软件导刊2025，Vol.24Issue(10)：56-64,9.DOI:10.11907/rjdk.251271
基于图深度强化学习的电力网络通信自趋优控制方法
A Self-trending Optimization Control Method for Power Network Communication Based on Graph Deep Reinforcement Learning
摘要
Abstract
Under the framework of smart grid development,electric power communication networks face challenges in real-time operations,transmission reliability,and intelligent management.This paper proposes a self-convergent optimization control strategy,RiskQuant-GRL(RQGRL),which integrates risk quantization and graph deep reinforcement learning.By combining Graph Convolutional Networks(GCN)with Deep Q-Learning(DQN),RQGRL aims to enhance network intelligence in resource scheduling and path planning.Specifically,it em-beds a GCN into a Deep Reinforcement Learning(DRL)framework to monitor the network state,make intelligent decisions,and provide opti-mal routing.By quantifying risk as edge weights and using weighted GCN,the model dynamically optimizes path selection and resource alloca-tion.Simulation results show that RQGRL improves bandwidth allocation efficiency by 6.6%over state-of-the-art DRL solutions and excels in link failure recovery,maintaining efficient network operation even under high concurrency.关键词
深度强化学习/图神经网络/深度Q学习算法/路径推荐Key words
deep reinforcement learning/graph neural network/deep Q-learning algorithms/path recommendation分类
教育学引用本文复制引用
江颖洁,魏永静,孙超,田安琪,张彦,朱尤祥..基于图深度强化学习的电力网络通信自趋优控制方法[J].软件导刊,2025,24(10):56-64,9.基金项目
国网山东省电力公司科技项目(520627240007) （520627240007）
