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Regular Paper | Open Access

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

Qiyue Li1( ), Yadong Zhu1, Jinjin Ding2, Weitao Li1, Wei Sun1, Lijian Ding3
Hefei University of Technology; and Engineering Technology Research Center of Industrial Automation, Hefei 230009, China
Electric Power Research Institute of State Grid Anhui Electric Power Co., Ltd., Hefei 230000, China
Hefei University of Technology, Hefei 230009, China
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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.

Keywords

smart griddeep reinforcement learningfault detectioncommunication delayCloud-edge collaboration

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 3,
May 2024
Pages 1220-1230
DOI: 10.17775/CSEEJPES.2021.02390
Cite this article:
Li Q, Zhu Y, Ding J, et al. Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid. CSEE Journal of Power and Energy Systems, 2024, 10(3): 1220-1230. https://doi.org/10.17775/CSEEJPES.2021.02390

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Received: 03 March 2021
Revised: 12 June 2021
Accepted: 09 July 2021
Published: 18 August 2022
© 2021 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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