电气技术2026，Vol.27Issue(5)：20-27,37,9.
基于强化学习策略的电网负荷频率控制
Power grid load frequency control based on reinforcement learning strategies
摘要
Abstract
To achieve precise control of load frequency in new power systems and effectively address the challenges of nonlinearity,randomness,and uncertainty caused by the integration of large-scale renewable energy,an Actor-Critic deep reinforcement learning algorithm based on radial basis function(RBF)network is adopted to construct an load frequency control(LFC)strategy with adaptive proportional-integral-derivative(PID)control.First,the stable control structure of the power system and the dilemmas faced by LFC are analyzed.The advantages of intelligent control strategies are expounded,and modeling is completed,followed by the design of an adaptive optimization system.Through Matlab simulation,the controller is compared and analyzed with the particle swarm optimization(PSO)-tuned controller.The results show that the proposed controller significantly reduces the four error parameters of area control error(ACE)in the two areas,demonstrates stronger performance in minimizing ACE and reducing deviations,effectively improves the adaptability and robustness of LFC,and provides a new method with both theoretical and engineering values for the construction of new power systems.关键词
负荷频率控制/自适应系统/强化学习/Actor-Critic 算法Key words
load frequency control/adaptive system/reinforcement learning/Actor-Critic algorithm引用本文复制引用
葛亚菲,曾佳倩,宋启凡,张璐..基于强化学习策略的电网负荷频率控制[J].电气技术,2026,27(5):20-27,37,9.基金项目
国家自然科学基金青年科学基金项目(C类)(62403354)天津工业大学大学生创新创业训练计划项目(202510058098) （C类）
