On Building Predictive Digital Twin Incorporating Wave Predicting Capabilities: Case Study on UMaine Experimental Campaign - FOCAL
- University of Maine, Orono, ME (United States)
- Norwegian University of Science and Technology (NTNU), Trondheim (Norway)
- University of Rhode Island, Narragansett, RI (United States)
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.
- Research Organization:
- Univ. of Maine, Orono, ME (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- Grant/Contract Number:
- SC0022103
- OSTI ID:
- 2441163
- Journal Information:
- Journal of Physics. Conference Series, Vol. 2745, Issue 1; Conference: WindEurope Annual Event, Bilbao (Spain), 20-22 Mar 2024; ISSN 1742-6588
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
Digital twin
Predictive model
Offshore wind energy
Experimental campaign
Ocean waves
Artificial inteligence
Long short term memory
RNN
Wave reconstruction prediction models
Floating offshore wind turbines
Wind-wave basin testing
Kalman filter
Time-domain analysis
Phase-resolved predictions
Sensitivity analysis