Data-Driven Unsupervised Stator Interturn Short-Circuit Fault Detection for Doubly Fed Induction Generator Wind Turbines

Abstract Inter-turn short-circuit (ITSC) faults in doubly fed induction generators (DFIGs) pose a significant challenge for reliable wind turbine operation. Existing fault–detection approaches often rely on data that neglect key aeroelastic, electromechanical, control, and grid interactions, limiting their applicability to real operating conditions. To address this limitation, this study develops a coupled OpenFAST-Simulink simulation framework capable of generating physically realistic datasets of healthy operation and ITSC faults across multiple severity levels. Leveraging this data, an unsupervised DL fault-detection scheme based on an autoencoder is proposed, trained exclusively on healthy data and evaluated using faulty scenarios. The results show effective detection of medium and high severity faults, with longer detection times as severity decreases due to weaker system disturbances.