Intelligent Terminal Task Scheduling Strategy for Self-Healing Distribution Networks
Lishun Ding, Zhihong Luo, Hui Yu, Rui‐Sheng Wang · 2025
As a crucial component of self-healing distribution networks, intelligent terminals play a vital role, with the efficiency of their task scheduling being particularly important for fault diagnosis and restoration in distribution networks. This paper proposes a quantum genetic algorithm (QGA) based on fog computing, aiming to reduce device energy consumption and shorten terminal computation time. Firstly, the task scheduling problem for terminal nodes is modeled based on fog computing and edge computing paradigms. Subsequently, under the constraints of task dependency, data transmission, response time, and computation cost, the QGA is employed to solve the problem through simulation experiments. Finally, comparative case studies are conducted to verify the significant advantages of the QGA in terms of energy consumption and computation time within the task scheduling strategy, which can improve the self-healing ability and operational efficiency of distribution networks.