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1- Babol Noshirvani University of Technology, Babol, Iran
Abstract: (107 Views)
Accurate forecasting of operational parameters is essential for predictive maintenance and digital twinning of offshore wind turbines. Using a unique dataset from the Levenmouth 7MW demonstration turbine, we compare a purely data-driven stacked ensemble model (StackedRidge) with a novel physics-informed neural network (GET-PINN) that incorporates the Energy Gradient (K) parameter from Energy Gradient Theory (GET). The StackedRidge model achieves superior predictive accuracy (RMSE = 0.2976, R² = 0.9731) for barometric pressure signals. In contrast, the GET-PINN provides valuable physics-aware diagnostics by jointly estimating the flow instability parameter K, supporting the detection of phenomena such as vortex-induced vibrations (VIV), albeit with higher forecasting error. These results highlight the complementary strengths of the two approaches: the stacked ensemble for high-fidelity point forecasting and the GET-PINN for interpretable, physics-guided maintenance decision support in operational wind farm digital twins.
Keywords: Floating Offshore Wind Turbine (FWOT), Physics-Informed Neural Networks (PINN), Gradient Energy Theory (GET), Digital Twin, Hybrid ML
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Highlights
(1) StackedRidge forecasts pressure with RMSE=0.298 and R²=0.973.
(2) GET-PINN jointly predicts target and flow instability K.
(3) K exceeding 385 signals vortex-induced vibration risk.
(4) Data-driven ensemble outperforms PINN for autocorrelated signals.
(5) Hybrid model merges high accuracy with physics interpretability.
(1) StackedRidge forecasts pressure with RMSE=0.298 and R²=0.973.
(2) GET-PINN jointly predicts target and flow instability K.
(3) K exceeding 385 signals vortex-induced vibration risk.
(4) Data-driven ensemble outperforms PINN for autocorrelated signals.
(5) Hybrid model merges high accuracy with physics interpretability.
Type of Study: Research Paper |
Subject:
Offshore Hydrodynamic
Received: 2025/11/20 | Accepted: 2026/04/21
Received: 2025/11/20 | Accepted: 2026/04/21
Audio file of the article abstract [MP3 2138 KB] (13 Download)
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