Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid

Qiyue Li; Yadong Zhu; Jinjin Ding; Weitao Li; Wei Sun; Lijian Ding

doi:10.17775/CSEEJPES.2021.02390

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Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid

Regular Papers | 更新时间：2026-10-15

- Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid
- Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第3期页码：1220-1230
- 作者机构：
  
  1. Engineering Technology Research Center of Industrial Automation, Hefei University of Technology,Hefei,China
  2. Electric Power Research Institute of State Grid Anhui Electric Power Co., Ltd,Hefei,China
  3. Hefei University of Technology,Hefei,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.02390
  中图分类号：
- 纸质出版：2024-05-01
- 稿件说明：
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Qiyue Li, Yadong Zhu, Jinjin Ding, 等. Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(3):1220-1230.

Qiyue Li, Yadong Zhu, Jinjin Ding, et al. Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid[J]. CSEE Journal of Power and Energy Systems, 2024, 10(3): 1220-1230.
Qiyue Li, Yadong Zhu, Jinjin Ding, 等. Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(3):1220-1230. DOI： 10.17775/CSEEJPES.2021.02390.

Qiyue Li, Yadong Zhu, Jinjin Ding, et al. Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid[J]. CSEE Journal of Power and Energy Systems, 2024, 10(3): 1220-1230. DOI： 10.17775/CSEEJPES.2021.02390.

摘要

Abstract

Real-time fault detection is important for operation of smart grid. It has become a trend of future development to design an anomaly detection system based on deep learning by using the powerful computing power of the cloud. However

delay of Internet transmission is large

which may make the delay time of detection and transmission go beyond the limits. However

the edge-based scheme may not be able to undertake all data detection tasks due to limited computing resources of edge devices. Therefore

we propose a cloud-edge collaborative smart grid fault detection system

next to which edge devices are placed

and equipped with a lightweight neural network with different precision for fault detection. In addition

a sub-optimal and real-time communication and computing resource allocation method is proposed based on deep reinforcement learning. This method greatly speeds up solution time

which can meet the requirements of data transmission delay

maximize the system throughput

and improve communication efficiency. Simulation results show the scheme is superior in transmission delay and improves real-time performance of the smart grid detection system.

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