Modeling and Analysis of PMSMs under Inter Turn Short Faults

Choi, Jun-Hyuk; Gu, Bon-Gwan; Won, Chung-Yuen

doi:10.5370/jeet.2013.8.5.1243

Cited by 13 publications

(6 citation statements)

References 20 publications

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“…Based on the literature and JMAG tutorials, as inter-turn fault is symmetric on the axial axis of the motor, it will be sufficient to perform 2D FEM analysis. This is in conformity with many previous research works, which used 2D FEM to investigate motor performance under faults [42]- [44]. Therefore, in this paper, a 2D FEM of a 1 hp interiormount LSPMSM with a stator inter-turn fault is created using JMAG.…”

Section: Lspmsm Jmag Fault Modelsupporting

confidence: 77%

Neural Network-Based Diagnostic Tool for Detecting Stator Inter-Turn Faults in Line Start Permanent Magnet Synchronous Motors

et al. 2019

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Three-phase line-start permanent magnet synchronous motors are considered among the most promising types of motors in industrial applications. However, these motors experience several faults, which may cause significant financial losses. This paper proposed a feed-forward neural network-based diagnostic tool for accurate and fast detection of the location and severity of stator inter-turn faults. The input to the neural network is a group of representative statistical and frequency-based features extracted from the steady-state three-phase stator current signals. The current signals with different numbers of shorted turns and loading conditions are captured using the developed finite element JMAG TM model for interior mount LSPMSM. In addition, an experimental set-up was built to validate the finite element model and the proposed diagnostics tool. The simulation and experimental test results showed an overall accuracy of 93.125% in detecting the location and the size of inter-turn, whereas, the accuracy in detecting the location of the fault is 100%.

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Section: Lspmsm Jmag Fault Modelsupporting

confidence: 77%

Neural Network-Based Diagnostic Tool for Detecting Stator Inter-Turn Faults in Line Start Permanent Magnet Synchronous Motors

et al. 2019

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“…Based on the literature and on the JMAG tutorials, as asymmetry fault is symmetric on the axial axis of the motor, it will become sufficient to perform 2D FEM analysis. This is in conformity with many previous research works that used 2D FEM to investigate motor performance under faults [27][28][29]. This section summarises the required steps for creating the FEM of the motor under study with the stator experiencing asymmetrical windings using the JMAG TM software.…”

Section: Mathematical Modelling Of Asymmetrical Stator Interiormount supporting

confidence: 71%

Modelling of interior‐mount LSPMSM under asymmetrical stator winding

Maraaba

Al-Hamouz

Milhem

et al. 2018

IET Electric Power Applications

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Reliable and safe operation of electric motors in the industry is highly desirable. Accurate modelling is the first step in developing a diagnostic tool for different types of failures. This study presents a generalised dq-model for interior-mount line start permanent magnet synchronous motors under asymmetrical stator phase's windings. The presented dq-model has been derived using winding theorem. Implementation and simulation of the derived dq-model has been done using MATLAB/ SIMULINK software. For validation purposes, interior-mount line start permanent magnet synchronous motor (LSPMSM) under this fault condition is implemented using the JMAG TM-finite-element-based software. The performance of a 1-hp interior-mount LSPMSM motor under different loading levels and stator winding asymmetric conditions has been investigated using the simulated MATLAB and JMAG models. Simulation results of MATLAB and JMAG are in very good agreement and show that under asymmetrical stator winding condition, oscillations are obvious in the torque characteristics of the motor. In addition, the torque experience high oscillations at a steady state due to the asymmetry between stator phases. Finally, investigating torque frequency spectrum showed that they can be good signatures in diagnosing asymmetrical stator winding faults in interior-mount LSPMSM.

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“…The motor parameters are dependent on current and temperature. Due to high temperatures, or high currents, turn faults [4][5][6] in the stator may occur. Current can be easily measured using the current sensors of the inverter.…”

Section: Introductionmentioning

confidence: 99%

Temperature Estimation of Stator Winding in Permanent Magnet Synchronous Motors Using d-Axis Current Injection

et al. 2018

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This paper presents a stator winding temperature detection method for permanent magnet synchronous motors (PMSMs) using a motor parameter estimation method. PMSM performance is highly dependent on the motor parameters. However, the motor parameters vary with temperature. It is difficult to measure motor parameters using a voltage equation without additional sensors. Herein, a stator winding temperature estimation method based on a d-axis current injection method is proposed. The proposed estimation method can be used to obtain stator temperatures and to achieve reliable operation. The validity of the proposed method is verified through simulations and experimental results.

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Modeling and Analysis of PMSMs under Inter Turn Short Faults

Cited by 13 publications

References 20 publications

Neural Network-Based Diagnostic Tool for Detecting Stator Inter-Turn Faults in Line Start Permanent Magnet Synchronous Motors

Neural Network-Based Diagnostic Tool for Detecting Stator Inter-Turn Faults in Line Start Permanent Magnet Synchronous Motors

Modelling of interior‐mount LSPMSM under asymmetrical stator winding

Temperature Estimation of Stator Winding in Permanent Magnet Synchronous Motors Using d-Axis Current Injection

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