• Journal of the Korean Society of Industry Convergence
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• v.2 no.2
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• pp.91-102
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• 1999

The paper describes a robust adaptive control algorithm for induction motor drive without speed sensor at low speed range. The control algorithm use only current sensors in a space vector pulse width modulation within loop control with rotor speed estimation and voltage source inverter. On-line rotor speed estimation is based on utilizing parallel model reference adaptive control system. MRAC of the modified flux model for flux and rotor speed estimator uses dual-adaptation mechanism, ${\omega}_r$ and ${\omega}_e$ scheme. The estimated flux components in the model can be compensated from the effects of offset errors on pure integrals. It can be compensated to the parameter variations and torque fluctuation with speed estimation in less then 10 rad/sec. In a simulation, the proposed induction motor control algorithm without speed sensor at very low speed range are shown to operate very well in spite of variable rotor time constant and fluctuating load without change the controller parameters. The suggested control strategy and estimation method have been validated by simulation study, and it proposed the designed system for the implementation using TI320C31 DSP/ASIC controller.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.195-197
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• 2005

This paper is proposed adaptive fuzzy neural network(AFNN) and artificial neural network(ANN) based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed control and estimation of speed of induction motor using fuzzy and neural network. 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. This paper is proposed the experimental results to verify the effectiveness of the new method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.10
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• pp.59-68
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed high performance control of induction motor drive using multi adaptive fuzzy controller. This controller has been performed for speed control with fuzzy adaptation mechanism (FAM)-PI, current control with model reference adaptive fuzzy control(MFC) and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM-PI, MFC and ANN controller. The performance of proposed controller is evaluated by analysis for various operating conditions using parameters of induction motor drive. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Journal of Electrical Engineering and Technology
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• v.2 no.1
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• pp.55-60
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• 2007

This paper proposes a hydraulic pump system that uses a variable speed SR drive and constant capacity pump. For the design of the SRM (Switched Reluctance Motor) and digital controller, base speed and rating torque are determined from the mechanical specifications of the hydraulic pump. In order to minimize the power consumption during the maintaining of preset oil-pressure, the pressure control system changes the maximum oil-pressure band and flow rate according to the motor speed. The DSP control system adjusts the oil-pressure and the speed of the SRM from the pressure sensor signal, due to conservation of power consumption by the hydraulic pump. A 2.2Kw, 12/8 pole SR motor and DSP based digital controller are designed and tested with experimental set-up. The test results indicate that the system has some good features such as high efficiency and rapid response characteristics.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.6
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• pp.77-82
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• 2005

This paper is proposed fuzzy neural network(FNN) and artificial neural network(ANN) based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed control and estimation of speed of induction motor using fuzzy and neural network. 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 experimental results to verify the effectiveness of the new method.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1068-1069
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• 2015

Wind turbines have an enormous potential for decentralized electricity generation. In recent years, there has been an increasing worldwide interest in small/medium wind systems. This paper presents the results of power performance testing conducted on a 50 kW turbine located in Yeonggwang test-bed. The turbine system is a pitch, active yaw, variable speed, upwind, three blade with a direct drive PMSG. This thesis covers the operation of variable speed wind turbines with pitch-yaw control. The system considered is controlled to generate maximum energy while minimizing loads. The data include power, wind speed, and direction from meteorological towers, and nacelle anemometer readings and output from turbine. The analysis concentrates on the effect of the load on the power-wind speed curve of the turbine.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.380-388
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• 2016

Using the magnet gear, it is possible to transmit power without mechanical contact. As the drive shaft in a magnet gear-based speed reducer system is isolated from the drive shaft, the system is a two-inertia resonance system that should cope with an external load with the limited air-gap stiffness. On the other hand, the drive shaft or low-speed side is controlled only by the torque of the drive shaft through an air-gap, and the excessive oscillation or the slip can then be generated because of an abrupt disturbance that is different from the general mechanical gear system. Therefore, the disturbance loaded at the low speed side should be measured or estimated, and considered in the control of the driving shaft. This paper proposes a novel full-state feedback controller with a reduced-order observer for the speed reducer system using a magnet gear with a unified harmonic modulator. The control method was verified by simulation and experiment. To estimate the load at the low speed side, a novel observer was designed, in which the new state variable is introduced and the new state equation is formulated. Using a full-state feedback controller including the observer, the test result against disturbance was compared with two D.O.F PI speed controllers. The pole slip was compensated within relatively a short time, and the simulation result about the estimated variable shows a similar tendency to the test result. The test results showed that the magnet gear-based reducer can be applied to an accurate servo system.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.579-582
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• 1989

The air-cooled waterwheel generator has a fan connected to waterwheel shaft or motor driven fan or fans. The fans are operated at constant speed, constant input, regardless of generator loss which is varied according to generator output and coolant the perature. Energy savings may he possible if the cooling air flow is controlled according to generato output and air temperature depending on season. The simulation and experience have been done on the 22.6 KVA Waterwheel generator by using AC variable speed drive. The results gave us los cut of generator.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1084-1086
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• 2003

3-Phase BLDC Motor, which is generally used, has limited usage in high speed, due to pulsation torque occurring commutation and variable speed. To solve this problem, it is necessary to increase invariable, pole number, or the number of slots. Currently, due to rapid growth of semiconductor technology and trend toward smaller and cheaper production of switching device, multi-phase BLDC Motor for high speed, high output power is being actively pursued to produce. In this paper 7-Phase BLDC Motor drive system is designed and Vector modulator is studied.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1548-1555
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• 2017

This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition to conventional neural network are explored for improving the modeling accuracy of SRM. The neural structures are trained with the data obtained by modeling of SRM using Finite Element Analysis (FEA) and the trained neural network is incorporated in the model of SRM drive. The results signify the modeling accuracy with GRNN model. The closed loop drive simulation is performed in MATLAB/Simulink environment and the closeness of the results in comparison with the experimental prototype validates the modeling approach.