An Effective Approach for Rotor Electrical Asymmetry Detection in Wind Turbine DFIGs

Determining the magnitude of particular fault
signature components (FSCs) generated by wind turbine
(WT) faults from current signals has been used as an
effective way to detect early abnormalities. However, the
WT current signals are time-varying due to the constantly
varying generator speed. The WT frequently operates with
the generator close to synchronous speed, resulting in
FSCs manifesting themselves in the vicinity of the supply
frequency and its harmonics, making their detection more
challenging. To address this challenge, the detection of rotor
electrical asymmetry in WT doubly-fed induction generators
(DFIGs), indicative of common winding, brush gear
or high resistance connection faults, has been investigated
using a test-rig under three different driving conditions,
and then an effective extended Kalman filter (EKF) based
method is proposed to iteratively estimate the FSCs and
track their magnitude. The proposed approach has been
compared with a continuous wavelet transform (CWT) and
an iterative localized discrete Fourier-transform (IDFT). The
experimental results demonstrate that the CWT and IDFT
algorithms fail to track the FSCs at low load operation
near synchronous speed. In contrast, the EKF was more
successful in tracking the FSCs magnitude in all operating
conditions, unambiguously determining the severity of the
faults over time and providing significant gains in both
computational efficiency and accuracy of fault diagnosis.