基于DDQN的电力传感网资源分配算法  

DDQN Based Resource Allocation Algorithm for Power Sensor Networks

作　　者：朱雪琼 胡成博 杨景刚 路永玲 ZHU Xueqiong;HU Chengbo;YANG Jinggang;LU Yongling(State Grid Jiangsu Electric Power Co.,Ltd.Research Institute,Nanjing 211103,China)

机构地区：[1]国网江苏省电力有限公司电力科学研究院,江苏南京211103

出　　处：《中国电力》2023年第11期60-66,共7页Electric Power

基　　金：国家电网有限公司科技项目(与电力传感装置融合的低功耗宽窄融合无线传感网络系统研发及应用,5108-202218280A-2-201-XG)。

摘　　要：电力传感网可以用于对电力网络的设备工作状态和工作环境等信息实时采集和获取,对于电力网络设施的实时监控与快速响应具有重要作用。针对系统在数据排队时延和丢包率上的特殊要求,提出了一种基于强化学习的电力传感网资源分配方案。在资源受限的情况下,通过资源分配算法来优化传感器节点的排队时延和丢包率,并将该优化问题建模为马尔可夫决策过程(Markov decision process,MDP),通过双深度Q网络(double deep Q-learning,DDQN)来对优化目标函数求解。仿真结果与数值分析表明,所提方案在收敛性、排队时延和丢包率等方面的性能均优于基准方案。The power sensor network can collect and obtain information on the power grid equipment's working status and working environment in real time,which plays an important role in monitoring and quick response of the grid facilities.Aiming at the special requirements of system such as data queuing time delay and packet loss rate,this paper proposes a resource allocation scheme based on reinforcement learning(RL)for power sensing networks.Under the resource constraints,the scheme optimizes the queuing time and packet loss rate of sensor nodes through the resource allocation algorithms,and the optimization problem is modeled as a Markov decision process(MDP),which is solved by double deep Q-network(DDQN)algorithm.Simulation results and numerical analysis show that the proposed scheme outperforms the benchmark scheme in convergence,time delay and packet loss rate.

关 键 词：电力传感网 资源分配 马尔可夫决策过程 双深度Q网络 

分 类 号：TM73[电气工程—电力系统及自动化] TP212.9[自动化与计算机技术—检测技术与自动化装置]