters, 2017, 6(6): 774−777 doi: 10.1109/LWC.2017.2740927
[99]	99 Yuan J, Li X Y. A reliable and lightweight trust computing mechanism for IoT edge devices based on multi-source feedback information fusion. IEEE Access, 2018, 6: 23626−23638 doi: 10.1109/ACCESS.2018.2831898
[100]	100 Zhang C, Zhao H L, Deng S G. A density-based offloading strategy for IoT devices in edge computing systems. IEEE Access, 2018, 6: 73520−73530 doi: 10.1109/ACCESS.2018.2882452
[101]	Zhou Z, Chen X, Li E, et al. Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing. arXiv preprint arXiv: 1905.10083, 2019.
[102]	102 Rahman A, Rashid M, Hossain M S, Hassanain E, Alhamid M F, Guizani M. Blockchain and IoT-based cognitive edge framework for sharing economy services in a smart city. IEEE Access, 2019, 7: 18611−18621 doi: 10.1109/ACCESS.2019.2896065
[103]	103 Hao Y X, Chen M, Hu L, Hossain M S, Ghoneim A. Energy efficient task caching and offloading for mobile edge computing. IEEE Access, 2018, 6: 11365−11373 doi: 10.1109/ACCESS.2018.2805798
[104]	104 Wang C M, Liang C C, Yu F R, Chen Q B, Tang L. Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE Transactions on Wireless Communications, 2017, 16(8): 4924−4938 doi: 10.1109/TWC.2017.2703901
[105]	105 Yu S, Langar R, Fu X M, Wang L, Han Z. Computation offloading with data caching enhancement for mobile edge computing. IEEE Transactions on Vehicular Technology, 2018, 67(11): 11098−11112 doi: 10.1109/TVT.2018.2869144
[106]	张江丰, 王飞跃, 苏烨, 陈波, 汪自翔, 孙坚栋, 等. 基于多源数据的电网一次调频能力平行计算研究. 自动化学报[Online], available: https://www.cnki.net/KCMS/detail/11.2109.TP.20191031.1702.004.html, December 31, 2019 Zhang Jiang-Feng, Wang Fei-Yue, Su Ye, Chen Bo, Wang Zi-Xiang, Sun Jian-Dong, et al. Power grid primary frequency control ability parallel computing based on multiple data. Acta Automatica Sinica[Online], available: https://doi.org/10.16383/j.aas.c190512, December 31, 2019
[107]	高汝武. 数字化变电站继电保护技术. 杭州: 浙江人民出版社, 2012 Gao Ru-Wu. Digital Substation Relay Protection Technology. Hangzhou: Zhejiang People′s Publishing House, 2012