Research on low-energy consumption automatic real-time regulation of cascade gates and pumps in open-canal based on reinforcement learning

Abstract

Cascade gates and pumps are common hydraulic structures in the open-canal section of water transfer projects, characterized by high energy consumption and substantial costs, making it challenging to regulate. By implementing cascade gates regulation to control the hydraulic process, lift distribution of pump stations can be optimized, thus enhancing operational efficiency and reducing energy consumption. However, the selection of control models and parameter optimization is difficult because hydraulic processes are nonlinear, high-dimensional, large hysteresis, strong coupling, and time-varying. This study considers minimum energy consumption of pump stations as the regulation objective and employs the reinforcement learning (RL) algorithm for optimization regulation (OR) within a typical canal section of the Jiaodong Water Transfer Project. Our results demonstrate that after regulating, OR can precisely control the water level to achieve the high efficiency lift interval of pump station, enhancing efficiency by 4.12–6.02% compared to previous operation. Moreover, using optimized hyperparameters group, the RL model proves robust under different work conditions. The proposed method is suitable for complex hydraulic processes, highlighting its potential to support more effective decision-making in water resources regulation.