• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.416-424
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• 2016

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.

• Journal of Magnetics
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• v.21 no.3
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• pp.413-420
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• 2016

The permanent magnet synchronous motor has high efficiency driving performance and high power density output characteristics compared with other motors. In addition, it has good regenerative operation characteristics during braking and deceleration driving condition. For this reason, permanent magnet synchronous motor is generally applied as a power train motor for electrical vehicle. In permanent magnet synchronous motor, the most probable causes of fault are demagnetization of rotor's permanent magnet and short of stator winding turn. Therefore, the demagnetization fault of permanent magnet and turn fault of stator winding should be detected quickly to reduce the risk of accident and to prevent the progress of breakdown of power train system. In this paper, the fault diagnosis method using high frequency low voltage injection was suggested to diagnose the demagnetization fault of rotor permanent magnet and the turn fault of stator winding. The proposed fault diagnosis method can be used to check the faults of permanent magnet synchronous motor during system check-up process at vehicle starting and idling stop mode. The feasibility and usefulness of the proposed method were verified by the finite element analysis.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.358-360
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• 2007

In this paper, an auto-diagnosis method of the stator winding fault for small induction motor is suggested. 3-phase stator currents are sampled, filtered, and transformed with Park's vector transformation. After then Park's vector patterns are obtained. To detect the stator winding fault automatically, a distortion ratio (id/iq) is newly defined and compared with the one of healthy motor, and the threshold levels are suggested. The 2-turn, 4-turn, 8-turn winding fault are tested with no load, 25%, 50%, 75%, and 100% rated load. The distortion ratio of the Park's vector patterns are increased as the increase of the faulted turns, but are same as the increase of the load.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2326-2333
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• 2015

Stator turn faults in permanent magnet synchronous motors (PMSMs) are more dangerous than those in induction motors (IMs) because of the presence of spinning rotor magnets that can be turned off at will. Condition monitoring and fault detection and diagnosis of the PMSM have been receiving a growing amount of attention among scientists and engineers in the past few years. The aim of this study is to propose a new detection technique of stator winding faults in a three-phase PMSM. This technique is based on the image analysis and recognition of the stator current Concordia patterns, and will allow the identification of turn faults in the stator winding as well as its correspondent fault index severity. A test bench of a vector controlled PMSM motor behaviors under short circuited turn in two phases stator windings has been built. Some experimental results of the phase to phase short circuits have been performed for diagnosis purpose.

• Journal of Electrical Engineering and Technology
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• v.6 no.5
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• pp.666-670
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• 2011

In this paper, a method for detecting broken rotor bar and stator winding fault in a low voltage squirrel-case induction motor using an air-gap flux variation analysis is proposed to develop a simple and low cost diagnosis technique. To measure the leakage flux in radial direction, a radial flux sensor is designed as a search coil and installed between stator slots. The proposed method is able to identify two kinds of motor faults by calculating load condition of motors and monitoring abnormal signals those are related with motor faults. Experimental results obtained on 7.5kW three-phase squirrel-cage induction motors are discussed to verify the performance of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.2
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• pp.136-141
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• 2009

An AC generator is an important components in producing a electric power and so it requires highly reliable protection relays to minimize the possibility of demage occurring under fault conditions. Conventionally, a DFT based RDR has been used for protecting the generator stator winding. However, when DFTs based on Fourier analysis are used, it has been pointed out that defects can occur during the process of transforming a time domain signal into a frequency domain one which can lead to loss of time domain information. This paper proposes the internal fault detection and fault type discrimination for the stator winding by applying the detailed coefficients by Daubechies Wavelet Transform to overcome the defects in the DFT process. For the case studies reported in the paper, a model system was established for the simulations utilizing the ATP, and this verified the effectiveness of the proposed technique through various off-line tests carried out on the collected data. The propose method is shown to be able to rapidly identify internal fault and did not operate a miss-operation for all the external fault tested.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1154-1157
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• 2004

During normal machine operation, partial discharge(PD) measurements were performed with turbine generator analyzer(TGA) in imitation stator winding of high voltage motors. The motor was energized to 4.47kV, 6.67, respectively. Applied voltage to Imitation winding was used two voltage level, 4.47[KV]and 6.67[KV]. Motors having imitation stator winding were installed with 80pF capacitive couplers at the terminal box Case of PD Pattern regarding applied voltage phase angel, the PD patterns were displayed two dimensional and three dimensional. TGA summarizes each plot with two quantities such as the normalized quantity number(NQN) and the peak PD magnitude(Qm). As the result, we could discrimidate using TGA the difference of internal and surface discharge for imitation stator winding. We have used the other technique, in order to feature extraction of faulty signals on stator winding, Daubechies Discrete wavelet transform and Harmonics analysis(FFT) about faulty signals.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2658-2660
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• 2005

This paper presents a stator winding fault diagnosis technique of induction motor on the PC - based virtual instrumentation system designed using the graphical programming language LabVIEW. This method collects the 3-phase current signals using the current probe amplifier and PXI/DAQ system then the preprocessing removes the noise using LPF, after then this method transforms the stator current to Park's vector and obtains the each Park's Vector pattern and detects stator winding fault by comparing the obtained faulted pattern with the healthy pattern. This proposed LabVIEW based diagnosis system is applied to the 3 phase 1 hp induction motor and obtained the reasonable results under no load condition. The test results give us the possibility a simple and realistic on-line winding fault diagnosis system.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.10
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• pp.965-975
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• 2005

In the case of the fault in stator windings of a high voltage motor. it facilitates certain destructive characteristics in insulations. This will result in a decreased reliability in power supplies and will prevent the generation of electricity, which will result in huge economic losses. This study simulates motor windings using normal windings and four faulty windings for an actual fault in stator winding of a high voltage motor. The partial discharge signals produced in each faulty winding were measured using an 80 PF epoxy/mica coupler sensor. In order to quantified signal waves its a way of feature extraction for each faulty signal, the signal wave of winding was quantified to measure the degree of skewness shape and kurtosis, which are both types of statistical parameters, using a discrete wavelet transformation method for each faulty type. Wave types present different types lot each faulty type, and the skewness and kurtosis also present different quantified values. The result of feature extraction was used as a preprocessing stage to identify a certain fault in stater windings. It is evident that the type of faulty signals can be classified from the test results using faulty signals that were randomly selected from the signal, which was not applied in the training after the training and learning period, by applying it to a back-propagation algorithm due to the supervising and learning method in a neural network in order to classify the faulty type. This becomes an important basis for studying diagnosis methods using the classification of faulty signals with a feature extraction algorithm, which can diagnose the fault of stator windings in the future.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.3
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• pp.197-203
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• 2009

This paper presents a novel method to diagnose magnetization faults produced during magnetization process using stator coils of a brushless dc motor with concentrated windings. It is demonstrated that the stator coil magnetization faults can cause a drop of energy efficiency of the brushless dc motor through computer simulations using a magnetization fault model and efficiency test using compressors. The proposed method diagnoses the stator coil magnetization faults by using an inverter during the brushless dc motor driving test after the magnetization process is completed. An experimental study on the brushless dc motor for compressor shows that the stator coil magnetization faults can be detected with high sensitivity.