• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1996년도 하계학술대회 논문집 A
• /
• pp.267-269
• /
• 1996

Servo systems became indispensable to applications such as industrial robots and numerically controlled machinery. Especially, induction motor drives are widely used as ac-servo system owing to the fact that it is maintenance-free. At the present time, Quick torque control methods such as vector control have been employed that enables an induction motor to attain as quick torque response as a dc motor. However, these methods can not be realized without knowing several motor parameters accurately, because the methods need them to calculate flux or voltage command. Most of all, secondary resistance has to be identified accurately, because it's value varies greatly for operation of induction motors. In this paper, a new identification method of secondary resistance based on quick torque control system of induction motors is proposed. The proposed method is derived theoretically from motor circuit equation and can be realized very simply by detecting primary current and voltage command of the motor. Through the numerical simulation considered using PWM inverter, the validity of the proposed method was successfully confirmed.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1998년도 하계학술대회 논문집 F
• /
• pp.1984-1986
• /
• 1998

This paper presents an optimal efficiency control for the inverter fed induction machine system using neural network. The motor speed and the load torque vary the efficiency characteristics of an induction motor. The optimal slip frequency has nonlinearity varied by the load torque as well as the motor speed. The induction motor is driven using the inverter system and the indirect vector control method which input is slip frequency. The neural network for estimating the optimal slip frequency has two input layer(the motor speed and the load torque) and one output layer(the optimal slip frequency that minimize the input power). Learning algorithm of the neural network is the back-propagation. Using the equivalent circuit including the nonlinearity of the induction motor, the loss reduction is analyzed quantitatively. Experimental results are shown noticeable power savings by proposed scheme in high speed and light load conditions.

• 전력전자학회논문지
• /
• 제11권1호
• /
• pp.38-45
• /
• 2006

본 논문은 유도기의 정밀위치제어를 얻고자 외란 변화에 대하여 정밀위치 제어를 할 수 있는 외란 관측기를 적용하였다. 제안된 알고리즘은 외란 변화에 대한 유도전동기의 정밀 제어에 강인함을 보이고 있다. 모든 게인의 튜닝은 디지털 제어 이론에 입각하여 이산 영역에서 구하였다. 또 시뮬레이션은 시뮬링크를 사용하여 그 구성을 매우 간편하게 하였다. DS1102 보드를 이용한 유도기 정밀위치 제어 결과는 제안된 시스템의 유용성을 보인다.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2000년도 추계학술대회 논문집
• /
• pp.680-687
• /
• 2000

In this paper we present a new vector control scheme for induction motor. An exact knowledge of the rotor flux position is essential for a high-performance vector control. The position of the rotor flux is measured in the direct scheme or estimated in the indirect schemes. Since the estimation of the flux position requires a priori knowledge of the induction motor parameters, the indirect schemes are machine parameter dependent. The rotor resistance and stator resistance among the parameters change with temperature. Variations in the parameters of induction machine cause deterioration of both the steady state and dynamic operation of the induction motor drive. Several methods have been presented to minimize the consequences of parameter sensitivity in indirect scheme. In this paper new estimation scheme of rotor flux position is presented to eliminate sensitivity due to resistance change with temperature. Simulation results are used to verify the performance of the proposed vector control scheme.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
• /
• 제49권10호
• /
• pp.675-682
• /
• 2000

An easier implementation method of the field oriented control of induction machine using Matlab/Simulink and dSpace board is proposed in this paper. The total system for the control of induction motor is modeled with the help of Matlab/Simulink. These models consist of induction motor block, SVPWM inverter block and control algorithm block, etc. And this system is simultaneously simulated and experimented in Matlab/Simulink environment with dSpace borad (DS1102). It is possible that Matlab and dSpace board compiler can make $^'{\ast}.c'\;and\;^'{\ast}.obj'$ file of models designed in Matlab/Simulink environment automatically. Both Simulation and experimental results are given.

• Journal of information and communication convergence engineering
• /
• 제3권2호
• /
• pp.105-109
• /
• 2005

This paper proposed a fault diagnosis technique of induction motors winding fault based on an artificial neural network (ANN). This method used Park's vector pattern as input data of ANN. The ANN are firstly learned using this pattern, and then classify between 'healthy' and 'winding fault' (with 2, 10, and 20 shorted turn) induction motor under 0, 50, and $100\%$ load condition. Also the possibility of classification of untrained turn-fault and load condition are tested. The proposed method has been experimentally tested on a 3-phase, 1 HP squirrel-cage induction motor. The obtained results provided a high level of accuracy especially in small turn fault, and showed that it is a reliable method for industrial application

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1996년도 하계학술대회 논문집 A
• /
• pp.476-478
• /
• 1996

This paper is on speed control of induction motor using space vector PWM. Indirect vector control which controls independantly flux and torque current component in order to drive induction motor, is applied for driving motor. Voltage sourced inverter with space vector PWM is used to generate the practically perfect sinusoidal flux density in induction motor. The appropriateness of speed control is proven by appling IP(Integral-proportional) controller which is known to have a good speed response and still to have less overshoot than the now used PI(Proportional-Integral) controller.

• Journal of Advanced Marine Engineering and Technology
• /
• 제23권6호
• /
• pp.738-747
• /
• 1999

Vector controlled induction motor have been widely used in high performance applications. How-ever the performance is sensitive to the variations of motor parameters especially the rotor time constant which varies with the temperature and the saturation of the magnetizing inductance. In this paper the authors propose new identifying method for time-varying parameters of an induction motor which is based on adaptive vector control with serial block algorithm. Vector con-trol system realized on synchronous frame and parameter identification system realized on sta-tionary frame are not easily affected by the vector control frame. Parameter mismatch in the control system results in heavy transient variation in speed and torque response. In order to compensate degradation of the responses at the middle and low speed region adaptive identifier is introduced. To verify the feasibility of this technique compute simu-lations carried out.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1998년도 하계학술대회 논문집 B
• /
• pp.503-505
• /
• 1998

The position control of an induction motor using Feedforward Neural Networks(FNNs) was studied in this paper. A teaching signal was obtained from sliding surface without a particular signal. And the FNNs team through the back propagation algorithm so as to reduce the error between the real position of the motor and the reference value. The structure of a controller was designed simply, for the fast calculating response which is certainly necessary for induction motor position control. And to show the superiority of this controller, 3-phase vector control induction motor whose power capacity is 2.2kw was modeled, and it was simulated.

• Journal of Advanced Marine Engineering and Technology
• /
• 제30권1호
• /
• pp.169-175
• /
• 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.