• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.330-335
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• 1998

In this paper the robust vector control method of induction motor for the purpose of improving the system performance deterioration caused by parameter variations is proposed. The estimations of the stator current and the rotor flux are obtained by the full order state observer with corrective prediction error feedback. and the adaptive scheme is constructed to estimate the rotor speed with the error signal between real and estimation value of the stator current. Adaptive sliding observer based on the variable structure control is applied to parameter identification. Consequently predictive current control and speed sensorless vector control can be obtained simultaneously regardless of the parameter variations.

• Proceedings of the KIEE Conference
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• 1998.07f
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• pp.1922-1924
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• 1998

In this paper, the robust vector control method of Induction Motor for the purpose of improving the system performance deterioration caused by parameter variations is proposed. The full order state observer estimates the stator current and the rotor flux by using the state prediction of state variables. And, the motor speed is estimated without speed sensor using the full order state observer. Also, the parameter variation is compensate by the Sliding Observer. By using this method, speed sensorless control and current contol with no affection of the parameter variation can be obtained simultaneously.

• 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 KIPE Conference
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• 2018.07a
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• pp.60-62
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• 2018

본 논문에서는 유도전동기의 회전자 시정수 오차에 강인한 센서리스 전차원(Full-Order) 자속(Flux) 관측기(Observer)를 제안하였다. 이 관측기는 고정자 자속과 회전자 자속을 동기좌표계상에서 추정한다. 고정자 자속에서 누설 자속을 뺀 공극 자속을 기준으로 제어를 수행하며, 동기좌표계가 공극 자속을 빠르게 따라갈 수 있는 방법을 제안하였다. 제안된 방법을 적용할 때, 기존의 회전자 자속을 직접 이용하는 방법보다 중-고속 영역에서 회전자 시정수 오차의 영향이 감소함을 확인하였다. 또한 기존 관측기와 달리 제안된 관측기에서 적응제어기(Adaptive Controller)는 전동기의 회전 주파수가 아닌, 전동기의 슬립 주파수 만을 추정하므로, 회전자 자속 오차에 민감한 적응제어기의 역할을 축소하였다. 시뮬레이션 및 실험으로 이론의 타당성 및 효과를 검증하였다.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1036-1038
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• 2000

This paper presents an implementation of digital motion control system of induction motor vector drives with a direct torque control(DTC) using the 16bit DSP TMS 320F240. The DSP controller enable enhanced real time algorithm and cost-effective design of intelligent controllers for induction motors which can be yield enhanced operation, fewer system components, lower system cost, increased efficiency and high performance. The system presented are stator flux observer of current model that inputs are current sensing of motor terminal and rotor angle, and optimal switching look-up table by using fully integrated control software. The developed system are shown a good motion control response characteristic results and high performance features using 2.2Kw general purposed induction motor.

• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.3-6
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• 2002

In a conventional speed sensorless stator flux-oriented(SFO) induction motor drive system, when the estimated speed is transformed into the sample-data model using the first-forward difference approximation, the sampled data model has a modeling error which, in turn, produces an error in the rotor speed estimation. The error included in the estimated speed is removed by the use of a low pass filter (LPF). As the result, the delay of the estimated speed occurs in transients by the use of the LPF This paper investigates the problem of a conventional speed sensorless SFO system due to the delay of estimated speed in the filed weakening region. In addition, this paper proposes a method to estimate exactly speed by using Luenberger observer, The proposed method is verified by experiment with a 5-hp induction motor drive.

• Proceedings of the KIPE Conference
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• 2001.12a
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• pp.7-10
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• 2001

To obtain a high performance in a direct vector controlled induction machine, it is essential to correct estimation of rotor flux. The accuracy of flux observers for induction machines inherently depends on parameter sensitivity. This paper presents an analysis method for conventional flux observers using Parameter Sensitivity. The Parameter sensitivity is defined as the ratio of the percentage change in the system transfer function to the percentage change of the parameter variation. We define the ratio between real flux and estimated flux as the transfer function, and analyzed a parameter sensitivity of this transfer function.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1176-1178
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• 2001

To obtain a high performance in a direct vector controlled induction machine, it is essential to correct estimation of rotor flux. The accuracy of flux observers for induction machines inherently depends on parameter sensitivity. This paper presents an analysis method for conventional flux observers using parameter Sensitivity. The Parameter sensitivity is defined as the ratio of the percentage change in the system transfer function to the percentage change of the parameter variation. We define the ratio between real flux and estimated flux as the transfer function, and analyzed a parameter sensitivity of this transfer function.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1185-1201
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• 2018

This paper presents a sensorless direct torque control (DTC) combined with sliding mode approach (SM) and space vector modulation (SVM) to achieve mainly a high performance and reduce torque and flux ripples of a parallel-connected two five-phase permanent magnet synchronous machine (PMSM) drive system. In order to increase the proposed drive robustness and decrease its complexity and cost, the rotor speeds, rotor positions, fluxes as well as torques are estimated by using a sliding mode observer (SMO) scheme. The effectiveness of the proposed sliding mode observer in conjunction with the sliding mode control based DTC is confirmed through the application of different load torques for wide speed range operation. Comparison between sliding mode control and proportional integral (PI) control based DTC of the proposed two-motor drive is provided. The obtained speeds, torques and fluxes responses follow their references; even in low and reverse speed operations, load torques changes, and machines parameters variations. Simulation results confirm also that, the ripples of the torques and fluxes are reduced more than 3.33% and 16.66 %, respectively, and the speed overshoots and speed drops are reduced about 99.85% and 92.24%, respectively.