Comparative evaluation of data-based estimators for wave-induced force in wave energy converters

Autores
Saavedra, Marcos David; Faedo, Nicolás Ezequiel; Inthamoussou, Fernando Ariel; Mosquera, Facundo; Garelli, Fabricio
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Wave energy conversion technology emerges as a promising approach to renewable energy generation, offering a consistent and predictable power source that complements intermittent renewable energy sources such as solar and wind power. Achieving optimal ocean wave energy absorption requires precise knowledge of the so-called wave excitation force, which is typically estimated through model-based techniques reliant on accurate system descriptions. However, uncertainties inherent to hydrodynamic modelling often limit the reliability of these approaches. To address this challenge, this paper presents a comprehensive evaluation of model-free data-based estimators, for wave excitation torque estimation in Wavestar like wave energy converters (WECs). The study examines various neural network architectures, including static models (feedforward networks) and those incorporating temporal dynamics (recurrent neural networks and long short-term memory networks). The analysis examines the impact of utilising multiple input combinations, ranging from motion variables to configurations enhanced with surrounding wave height measurements from the device’s vicinity. Input selection is guided by correlation analysis and spectral coherence evaluation to ensure physical relevance and practical feasibility. Estimators are trained and tested using experimental data obtained from a comprehensive wave tank campaign emulating diverse sea state conditions. The results demonstrate that architectures incorporating temporal considerations achieve superior performance, particularly under wide-banded sea states. A comparative analysis with a model-based estimator, implemented via a Kalman–Bucy Filter with a harmonic oscillator expansion, highlights the advantages of neural networks, especially under challenging conditions where model-based approaches face significant limitations. These findings underscore the capability of data-based strategies to reduce dependence on potentially complex and uncertain analytical models, offering a promising alternative for improving WEC control systems.
Fil: Saavedra, Marcos David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Faedo, Nicolás Ezequiel. Politecnico di Torino; Italia
Fil: Inthamoussou, Fernando Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Mosquera, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Garelli, Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Materia
Wave energy converters
Wave excitation force
Data-based estimation
Artificial neural networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/275675
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Comparative evaluation of data-based estimators for wave-induced force in wave energy convertersSaavedra, Marcos DavidFaedo, Nicolás EzequielInthamoussou, Fernando ArielMosquera, FacundoGarelli, FabricioWave energy convertersWave excitation forceData-based estimationArtificial neural networkshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Wave energy conversion technology emerges as a promising approach to renewable energy generation, offering a consistent and predictable power source that complements intermittent renewable energy sources such as solar and wind power. Achieving optimal ocean wave energy absorption requires precise knowledge of the so-called wave excitation force, which is typically estimated through model-based techniques reliant on accurate system descriptions. However, uncertainties inherent to hydrodynamic modelling often limit the reliability of these approaches. To address this challenge, this paper presents a comprehensive evaluation of model-free data-based estimators, for wave excitation torque estimation in Wavestar like wave energy converters (WECs). The study examines various neural network architectures, including static models (feedforward networks) and those incorporating temporal dynamics (recurrent neural networks and long short-term memory networks). The analysis examines the impact of utilising multiple input combinations, ranging from motion variables to configurations enhanced with surrounding wave height measurements from the device’s vicinity. Input selection is guided by correlation analysis and spectral coherence evaluation to ensure physical relevance and practical feasibility. Estimators are trained and tested using experimental data obtained from a comprehensive wave tank campaign emulating diverse sea state conditions. The results demonstrate that architectures incorporating temporal considerations achieve superior performance, particularly under wide-banded sea states. A comparative analysis with a model-based estimator, implemented via a Kalman–Bucy Filter with a harmonic oscillator expansion, highlights the advantages of neural networks, especially under challenging conditions where model-based approaches face significant limitations. These findings underscore the capability of data-based strategies to reduce dependence on potentially complex and uncertain analytical models, offering a promising alternative for improving WEC control systems.Fil: Saavedra, Marcos David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Faedo, Nicolás Ezequiel. Politecnico di Torino; ItaliaFil: Inthamoussou, Fernando Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Mosquera, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Garelli, Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaSpringer2025-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/275675Saavedra, Marcos David; Faedo, Nicolás Ezequiel; Inthamoussou, Fernando Ariel; Mosquera, Facundo; Garelli, Fabricio; Comparative evaluation of data-based estimators for wave-induced force in wave energy converters; Springer; Journal of Ocean Engineering and Marine Energy; 9-2025; 1-142198-6452CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/10.1007/s40722-025-00427-4info:eu-repo/semantics/altIdentifier/doi/10.1007/s40722-025-00427-4info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-12-03T09:24:40Zoai:ri.conicet.gov.ar:11336/275675instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-12-03 09:24:41.079CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Comparative evaluation of data-based estimators for wave-induced force in wave energy converters
title Comparative evaluation of data-based estimators for wave-induced force in wave energy converters
spellingShingle Comparative evaluation of data-based estimators for wave-induced force in wave energy converters
Saavedra, Marcos David
Wave energy converters
Wave excitation force
Data-based estimation
