• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1151-1152
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• 2011

본 논문에서는 부스트-플라이백(Integrated Boost-Flyback Converter, IBFC) 직렬 연결 구조 컨버터의 동작 특성 해석 및 정확한 제어기 설계를 위한 개선된 모델링 방법을 제시한다. 주 스위치에 의해 IBFC의 부스트, 플라이백 컨버터가 서로 다른 도통 모드로 동시에 동작 하기 때문에 2대 컨버터의 모델링과 회로 해석을 위한 이론적인 모델링 접근방법과 수학적인 계산과정이 복잡하다. 따라서 IBFC를 등가 전류 소스를 포함한 부스트, 플라이백 컨버터로 각각 나누어 상태 공간 평균화 방법을 이용하여 회로 방정식을 독립적으로 유도한 후, 이 회로 방정식을 종합하여 IBFC의 완전한 상태 공간 방정식을 얻을 수 있다. 제안된 방법은 IBFC의 복잡한 모델링을 간단하게 해 주며, 수학적인 계산 과정도 간소화 시킬 수 있는 장점이 있다. 이를 바탕으로 정상 상태 해석 및 높은 출력 전압 추종 제어기를 설계하였다. 시뮬레이션과 실험 결과를 제시하여 제안된 방법의 유효함을 검증하였다.

• Proceedings of the KIPE Conference
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• 2009.11a
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• pp.152-154
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• 2009

태양광발전 시스템에서 사용되는 컨버터는 기존의 입출력 관계로 모델링할 수 없다. 이는 태양광 전지를 컨버터의 입력단으로 사용하여, 모델의 입력 전류와 전압이 수시로 변동하기 때문이다. 이런 오류가 있는 방법으로 모델링하여 설계한 제어기가 좋은 성능을 내기는 어렵다. 본 논문에서는 기존의 입출력 관계를 반대로 한 벅 컨버터의 새로운 모델을 제시한다. 모델링은 상태공간평균화 방법(State-Space Averaging Method)을 사용하였다.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.368-369
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• 2013

본 논문에서는 배터리 저장 시스템을 위한 2단 인버터의 동작 특성 해석과 정확한 제어기 설계를 위한 모델링 방법을 제안한다. 한 번에 2단 인버터의 회로 해석 및 동특성 방정식을 유도하는 것은 매우 복잡하고 어렵다. 따라서 2단 인버터를 등가 전류원을 포함하는 양방향 컨버터, 단상 풀 브릿지 인버터로 각각 나누어 회로방정식을 유도한 후, 상태 공간 평균화 방법을 이용하여 소신호 모델을 얻을 수 있다. 제안된 방법의 장점은 2단 인버터의 복잡한 수학과정을 간소화하여 모델링한 점이다. 이를 바탕으로 양방향 컨버터에서는 배터리 입력 전류제어기를 설계하고, 단상 풀 브릿지 인버터에서는 DC-Link 전압 및 계통전류 제어기를 설계하였다. 시뮬레이션과 실험결과를 통해 제안된 방법의 타당성을 검증하였다.

• Journal of the Korean Solar Energy Society
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• v.34 no.2
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• pp.1-7
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• 2014

Generally, buck converter controller is designed to control the output voltage of the converter. However, design of the controller in a photovoltaic power conditioning system is different from theoretical design guideline. The controller in a photovoltaic power conditioning system controls the input voltage of the converter (the output voltage of the solar cell) to meet a maximum power point tracking (MPPT) performance. In this study, a new model for buck converter used in a photovoltaic power conditioning system is proposed, which is linearized after state-space averaging in each period. Also, mathematical expression of the modeled buck converter is interpreted separately as small and large signals; therefore its appropriateness is measured to design linear voltage and current controller.

