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In floating offshore wind turbines (FOWTs), mooring lines face fatigue degradation under wind and hydrodynamic loads, demanding real-time monitoring for safety. Conventional methods, like the TN or SN curve, mandate stress fluctuations under tensile loads to be measured from each mooring line via integrating costly underwater sensors, potentially inflating the cost of energy. This study introduces a sequence-to-sequence (Seq2Seq) surrogate model for real-time fatigue monitoring of a FOWT mooring system, offering a novel approach to estimating mooring line fatigue by connecting platform displacement to tension variations. A 5MW OC4 semi-submersible wind turbine model is used to simulate the dataset, train the Seq2Seq model, and establish the intricate connection between platform motion and tension fluctuations across diverse sea conditions. This surrogate model effectively captures the nonlinear relationship, allowing real-time predictions of fatigue damage in mooring lines.

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Fatigue Damage Assessment of FOWT Mooring Lines Using Sequence-to-Sequence Based Indirect Sensing

  • 会议论文

  • 发表日期：

    2026年

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  • Rohit Kumar,
  • Subhamoy Sen,
  • Arvind Keprate

摘要

In floating offshore wind turbines (FOWTs), mooring lines face fatigue degradation under wind and hydrodynamic loads, demanding real-time monitoring for safety. Conventional methods, like the TN or SN curve, mandate stress fluctuations under tensile loads to be measured from each mooring line via integrating costly underwater sensors, potentially inflating the cost of energy. This study introduces a sequence-to-sequence (Seq2Seq) surrogate model for real-time fatigue monitoring of a FOWT mooring system, offering a novel approach to estimating mooring line fatigue by connecting platform displacement to tension variations. A 5MW OC4 semi-submersible wind turbine model is used to simulate the dataset, train the Seq2Seq model, and establish the intricate connection between platform motion and tension fluctuations across diverse sea conditions. This surrogate model effectively captures the nonlinear relationship, allowing real-time predictions of fatigue damage in mooring lines.