Artificial intelligence-based early warning and self-healing technology for distribution edge IoT networks
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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).

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    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.

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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

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  • Received:November 15,2021
  • Revised:
  • Adopted:
  • Online: August 25,2023
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