• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.111-116
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• 2022

The causes of motor burnout include overload, phase loss, restraint, interlayer short circuit, winding ground fault, instantaneous overvoltage, and the rotor contacting the stator, leading to insulation breakdown, leading to breakdown or electrical accidents. Therefore, equipment failure causes not only loss due to cost required for equipment maintenance/repair, but also huge economic loss due to productivity decrease due to process stop because the process itself including the motor is stopped. The current level of technology for diagnosing motor failures uses vibration, heat, and power analysis methods, but there is a limit to analyzing the problems only after a considerable amount of time has passed according to the failure. Therefore, in this paper, a device and algorithm for measuring insulation resistance using DC AMP signal was applied to an industrial motor to solve this problem. And by following the insulation resistance state value, we propose a diagnosis of deterioration and failure of the motor that cannot be solved by the existing method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.4
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• pp.45-56
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• 2011

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.140-143
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• 1991

This paper describes a compensation method for the rotor resistance variation of induction machines in speed sensor-less vector control system using MRAS(model reference adaptive system). In case of rotor resistance variation, the analysis of the conventional speed sensor-less vector control system using MRAS is presented and the compensation method for rotor resistance variation using Fuzzy logic is proposed. In order to confirm the performance of the proposed algorithm, computer simulation is performed.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.510-513
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• 1996

This paper consists of the speed sensorless vector control of induction motors with the estimation of rotor resistance. In the application of variable-speed induction motor drives, if an inaccurate rotor resistance is used because the rotor resistance can change due to skin effects and temperature variables, it is difficult to achieve a collect field orientation. In this paper, to overcome these difficulties adaptive algorithm is designed for rotor resistance identification at the beginning of the transient state. And an adaptive flux observer is used for the purpose of estimating rotor flux and speed in the speed sensorless scheme. Computer simulations are carried out to verity the validity of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.50-52
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• 2006

This paper Proposes a new method of on-line estimation for rotor resistance of the induction motor in the indirect vector controlled drive, using artificial neural network (ANN). The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and actual state variable of a neural network model is back propagated to adjust the weight of a neural network model, so that the actual state variable tracks the desired value. The performance of rotor resistance estimator and torque and flux responses of drive, together with these estimators, are investigated variations rotor resistance from their nominal values. The rotor resistance are estimated analytically, using the proposed ANN in a vector controlled induction motor drive.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1656_1657
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• 2009

This paper presents a new method for estimating rotor resistance of induction motor. The rotor resistance changes dramatically with temperature and frequency. Speed is controlled by PID as it is simplest and most intuitive control method. The change in rotor resistance has a great influence on the performance of IM. In this paper rotor resistance is estimated using Non Linear Disturbance Observer. The model uncertainty and system non linearity are treated as disturbance in this method. Using NDO it does not require an accurate dynamic model to achieve high precision motor control. Controller with NDO has more superior tracking performance. Simulation results are presented to show the validity of the proposed controller.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.647-648
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• 2015

최근 최저효율제를 대비한 프리미엄급 고효율 전동기의 필요성이 대두됨에 따라 유도전동기를 대체할 수 있는 고효율 전동기로써 영구자석 매입형 동기 릴럭턴스 모터(PMA-SynRM)가 각광받고 있다. 하지만 모터의 속도와 토크를 제어하기 위해 회전자 속도와 위치 정보를 측정할 수 있는 센서가 필요하고, 이 때 센서 설치의 공간적 문제, 센서의 유지/보수 및 시스템의 추가 비용 발생 등 많은 문제점들이 발생한다. 따라서 센서를 제거하기 위한 센서리스 위치 및 속도 제어에 관한 연구가 폭 넓게 진행되고 있다. 하지만 모터를 운전함에 따라 저항이나 역기전력 상수, 인덕턴스 등의 제정수의 변동이 발생하게 되고 센서리스 제어 시 이러한 변동에 의해서 추정 속도 및 위치에 오차가 발생하게 된다. 본 논문에서는 이러한 오차의 보상을 위해 MRAS(Model Reference Adaptive System) 방식을 적용한 센서리스 제어를 제안한다.

• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.113-115
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• 2001

This paper presents the rotor parameter identification based on the MRAS theory and the speed estimation using ANN for the sensorless vector control of induction motor. The motor speed is estimated using ANN model which contains the rotor parameter. And the rotor parameter is identified using MRAS scheme which contains the rotor speed. The rotor speed estimate converges to its actual value as the rotor parameter error converges toward the zero. The simulation using Matlab/Simulink is performed to show the effectiveness of the proposed scheme.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.357-359
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• 1994

This paper consists of the vector control of three phase induction motors which has robustness against disturbances and parameter variations by the TDOF (Two Degree Of Freedom) theory. Using the TDOF theory, induction motor is robustly controlled for resistance variations and disturbances. Adaptive observer is used for the purpose of estimating the stator fluxes, the stator currents, and the parameters. The proposed control netted is verified by computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.125-130
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• 2001

This paper presents an indirect field-oriented (IFO) induction motor position servo drives which uses the model following adaptive controller with the artificial neural network(ANN)-based rotor resistance estimator. The model reference adaptive system(MRAS)-based 2-layer ANN estimates the rotor resistance on-line and a linear model-following position controller is designed by using the estimated the rotor resistance value. At the end, a fuzzy logic system(FLS) is added to make the position controller robust to the external disturbances and the parameter variations. The simulation results show the effectiveness of the proposed method.