Analysis of inter-turn fault symptoms for the converter-fed induction motor based on the phase-shift calculation

Kowalski, C. T.; Orłowska-Kowalska, Teresa; Wierzbicki, Robert; Wolkiewicz, Marcin

doi:10.1109/iecon.2010.5675029

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…In addition, both methods are limited in mains-fed condition, as they don't deal with the influence of the variable frequency in systems driven by the inverter. Recently, in [25] the inverter-fed fault detection is achieved using the phaseshift of fundamental harmonic. But the fault indicator of the shorted turns varies with the load and frequency conditions even though the IM is under the same fault severity.…”

Section: Introductionmentioning

confidence: 99%

Stator inter-turn fault detection for the converter-fed induction motor based on the adjacent-current phase-shift

Liu

Huang

Cheng

et al. 2014

2014 17th International Conference on Electrical Machines and Systems (ICEMS)

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This paper presents a sensorless on-line Motor Current Signature Analysis (MCSA) method to detect and locate an inter-turn short-circuited fault in the stator winding of the induction machine (IM). It is appropriate for the monitoring of Volts/Hertz controlled inverter-fed systems. The phase-shifts between adjacent line currents of both fundamental harmonic and principal slot harmonic (PSH) are analyzed. Then the fault severity and the fault location feature are derived, respectively. For the purpose of phase signal extraction, synchronous reference frame filter and adaptive phase locked loop (PLL) are integrated into the monitoring system. The robustness and effectiveness of the proposed scheme has been validated by means of laboratory tests.

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Section: Introductionmentioning

confidence: 99%

Stator inter-turn fault detection for the converter-fed induction motor based on the adjacent-current phase-shift

Liu

Huang

Cheng

et al. 2014

2014 17th International Conference on Electrical Machines and Systems (ICEMS)

View full text Add to dashboard Cite

show abstract

“…Most of the significant methods and related techniques are based on monitoring one or more signals and the use of advanced signal processing methods. An overview of these methods is presented in detail in [18][19][20][21][22][23][24][25][26][27], where the ITSCs are detected in the stator phase current [18], in the phase shift between phase currents, phase shift between current and stator voltage [14,19], axial flux [2,20], instantaneous active and reactive power [21] or mechanical vibrations [22]. The most popular signal processing methods are based on motor current signature analysis (MCSA) [1,2,7], symmetrical components analysis (SCA) [23][24][25][26][27][28][29] and the stator current Park's vector approach analysis [29,30].…”

Section: Introductionmentioning

confidence: 99%

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

et al. 2019

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Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values.

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Modeling and simulation of stator turn faults. Detection based on stator circular current and neutral voltage

Maouche

Boussaid

Boucherma

et al. 2013

2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)

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Analysis of inter-turn fault symptoms for the converter-fed induction motor based on the phase-shift calculation

Cited by 4 publications

References 20 publications

Stator inter-turn fault detection for the converter-fed induction motor based on the adjacent-current phase-shift

Stator inter-turn fault detection for the converter-fed induction motor based on the adjacent-current phase-shift

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

Modeling and simulation of stator turn faults. Detection based on stator circular current and neutral voltage

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