• Proceedings of the KIEE Conference
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• 1998.07f
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• pp.2094-2096
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• 1998

In this paper, a new speed estimation method using neural networks is proposed and speed sensorless vector control is realized with the estimated speed. The effectiveness and the usefulness of the proposed algorithms are thoroughly verified with the experiments on the fully-digitalized 2.2kW induction motor drive systems.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.05a
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• pp.449-454
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• 2005

Brushless DC Motor(PM BLDCM) is widely used in industrial applications due to its high efficiency and power density. In order to increase reliability and reduce system cost, this paper studies particularly applicable method for sensorless PM BLDCM drive system. The resulting third harmonic signal keeps a constant phase relationship with the rotor flux for any motor speed and load condition, and is practically free of noise that can be introduced by the inverter switching, making this a robust sensing method. As a result, the method described here is not sensitive to filtering delays, allowing the motor to achieve a good performance over a wide speed range. In addition, a simple starting method and a speed estimation approach are also proposed.

• Proceedings of the KIEE Conference
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• 1992.07b
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• pp.1196-1198
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• 1992

In field oriented control of Induction motors, speed sensor is required, which reduces the sturdiness of drive system and together with the expenditure of hardware for faultless transmission and processing of sensor signals it causes considerable expenses. These expensive sensors can be replaced by speed sensorless concept. And for good control, the knowledge of the rotor flux component of the rotor resistance are needs. Thus, this paper is based on a Extended Kalman Filter( EKF ) that estimates the state variables that are required for the control by only measuring the line voltages and currents of the machine. The rotor time constant and speed estimated by the EKF shows satisfactory agreement with the real values, with the simulation approaches.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.11
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• pp.1894-1899
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• 2016

In this paper, because the position sensor represents the important factor in BLDC (Brushless DC) motor drives, BLDC motor is necessary that the three Hall-sensors evenly be distributed around the stator circumference in case of the 3 phase motor. The Hall-sensor is set up in this motor to detect the main flux from the rotor. So the output signal from Hall-sensor is used to drive IGBT to control the stator winding current. However, in case of breakdown Hall sensor, we research that the estimation algorithm of Hall sensor signal to detect rotor position and for the speed feedback signals with BLDC motor whose six stator and two rotor designed. In addition, this paper presents a sensorless speed control of BLDC Motor using terminal voltage of the one phase. Rotor position information is extracted by indirectly sensing the back EMF from only one of the three terminal voltages for a three-phase BLDC motor.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.1
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• pp.78-84
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• 2011

Using sensors to detect current or voltage of motors is disadvantaged because motor is exposed to vibration, impacts, corrosion, high temperature and humidity in the machinery structure like HEV. In the case of IPMSM, position information is included not only in the flux or EMF term but also in the rotator inductance because of its saliency. To solve this problem, a new mathematical model of IPMSMs on fixed frame is proposed and an extended EMF includiing rotating position information is defined to estimate extended EMF. A strong low-pass filter through the variable cut-off frequency using velocity function was proposed. This makes it is possible to estimate extended EMF by least order disturbance observer. The proposed method was proved through the experiment.

• Journal of Power Electronics
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• v.14 no.6
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• pp.1243-1253
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• 2014

The phase inductance of a switch reluctance motor (SRM) is significantly nonlinear. With different saturation conditions, the phase inductance shape is clearly changed. This study focuses on the relationship between coefficient and current in an inductance model with ignored harmonics above the order of 3. A position estimation method based on the variable coefficient inductance model is proposed in this paper. A four-quadrant sensorless control system of the SRM drive is constructed based on the relationship between variable coefficient inductance and rotor position. The proposed algorithms are implemented in an experimental SRM test setup. Experimental results show that the proposed method estimates position accurately in operating two/four-quadrants. The entire system also has good static and dynamic performance.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.526-530
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• 1999

This paper presents a newly developed speed sensorless drive using Neural Network algorithm. Neural Network algorithm can be divided into three categories. In the first one, a Back Propagation-based NN algorithm is well-known to gradient descent method. In the second scheme, a Extended Kalman Filter-based NN algorithm has just the time varying learning rate. In the last scheme, a Recursive Least Square-based NN algorithm is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. The number of iterations required to converge and the mean-squared error between the desired and actual outputs is compared with respect to each method. The theoretical analysis and experimental results are discussed.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.4
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• pp.185-191
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• 2005

In this paper, a new approach for the Synchronous Reluctance Motor control which ensures producing Maximum Torque per Ampere(MTPA) over the entire field weakening region is presented. In addition, This paper presents a speed sensorless control scheme of SynRM using artificial neural network. Also, by adjusting the base speed for the field weakening operation according to the flux level, the current and voltage limit, the smooth and precise transition into the field weakening operation can be achieved. The proposed scheme is verified validity through simulation.

• 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.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.7
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• pp.930-936
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• 2003

A new method of direct current motor drive, which requires neither shaft encoder nor speed estimator, is presented. The proposed scheme is based on decreasing current gap between a numerical model and an actual motor. By supplying the identical instantaneous voltage to both model and motor in the direction of reducing the current difference, the rotor approaches to the model speed, that is, reference value. The performance of direct current motor drives without speed sensor is generally poor at very low speed. However, in this system, it is possible to obtain good speed performance in the low speed range.