Real-Time Task Fault-Tolerant Scheduling Algorithm for Dynamic Monitoring Platform of Distribution Network Operation under Overload of Distribution Transformer

Authors

  • Hancong Huangfu Foshan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangdong, China
  • Yongcai Wang Foshan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangdong, China
  • Jiang Jiang Guangdong Power Grid, Guangzhou, China

DOI:

https://doi.org/10.4108/eetsis.v10i3.3158

Keywords:

Edge computing, wireless communication, task scheduling, dynamic monitoring

Abstract

This paper proposes a real-time task fault-tolerant scheduling algorithm for a dynamic monitoring platform of distribution network operation under overload of distribution transformers. The proposed algorithm is based on wireless communication and mobile edge computing to address the challenges faced by distribution networks in handling the increasing load demand. For the considered system, we evaluate the system performance by analyzing the communication and computing latency, from which we then derive an analytical expression of system outage probability to facilitate the performance evaluation. We further optimize the system design by allocating computing resources for multiple mobile users, where a greedy-based optimization scheme is proposed. The proposed algorithm is evaluated through simulations, and the results demonstrate its effectiveness in reducing task completion time, improving resource utilization, and enhancing system reliability. The findings of this study can provide a basis for the development of practical solutions for the dynamic monitoring of distribution networks.

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Published

11-05-2023

How to Cite

1.
Huangfu H, Wang Y, Jiang J. Real-Time Task Fault-Tolerant Scheduling Algorithm for Dynamic Monitoring Platform of Distribution Network Operation under Overload of Distribution Transformer. EAI Endorsed Scal Inf Syst [Internet]. 2023 May 11 [cited 2024 Dec. 25];10(4):e15. Available from: https://publications.eai.eu/index.php/sis/article/view/3158