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

Fabian G. Pierart (), Pedro G. Campos, Cristian E. Basoalto, Jaime Rohten and Thomas Davey
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Fabian G. Pierart: Department of Mechanical Engineering, College of Engineering, Universidad del Bío-Bío, 4051381 Collao Avenue, Concepción 1202, Chile
Pedro G. Campos: Department of Information Systems, Universidad del Bío-Bío, Concepción 1202, Chile
Cristian E. Basoalto: Department of Mechanical Engineering, College of Engineering, Universidad del Bío-Bío, 4051381 Collao Avenue, Concepción 1202, Chile
Jaime Rohten: Department of Electric and Electronic Engineering, College of Engineering, Universidad del Bío-Bío, 4051381 Collao Avenue, Concepción 1202, Chile
Thomas Davey: School of Engineering, Institute for Energy Systems, FloWave Ocean Energy Research Facility, The University of Edinburgh, Max Born Crescent, Edinburgh EH9 3BF, UK

Energies, 2024, vol. 17, issue 20, 1-13

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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