• Proceedings of the KIEE Conference
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• 2003.10b
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• pp.203-205
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• 2003

This paper is proposed a newly developed approach to identify the mechanical speed of an induction motor based on artificial neural networks technique. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• Proceedings of the KIPE Conference
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• 2004.07a
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• pp.269-273
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• 2004

It is important to estimate the rotor speed for field weakening operation in the speed sensorless stator flux oriented (SFO) induction motor drive. Several methods have been reported to estimate exactly the speed in the speed sensorless system. In this paper, we apply two observer-based methods, the Luenberger observer (LO) and the Kalman filter (KF), to SFO induction motor drive in order to achieve a speed sensorless operation in field weakening region. Two control methods are reviewed and discussed. The operation characteristics of these methods in the field weakening region is compared by simulation and experiment.

• Journal of Power Electronics
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• v.9 no.4
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• pp.582-592
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• 2009

This paper proposes a new speed sensorless direct torque and flux controlled interior permanent magnet synchronous motor (IPMSM) drive. Closed-loop control of both the torque and stator flux linkage are achieved by using two proportional-integral (PI) controllers. The reference voltage vectors are generated by a SVM unit. The drive uses an adaptive sliding mode observer for joint stator flux and rotor speed estimation. Global asymptotic stability of the observer is achieved via Lyapunov analysis. At low speeds, the observer is combined with the high frequency signal injection technique for stable operation down to standstill. Hence, the sensorless drive is capable of exhibiting high dynamic and steady-state performances over a wide speed range. The operating range of the direct torque and flux controlled (DTFC) drive is extended into the high speed region by incorporating field weakening. Experimental results confirm the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.171-175
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• 1998

This paper proposes a speed sensorless vector control of induction motor using a minimum-order EKF(extended kalman filter). Minimum-order EKF has the advantage of reducing the computational estimation cost because the stator current is not estimated. EKF does not deteriorate the performance of the overall speed control system, even though the measurements are relatively noisy. The estimated rotor speed is used for vector control and overall speed control. Computer simulations of the speed sensorless vector control are carried out to test the usefulness of the minimum-order EKF algorithm.

• Journal of the Korean Society of Propulsion Engineers
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• v.1 no.2
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• pp.12-21
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• 1997

Nowadays, non-pollution energy sources have been strongly needed because of the exhaustion of fossil fuels and serious environmental problems. Because wind energy can be enormously obtained from natural atmosphere, this type of energy has lots of advantages in a economic and pollution point of view. This study has established the aerodynamic and structural design procedure of the rotor blade with an appropriate aerodynamic performance and structural strength for the 500㎾ medium class wind turbine system. The aerodynamic configuration of the rotor blade was determined by considering the wind condition in the typical local operation region, and based on this configuration aerodynamic performance analysis was performed. The rotor blade has the shell-spar structure based on glass/epoxy composite material and is composed of shank including metal joint parts and blade. Structural design was done by the developed design program in this study and structural analysis, for instance stress analysis, mode analysis and fatigue life estimation, was performed by the finite element method. As a result, a medium scale wind turbine rotor blade with starting characteristics of 4m/s wind speed, rated power of 500㎾ at 12m/s wind speed and over 20 years fatigue life has been designed.

• Journal of Power System Engineering
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• v.18 no.5
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• pp.129-136
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• 2014

This paper proposes improvement on sensorless vector control performance of a permanent magnet synchronous motor (PMSM) with sliding mode observer. An adaptive observer gain and second order cascade low-pass filter (LPF) were used to improve the estimation accuracy of the rotor position and speed. The adaptive observer gain was applied to suppress the chattering intensity and obtained by using the Lyapunov's stability criterion. The second order cascade LPF was designed for the system to escalate the filtering performance of the back-emf estimation. Furthermore, genetic algorithm was used to optimize the system PI controller's performance. Simulation results showed the effectiveness of the suggested improvement strategy. Moreover, the strategy was useful for the sensorless vector control of PMSM to operate on the low-speed area.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.727-728
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• 2006

This paper is proposed robust control based on the vector controlled induction motor drive with adaptive fuzzy learning control(AFLC). The fuzzy logic principle is first utilized for the control rotor speed. AFLC scheme is then proposed in which the adaptation mechanism is executed using fuzzy logic. Also, this paper is proposed estimation of speed of induction motor using ANN Controller. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. This paper is proposed the analysis results to verify the effectiveness of the new method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.886-890
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• 2011

Induction motors are relatively cheap and rugged machines. For the vector control of induction motors, a position or speed sensor is needed. But a speed sensor increases motor cost and reduces reliability in harsh environment. Recently, many studies have been performed for sensorless speed control. This paper investigates an improved flux observer with the parameter error compensation for a sensorless induction motor. The proposed algorithm is verified through simulation and experiment.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.1
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• pp.1-7
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• 2005

This paper is proposed the speed and position sensorless control of surface permanent magnet synchronous motor(SPMSM) with adaptive fuzzy and observer. A adaptive fuzzy controller is applied for speed control of SPMSM drive. A adaptive state observer is used for the mechanical state estimation of the motor. The observer was developed based on nonlinear model of SPMSM, that employs a d - q rotating reference frame attached to the rotor. A adaptive observer is implemented to compute the speed and position feedback signal. The validity of the proposed sensorless scheme is confirmed by various response characteristics.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.139.4-139
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• 2001

The thesis propose the sensorless vector control method that estimates the rotor speed using stator current. The estimated speed is used as feedback in a vector control system. The conventional MRAS structure has a problem the error output is decreasing as estimated speed error is increasing and the estimation performance is not robust when mutual inductance has been changed. In the proposed method, error output is proportional to estimated speed error. The described technique is less complex, robust to variations of mutual inductance. This new method can achieve much wider bandwidth speed control than that of the conventional MRAS structure.