• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.420-423
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.

• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.106-108
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• 2008

This paper deals with the design for speed controller to drive PMSM by matrix converter without DC-link circuit as the power conversion system of AC servo motor drive. To design the speed controller of PMSM drive, the closed-loop transfer function of speed controller is calculated and then the frequency-domain response characteristics are analyzed by bode plot using Matlab. Based on the results by bode plot, the speed control gains are determined. As the real effects of controller designed in the frequency-domain display in the time-domain, the performance of speed controller is confirmed by the step response of speed controller. The design examples are shown and its validity of the design method mentioned in the paper is verified through PSIM simulation.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1908-1916
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• 2018

This paper proposes a controller design method for suppressing the resonance generated in the slave motor in the middle and low speed operation range, according to the load and parameter differences between two motors, during parallel operation using the master and slave method that controls two surface permanent magnet synchronous motors connected in parallel by a single inverter. The proposed resonance suppression controller is directly obtained by analyzing the resonance characteristics, using the lead controller method. Therefore, it is possible to fundamentally reduce trial and error to set the controller gain. In addition, because the proposed resonance suppression controller was designed as a lead controller, the stability region of the system increased owing to the added zero point, making the system robust with respect to parametric variations. Simulations and experiments confirmed the usefulness of the proposed method and the system's robustness with respect to parametric variations.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.225-227
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed artificial intelligent-PI(AIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.

• Journal of Power Electronics
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• v.13 no.2
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• pp.313-321
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• 2013

This paper describes the operation analysis results of a communication-based DC micro-grid using a hardware simulator developed in the lab. The developed hardware simulator is composed of distributed generation devices such as wind power, photovoltaic power and fuel cells, and energy storage devices such as super-capacitors and batteries. Whole system monitoring and control was implemented using a personal computer. The power management scheme was implemented in a main controller based on a TMS320F28335 chip. The main controller is connected with the local controller in each of the distributed generator and energy storage devices through the communication link based on a CAN or an IEC61850. The operation analysis results using the developed hardware simulator confirm the ability of the DC micro-grid to supply the electric power to end users.

• Proceedings of the KIPE Conference
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• 2013.11a
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• pp.79-80
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• 2013

MMC(Modular Multilevel Converter)는 여러 개의 Power Module을 직렬로 연결하여 정현파에 가까운 고전압의 파형을 얻을 수 있는 토폴로지로 대용량 전력변환 분야의 요구를 만족하면서 전력 품질을 향상시킬 수 있어 근래에 상당히 주목받고 있다. 당사에서는 5Mvar급 STATCOM(STATic synchronous COMpensator)을 MMC 형태로 제작하였다. 제작된 5Mvar급 STATCOM은 한 상당 12대의 Power Module로 구성하여 25-Level로 제작되었다. 제어시스템은 DSP(Digital Signal Processor)를 이용하였으며, 하나의 Main Controller와 다수의 Cell Controller, FPGA 보드 등으로 구성되어 있다. Controller 간의 상호 정보를 교환하기 위해 CAN 통신을 이용하였고, Power Module의 스위칭을 위한 보드는 각각에 연결되어 있으며, Cell Controller보드와는 절연을 위해 광신호로 연결하였다. 본 논문에서는 MMC 시스템의 제어기간 CAN 통신인터페이스와 Power Module의 PWM 동기화에 대해 설명을 하였다.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.12
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• pp.535-541
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• 2002

This paper proposes an adaptive fuzzy controller based fuzzy logic control for high performance of permanent magnet synchronous motor(PMSM) drive. In the proposed system, fuzzy control is sued to implement the direct controller as well as the adaptation mechanism. 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 adaptive fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed controller is confirmed by performance results for PMSM drive system.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.47-49
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• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism-fuzzy neural networks(ALM-FNN) controller that is implemented using adaptive, fuzzy control and neural networks. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.6 no.2
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• pp.182-191
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• 2011

Genetic algorithms (GA) and particle swarm optimization (PSO) are the most famous optimization techniques among various modern heuristic optimization techniques. These two approaches identify the solution to a given objective function, but they employ different strategies and computational effort; therefore, a comparison of their performance is needed. This paper presents the application and performance comparison of the PSO and GA optimization techniques for a static synchronous series compensator-based controller design. The design objective is to enhance power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem, and both PSO and GA optimization techniques are employed to search for the optimal controller parameters.

• Journal of Power Electronics
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• v.8 no.1
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• pp.23-34
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• 2008

This paper deals with a Direct Torque Control (DTC) of an Interior Permanent Magnet Synchronous Motor (IPMSM) for the Electric Vehicle (EV) propulsion system using a Neural Network (NN). The Conventional DTC with optimized switching lookup table and three level torque controller generates relatively large torque ripples in an electric vehicle motor drive. For reducing the torque ripples, a three level torque controller is hereby replaced by the five level torque controller. Furthermore, the switching lookup table of the five level torque controller based DTC is replaced with a Neural Network. These DTC schemes of an IPMSM drive are simulated using MATLAB/SIMULINK. The simulated results are compared with the conventional DTC and it is found that the ripples in the torque, as well as in the stator current, are reduced drastically.