Affiliation:
1. a State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, China
2. b Key Laboratory of Digital Twin Watershed, Ministry of Water Resources, Beijing 100048, China
Abstract
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.
Funder
National Natural Science Foundation of China
Shandong Province Water Diversion Project Operation and Maintenance Center Cooperation Project