Calculation method for mooring monitoring data of floating wind turbines using LSTM model
Addressing the challenge of accurately converting the monitoring declination of floating wind turbine into mooring tension responses,this study introduces a hybrid neural network model that integrates Long Short-Term Memory(LSTM)networks with Fully Connected Networks(FCNs).Initially,a coupled time-domain simulation model considering wind,wave and current interactions was constructed for the in-service"Fuyao"floating wind turbine to obtain mooring tension and declination data under various conditions,forming the dataset for neural network training and validation.Within the framework of Keras,a combined network model of FCN and LSTM was developed and trained using the Adam optimizer,and the network parameters with the minimal validation loss were ultimately preserved.Comparative analysis with traditional catenary equation predictions revealed that the proposed neural network model demonstrated enhanced accuracy in forecasting mooring tension in terms of time series,mean,standard deviation and peak values.The prediction errors for the maximum mooring tension of the two are 6.4%and 17.1%respectively.
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