Experimental Implementation of Reinforcement Learning Applied to Maximise Energy from a Wave Energy Converter

Abstract

Wave energy has the potential to provide a sustainable solution for global energy demands, particularly in coastal regions. This study explores the use of reinforcement learning (RL), specifically the Q-learning algorithm, to optimise the energy extraction capabilities of a wave energy converter (WEC) using a single-body point absorber with resistive control. Experimental validation demonstrated that Q-learning effectively optimises the power take-off (PTO) damping coefficient, leading to an energy output that closely aligns with theoretical predictions. The stability observed after approximately 40 episodes highlights the capability of Q-learning for real-time optimisation, even under irregular wave conditions. The results also showed an improvement in efficiency of 12% for the theoretical case and 11.3% for the experimental case from the initial to the optimised state, underscoring the effectiveness of the RL strategy. The simplicity of the resistive control strategy makes it a viable solution for practical engineering applications, reducing the complexity and cost of deployment. This study provides a significant step towards bridging the gap between the theoretical modelling and experimental implementation of RL-based WEC systems, contributing to the advancement of sustainable ocean energy technologies.

Keywords

• experimental validation
• machine learning
• resistive control
• wave energy