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

• Published in: Energies (Basel) Vol. 17; no. 20; p. 5087
• Main Authors: Pierart, Fabian G., Campos, Pedro G., Basoalto, Cristian E., Rohten, Jaime, Davey, Thomas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Algorithms; Alternative energy sources; Control algorithms; Data mining; Deep learning; Energy; Energy industry; experimental validation; machine learning; Mathematical models; resistive control; wave energy; Wave power
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17205087

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.