• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.9
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• pp.569-575
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control Performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation. and steady- state accuracy and transient response.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.570-572
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• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.222-224
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• 2002

This paper investigates the adaptive control of a fuzzy logic based speed and flux controller for a vector controlled induction motor drive. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the model reference adaptive control(mAC) fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed MRAC fuzzy controller is confirmed by performance results for induction motor drive system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.12
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• pp.854-861
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• 1988

The current source inverter-fed induction motor(CSIM) drive is widely used in industry because of its four quadrant operation, fuseless protection, fuseless protection, and ruggedness. the CSIM drive system, however, has shortcomins such as slow response and dynamic stability to load torque disturbance and reference speed change. Such a disadvantages can be compensated considerably by means of introducing additional angle angle control loop. The angle control method is dependent upon the inverter type. In this paper, simultaneus recovery and commutation inverter(SRCI) which is developed recently is considered.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.10
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• pp.82-93
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• 2006

In this paper, filters are designed to reduce transients overvoltage in inverter fed high-voltage large-capacity induction motor drive system. Design issues for a LCR filter at the inverter output terminals to reduce the dv/dt of the inverter output pulse and a RC filter at the induction motor input terminals to match the characteristic impedance between cable and induction motor are examined in detail. These filters are modeled to be suitable to high-voltage large-capacity induction motor. The performance of the filter is evaluated through simulation using EMTP(ElectroMagnetic Transients Program). We presented filters that used high voltage large-capacity induction Motor on the basis of this. Effect of the filter is analyzed for variation of the cable length. Characteristics of filters are analyzed to reduce harmonic in voltage waveform of induction motor input terminal. The switching surge voltage became the major cause to occur the insulation failure by serious voltage stress in the stator winding of induction motor. Filter for to mitigate transients overvoltage presents a required component in drive system of high-voltage large-capacity induction motor. Also, proposed filters are proved through simulation using EMTP.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.20-24
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• 1989

The time optimal position control scheme can be repeatedly taken from the initial state of a dynamic system to a desired one as fast as possible at the industrial drives. In this case, the machine parameters will vary due to temperature, frequency, and saturation effects. In particular, the rotor resistance value changes dramatically with temperature and frequency. These changes affect the command values of the stator current components and slip speed. There is a mismatch between the commanded variables and actual variables of the induction motor drive, and this situation leads to decoupling of the vector controller from the plant, i.e the induction motor. Consequences of such decoupling include the initiation of oscillations of the rotor flux and unsuitable switching of electromagnetic torque of the induction motor servo drive. Therefore, a rotor resistance parameter compensating method for the induction motor is described.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.22-33
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• 2011

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.6
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• pp.368-375
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• 1988

This paper is concerned with the design of the optimal gains of the controller in the speed control system for the induction motor controlled by the microprocessor. The system is modelled with the discrete-time state equation, considering the time delay, for the facility of the optimization techniques. Introducing the conjugate gradient descent method, as the optimization technique, are derived the optimal gains, the gains which give the best transient characteristics. At the optimal gains obtained, the theoretcal transient responses are verified by experimental ones on a 5HP induction motor drive system.

• Journal of Power Electronics
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• v.8 no.3
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• pp.268-274
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• 2008

This paper presents a proposed position controller for a vector controlled induction motor. The position controller design depends on the rotational motion equations and a classical speed controller (CSC) performance. The CSC is designed to have the ability to track variable reference inputs and to provide a predefined system performance. Standard position controller in industry is presented to analyze its performance and its drawbacks. Then the proposed position controller is designed, based on the well defined rotational motion equations. The proposed position controller and the CSC are applied to control the position and speed of the vector controlled induction motor with different ratings. Simulation results at different operating conditions are presented to evaluate the proposed controllers' performance. The results show that the CSC can drive the motor with a predefined speed performance and can track a variable reference speed with an approximately zero steady state error. The results also show that the proposed position controller has the ability to effect high-precision positioning in a limited time and to track a variable reference position with a zero steady state error.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.3133-3141
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• 2011

The next generation domestic high speed railway system is a power distributed type and uses vector control method for motor speed control. Nowadays, inverter driven induction motor system is widely used. However, recently PMSM drives are deeply considered as a alternative candidate instead of an induction motor drive system due to their advantages in efficiency, noise reduction and maintenance. The next-generation high speed train is composed of 2 converter units, 4 inverter units, and 4 Traction Motor units. Each motor is connected to the inverter directly. In this paper, the effect of IPMSM parameter variations to the system operation characteristics of the multi inverter drive high speed train system are investigated. The parallel connected inverter input-output characteristics are analyzed to the parameter mismatches of IPMSM using the 1C1M control simulator based on Matlab/Simulink.