An Artificial Neural Network-Based Approach for Instantaneous Estimation of the Sea Surface Elevation in a Wave Farm

Many control strategies to maximise the captured power by wave energy converters depend on the knowledge of the incoming wave in a short term future. However, the wave prediction may be unpractical in a wave farm, since many measurement systems would be required. As a part of a prediction system, this paper presents an estimator of the instantaneous sea surface elevation in an open-field sea area for potential wave farm deployment based on a single measurement point. The approach is based on a time delay artificial neural network and the paper explores the performance of the estimation for a given field and the sensitivity to different sea characteristics. The proposed realisation of the artificial neural network is found to be accurate and robust, resulting in a useful tool for a wave prediction system in a farm for control purposes.

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Publication Details

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UPM Digital Archive (Technical University of Madrid)
Published
2025-10-16
Primary Topic
Wave and Wind Energy Systems
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article
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article

An Artificial Neural Network-Based Approach for Instantaneous Estimation of the Sea Surface Elevation in a Wave Farm

Sergio Martínez González, Hugo Rocha Mendonca, Rosa María de Castro Fernández
UPM Digital Archive (Technical University of Madrid)
Wave and Wind Energy Systems
article

An Artificial Neural Network-Based Approach for Instantaneous Estimation of the Sea Surface Elevation in a Wave Farm

Sergio Martínez González, Hugo Rocha Mendonca, Rosa María de Castro Fernández
article en

Abstract

Many control strategies to maximise the captured power by wave energy converters depend on the knowledge of the incoming wave in a short term future. However, the wave prediction may be unpractical in a wave farm, since many measurement systems would be required. As a part of a prediction system, this paper presents an estimator of the instantaneous sea surface elevation in an open-field sea area for potential wave farm deployment based on a single measurement point. The approach is based on a time delay artificial neural network and the paper explores the performance of the estimation for a given field and the sensitivity to different sea characteristics. The proposed realisation of the artificial neural network is found to be accurate and robust, resulting in a useful tool for a wave prediction system in a farm for control purposes.

UPM Digital Archive (Technical University of Madrid)
Affordable and clean energy
Openalex Percentile: Top 31%
Wave and Wind Energy Systems
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