Abstract
Due to the global commitment to mitigate climate warming by replacing fossil fuels, the global wind power installed capacity continues to increase. The reliability of wind turbines is a vital factor in ensuring their safety and profitability. Early fault detection can significantly reduce the maintenance costs of wind farms. This paper constructs a Conv1d-GRU-MHA model for detecting blade structural damage in floating offshore wind turbines (FOWTs) operating under complex conditions. This model combines a one-dimensional convolutional neural network (Conv1d), gated recurrent units (GRU), and multi-head attention mechanism (MHA) to provide accurate detection of blade structural damage. This amalgamation enables the model to comprehend diverse scales of sequence data, effectively capturing intricate patterns and abstract features. The NREL OpenFAST software was used to model a 5 MW wind turbine, where structural damage was introduced on the blades. Acceleration data was collected from blades and tower in different directions and under different wind turbulence intensities (WTI). The Conv1d-GRU-MHA network predicts blade health with an accuracy of up to 97.58 % under the 5 % turbulence level background, with both precision and recall being superior to the comparative models.
Published Version
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