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Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

Author

Listed:
  • Mohammad Barooni

    (Ocean Engineering and Marine Sciences, Florida Institute of Technology, Melbourne, FL 32901, USA
    These authors contributed equally to this work.)

  • Deniz Velioglu Sogut

    (Ocean Engineering and Marine Sciences, Florida Institute of Technology, Melbourne, FL 32901, USA
    These authors contributed equally to this work.)

Abstract

The design and optimization of floating offshore wind turbines (FOWTs) pose significant challenges, stemming from the complex interplay among aerodynamics, hydrodynamics, structural dynamics, and control systems. In this context, this study introduces an innovative method for forecasting the dynamic behavior of FOWTs under various conditions by merging Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) network. This model outperforms traditional numerical models by delivering precise and efficient predictions of dynamic FOWT responses. It adeptly handles computational complexities and reduces processing duration, while maintaining flexibility and effectively managing nonlinear dynamics. The model’s prowess is showcased through an analysis of a spar-type FOWT in a multivariate parallel time series dataset using the CNN–GRU structure. The outcomes are notably promising, underscoring the model’s proficiency in accurately forecasting the performance of FOWTs.

Suggested Citation

  • Mohammad Barooni & Deniz Velioglu Sogut, 2024. "Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model," Clean Technol., MDPI, vol. 6(2), pages 1-14, March.
    • Handle: RePEc:gam:jcltec:v:6:y:2024:i:2:p:21-431:d:1366391
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    References listed on IDEAS

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    1. Choe, Do-Eun & Kim, Hyoung-Chul & Kim, Moo-Hyun, 2021. "Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades," Renewable Energy, Elsevier, vol. 174(C), pages 218-235.
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