Diagnosis of Rotor Winding Inter-turn Short Circuit Fault in Wind Turbine Based on DFIG using Hybrid TSA/DWT Approach

Sabir, Hamza; Ouassaid, Mohammed; Ngote, Nabil

doi:10.1109/irsec.2018.8703006

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…From an energetic aspect, the consumed energy at each level of decomposition is calculated to find and prove the frequency bands containing the frequency of the defect [ 35 ]. Especially, for both faulty and healthy cases under motor at no-load, half-load, and full-load.…”

Section: Experimental Results and Validationmentioning

confidence: 99%

An experimental method for diagnostic of incipient broken rotor bar fault in induction machines

Sabir

Ouassaid

Ngote

2022

Heliyon

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Section: Experimental Results and Validationmentioning

confidence: 99%

An experimental method for diagnostic of incipient broken rotor bar fault in induction machines

Sabir

Ouassaid

Ngote

2022

Heliyon

Self Cite

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“…Swana and Doorsamy [2] investigates a short-circuit fault involving the inter-turn windings of both the stator and rotor. Additional studies on stator faults are presented in [3], [4], while rotor fault research is documented in studies [5], [6]. The majority of fault detection techniques utilize machine learning methods, including wavelet analysis [5], [7], [8], fast Fourier transform (FFT) [9], current signature analysis [4], [10],…”

Section: Introductionmentioning

confidence: 99%

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

Fadzail,

Zali,

Mid

2024

IJECE

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The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.

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“…Additionally, DWT has been utilized to diagnose imbalances in both the stators and rotors of WRIMs, eliminating the need for slip assessment [ 27 ]. 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 ].…”

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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Diagnosis of Rotor Winding Inter-turn Short Circuit Fault in Wind Turbine Based on DFIG using Hybrid TSA/DWT Approach

Cited by 10 publications

References 27 publications

An experimental method for diagnostic of incipient broken rotor bar fault in induction machines

An experimental method for diagnostic of incipient broken rotor bar fault in induction machines

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

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

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