• Journal of Power System Engineering
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• v.5 no.3
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• pp.95-103
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• 2001

This study is applied to common induction motor, and vector control is realized by using an indirect type of induction motor which has a simple composition. In this study extended Kalman filter is used from control theoretical viewpoint, and primary resistance and secondary resistance which change according to the temperature of motor are simultaneously estimated. This paper aims to research an indirect vector control in which the secondary resistance obtained from this estimation is consistent with secondary flux. This estimation is made by on-line estimation, but on-line estimation is difficult because extended Kalman filter takes long time in computation time. So off-line estimation was made on the assumption that the variation of temperature in motor is slow temporally.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.1
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• pp.169-175
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• 2006

This paper describes how an Artificial Neural Network(ANN) can be employed to improve a speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the motor resistance, which enable the sensorless speed control method to work more accurately. Flux Observer is used for speed estimation in this system. Obviously the accuracy of the speed control of motor is dependent upon how well the parameters of the induction machine are known. These parameters vary with the operating conditions of the motor; both stator resistance(Rs) and rotor resistance(Rr) change with temperature, while the stator leakage inductance varies with load. This paper proposes a parameter compensation technique using artificial neural network for accurate speed estimation of induction motor and simulation results confirm the validity of the proposed scheme.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.11a
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• pp.47-48
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• 2005

This paper describes how an Artificial Neural Network(ANN) can be employed to improve a speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the motor resistance, which enable the sensorless speed control method to work more accurately. Flux Observer is used for speed estimation in this system. Obviously the accuracy of the speed control of motor is dependent upon how well the parameters of the induction machine are known. These parameters vary with the operating conditions of the motor; both stator resistance(Rs) and rotor resistance(Rr) change with temperature, while the stator leakage inductance varies with load. This paper proposes a parameter compensation technique using artificial neural network for accurate speed estimation of induction motor and simulation results confirm the validity of the proposed scheme.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11a
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• pp.65-68
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• 2001

In the vector control methods of induction motor, the stator current is divided into the flux and torque component current. By controlling these components respectively, the methods control independently flux and torque as in the DC motor and improve the control effects. To apply the vector control methods, the position of the rotor current is identified. The indirect vector control use the parameters of the machine to identify the position of rotor flux. But due to the temperature rise during machine operation, the variation of rotor resistance degrades the vector control. To solve the problem, the q-axis is aligned to reference frame without phase difference by comparing the real flux component with the reference flux component. Then to compensate the slip, PI controller is used. The proposed method keeps a constant slip by compensating the gain of direct slip frequency when the rotor resistance of induction motor varies. To prove the validations of the proposed algorithm in the paper, computer simulations is executed.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.12
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• pp.1218-1229
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• 1991

The time optimal position control design can be repeatedly taken from the initial state of a dynamic system to a desired one as fast as possible in the industrial drives. In this case, an induction machine parameters will vary due to temperature, frequency, and saturation effects. In particular, the rotor resistance changes critically with temperature and frequency. These changes affect the command values of the stator current components and slip speed. There is a mismatch between the commanded variables and actual ones of the induction motor drive, and this situation leads to coupling of the vector controller from the plant, i.e. the induction motor . Consequences of such a coupling include the initiation of oscillations of the rotor flux and unsuitable switching of electromagnetic torque for the induction motor servo drive. Therefore, this paper describes a rotor resistance parameter compensating method for the induction motor, And the validity of the proposed design method is confirmed by simulation studies and experiment results.

• 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.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.3
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• pp.288-294
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• 2013

This paper introduces a novel method of on-line identifying the stator resistance and leakage inductance of dual three phase induction motor (DTPIM). According to the machine mathematical model, the stator resistance and leakage inductance can be estimated using the voltage and current values in harmonic subspace. Thus a method of voltage vector injection in harmonic subspace (VVIHS) is proposed, which causes currents in harmonic space. Then the errors between command and actual harmonic currents are utilized to regulate the machine parameters, including stator resistance and leakage inductance. The principle is presented and analyzed in detail. Experimental results prove the feasibility and validity of proposed method.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.742-747
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• 2006

This paper describes online stator-resistance estimation of a linear induction motor(LIM) with cage-type secondary using direct thrust control(DTC), where the resistance value is derived from stator-winding temperature estimation using thermocouple. In this paper, corrected stator resistance has an error in actuality measurement resistance. So compensation coefficient $\kappa$ which is decided through comparison and verifying several times relation of calculated resistance and measured motor line-line resistance. The stator-winding temperature information can also be used for monitoring, protection, and fault-tolerant control of the machine. Also, this paper reports the LIM's responses of the flux measured by the proposed stator resistance compensation algorithm.

• Journal of Power Electronics
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• v.19 no.3
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• pp.751-760
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• 2019

Since parameter mismatch seriously impacts the efficiency and stability of induction motor drives, it is important to accurately estimate the rotor and stator resistance. This paper introduces a method to directly calculate the rotor flux that is independent of stator and rotor resistance and electrical angle. It is based on obtaining the rotor and stator resistance using the model reference adaptive system (MRAS) method. The method has a lower computation burden and less adaptation time when compared with other rotor resistance estimation methods. This paper builds three coordinate frames to analyze the rotor flux error and rotor resistance error. A number of implementation issues are also considered.

• Korean Journal Plant Pathology
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• v.14 no.4
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• pp.319-324
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• 1998

Induction of systemic acquired resistance (SAR) against phytophthora blight and pathogenesis-related (PR) protein accumulation by TMV infection in pepper plant (Capsicum annuum cv. Nockwang) were examined to understand the mechanism of the systemic acquired resistance in pepper plant. The zoospore suspension of Phytophthora capsici was inoculated on stem of pepper plant in which TMV-pepper strain had been inoculated on fully expanded upper leaves, and thephytopha blight incidence was examined. Both disease severity and lesion length of phytophthora blight were much smaller in TMV pre-inoculated pepper plant than in uninoculated control plants. The phytophthora blight incidence was decreased about 50% in the TMV pre-inoculated pepper, compared to the uninoculated control plant at 10 days after P. capsici inoculation. Accumulation of PR1 and PR5 proteins in intercellular fluid of TMV-inoculated and uninoculated upper leaves were monitored by immuno-blot with tobacco P1b and PR5a, antibody during induction of SAR. PR1 and PR5 were detected from 24 hours after TMV inoculation in both TMV-inoculated and uninouclated upper leaves, and increased rapidly in TMV-inoculation in uninoculated upper leaves were defoliated. PR5 could be detected upto 20 days after TMV inoculation in uninoculated upper leaves. These results suggest that TMV infection induces SAR against phytophthora blight in pepper plant, and that PR proteins are accumulated very rapidly during induction of SAR and maintained for quite long time in pepper plant.