Won Seok Oh

A New Switching Strategy for Pulse Width Modulation (PWM) Power Converters

This paper presents a new switching strategy for pulse width modulation (PWM) power converters. Since the proposed strategy uses independent on/off switching action of the upper or lower arm according to the polarity of the current, the dead time is not needed except instant of current polarity change. Therefore, it is not necessary to compensate the dead time effect and the possibility of arm short is strongly eliminated. The current control of PWM power converters can easily adopt the proposed switching strategy by using the polarity information of the reference current instead of the real current, thus eliminating the problems that commonly arise from real current detection. In order to confirm the usefulness of the proposed switching strategy, experimental tests were done using a single-phase inverter with passive loads, a three-phase inverter for induction motor drives, a three-phase ac/dc PWM converter, a three-phase active power filter, and a class-D amplifier, the results of which are presented in this paper

Various design techniques to reduce cogging torque in flux-reversal machines

This paper introduces flux-reversal machine (FRM) and presents the design of a FRM to reduce the cogging torque. The effect of the design parameters on the characteristic and cogging torque is analyzed by finite element method (FEM). The considered design parameters are bifurcated teeth, chamfered magnet poles, chamfered rotor tooth tips and rotor skewing. As a result, we can find the optimum model reduced cogging torque and torque ripple in 6/8 FRM.

A new switching strategy for PWM power converters

This paper presents a new switching strategy for PWM power converters. The proposed switching strategy is a kind of dead time minimization method. The proposed method can reduce the discontinuous phenomenon of the current, which occurs in the conventional dead time minimization method, at the changing instant of the current polarity. Although the dead time is applied in every current polarity changing points, the dead time should be not always adopted in the proposed method. Especially, in the current control of PWM power converters, the proposed switching strategy can be easily constructed by using the polarity information of the reference current instead of the real current. Therefore, there are no more problems that would be caused by real current detection. In this paper, in order to confirm the usefulness of the proposed switching strategy, experimental results that are applied in single phase inverter with passive loads, three phase inverter for induction motor drives, three phase AC/DC PWM converter, three phase active power filter and class-D amplifier are presented.

Self tuning neural network controller for induction motor drives

In this paper, recurrent artificial neural network (RNN) based self tuning speed controller is proposed for the high performance drives of induction motor. RNN provides a nonlinear modeling of motor drive system and could give the information of the load variation, system noise and parameter variation of induction motor to the controller through the on-line estimated weights of corresponding RNN. Thus, proposed self tuning controller can change gains of the controller according to system conditions. The gains are composed with the weights of R-NN. For the on-line estimation of the weights of RNN, extended Kalman filter (EKF) algorithm is used. Self tuning controller that is adequate for the speed control of induction motor is designed. The availability of the proposed controller is verified through the MATLAB simulation with the comparison of conventional PI controller.

Optimized neural network speed control of induction motor using genetic algorithm

For the high performance drives of induction motor, recurrent artificial neural network (RNN) based self tuning speed controller is proposed. RNN provides a nonlinear modeling of motor drive system and could give the information of the load variation, system noise and parameter variation of induction motor to the controller through the on-line estimated weights of corresponding RNN. Self tuning controller can change gains of the controller according to system conditions. The gains are composed of the weights of RNN. For the on-line estimation of the weights of RNN, extended Kalman filter (EKF) algorithm should be used. In order to design EKF with optimal constants, simple genetic algorithm is proposed. Genetic algorithm can follow the optimal estimation constants without trial and error efforts. The availability of the proposed controller is verified through the MATLAB and Simulink simulation with the comparison of conventional controller. The simulation results show a significant enhancement in shortening development time and improving system performance over a traditional manually tuned EKF estimation algorithm based neural network controller

Self-tuning speed controller for induction motor drives

A genetic based self-tuning speed controller is proposed for the high-performance drives of induction motors. Nonlinear system identification of a motor drive system is required and the results of system identification could provide the controller with information regarding the load variation, system noise, and parameter variation of the induction. The proposed self-tuning controller can change the gains of the controller according to system conditions. For the estimation of system parameters, a genetic algorithm is used. A self-tuning controller is designed that is adequate for the speed control of the induction motor. The availability of the proposed controller is verified.

A microprocessor based single stage power supply using fly-back converter for LED lightings

This paper presents a single stage power supply for LED lightings. The proposed power supply designed using fly-back converter topology, which is directly controlled by a microprocessor. Although the proposed circuit does not sense the AC input current and has not AC input voltage feed-forward, it can achieve high power factor. The proposed power supply directly regulates the output power for LED loads using the PWM and PFM control of the fly-back converter without additional regulator. A prototype set-up has been built and tested. Through the experiment with a prototype set-up, the validity of the proposed circuit is verified.

Genetic based self tuning speed controller for induction motor drives

In this paper, a genetic based self-tuning speed controller is proposed for the high-performance drives of induction motors. For a high-performance drives, nonlinear system identification of a motor drive system is required and the results of system identification could provide the controller with information regarding the load variation and parameter variation of the motor. The proposed controller can change the gains of the controller according to system conditions. The gain is composed with system parameter of drive system. For the estimation of system parameters, a genetic algorithm is used. A self-tuning controller is designed that is adequate for the speed control of the induction motor. The availability of the proposed controller is verified through MATLAB/Simulink simulations and is compared with the conventional PI controller.

Analysis of the errors on the output voltage of inverter caused by switching dead times

The dead time which is inserted in switching signals of PWM voltage source inverters distorts its output. As a result, the deviations of real fundamental voltage and phase compared with the reference are occurred. And also the harmonics of its output are increased. In this paper, numerical analysis of the error voltage on the output of inverter according to the switching dead time is presented. And the calculation results of fundamental voltage gain and phase deviations are presented.

Load variation compensated neural network speed controller for induction motor drives

In this paper, a recurrent artificial neural network (RNN) based self-tuning speed controller is proposed for the high-performance drives of induction motors. The RNN provides a nonlinear modeling of a motor drive system and could provide the controller with information regarding the load variation, system noise, and parameter variation of the induction motor through the online estimated weights of the corresponding RNN. Thus, the proposed self-tuning controller can change the gains of the controller according to system conditions. The gain is composed with the weights of the RNN. For the on-line estimation of the RNN weights, an extended Kalman filter (EKF) algorithm is used. A self-tuning controller is designed that is adequate for the speed control of the induction motor. The availability of the proposed controller is verified through MATLAB simulations and is compared with the conventional PI controller.