Artificial intelligence-based early warning and self-healing technology for distribution edge IoT networks
CSTR:
Author:
Affiliation:

1.Meizhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Meizhou, Guangdong 514199, P. R. China;2.School of Big Data and Software Engineering, Chongqing University, Chongqing 401331,P. R. China;3.Electric Power Dispatching and Control Center, Guangdong Power Grid Company Ltd., Guangzhou 510062, P. R. China

Clc Number:

TM76;TM73

Fund Project:

Supported by National Natural Science Foundation of China (62072065), and Science and Technology Projects of China Southern Power Grid (GDKJXM20198151).

  • Article
  • | |
  • Metrics
  • |
  • Reference [17]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    The distribution of IoT (Internet of Things) is the last link in the construction of ubiquitous power IoT. It possesses characteristics such as short power supply path and high load density, which bring about significant challenges in terms of protection and control. To address these challenges, the establishment of an early warning and self-healing strategy for the distribution network is crucial, enabling the formation of a smart distribution network with flexible operation mode, timely fault warning and perfect fault self-healing. This paper proposes a security defense technology framework applicable to the firmware of the distribution IoT edge network. The framework protects the reliability matrix of each firmware present in the edge devices, while the edge devices are interconnected through the edge servers, forming a technical solution for distribution edge IoT that incorporates security warning and self-healing capabilities. Finally, the feasibility of this scheme is verified by simulation experiments conducted under different environmental conditions.

    Reference
    [1] 冯杨. 电网数字化转型下面临的安全形式与保障措施研究[J]. 通信电源技术, 2021, 38(3): 206-208.Feng Y. Research on security forms and safeguard measures in the digital transformation of power grid[J]. Telecom Power Technology, 2021, 38(3): 206-208.(in Chinese)
    [2] 王琨, 杜亮, 马来·对山拜, 等. 面向智能电网应用的电力大数据关键技术研究[J]. 微型电脑应用, 2021, 37(8): 123-126.Wang K, Du L, Ma L, et al. Research on key technologies of power big data for smart grid application[J]. Microcomputer Applications, 2021, 37(8): 123-126.(in Chinese)
    [3] Wang Q P, Group X, Bo Z Q, et al. Integrated wide area protection and control for power grid security[J]. CSEE Journal of Power and Energy Systems, 2019, 5(2): 206-214.
    [4] Guo Q, Zhu Y H, Chang D X, et al. A remote test method for power grid security and stability control system and its engineering application[J]. E3S Web of Conferences, 2021, 260: 02006.
    [5] 王瑾, 裴亮. 基于深度学习的电网调控系统异常检测与多阶段风险预警[J]. 沈阳工业大学学报, 2021, 43(6): 601-607.Wang J, Pei L. Anomaly detection and multi-stage risk pre-warning technology of power grid control system based on deep learning [J]. Journal of Shenyang University of Technology, 2021, 43(6): 601-607. (in Chinese)
    [6] Zhang S, Luo X C, Litvinov E. Serverless computing for cloud-based power grid emergency generation dispatch[J]. International Journal of Electrical Power & Energy Systems, 2021, 124: 106366.
    [7] 王安娜, 刘坐乾, 杨铭如, 等. 基于BP-ART混合神经网络的电路故障诊断新方法[J]. 系统工程与电子技术, 2010, 32(4): 873-876.Wang A N, Liu Z Q, Yang M R, et al. Novel method for circuit fault diagnosis based on the BP-ART hybrid neural network[J]. Systems Engineering and Electronics, 2010, 32(4): 873-876.(in Chinese)
    [8] 李学军, 李平, 蒋玲莉. 类均值核主元分析法及在故障诊断中的应用[J]. 机械工程学报, 2014, 50(3): 123-129.Li X J, Li P, Jiang L L. Class mean kernel principal component analysis and its application in fault diagnosis[J]. Journal of Mechanical Engineering, 2014, 50(3): 123-129.(in Chinese)
    [9] 曹源, 马连川, 李旺. 铁道信号系统安全计算机状态监测方法[J]. 交通运输工程学报, 2013, 13(3): 107-112.Cao Y, Ma L C, Li W. Monitoring method of safety computer condition for railway signal system[J]. Journal of Traffic and Transportation Engineering, 2013, 13(3): 107-112.(in Chinese)
    [10] 周真, 周浩, 马德仲, 等. 风电机组故障诊断中不确定性信息处理的贝叶斯网络方法[J]. 哈尔滨理工大学学报, 2014, 19(1): 64-68.Zhou Z, Zhou H, Ma D Z, et al. Method of Bayesian network for uncertainty information processing of wind turbines fault diagnosis[J]. Journal of Harbin University of Science and Technology, 2014, 19(1): 64-68.(in Chinese)
    [11] 蒋勇, 赵作鹏. 多属性加权模糊贝叶斯的复杂网络故障自修复技术[J]. 计算机应用研究, 2015, 32(8): 2378-2381.Jiang Y, Zhao Z P. Complex network fault self-repair mechanism with multi-attribute weighted fuzzy Bayesian[J]. Application Research of Computers, 2015, 32(8): 2378-2381.(in Chinese)
    [12] 杨丽君, 于琦, 魏玲玲, 等. 基于移动多代理动态联盟的配电网故障恢复研究[J]. 电工技术学报, 2016, 31(8): 147-155.Yang L J, Yu Q, Wei L L, et al. A distribution network fault recovery study on the dynamic alliance of mobile multi-agent[J]. Transactions of China Electrotechnical Society, 2016, 31(8): 147-155.(in Chinese)
    [13] 李学平, 卢志刚, 刘照拯, 等. 含分布式电源的配电网多故障抢修的多代理策略研究[J]. 电工技术学报, 2013, 28(8): 48-55.Li X P, Lu Z G, Liu Z Z, et al. Multi-agent strategy of distribution networks multi-faults rush-repair with distributed generators[J]. Transactions of China Electrotechnical Society, 2013, 28(8): 48-55.(in Chinese)
    [14] 黄弦超, 杨雨,范闻博. 配电网故障抢修与供电恢复联合优化模型[J]. 电力系统自动化, 2014, 38(11):68-73.Huang X C, Yang Y, Fan W B. Combined optimization model for maintenance scheduling and service restoration of distribution system[J]. Automation of Electric Power Systems, 2014, 38(11):68-73. (in Chinese)
    [15] Diffie W, Hellman M. New directions in cryptography[J]. IEEE Transactions on Information Theory, 1976, 22(6): 644-654.
    [16] Kaur G, Madaan N. A comparative study of AES encryption decryption[J]. Journal of Innovation and Social Science Research, 2015, 2(6): 84-88.
    [17] Shamir A. How to share a secret[J]. Communications of the ACM, 1979, 22(11): 612-613.
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

李伟青,陈虹宇,赵瑞锋,胡春强.配电边缘物联网网络预警及自愈方案[J].重庆大学学报,2023,46(8):11~19

Copy
Share
Article Metrics
  • Abstract:428
  • PDF: 575
  • HTML: 87
  • Cited by: 0
History
  • Received:November 15,2021
  • Online: August 25,2023
Article QR Code