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。
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.
作者
陈刚
CHEN Gang(China Oilfieid Services Limited,Shenzhen,Guangdong 518067,China)
出处
《自动化应用》
2024年第23期48-50,共3页
Automation Application
关键词
强化学习
分布式
自动化控制
reinforcement learning
distributed
automated control
