Current-based fault detection and identification for wind turbine drivetrain gearboxes

Abstract

This paper proposes a new fault detection and identification framework for drivetrain gearboxes of wind turbines equipped with doubly-fed induction generators (DFIGs) based on the fusion of DFIG stator and rotor current signals. First, the characteristic frequencies of gearbox faults in DFIG stator and rotor currents are analyzed. Different time- and frequency-domain features of gearbox faults in DFIG stator and rotor current signals are then defined and the methods to extract these features are introduced. These features are used as the inputs of multiclass support vector machines (SVMs) with probabilistic outputs for fault mode identification. Different schemes that use a single stator or rotor current signal or both stator and rotor current signals for the feature- or decision-level information fusion are designed. Experimental results obtained from a DFIG wind turbine drivetrain test rig are provided to validate the proposed current-based fault detection and identification framework.