Routing Algorithm for Power Communication Networks Based on Serivce Differentiated Transmission Requirements
薛松萍 1高德荃 2赵子岩 2林彧茜 3广泽晶 2张大卫1
扫码查看
点击上方二维码区域，可以放大扫码查看
作者信息
1. 华中科技大学电子信息与通信学院,湖北武汉 430074
2. 国家电网有限公司信息通信分公司,北京 100761
3. 国网福建省电力有限公司,福建福州 350003
折叠
摘要
电力通信网负责传递控制指令、收集状态数据,对保障电网的稳定运作至关重要.针对电力通信网络中多约束条件下的智能路由问题,提出了一种结合消息传递神经网络(message passing neural network,MPNN)与深度强化学习算法的智能路由算法.通过Tensor flow框架实现,在Open AI Gym构建的模拟环境进行验证.算法在超过8000次的训练迭代后呈现出显著的性能提升,表现出了较传统最短路径和负载均衡算法更优越的路由选择能力.同时,在新拓扑图的泛化测试和链路故障模拟实验中,也显示出较强的适应性和鲁棒性.
Abstract
The electric power communication network,pivotal in ensuring the stable operation of the power grid,is tasked with transmitting control instructions and collecting status data.Addressing the intelligent routing challenge within the electric power communication network under multiple constraints,we propose an innovative routing algorithm that seamlessly integrates Message Passing Neural Network (MPNN) with deep reinforcement learning algorithms.Implemented through the TensorFlow framework,this algorithm has been rigorously validated in a simulation environment constructed using OpenAI Gym.After undergoing over 8,000 training iterations,the algorithm demonstrates remarkable performance enhancements,outperforming traditional shortest path and load balancing algorithms in terms of routing selection capabilities.Furthermore,it has exhibited robust adaptability and resilience in generalization tests on new topology maps and link failure simulation experiments.
关键词
电力通信网/路由优化/消息神经网络/深度强化学习/多约束条件
Key words
power communication network/routing optimization/message passing neural network/deep reinforcement learning/multiple constraints
维普
万方数据