Artificial neural networks
title_short Comparative evaluation of data-based estimators for wave-induced force in wave energy converters
title_full Comparative evaluation of data-based estimators for wave-induced force in wave energy converters
title_fullStr Comparative evaluation of data-based estimators for wave-induced force in wave energy converters
title_full_unstemmed Comparative evaluation of data-based estimators for wave-induced force in wave energy converters
title_sort Comparative evaluation of data-based estimators for wave-induced force in wave energy converters
dc.creator.none.fl_str_mv Saavedra, Marcos David
Faedo, Nicolás Ezequiel
Inthamoussou, Fernando Ariel
Mosquera, Facundo
Garelli, Fabricio
author Saavedra, Marcos David
author_facet Saavedra, Marcos David
Faedo, Nicolás Ezequiel
Inthamoussou, Fernando Ariel
Mosquera, Facundo
Garelli, Fabricio
author_role author
author2 Faedo, Nicolás Ezequiel
Inthamoussou, Fernando Ariel
Mosquera, Facundo
Garelli, Fabricio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Wave energy converters
Wave excitation force
Data-based estimation
Artificial neural networks
topic Wave energy converters
Wave excitation force
Data-based estimation
Artificial neural networks
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Wave energy conversion technology emerges as a promising approach to renewable energy generation, offering a consistent and predictable power source that complements intermittent renewable energy sources such as solar and wind power. Achieving optimal ocean wave energy absorption requires precise knowledge of the so-called wave excitation force, which is typically estimated through model-based techniques reliant on accurate system descriptions. However, uncertainties inherent to hydrodynamic modelling often limit the reliability of these approaches. To address this challenge, this paper presents a comprehensive evaluation of model-free data-based estimators, for wave excitation torque estimation in Wavestar like wave energy converters (WECs). The study examines various neural network architectures, including static models (feedforward networks) and those incorporating temporal dynamics (recurrent neural networks and long short-term memory networks). The analysis examines the impact of utilising multiple input combinations, ranging from motion variables to configurations enhanced with surrounding wave height measurements from the device’s vicinity. Input selection is guided by correlation analysis and spectral coherence evaluation to ensure physical relevance and practical feasibility. Estimators are trained and tested using experimental data obtained from a comprehensive wave tank campaign emulating diverse sea state conditions. The results demonstrate that architectures incorporating temporal considerations achieve superior performance, particularly under wide-banded sea states. A comparative analysis with a model-based estimator, implemented via a Kalman–Bucy Filter with a harmonic oscillator expansion, highlights the advantages of neural networks, especially under challenging conditions where model-based approaches face significant limitations. These findings underscore the capability of data-based strategies to reduce dependence on potentially complex and uncertain analytical models, offering a promising alternative for improving WEC control systems.
Fil: Saavedra, Marcos David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Faedo, Nicolás Ezequiel. Politecnico di Torino; Italia
Fil: Inthamoussou, Fernando Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Mosquera, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Garelli, Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
description Wave energy conversion technology emerges as a promising approach to renewable energy generation, offering a consistent and predictable power source that complements intermittent renewable energy sources such as solar and wind power. Achieving optimal ocean wave energy absorption requires precise knowledge of the so-called wave excitation force, which is typically estimated through model-based techniques reliant on accurate system descriptions. However, uncertainties inherent to hydrodynamic modelling often limit the reliability of these approaches. To address this challenge, this paper presents a comprehensive evaluation of model-free data-based estimators, for wave excitation torque estimation in Wavestar like wave energy converters (WECs). The study examines various neural network architectures, including static models (feedforward networks) and those incorporating temporal dynamics (recurrent neural networks and long short-term memory networks). The analysis examines the impact of utilising multiple input combinations, ranging from motion variables to configurations enhanced with surrounding wave height measurements from the device’s vicinity. Input selection is guided by correlation analysis and spectral coherence evaluation to ensure physical relevance and practical feasibility. Estimators are trained and tested using experimental data obtained from a comprehensive wave tank campaign emulating diverse sea state conditions. The results demonstrate that architectures incorporating temporal considerations achieve superior performance, particularly under wide-banded sea states. A comparative analysis with a model-based estimator, implemented via a Kalman–Bucy Filter with a harmonic oscillator expansion, highlights the advantages of neural networks, especially under challenging conditions where model-based approaches face significant limitations. These findings underscore the capability of data-based strategies to reduce dependence on potentially complex and uncertain analytical models, offering a promising alternative for improving WEC control systems.
publishDate 2025
dc.date.none.fl_str_mv 2025-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/275675
Saavedra, Marcos David; Faedo, Nicolás Ezequiel; Inthamoussou, Fernando Ariel; Mosquera, Facundo; Garelli, Fabricio; Comparative evaluation of data-based estimators for wave-induced force in wave energy converters; Springer; Journal of Ocean Engineering and Marine Energy; 9-2025; 1-14
2198-6452
CONICET Digital
CONICET
url http://hdl.handle.net/11336/275675
identifier_str_mv Saavedra, Marcos David; Faedo, Nicolás Ezequiel; Inthamoussou, Fernando Ariel; Mosquera, Facundo; Garelli, Fabricio; Comparative evaluation of data-based estimators for wave-induced force in wave energy converters; Springer; Journal of Ocean Engineering and Marine Energy; 9-2025; 1-14
2198-6452
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/10.1007/s40722-025-00427-4
info:eu-repo/semantics/altIdentifier/doi/10.1007/s40722-025-00427-4
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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