Ultra-Short-Term Mooring Forces Forecasting for Floating Wind Turbines With Response-Frequency-Informed Deep Learning and On-Site Data

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Abstract Accurate dynamic response forecasting is crucial for the operational monitoring, maintenance, and dynamic control of floating wind turbines (FWT). In this study, an ultra-short-term forecasting model of mooring line tension for a full-size FWT is developed by combining a long short-term memory (LSTM) encoder–decoder network with frequency decomposition (FD), i.e., the LSTM-FD method. After presenting the principles of the LSTM-FD-based ultra-short-term forecasting model, full-scaled measurement data from the Hywind Scotland wind farm are used to validate and demonstrate the accuracy of the proposed model. The result shows that the LSTM-FD method has good consistency between different datasets, and higher accuracy than the LSTM without frequency decomposition. For instance, achieving a 10% enhancement in the accuracy of maximum forecasting for line 1 bridle 1 over a 60-s horizon. More importantly, compared to traditional methods, LSTM-FD improves accuracy by using frequency decomposition to better capture changes in mooring forces of FWT across different frequency ranges. In summary, the proposed method can facilitate more precise and timely maintenance scheduling, reduce operational costs, and enhance the overall safety of FWT operations by mitigating the risk of mooring line failures.