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    Analysis of Stator Inter-turn Short-circuit Fault Signatures for Inverter-fed Permanent Magnet Synchronous Motors
    (Ieee, 2016) Cira, Ferhat; Arkan, Muslum; Gumus, Bilal; Goktas, Taner
    It is quite important to detect stator short-circuit fault, which is the most common fault type, at incipient stage. It is possible to carry out fault detection using Motor Current Signature Analysis (MCSA) method. In this study, stator current and voltage space vectors of PMSMs were analyzed with MCSA under various load torque, speed and fault percentages conditions. The Negative & positive harmonics were obtained by applying Fast Fourier Transform (FFT) to space vectors of stator current and voltage. It is suggested that by using the obtained fault signatures, stator inter-turn fault estimation can be achieved accurately. The results of comprehensive analysis carried out under various load torque and speed conditions show that characteristics fault signatures are both present in the current and the voltage space vectors spectra.
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    Detection of Stator Winding Inter-Turn Short Circuit Faults in Permanent Magnet Synchronous Motors and Automatic Classification of Fault Severity via a Pattern Recognition System
    (Springer Singapore Pte Ltd, 2016) Cira, Ferhat; Arkan, Muslum; Gumus, Bilal
    In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.
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    A New Approach to Detect Stator Fault in Permanent Magnet Synchronous Motors
    (Ieee, 2015) Cira, Ferhat; Arkan, Muslum; Gumus, Bilal
    In this paper, detection of the stator winding inter-turn short circuit fault (SWISCF) in surface-mounted permanent magnet synchronous motors (SPMSMs) and classification of the fault severity via pattern recognition system (PRS) are presented. In order to automatically detect stator winding short circuit fault and to estimate severity of this fault, artificial neural network (ANN) based PRS has been used. It has been observed that the amplitude of the 3rd harmonics of the current is the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To increase the fault clasification accuracy of PRS both fundamental (1st) and 3rd harmonics are used. In order to validate proposed method experimental results are presented.