On Building Predictive Digital Twin Incorporating Wave Predicting Capabilities: Case Study on UMaine Experimental Campaign - FOCAL
Author(s): |
Yuksel R. Alkarem
Kimberly Huguenard Richard W. Kimball Babak Hejrati Ian Ammerman Amir R. Nejad Jacob Fontaine Reza Heshami Stéphan Grilli |
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Medium: | journal article |
Language(s): | English |
Published in: | Journal of Physics: Conference Series, 1 April 2024, 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. |
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data sheet - Reference-ID
10777704 - Published on:
12/05/2024 - Last updated on:
12/05/2024