• 한국태양에너지학회:학술대회논문집
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• 2009.11a
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• pp.377-382
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• 2009

modelling of the buck converter in photovoltaic power conditioning system is not a possibility of doing with input-output relationship from existing procedures. Because the input current and voltage of the buck converter in fluctuate at any time. The controller which design with the method which has like this error cannot have a good efficiency. In this paper, firstly, in order to design accurate controller of buck converter, new model is proposed. The modeling used a state-space averaging method and came to accomplish. Secondly, the process which design the controller is described. Finally, the simulation results are analyzed.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.11
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• pp.215-222
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• 2018

In this paper, the research on the energy storage system adapting super-capacitor has been performed. The most advanced features compared to the conventional lead-acid battery systems is that it can obtain high power capability due to the super capacitor power characteristics. The suggested system can attain high power in short times and achieve high power quality improvements. The application areas are power quality improvement system, motor start power which requires high power during transient times. The energy conversion system consists of bi-directional converter and inverter and advantages of high speed, high power charging and discharging performances. The design steps for the two loop controller of the bi-directional inverter are suggested and verified by the experiment and manufacturing. The two loop controller design starts from linearized transfer function which is calculated from the state averaging model including state decoupling method. The current controller requirements are 20% overshoot and settling time and voltage controller are no overshoot and settling time which is 10 times longer than current controller. The design is verified from the step input response. The designed controllers have unity power factor characteristics and thus can improve the power quality of the grid. It also has fast response time and zero steady state error.

• Investigative Magnetic Resonance Imaging
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• v.12 no.1
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• pp.8-19
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• 2008

Purpose : To investigate the 3-bond and spatial connectivity of human brain metabolites by scalar coupling and dipolar nuclear Overhauser effect/enhancement (NOE) interaction through 2D- correlation spectroscopy (COSY) and 2D- NOE spectroscopy (NOESY) techniques. Materials and Methods : All 2D experiments were performed on Bruker Avance 500 (11.8 T) with the zshield gradient triple resonance cryoprobe at 298 K. Human brain metabolites were prepared with 10% $D_2O$. Two-dimensional spectra with 2048 data points contains 320 free induction decay (FID) averaging. Repetition delay was 2 sec. The Top Spin 2.0 software was used for post-processing. Total 7 metabolites such as N-acetyl aspartate (NAA), creatine (Cr), choline (Cho), lutamine (Gln), glutamate (Glu), myo-inositol (Ins), and lactate (Lac) were included for major target metabolites. Results : Symmetrical 2D-COSY and 2D-NOESY pectra were successfully acquired: COSY cross peaks were observed in the only 1.0-4.5 ppm, however, NOESY cross peaks were observed in the 1.0-4.5 ppm and 7.9 ppm. From the result of the 2-D COSY data, cross peaks between the methyl protons ($CH_3$(3)) at 1.33 ppm and methine proton (CH(2)) at 4.11 ppm were observed in Lac. Cross peaks between the methylene protons (CH2(3,$H{\alpha}$)) at 2.50ppm and methylene protons ($CH_2$,(3,$H_B$)) at 2.70 ppm were observed in NAA. Cross peaks between the methine proton (CH(5)) at 3.27 ppm and the methine proton (CH(4,6)) at 3.59 ppm, between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(4,6)) at 3.59 ppm, and between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(2)) at 4.05 ppm were observed in Ins. From the result of 2-D NOESY data, cross peaks between the NH proton at 8.00 ppm and methyl protons ($CH_3$) were observed in NAA. Cross peaks between the methyl protons ($CH_3$(3)) at 1.33 ppm and methine proton (CH(2)) at 4.11 ppm were observed in Lac. Cross peaks between the methyl protons (CH3) at 3.03 ppm and methylene protons (CH2) at 3.93 ppm were observed in Cr. Cross peaks between the methylene protons ($CH_2$(3)) at 2.11 ppm and methylene protons ($CH_2$(4)) at 2.35 ppm, and between the methylene protons($CH_2$ (3)) at 2.11 ppm and methine proton (CH(2)) at 3.76 ppm were observed in Glu. Cross peaks between the methylene protons (CH2 (3)) at 2.14 ppm and methine proton (CH(2)) at 3.79 ppm were observed in Gln. Cross peaks between the methine proton (CH(5)) at 3.27 ppm and the methine proton (CH(4,6)) at 3.59 ppm, and between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(2)) at 4.05 ppm were observed in Ins. Conclusion : The present study demonstrated that in vitro 2D-COSY and NOESY represented the 3-bond and spatial connectivity of human brain metabolites by scalar coupling and dipolar NOE interaction. This study could aid in better understanding the interactions between human brain metabolites in vivo 2DCOSY study.