<ul><li>Intelligent condition monitoring of wind turbines is essential for reducing downtimes. Machine learning models trained on wind turbine operation data are commonly used to detect anomalies and operation faults.</li><li>A novel generative deep learning approach is presented to make SCADA samples from wind turbines with limited training data resemble those with representative training data.</li><li>The proposed technique improves fault diagnosis in wind turbines with scarce data, achieving similar anomaly scores to models trained with abundant data.</li><li>This research direction provides a promising solution for improving anomaly detection and fault diagnosis in wind farms with limited training data.</li></ul>

Fault Diagnosis in New Wind Turbines using Knowledge from Existing Turbines by Generative Domain Adaptation

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