Abstract
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.
Highlights
I N RECENT years, wind energy has experienced substantial growth compared to other forms of power generation
This paper proposed the use of an extended Kalman filter (EKF) in the detection of rotor electrical unbalance fault, indicative of common winding, brush gear, or high resistance connection faults, in a wind turbine (WT) doubly fed induction generator (DFIG)
The EKF performance was compared with that of a continuous wavelet transform (CWT) and an iterative localized discrete Fourier-transform (IDFT) in terms of its ability to track a series of fault frequencies associated with three different unbalance condition levels and for three different simulated transient operating regimes using data generated by a test rig
Summary
I N RECENT years, wind energy has experienced substantial growth compared to other forms of power generation. The diagnostic application of stator current signature analysis to detect DFIG rotor asymmetry conditions has been studied on laboratory test rigs, simulation studies [5], [8], [9], [21], [22], or analytical formulations of fault frequencies [10], [11]. In these conditions, the FSCs are difficult to detect or differentiate using existing methods, which may lead to an increase in the false alarms for these conditions This problem has not received attention in reported literature despite the fact that actual WTs frequently operate at low load conditions where the generator rotational speed is close to the synchronous speed, motivating the research in this study to propose potential solutions.
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