水力发电学报2026，Vol.45Issue(6)：23-36,14.DOI:10.11660/slfdxb.20260603
水泵水轮机转速强化学习控制策略
Reinforcement learning approach for speed control of pump turbines
摘要
Abstract
Hydropower serves as a critical renewable energy source that is often used for essential peak-shaving and frequency regulation for power grids;the agility of hydropower units'speed-governing system in response to load fluctuations directly impacts power quality and grid stability.However,the generating units usually operate across diverse conditions and suffer from severe nonlinearities,posing a huge challenge to the conventional method of proportional-integral-derivative(PID)control.To enhance system control performance and robustness,this paper describes a new intelligent control strategy that is based on the Soft Actor-Critic(SAC)reinforcement learning algorithm.By using the strategy and a nonlinear pump-turbine governing system,first a framework is constructed to train a network of Nonlinear Autoregressive with Exogenous Input Long Short-Term Memory(NARX-LSTM)as a surrogate model.It uses one module for LSTM-based error-correction to raise model accuracy.Then,this error-corrected NARX-LSTM environment is leveraged for iterative training of the SAC agent.Simulation results demonstrate that the new method outperforms traditional PID control in response speed and overshoot suppression across multiple operating points.And,the strategy exhibits superior resilience to operational transitions with minimal fluctuations.This study has verified the efficacy of reinforcement learning in achieving a complicated industrial control,and a promising new paradigm for hydropower speed regulation.关键词
水泵水轮机/水轮机调速/强化学习/SAC算法/带外源输入的非线性自回归-长短期记忆网络Key words
pump turbine/turbine speed control/reinforcement learning/soft actor-critic algorithm/nonlinear autoregressive with exogenous input long short-term memory network分类
能源科技引用本文复制引用
肖文圣,何佳,赵忠盖,郎彦东,陈金保..水泵水轮机转速强化学习控制策略[J].水力发电学报,2026,45(6):23-36,14.基金项目
湖北省智慧水电技术创新中心开放研究基金(HBCXZX-JJ-202409) （HBCXZX-JJ-202409）
