Publicación: EXPERIMENTAL IMPLEMENTATION OF REINFORCEMENT LEARNING APPLIED TO MAXIMISE ENERGY FROM A WAVE ENERGY CONVERTER
Fecha
2024
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Energies
Resumen
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.
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wave energy, resistive control, machine learning, experimental validation
