中国电机工程学报2026，Vol.46Issue(12)：5033-5046,中插17,15.DOI:10.13334/j.0258-8013.pcsee.250370
基于类脑记忆协同经验回放算法的自动发电控制
Automatic Generation Control Based on Brain-inspired Memory Cooperative Experience Replay Algorithm
摘要
Abstract
The large-scale integration of renewable energy sources and electric vehicle clusters has introduced strong stochastic disturbances,which amplify frequency oscillations and challenge the performance of automatic generation control(AGC).However,existing reinforcement learning approaches exhibit low efficiency in utilizing experience samples,which hinders their ability to accurately reflect the dynamic state changes of the AGC system,thereby leading to suboptimal control strategies.To overcome these limitations,this paper proposes a twin delayed deep deterministic policy gradient algorithm for AGC,incorporating brain-inspired memory cooperative experience replay.By integrating short-term,long-term,and expert experience samples,it cooperatively utilizes and interactively samples multi-source experiences,enabling the agent to consider real-time feedback,historical data,and external guidance for more accurate state reflection and optimal control.The simulation results of both the two-area load frequency control model with electric vehicle cluster access and the four-area model of the Central China power grid indicate that the proposed algorithm can effectively suppress frequency oscillations and improve the performance of AGC.关键词
自动发电控制/强化学习/人脑记忆/经验回放Key words
automatic generation control/reinforcement learning/human brain memory/experience replay分类
信息技术与安全科学引用本文复制引用
席磊,苏磊,施宇,宋浩杰,李宗泽..基于类脑记忆协同经验回放算法的自动发电控制[J].中国电机工程学报,2026,46(12):5033-5046,中插17,15.基金项目
国家自然科学基金项目(52277108,52477104) （52277108,52477104）
宜昌市自然科学研究项目(A23-2-001).Project Supported by National Natural Science Foundation of China(52277108,52477104) （A23-2-001）
Yichang Municipal Natural Science Foundation(A23-2-001). （A23-2-001）
