• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.369-370
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• 2017

In this paper, a model predictive voltage control (MPVC) for the DC-DC buck-boost converters is proposed. It provides a fast seamless bidirectional control method to maintain the DC grid voltage, battery voltage and current within predefined limits. In addition, an inner current control loop is not employed, so that the bandwidth of controller can be higher compared with the PI controller.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.742-751
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• 2018

Electric vehicles (EV) are emerging as the future transportation vehicle reflecting their potential safe environmental advantages. Vehicle to Grid (V2G) system describes the hybrid system in which the EV can communicate with the utility grid and the energy flows with insignificant effect between the utility grid and the EV. The paper presents an optimal power control and energy management strategy for Plug-In Electric Vehicle (PEV) charging stations using Wind-PV-FC-Battery renewable energy sources. The energy management optimization is structured and solved using Multi-Objective Particle Swarm Optimization (MOPSO) to determine and distribute at each time step the charging power among all accessible vehicles. The Model-Based Predictive (MPC) control strategy is used to plan PEV charging energy to increase the utilization of the wind, the FC and solar energy, decrease power taken from the power grid, and fulfil the charging power requirement of all vehicles. Desired features for EV battery chargers such as the near unity power factor with negligible harmonics for the ac source, well-regulated charging current for the battery, maximum output power, high efficiency, and high reliability are fully confirmed by the proposed solution.

• Journal of Power Electronics
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• v.12 no.4
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• pp.595-603
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• 2012

This paper presents a full digital control strategy for parallel connected modular inverter systems. Each modular inverter is a high frequency (HF) AC link inverter which is composed of a HF inverter and a HF transformer followed by a cycloconverter. To achieve equal sharing of the load current and to suppress the circulating currents among the modules, a three-loop control strategy, consisting of a common output voltage regulation (OVR) loop, individual circulating current suppression (CCS) loops and individual inner current tracking (ICT) loops, is proposed. The ICT loops are implemented with predictive current control from which high precision current tracking can be obtained. The effectiveness of the proposed control strategy is verified by simulation and experimental results from parallel connected two full-bridge HF AC link inverter modules.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.180-181
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• 2019

This paper proposes a method for improving the power quality of PMSG-based wind energy conversion system based on model predictive control in DC Microgrid. The MPC has a fast dynamic response. However, a large torque ripple deteriorating power quality is generated by a large and fixed switching period. On the other hand, the proposed method improves the power quality by setting the sampling time having zero torque error. The validity of the proposed method is verified through PSIM simulation.

• Journal of Power Electronics
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• v.17 no.3
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• pp.695-703
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• 2017

In order to satisfy the urgent requirements for the overheating protection of induction motors, an approach that can be used to identify motor temperature has been proposed based on the rotor slots harmonic (RSH) in this paper. One method to accomplish this is to improve the calculation efficiency of the RSH by predicting the stator winding distribution harmonic order by analyzing the harmonics spectrum. Another approach is to increase the identification accuracy of the RSH by suppressing the influence of voltage flashes or current surges during temperature estimation based on model predictive control (MPC). First, an analytical expression of the stator inductance is extracted from a steady-state positive sequence motor equivalent circuit model developed from the rotor flux field orientation. Then a procedure that applies MPC for reducing the identification error of the rotor temperature caused by voltage sag or swell of the power system is given. Due to this work, the efficiency and accuracy of the RSH have been significantly improved and validated our experiments. This work can serves as a reference for the on-line temperature monitoring and overheating protection of an induction motor.

• International Journal of Automotive Technology
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• v.6 no.3
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• pp.243-249
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• 2005

To improve the ride comfort and handling characteristics of a vehicle, an active suspension which is controlled by external actuators can be used. An active suspension can control the vertical acceleration of a vehicle and the tire deflection to achieve the desired suspension goal. For this purpose, Model Predictive Control (MPC) scheme is applied with the assumption that the preview information of the oncoming road disturbance is available. The predictive control approach uses the output prediction to forecast the output over a time horizon and determines the future control over the horizon by minimizing the performance index. The developed method is applied to a half car model of four degrees-of-freedom and numerical simulations show that the MPC controller improves noticeably the ride qualities and handling performance of a vehicle.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.133-142
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• 2023

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.

• Journal of Power Electronics
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• v.15 no.3
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• pp.712-720
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• 2015

A model predictive current control (MPCC) method that does not employ a cost function is proposed. The MPCC method can decrease common-mode voltages in loads fed by three-phase voltage-source inverters. Only non-zero-voltage vectors are considered as finite control elements to regulate load currents and decrease common-mode voltages. Furthermore, the three-phase future reference voltage vector is calculated on the basis of an inverse dynamics model, and the location of the one-step future voltage vector is determined at every sampling period. Given this location, a non-zero optimal future voltage vector is directly determined without repeatedly calculating the cost values obtained by each voltage vector through a cost function. Without utilizing the zero-voltage vectors, the proposed MPCC method can restrict the common-mode voltage within ± V_dc/6, whereas the common-mode voltages of the conventional MPCC method vary within ± V_dc/2. The performance of the proposed method with the reduced common-mode voltage and no cost function is evaluated in terms of the total harmonic distortions and current errors of the load currents. Simulation and experimental results are presented to verify the effectiveness of the proposed method operated without a cost function, which can reduce the common-mode voltage.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.1
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• pp.18-26
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• 2017

This paper proposes the model predictive control with space vector modulation (SVM) method for current control of voltage-source inverter. Unlike the conventional method using a limited number of voltage vectors by switching states, the proposed method can consider various voltage vectors to identify the optimized voltage vector. The various voltage vectors are obtained by subdividing existing voltage vectors. The optimized voltage vector that minimizes the cost function is selected and applied to the inverter by using the SVM. The various voltage vectors and SVM reduce current ripples in the output AC side of the inverter compared with the conventional method. The effectiveness and performance of the proposed method are verified through simulation and experiment with a three-phase two-level voltage-source grid-connected inverter.

• Proceedings of the KIPE Conference
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• 2014.11a
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• pp.21-22
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• 2014

본 논문은 5-레벨 NPC/H-브릿지 (Neutral Point Clamped/H-bridge) 인버터의 최적 제어 세트 (finite-control-set) 모델 예측 제어 (MPC: Model Predictive Control) 방법을 제안한다. NPC/H-브릿지 인버터의 출력 전류 제어 및 DC-link 커패시터 전압 균형을 유지하기 위해 출력 전류와 DC-link 커패시터 전압을 예측하고, 하나의 비용 함수 (cost function)을 통해 최적의 스위칭 상태를 출력한다. PSIM 시뮬레이션을 통해 제안된 제어 알고리즘의 검증하였다.