• 전력전자학회:학술대회논문집
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• 전력전자학회 2004년도 전력전자학술대회 논문집(1)
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• pp.201-205
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• 2004

A method for driving and position sensing of TSRM(Toroidal Switched Reluctance Motor) using the search coil is presented in this paper. Position information of the rotor is essential for SRM drives. The rotor position sensor such as an opto-interrupter or high performance encoder is generally used for the estimation of rotor position. However, these discrete position sensors not only add complexity and cost to the system but also tend to reduce the reliability of the drive system. In order to solve these problems, in the proposed method, rotor position detection is achieved using the voltage waveforms induced by the time varying flux linkage in the search coils, and then the appropriate phases are excited to drive the SRM. But the search coil EMF is generated only when the motor rotates. Therefore the rotor position sensing method at standstill is also suggested.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1998년도 춘계종합학술대회
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• pp.371-376
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• 1998

This paper describes an approach to a speed estimation method to remove speed sensor of underwater robot's AC drive systems. AC motors have been widely used in the field of underwater robot's manipulator or propulsion system. Most of these AC motors for underwater use have usually filled oil to compensate the high pressure in deep-sea operation, where a resolver is adopted to feed back the speed of rotor But this kind of speed feedback devices gives rise to some defects arising from their mechanical complexity and numerous signal lines; a resolver needs 6 or 7 signal lines for proper operation. This paper presents a speed estimation method to improve these problems of induction motor, which is adopted as a prototype of AC motor. The proposed speed estimation method is based on the RFO(rotor flux orientation) vector control method of voltage-fed AC drives. Using the controller of voltage-fed AC drives, it is unnecessary to measure the voltage for the estimation of rotor speed, which reduces the effects of measurement error Numerical simulation is carried out to investigate the validity of the method and the effects of rotors resistance variation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.285-287
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• 2001

To obtain a high performance in a vector controlled induction machine, it is essential to know the instantaneous position of the rotor flux which depends on the rotor time constant. But the rotor time constant mainly varies due to the temperature rise in the motor winding, so real time compensating algorithm is necessary. This paper proposes that it uses short duration pulses added to the constant flux command current and then resultant torque command current produced by speed controller is utilized for the rotor resistance estimation. This method has advantages with a low computational requirement and does not require voltage sensors. The proposed method is proved by simulations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 추계학술대회 논문집 학회본부
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• pp.326-331
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• 1990

In the vector-controlled induction machine drive, mechanical sensors restrict the wide applications of high performance AC drives. So in resent years, many papers have been presented which doesn't need mechanical sensors, named by sensorless vector control. But sensorless control has a few serious problem, one of which Is poor speed estimation in case of incorrect rotor resistance (Rr) information. This paper describes the stator flux orientation speed control strategy with the speed estimation algorithm. and the method of adapting Rr change due to thermal heating. By proposed method. We can acquire precise speed estimation and higher performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 학술대회 논문집 전문대학교육위원
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• pp.160-164
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• 2007

MRAS observer, which is based on adaptive control theory, estimates speed and position by using optimal observer gains on the basis of Lyapunov stability theory. However, in case of MRAS theory, position estimation error is in existence because of non-linearity for inductance variation and limit cycles for position estimation. The adaptive sliding observer based on the variable structure control theory estimates the speed and position for zero of estimation error by using the sliding surface equal to the error between speed and position estimation. The binary observer estimates the rotor speed and rotor flux with alleviation of the high-frequency chattering, and retains the benefits achieved in the conventional sliding observer, such as robustness to parameter and disturbance variations. The speed and position sensorless control of SRM under the load and inductance variation is verified by the experimental results.

• Journal of Power Electronics
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• 제9권6호
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• pp.911-918
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• 2009

This paper presents a new sensorless control method for single-phase switched reluctance motors using induced electromotive force (EMF) due to the residual flux both on the stator and the rotor during phase commutation. The induced EMF falls to zero when the rotor pole moves away from the overlap with the stator pole. By detecting this instant, the speed and position of the rotor can be simply estimated. This method is very simple to implement and it is insensitive to variations in the system parameters as it does not require any stored magnetic data or offline inductance measurements but requires only measurements of the terminal voltage and a simple analog circuit. The proposed method is implemented on a 6/6 single-phase switched reluctance motor. However, it can also be implemented on a multiphase SRM regardless of the size, operation speed and switching mode of the motor hence making the proposed method viable to many applications. Simulation and experimental verification is provided to demonstrate the feasibility of the proposed method.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 Proceedings ICPE 98 1998 International Conference on Power Electronics
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• pp.23-28
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• 1998

We propose a nonlinear feedback controller that can control the induction motors with high dynamic performance by means of decoupling of motor speed and rotor flux. The nonlinear feedback controller needs the information on some motor parameters. Among them, rotor resistance varies greatly with machine temperature. A new recursive adaptation algorithm for rotor resistance which can be applied to our nonlinear feedback controller is also presented in this paper. The recursive adaptation algorithm makes the estimated value of rotor resistance track its real value. Some simulation results show that the adaptation algorithm for rotor resistance is robust against the variation of stator resistance and mutual inductance. In addition, it is computationally simple and has small estimation errors. To demonstrate the practical significance of our results, we present some experimental results.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1999년도 전력전자학술대회 논문집
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• pp.544-548
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• 1999

Using Observer, on the sensorless vector control system is a novel techniques for modern induction motor control. In this paper, a speed estimation algorithm of an induction motor using an extended kalman filter was proposed. Extended kalman filter can solve the problem, that have steady state error of estimated speed in flux and slip estimation method. The extended Kalman filter is employed to identify the speed of an induction motor and rotor flux based on the measured quantities such as stator current and DC link voltage. In order to confirming above proposal, computer simulation carried out using Matlab Simulink and show the effectiveness of the control drives for induction motor speed estimation.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 후기학술대회논문집
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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 Electrical Engineering and Technology
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• 제11권3호
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• pp.769-774
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• 2016

In the design of sensorless control system for induction motor, high-precision speed estimation is one of the most difficult problems. To solve this problem, the common method is model reference adaptive method (MRAS). MRAS requires accurate motor parameters to estimate rotor speed precisely. However, when motor is running, the variety of temperature and magnetic saturation will lead to the change of motor parameters such as stator resistance and rotor resistance, which will lower the accuracy of the speed estimation. To improve the accuracy and rapidity of speed estimation, this paper analyses the mutual MRAS speed identification based on rotor flux linkage, and proposes an improved mutual MRAS speed identification based on back-EMF. The improved method is verified by Simulink simulation and motor experimental platform based on DSP2812. The results of simulation and experiment indicate that the method proposed by this paper can significantly improve the accuracy of speed identification, and speed up the response of identification.