• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.10
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• pp.27-34
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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 Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.05a
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• pp.119-124
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• 2004

A new control method of spindle induction motor drive is proposed in this paper. In a conventional control method, the vector control was employed in the low speed range, while the constant V/f(volt-per-hertz) PWM control was employed in the high speed range. The two control modes were automatically selected at an intermediate speed. In a proposed control method, the direct vector-control with an encoder is employed in the low speed range(from 0 to 8,000rpm), and the vector control without an encoder is employed in th high speed(from 8,000 to 15,000rpm) by using flux observer and speed estimator. This paper describes problem of control method in a conventional spindle induction motor drive and proposes contort method as variation of motor speed.

• Journal of Power Electronics
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• v.6 no.3
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• pp.235-244
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• 2006

Recently, speed sensorless vector control for synchronous reluctance motors (SYRMs) has deserved attention because of its advantages. Although rotor angle calculation using flux estimation is a straightforward approach, the DC offset can cause an increasing pure integrator error in this estimator. In addition, this method is affected by parameter fluctuation. In this paper, to control the motor at the low speed region, a modified programmable cascaded low pass filter (MPCPLF) with sensorless online parameter identification based on a block pulse function is proposed. The use of the MPCLPF is suggested because in programmable, cascade low pass filters (PCLPF), which previously have been applied to induction motors, the drift increases vastly wl)en motor speed decreases. Parameter identification is also used because it does not depend on estimation accuracy and can solve parameter fluctuation effects. Thus, sensorless speed control in the low speed region is possible. The experimental system includes a PC-based control with real time Linux and an ALTERA Complex Programmable Logic Device (CPLD), to acquire data from sensors and to send commands to the system. The experimental results show the proposed method performs well, speed and angle estimation are correct. Also, parameter identification and sensorless vector control are achieved at low speed, as well as, as at high speed.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.430-433
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• 2001

When the vector control, which does not need a speed signal from a mechanical speed sensor, it is possible to reduce the cost of the control equipment and to improve the control performance in many industrial application. This paper describes a rotor speed identification method of induction motor based on the theory of flux model reference adaptive system. The estimator execute the rotor speed identification so that the vector control of the induction motor may be achieved. The improved auxiliary variable of the two model are introduced In perform accurate rotor speed estimation. The control system is composed of the PI controller for speed control and current controller using space voltage vector PWM technique. High speed calculation and processing for vector control is carried out by TMS320C31 digital signal processor. Validity of the proposed control method is verified through simulation and experimental result.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1092-1099
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• 2006

Displayed system equationally using accurate dynamic modeling of whole system including induction motor and load to analyze induction motor to normal condiction's action characteristic as well as transient characteristic using power converter device such as inverter in this paper. Also, presume adhesive power calculation through speed sensorless vector control and load torque disturbance observer for maximum tractive force control. Confirmed proposed algorithm through simulation and an experiment using railroad experiment equipment to embody control algorithm of such system. With relation of adhesive power about the wage speed by speed addition and subtraction of railway vehicle, embodied all sorts item by experiment equipment.