Bearing fault detection for direct-drive wind turbines via stator current spectrum analysis

Abstract

Bearing faults constitute a significant portion of all faults in wind turbine generators (WTGs). Current-based bearing fault detection has significant advantages over traditional vibration-based methods in terms of cost, implementation, and system reliability. This paper proposes a method based on stator current power spectral density (PSD) analysis for bearing fault detection of direct-drive WTGs. In the proposed method, appropriate interpolation/up-sampling and down-sampling algorithms are designed to convert the variable fundamental frequency of the stator current to a fixed frequency according to the estimated fundamental speed of the WTG. Consequently, the characteristic frequencies of bearing faults can be clearly identified form the resulting stator current PSD. Experimental results show that the proposed method can effectively detect bearing outer-race and inner-race defects for a direct-drive WTG.