Wound-rotor IM diagnosis method based on neutral voltage signal analysis

Dahi, Khalid; Elhani, Soumia; Guedira, Saïd

doi:10.1109/iecon.2014.7048618

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…For doubly fed induction generators (DFIGs), a combination of DWT and the time-synchronous averaging algorithm was employed to identify inter-turn short-circuit faults [ 28 ]. Furthermore, a novel approach using the Hilbert transform was suggested to identify defects in inter-turn short-circuit faults [ 29 ]. Additionally, promising diagnostic techniques such as acoustic emission analysis [ 30 , 31 ] and vibration signals [ 32 , 33 , 34 ] have been utilized for identifying various issues, including broken bars, bearing failures, voltage imbalances, and partial discharges.…”

Section: Introductionmentioning

confidence: 99%

Efficient Fault Detection of Rotor Minor Inter-Turn Short Circuit in Induction Machines Using Wavelet Transform and Empirical Mode Decomposition

Rehman

Jiao

Sun

et al. 2023

Sensors

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This paper introduces a novel approach for detecting inter-turn short-circuit faults in rotor windings using wavelet transformation and empirical mode decomposition. A MATLAB/Simulink model is developed based on electrical parameters to simulate the inter-turn short circuit by adding a resistor parallel to phase “a” of the rotor. The resulting high current in the new phase indicates the presence of the short circuit. By measuring the rotor and stator three-phase currents, the fault can be detected as the currents exhibit asymmetric behavior. Fluctuations in the electromagnetic torque also occur during the fault. The wavelet transform is applied to the rotor current, revealing an effective analysis of sideband frequency components. Specifically, changes in amplitude and frequency, particularly in d7 and a7, indicate the presence of harmonics generated by the inter-turn short circuit. The simulation results demonstrate the effectiveness of wavelet transformation in analyzing these frequency components. Additionally, this study explores the use of empirical mode decomposition to detect faults in their early stages, observing substantial changes in the instantaneous amplitudes of the first three intrinsic mode functions during fault onset. The proposed technique is straightforward and reliable, making it suitable for application in wind turbines with simple electrical inputs.

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