Publicación:
EXPERIMENTAL IMPLEMENTATION OF REINFORCEMENT LEARNING APPLIED TO MAXIMISE ENERGY FROM A WAVE ENERGY CONVERTER

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dc.creatorCRISTIAN EDUARDO BASOALTO CONTRERAS
dc.creatorFABIÁN GONZALO PIERART VÁSQUEZ
dc.creatorJAIME ADDIN ROHTEN CARRASCO
dc.creatorPEDRO GERÓNIMO CAMPOS SOTO
dc.date2024
dc.date.accessioned2025-01-10T15:51:24Z
dc.date.available2025-01-10T15:51:24Z
dc.date.issued2024
dc.description.abstractWAVE 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.
dc.formatapplication/pdf
dc.identifier.doi10.3390/en17205087
dc.identifier.issn1996-1073
dc.identifier.issn1996-1073
dc.identifier.urihttps://repositorio.ubiobio.cl/handle/123456789/13971
dc.languagespa
dc.publisherEnergies
dc.relation.uri10.3390/en17205087
dc.rightsPUBLICADA
dc.subjectwave energy
dc.subjectresistive control
dc.subjectmachine learning
dc.subjectexperimental validation
dc.titleEXPERIMENTAL IMPLEMENTATION OF REINFORCEMENT LEARNING APPLIED TO MAXIMISE ENERGY FROM A WAVE ENERGY CONVERTER
dc.typeARTÍCULO
dspace.entity.typePublication
ubb.EstadoPUBLICADA
ubb.Otra ReparticionDEPARTAMENTO DE INGENIERIA MECANICA
ubb.Otra ReparticionDEPARTAMENTO DE INGENIERIA MECANICA
ubb.Otra ReparticionDEPARTAMENTO DE INGENIERIA ELECTRICA Y ELECTRONICA
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.SedeCONCEPCIÓN
ubb.SedeCONCEPCIÓN
ubb.SedeCONCEPCIÓN
ubb.SedeCONCEPCIÓN
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