• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1079-1080
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• 2011

This paper deals with the operating characteristics of induction motors with broken rotor bar, stator winding inter-turn short and their complex fault conditions. The considered operating characteristics are phase current, torque and speed. Since the operating characteristics of induction motors are directly related to their slip conditions, this paper built the experimental set to adjust the speed of induction motor with a permanent magnet synchronous generator connected to a load bank. From the various experimental results, it is shown that the faults do not highly affect on the operating characteristics of induction motors in low slip conditions, but the fault characteristics can be easily found in larger slip conditions.

• 전기전자학회논문지
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• 제27권4호
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• pp.518-523
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• 2023

고정자 권선단락은 미세한 턴이 단락되어 급격히 고장이 심각해짐에 따라 ITSC의 진단이 중요시되고 있다. 그러나, 3상 유도전동기의 노이즈 및 손실등과 유사한 특징을 가짐에 따라 ITSC진단에 많은 어려움이 있다. 이를 효율적으로 진단하기 위해서 인공지능 기법으로 연구되고 있으나, 현장에서는 모델기반 기법이 두루 활용되고 있음에 따라 모델기반 기법에 대한 진단 성능개선 연구가 필요한 실정이다. 이에 본 논문에서는 회전하고 있는 자속에 변화를 무시하며, 전류 성분만을 이용할 수 있도록 Clarke변환 방법을 응용하여 진단방법을 제안하였다. 이에 30분간의 정상 및 ITSC 상태의 측정 결과, 정상상태를 ITSC 상태로 오인식하는 경우 0.2[%], ITSC상태를 정상상태로 오거부하는 경우 0.26[%]로 효율적인 진단 방법임을 실험을 통해 알 수 있었다.

• Journal of Power Electronics
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• 제8권2호
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• pp.181-191
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• 2008

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.182-184
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• 2006

A down-scaled simulator is developed to simulate typical faults in induction motor such as short-turn stator winding, broken rotor bar, dynamic and static air-gap eccentricity, bearing trouble, and mechanical unbalance. The simulator is used as an initial builder to develop design algorithm for real-time faults detecting system by processing an abnormal signal and characteristics in each fault.

• Journal of Power Electronics
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• 제12권1호
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• pp.208-214
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• 2012

This paper presents a novel approach based on the loci of instantaneous symmetrical components called "Wing Shape" which requires the measurement of three input stator currents and voltages to diagnose interturn insulation faults in three phase induction motors operating under different loading conditions. In this methodology, the effect of unbalanced supply conditions, constructional imbalances and measurement errors are also investigated. The sizes of the wings determine the loading on the motor and the travel of the wings while their areas determine the degree of severity of the faults. This approach is also applied to detect open circuit faults or single phasing conditions in induction motors. In order to validate this method, experimental results are presented for a 5 hp squirrel cage induction motor. The proposed technique helps improve the reliability, efficiency, and safety of the motor system and industrial plant. It also allows maintenance to be performed in a more efficient manner, since the course of action can be determined based on the type and severity of the fault.