Stator inter turns fault detection using discrete wavelet transform

Cherif, Hakima; Menacer, Arezki; Bessam, B.; Kechida, Ridha

doi:10.1109/demped.2015.7303681

Cited by 12 publications

(12 citation statements)

References 17 publications

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“…Moreover, the wavelet coefficients enable better understanding of the signal and are useful in feature extraction applications [29]. As can be seen, the original signal is passed through filters H and G to generate the approximate and detail components at the first level of decomposition, where G and H are orthogonal vectors with N elements [10,11,36]. For the second level, the approximate component is down-sampled by two, that is, its samples are halved.…”

Section: General Remarksmentioning

confidence: 99%

“…In order to establish an appropriate criterion to discriminate healthy and faulty motor conditions, the energy of each detail component [11] can be obtained by applying a moving data window with a length corresponding to one cycle of the fundamental frequency (50 Hz in this article), that is, N ¼ f s =f ¼ 5000=50 ¼ 100 samples per cycle, as below [11,39]:…”

Section: Sttfmentioning

confidence: 99%

“…Due to the orientation and perspective of this article in using stator terminal currents as the main fault detection foundation, a brief review of the non-invasive current-based techniques is provided. Some conventional current-based diagnostic methods are the positive-, negative-and zero-sequence current components analysis [6][7][8], motor current spectrum analysis (MCSA) [9][10][11][12][13][14][15], Park's vector approach [16,17], motor current bispectral analysis [18,19], and rotor current spectrum analysis [20,21].…”

Section: Introductionmentioning

confidence: 99%

“…Another term of comparison for the different diagnostic approaches is the signal processing technique used in their implementation. Fast Fourier Transform (FFT) [22][23][24][25], Short Time Fourier Transform (STFT) [26,27], Discrete Wavelet Transform (DWT) [4,10,11,21,26,[28][29][30][31][32][33][34], Stationary Wavelet Transform (SWT) [35][36][37][38], Dual-Tree Complex Wavelet Transform (DTCWT) [4,34], Wavelet Packet Transform (WPT) [39][40][41], Rational-dilation Wavelet Transform (RADWT) [42], Artificial Intelligence (AI) [43,44], Kalman filter [45,46] and Hilbert-Huang Transform (HHT) [47][48][49] are some of the techniques presented in the literature.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A secure and sensitive wavelet transform based technique for stator fault detection in the cases of line‐connected and inverter‐fed induction machines

Nazemi

Gallehdar²,

Haghjoo

et al. 2021

IET Electric Power Appl

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Here a new sensitive fault detection criterion is proposed, based on the stationary wavelet transform (SWT) decomposition components of the stator currents, for the online detection of stator winding turn-to-turn faults at an early stage of development. The energies of the desired detail components are calculated and the normalized Euclidean distance of the energy differences between the three phases is used as a new fault detection criterion. The robustness of the proposed technique against the presence of rotor turn-to-turn faults and loading variations is demonstrated. In addition, an adaptive threshold level as a function of the voltage unbalance factor is proposed to guarantee the robustness of the diagnostic technique against unbalanced voltage sources. Experimental results demonstrate the effectiveness and sensitivity of the proposed diagnostic approach for the detection of stator faults in line-connected and inverter-fed motors.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: General Remarksmentioning

confidence: 99%

Section: Sttfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A secure and sensitive wavelet transform based technique for stator fault detection in the cases of line‐connected and inverter‐fed induction machines

Nazemi

Gallehdar²,

Haghjoo

et al. 2021

IET Electric Power Appl

View full text Add to dashboard Cite

show abstract

“…The main disadvantage of using the transient signal is that it encompasses the full range of slip values within the transient signal of the machine, producing frequency components of a non-stationary nature [17]. Some approaches used in this field are: the wavelet transform-WT [16,18,19], empirical decomposition-EMD [20,21], fractional Fourier transform-FrFT [22], symmetrical component analysis [23], Gabor analysis [24], wavelet-SVM [25], Wigner-Ville distribution-WVD [21,26], and principal component analysis-PCA [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Transient envelope current analysis for inter-turn short-circuit detection in induction motor stator

Zaparoli

Baccarini

Filho

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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Electrical machines are subject to faults which, depending on the type and importance of the plant, can cause material, economic and social damage. The reliability required for electric motors constantly grows due to the importance of their applications and technological advancement. One of these possible faults is an inter-turn short circuit of the stator winding. This is a very severe failure because its evolution to a worst fault is very fast. Thus, the initial detection of the inter-turn short circuit is of fundamental importance to avoid further damage to the motor. Steady-state analysis is not adequate when applied in some cases, such as unloaded machines, loads with oscillating torque and power with voltage fluctuations. The present work brings a new study of the transient current to analyze the beginning of inter-turn short circuits in the induction motor stator windings. The proposed methodology extracts the second component from the transient envelope of the motor starting current signals by principal component analysis. These components reflect the asymmetry caused by the initial short circuit and carry the desired fault information. By variance analysis it becomes possible to monitor the evolution of a short-circuit fault. Numerical and experimental signals are provided to validate the effectiveness of the novel technique.

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Fault detection and fault severity calculation for rotor windings based on spectral, wavelet and ratio computation analyses of rotor current signals for a doubly fed induction generator in wind turbines

et al. 2020

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Stator inter turns fault detection using discrete wavelet transform

Cited by 12 publications

References 17 publications

A secure and sensitive wavelet transform based technique for stator fault detection in the cases of line‐connected and inverter‐fed induction machines

A secure and sensitive wavelet transform based technique for stator fault detection in the cases of line‐connected and inverter‐fed induction machines

Transient envelope current analysis for inter-turn short-circuit detection in induction motor stator

Fault detection and fault severity calculation for rotor windings based on spectral, wavelet and ratio computation analyses of rotor current signals for a doubly fed induction generator in wind turbines

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