• 전기학회논문지
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• 제66권2호
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• pp.394-401
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• 2017

Fault diagnosis is important due to the increasing demand of using interior permanent magnet synchronous machines (IPMSMs). In particular, an interturn fault is one of the most frequent electrical faults in IPMSMs. This paper proposes a fault indicator for diagnosis of interturn faults in IPMSMs. The fault indicator is developed by negative-sequence impedance. The effectiveness of the fault indicator to diagnose interturn faults was verified through various fault conditions.

• 전기학회논문지
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• 제67권1호
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• pp.52-60
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• 2018

The winding of the motor stator coil is broken due to external stress and various factors. If the proper current is not injected when interturn fault(ITF) occurs, the fault can easily be expanded and the motor can be finally destroyed, resulting in many problems with time costs and safety. In this paper, the power loss limit concept, which is the inherent durability of each motor, is applied to secure safety by controlling the total power loss of the motor within the limits. So, we propose an algorithm that can control maximum torque per minimum power loss based on constant torque curve and power loss limit. To verify the proposed method, the simulation and experimental results with an Interior permanent magnet synchronous motor(IPMSM) having an ITF are shown.

• 전기학회논문지
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• 제66권1호
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• pp.144-152
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• 2017

A motor model and off-line diagnosis method of the induction motor having an interturn fault(ITF) is studied. The proposed method is based on the magnitude comparison of the six impedance in the d-q plane. To prove the impedance unbalance, the induction motor model is presented with an ITF circuit loop with a fault resistance. Then, six impedance components in the stationary d-q plane are defined depending on the connected phase windings. Finding the maximum and minimum magnitude of the six impedance, the ITF and the faulty phase can be founded. To verify the proposed method, the experimental results with an induction motor having an ITF are shown.

• Journal of Power Electronics
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• 제15권1호
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• pp.288-297
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• 2015

This paper presents a novel approach to diagnose interturn insulation faults in three-phase transformers that operate at different loading conditions. This approach is based on the loci of instantaneous symmetrical components and requires the measurement of three input primary winding currents and voltages to diagnose faults in the transformer. The effect of unbalance supply conditions, load variations, constructional imbalance, and measurement errors when this methodology is used is also investigated. Wing size or length determines the loading on the transformer. Wing travel and area determine the degree of severity of fault. Experimental results are presented for a 400/200 V, 7.5 kVA transformer to validate this method.

• 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.

• Journal of Electrical Engineering and Technology
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• 제2권4호
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• pp.485-493
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• 2007

This paper deals with the problem of detection of induction motor incipient faults. Artificial Neural Network (ANN) approach is applied to detect two types of incipient faults (1). Interturn insulation and (2) Bearing wear faults in single-phase induction motor. The experimental data for five measurable parameters (motor intake current, rotor speed, winding temperature, bearing temperature and the noise) is generated in the laboratory on specially designed single-phase induction motor. Initially, the performance is tested with two inputs i.e. motor intake current and rotor speed, later the remaining three input parameters (winding temperature, bearing temperature and the noise) were added sequentially. Depending upon input parameters, the four ANN based fault detectors are developed. The training and testing results of these detectors are illustrated. It is found that the fault detection accuracy is improved with the addition of input parameters.