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 the 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, 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 magnitudes. 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.@en