Stator Short-Circuit Diagnosis in Induction Motors Using Mutual Information and Intelligent Systems

Bazan, Gustavo Henrique; Scalassara, Paulo Rogério; Endo, Wagner; Goedtel, Alessandro; Palácios, Rodrigo Henrique Cunha; Godoy, Wagner Fontes

doi:10.1109/tie.2018.2840983

Cited by 65 publications

(25 citation statements)

References 35 publications

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“…Standard VM and CM Equations (16) and (17) are based on a healthy IM model, therefore they cannot ensure proper estimation during ITSCs. This can be proven analytically using the mathematical model of a faulted IM (Equations (6) and (11)).…”

Section: (C) Analytical Substantiation Of Obtained Resultsmentioning

confidence: 99%

“…Furthermore, it was observed that for the non-faulted condition of the motor, the fault factor modulus equaled zero. The fault factor disappeared for these conditions, which resulted in the fact that the equations of the modified flux estimators (24) and (25) look the same as the equations of the standard estimators (16) and (17). Therefore, it can be concluded that the proposed modified flux estimators can be used during non-faulted conditions as well as faulted conditions of the motors.…”

Section: (C) Fault Severity Evaluationmentioning

confidence: 97%

“…Many studies have focused on the incipient detection of ITSCs [10,11,14] for open-loop control schemes [15][16][17][18], as well as for closed-loop vector control structures of the IM. Field-oriented control (FOC) [19][20][21][22], direct torque control with switching table (DTC-ST) [23,24], and direct torque control with space-vector modulation (DTC-SVM) [21] structures have been considered.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives

Dybkowski

Bednarz

2019

Energies

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This paper deals with fault-tolerant control (FTC) of an induction motor (IM) drive. An inter-turn short circuit (ITSC) of the stator windings was taken into consideration, which is one of the most common internal faults of induction machines. The sensitivity of the classic, well-known voltage and current models to the stator winding faults was analyzed. It has been shown that these classical state variable estimators are sensitive to induction motor parameter changes during stator winding failure, which results in unstable operation of the direct field-oriented control (DFOC) drive. From a safety-critical applications point of view, it is vital to guarantee stable operation of the drive even during faults of the machine. Therefore, a new FTC system has been proposed, which consists of new modified rotor flux estimators, robust to stator winding faults. A detailed description of the proposed system is presented herein, as well as the results of simulation and experimental tests. Simulation analyses were performed using MATLAB/Simulink software. Experimental tests were carried out on the experimental test bench with a dSpace DS1103 card. The proposed solution could be applied as an alternative rotor flux estimation technique for the modern FTC drive.

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Section: (C) Analytical Substantiation Of Obtained Resultsmentioning

confidence: 99%

Section: (C) Fault Severity Evaluationmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives

Dybkowski

Bednarz

2019

Energies

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“…Squirrel cage induction motors are widely used in the industrial sector, due to low-cost and low maintenance requirements, high reliability and easy adaptation to various mechanical loads. However, despite their robustness and reliability, induction motors are subjected to electrical or mechanical failures, which occurs due to prolonged and severe operating conditions such as overload, overvoltage, voltage and current unbalance, among others [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [1] utilize ANN and decision trees with delayed stator current signals for fault detection in the stator windings, while [12] exploit fuzzy logic strategy with stator current Concordia patterns and [13] use an induction motor model, together with a genetic algorithm, exploiting stator voltages and currents and also mechanical speed.…”

Section: Introductionmentioning

confidence: 99%

Stator Winding Fault Detection Using External Search Coil and Artificial Neural Network

et al. 2020

Self Cite

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This paper presents a methodology for winding stator fault detection of induction motors, using an external search coil, which is a noninvasive technique and can be applied during motor operation. The dispersion magnetic flux of the motor operating in abnormal conditions induces a voltage in the search coil that differs from a reference pattern corresponding to the healthy stator winding. Experimental data were obtained in a test bench using a 0.75 kW three-phase squirrel-cage induction motor with the stator winding modified to allow the introduction of short circuits. This work considered short circuits in one phase, involving 1%, 3%, 5% and 10% of the turns, with the motor loaded with a varying torque. Fault diagnosis is obtained through two models of artificial neural networks, implemented with the signals in the time domain. The obtained results demonstrated that the developed methodology presents difficulties in predicting short circuits in incipient stages, but for short circuits of higher severity, the behaviour improved substantially, being 100% successful for faults with 10% turns short-circuited.

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An accurate multiple cognitive classifier system for incipient short-circuit fault detection in induction generators

2021

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Stator Short-Circuit Diagnosis in Induction Motors Using Mutual Information and Intelligent Systems

Cited by 65 publications

References 35 publications

Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives

Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives

Stator Winding Fault Detection Using External Search Coil and Artificial Neural Network

An accurate multiple cognitive classifier system for incipient short-circuit fault detection in induction generators

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