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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月1日

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  • Marcos D. Saavedra,
  • Nicolás Faedo,
  • Fernando A. Inthamoussou,
  • Facundo D. Mosquera,
  • Fabricio Garelli

摘要

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.