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Floating Offshore Wind Turbines (FOWT) provided new potential in harvesting wind energy in far offshore deep-sea regions and contributed to the world decarbonization Net-Zero target. Providing structural health monitoring (SHM) is crucial for ensuring the structural integrity of FOWT in lifecycle. However, the SHM is technically challenging with high Operational and Maintenance Expenditure (OPEX). Recently, Digital Twin (DT) and advanced sensor technologies offer alternative solutions to provide effective strategy in SHM remotely. Data-driven DT with deep learning models can formulate highly nonlinear dynamics systems. Yet, these existing models only perform the “black box” prediction without explicitly modeling the spatial-temporal relationship and consider only homogenous loading exerted in contrast to the complicated loading combination of FOWT with wind, wave and sea current. To address the existing modelling limitations, a new Graph Neural Network (GNN)-Encoder-Decoder-Long Short-Term Memory (LSTM) surrogate model of FOWT is presented in this work, which can perform 50 times faster than the real-time of simulation data set with accurate prediction of wind turbine tower bottom forces in the dominant dynamic modes force-aft and side-side directions. The training data is based on the software QBlade simulation and focuses on the OC4 5MW DeepCwind FOWT structure. A holistic quantitative analysis is carried out to validate the tractable latent space vectors for this complex FOWT system.

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Discover the Tractable Latent Space of Floating Offshore Wind Turbine Based on a Novel GNN-Encoder-Decoder-LSTM Deep Learning Architecture

  • 会议论文

  • 发表日期：

    2025年

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  • Kobe Hoi-Yin Yung,
  • Qing Xiao,
  • Xiuqing Xing,
  • Atilla Incecik,
  • Chang Wei Kang

摘要

Floating Offshore Wind Turbines (FOWT) provided new potential in harvesting wind energy in far offshore deep-sea regions and contributed to the world decarbonization Net-Zero target. Providing structural health monitoring (SHM) is crucial for ensuring the structural integrity of FOWT in lifecycle. However, the SHM is technically challenging with high Operational and Maintenance Expenditure (OPEX). Recently, Digital Twin (DT) and advanced sensor technologies offer alternative solutions to provide effective strategy in SHM remotely. Data-driven DT with deep learning models can formulate highly nonlinear dynamics systems. Yet, these existing models only perform the “black box” prediction without explicitly modeling the spatial-temporal relationship and consider only homogenous loading exerted in contrast to the complicated loading combination of FOWT with wind, wave and sea current. To address the existing modelling limitations, a new Graph Neural Network (GNN)-Encoder-Decoder-Long Short-Term Memory (LSTM) surrogate model of FOWT is presented in this work, which can perform 50 times faster than the real-time of simulation data set with accurate prediction of wind turbine tower bottom forces in the dominant dynamic modes force-aft and side-side directions. The training data is based on the software QBlade simulation and focuses on the OC4 5MW DeepCwind FOWT structure. A holistic quantitative analysis is carried out to validate the tractable latent space vectors for this complex FOWT system.