Author(s): C Tubeuf; T Bernhardt; J Aus Der Schmitten; C Heitzinger; F Birkelbach; R Hofmann; A Maly

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Keywords: No Keywords

Abstract: Reinforcement learning (RL) offers a promising framework for controlling transient processes in hydraulic machines. In this study, we apply RL to the start-up control of a reversible pump-turbine in a simulation of a pumped storage power system. A physics-based simulation environment capturing the system’s key hydraulic and mechanical dynamics is developed to train a Proximal Policy Optimization agent. Compared to a conventional control approach, the RL-based policy yields a higher cumulative reward in simulation by significantly reducing start-up time and cumulative operational cost. The approach demonstrates the potential of RL in a simulated pump start-up case, highlighting its ability to autonomously discover efficient control strategies for highly dynamic processes, such as pump start-up, without relying on predefined procedural logic. Future work includes experimental validation on a laboratory-scale pump-turbine to further investigate the policy’s performance under real-world conditions.

DOI: https://doi.org/10.1088/1755-1315/1561/1/012035

Year: 2025