A Generalizable Model for Fault Detection in Offshore Wind Turbines Based on DeepLearning

Soorena Salari, Nasser Sadati

This paper presents a new deep learning-based model for fault detection in offshore wind turbines. To design a generalizable model for fault detection, we use 5 sensors and a sliding window to exploit the inherent temporal information contained in the raw time-series data obtained from sensors. The proposed model uses the nonlinear relationships among multiple sensor variables and the temporal dependency of each sensor on others that considerably increases the performance of fault detection model. A 10-fold cross-validation is used to verify the generalization of the model and evaluate the classification metrics. To evaluate the performance of the model, simulated data from a benchmark floating offshore wind turbine (FOWT) with supervisory control and data acquisition (SCADA) are used. The results illustrate that the proposed model would accurately disclose and classify more than 99% of the faults. Moreover, it is generalizable and can be used to detect faults for different types of systems.

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