基于强化学习的分布式电力自动化控制系统设计
Design of Distributed Electric Power Automation Control System Based on Reinforcement Learning
陈刚1
作者信息
- 1. 中海油田服务股份有限公司,广东 深圳 518067
- 折叠
摘要
提出一种基于强化学习的分布式电力自动化控制系统设计方案.该方案对分布式电力系统进行强化学习建模,设计了状态空间、动作空间和奖励函数.然后,利用深度确定性策略梯度(DDPG)算法训练智能体,使其能根据系统状态自主调整控制策略,以优化系统性能.结果表明,该方案能有效提高分布式电力系统的稳定性、可靠性和经济性.
Abstract
This paper design scheme for distributed power automation control system based on reinforcement learning is proposed.This scheme applies reinforcement learning modeling to distributed power systems and designs state space,action space,and reward function.Then,the deep deterministic policy gradient(DDPG)algorithm is used to train the agent to autonomously adjust the control strategy based on the system state,thereby optimizing system performance.The results indicate that this scheme can effectively improve the stability,reliability,and economy of distributed power systems.
关键词
强化学习/分布式/自动化控制Key words
reinforcement learning/distributed/automated control引用本文复制引用
出版年
2024
NETL
NSTL
万方数据