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On Building Predictive Digital Twin Incorporating Wave Predicting Capabilities: Case Study on UMaine Experimental Campaign - FOCAL

Author(s):








Medium: journal article
Language(s): English
Published in: Journal of Physics: Conference Series, , n. 1, v. 2745
Page(s): 012001
DOI: 10.1088/1742-6596/2745/1/012001
Abstract:

The response of floating wind turbines (FWT) are susceptible to stochastic wave variations. For the optimal operation of FWT, a comprehensive understanding of the phaseresolved wave dynamics and the consequential system response is crucial for real-time monitoring and control. A multi-variate, multi-step, long short term memory (MLSTM), a type of recurrent neural network (RNN) is used to capture complex system dynamics for real-time application. Results indicate that the integration of a wave prediction-reconstruction (WRP) model substantially enhances prediction accuracy by 50% on average relative to the baseline model. The improvement is consistent across various wave extremity and prediction horizons, thereby significantly broadening the scope for timely and precise predictive capabilities.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1088/1742-6596/2745/1/012001.
  • About this
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  • Reference-ID
    10777704
  • Published on:
    12/05/2024
  • Last updated on:
    12/05/2024
 
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