Severity Estimation of Interturn Short Circuit Fault for PMSM

Yuan, Qi; Bostanci, Emine; Zafarani, Mohsen; Akin, Bilal

doi:10.1109/tie.2018.2879281

Cited by 85 publications

(33 citation statements)

References 32 publications

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“…The radial component of the flux density can be given from (14) where is an airgap flux density, is residual flux density of PMs, is an airgap length and ℎ is the magnet thickness respectively.…”

Section: Calculation Of Airgap Magnetic Fields ( ) Based On Mec Calculusmentioning

confidence: 99%

“…However, the fault diagnostic methods adopted are collectively model-based strategy and signal processingbased techniques. The advanced research findings as reported in [14], analyze the severity estimation of SITF faults in PMSM considering the residual voltages and current. It is thus comprehensible from the literature which explores on the individual machine fault diagnosis but fails to report the combined effect of faults eventuating at the same time in a machine.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Classification of Stator Inter-Turn Fault and Demagnetization Effects in BLDC Motor Using Rotor Back-EMF and Radial Magnetic Flux Analysis

Usman

Rajpurohit

2020

IEEE Access

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This paper shall carry out unique study and investigation on the individual and combined effect of Stator Inter-turn Fault (SITF) and rotor demagnetization effects in a Surface Mounted Permanent Magnet (SPM)-type Brushless Direct Current (BLDC) motor. Comprehensive modeling of fault is accomplished using the well-established fault modeling techniques such as Electrical Equivalent Circuit (EEC), Magnetic Equivalent Circuit (MEC), Numerical Methods (NMs) and hybrid modeling-based approaches. The extensive study of SITF conditions using an Improved Winding Function Theory (IWFT) gives better analysis to winding inductance LS, under fault conditions, while modeling the demagnetization effects using MEC and NM based approaches gives more realistic magnetic field quantities. The outcomes obtained are further remodeled in the developed hybrid model of a BLDC motor to study the steady state performance of a machine in terms of rotor back-EMF, EB and radial magnetic flux density Bg. The characteristic performance accomplished through the hybrid model system possess better accuracy with less computational time and therefore finds suitability among efficient and fast diagnosis technique feasible for industrial applications. The significant detectable changes in EB and Bg under subjected fault conditions, assist in detection, identification and classification of faults in BLDC motor. By obtaining the similar outcome experimentally and under the load conditions, the proposed study on the classification of fault is validated.

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Section: Calculation Of Airgap Magnetic Fields ( ) Based On Mec Calculusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Classification of Stator Inter-Turn Fault and Demagnetization Effects in BLDC Motor Using Rotor Back-EMF and Radial Magnetic Flux Analysis

Usman

Rajpurohit

2020

IEEE Access

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“…In [3], the amplitude of current in the shorted circuits estimated through fluctuations on EMF is selected as the fault feature to identify the fault severity instead of estimating by the quantity of shorted-circuit turns of windings. In [4], a novel method based on injecting currents, is designed for evaluating the fault exponent by the variation of parameters, and this can prevent the complex training process brought from the data analysis. Moreover, the stator winding impedance obtained by injecting the pulsating-type voltage [5] and extending self-commissioning [6] is used to evaluate the on-line turn fault, which is instead of utilizing the current and back EMF signals.…”

Section: Introductionmentioning

confidence: 99%

Different Faults Diagnosis and Classification on‐Line in Permanent Magnet Synchronous Machines Using Improved Exponential Discriminant Analysis

Xiao

Gao

Hou

et al. 2020

IEEJ Transactions Elec Engng

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Reliability is an important performance for permanent magnet synchronous machine (PMSM) exploited to industrial applications and electric‐energy vehicles. Both air‐eccentricity and inter‐turn short circuit (ITSC) could induce the machine running in unbalanced status. Once not restored timely, the fault will lead to the whole drive system and its application damage. To enhance the reliability for PMSM, an effective fault diagnosis and classification method is necessary in advance. For this purpose, an improved exponential discriminant analysis (IEDA) algorithm is proposed and optimized. Further, the distortion characteristics of phase current signals with air‐eccentricity and ITSC are extracted and trained by the IEDA, which are applied for diagnosing whether the motor healthy or faulty and distinguishing fault types. Besides, the IEDA can not only identify fault type accurately but also estimate the fault degree precisely. Finally, compared to other common learning methods, several simulations and practical tests accompanying with diverse operating status are implemented, and their results can prove the proposed method has more accuracy and robustness than other methods. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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“…With the reduction of impedance, the ISCF can be determined accurately and quickly by comparing the real-time input impedance with that under normal conditions using the winding function theory for impedance calculation [16]. The PMSM is excited by a low sinusoidal voltage at the standstill condition, and the resistance and inductance obtained from the current response can be used to estimate the fault location and number of shorted turns [17]. Since the back EMF of the short-circuit phase will decrease, it can be used as a fault indicator.…”

Section: Introductionmentioning

confidence: 99%

Online detection of inter‐turn short‐circuit fault in dual‐redundancy permanent magnet synchronous motor

Yang

Chen

Hao

2020

IET Electric Power Appl

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An online detection method for the inter‐turn short‐circuit fault (ISCF) of a dual‐redundancy permanent magnet synchronous motor (DRPMSM) is proposed. First, the electromagnetic relationships in a DRPMSM without mutual inductances are analysed theoretically upon the occurrence of the ISCF. Then, a simulation model of the motor with ISCF is established, and the arithmetic mean for the ratio of the squared amplitude difference between two sets of space voltage vectors to squared frequency is used to identify the faulty winding by the difference between the back electromotive force and impedance of the dual winding. It is shown to be valid even with variations in the load, speed, contact resistance, and number of short‐circuit turns. The simulation and experimental results thus verify the effectiveness of the proposed method.

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Severity Estimation of Interturn Short Circuit Fault for PMSM

Cited by 85 publications

References 32 publications

Modeling and Classification of Stator Inter-Turn Fault and Demagnetization Effects in BLDC Motor Using Rotor Back-EMF and Radial Magnetic Flux Analysis

Modeling and Classification of Stator Inter-Turn Fault and Demagnetization Effects in BLDC Motor Using Rotor Back-EMF and Radial Magnetic Flux Analysis

Different Faults Diagnosis and Classification on‐Line in Permanent Magnet Synchronous Machines Using Improved Exponential Discriminant Analysis

Online detection of inter‐turn short‐circuit fault in dual‐redundancy permanent magnet synchronous motor

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