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비선형 부하에서 커패시터 전류 궤환을 통한 고성능 UPS 설계
이우철(Woo-Cheol Lee),이택기(Taeck-Kie Lee) 한국조명·전기설비학회 2012 조명·전기설비학회논문지 Vol.26 No.5
This paper presents a digital control solution to process capacitor current feedback of high performance single-phase UPS for non-linear loads. In all UPS the goal is to maintain the desired output voltage waveform and RMS value over all unknown load conditions and transient response. The proposed UPS uses instantaneous load voltage and filter capacitor current feedback, which is based on the double regulation loop such as the outer voltage control loop and inner current control loop. The proposed DSP-based digital-controlled PWM inverter system has fast dynamic response and low total harmonic distortion (THD) for nonlinear load. The control system was implemented on a 32-bit Floating-point DSP controller TMS320C32 and tested on a 5[KVA] IGBT based inverter switching at 11[Khz]. The validity of the proposed scheme is investigated through simulation and experimental results.
풍력터빈에서 서지 보호 장치와 접지의 적용에 따른 직격뢰 및 유도뢰의 낙뢰 과전압 저감 효과 분석
표광진(Kuang-Jin Pyo),이택기(Taeck-Kie Lee),김규호(Kyu-Ho Kim),우정욱(Jung-Wook Woo) 대한전기학회 2019 전기학회논문지 Vol.68 No.12
The ways to improve the safety of power facilities is to install surge protection devices and grounding method to protect against the lightning surge. This paper analyzes how much lightning overvoltages are reduced by giving different rated voltage to the surge protection device and applying different grounding method when the direct lightning strike and the lightning-induced is applied separately. This analysis also contains how the distance between wind turbine and the lightning strike point affects the lightning overvoltage created by the lightning-induced.
Neutral Beam Injection용 Arc Power Supply 설계
이희준(Hee Jun Lee),전범수(Bum Su Jun),류동균(Dong Kyun Ryu),이택기(Taeck Kie Lee),박선순(Seon Soon Park),원충연(Chung Yuen Won) 전력전자학회 2010 전력전자학술대회 논문집 Vol.2010 No.11
KSTAR NBI 시스템은 플라즈마의 온도를 높여 주기 위해 고 에너지의 중성 입자빔을 만들어서 토카막 플라즈마에 투입시키기 위한 중성 입사 장치이다. NBI 아크 전원 공급장치는 토카막 내부에 플라즈마를 만들어 주는 역할을 하는데 본 논문에서는 3상 다이오드 정류기, LC필터, 2.4㎾급 6 병렬 벅 컨버터로 설계하여 시뮬레이션과 실험을 통하여 확인 하였다.
이규상(Kyu Sang Lee),임희성(Hui Seong Lim),신수철(Soo Cheol Shin),박종민(Jong Min Park),이택기(Taeck Kie Lee),원충연(Chung Yuen Won) 전력전자학회 2010 전력전자학술대회 논문집 Vol.2010 No.11
최근 연료전지 기술의 발전으로 스택의 소형화 및 안전성이 확보됨에 따라 연료전지 자동차(FCEV)의 관심이 증가하고 있다. 본 논문에서는 변압기를 사용하는 풀-브리지 컨버터와 IPMSM 구동용 3상 인버터 구동 알고리즘을 제안하고 시뮬레이션을 통해 FCEV 적용 타당성을 검증 하였다.
풍력발전설비 낙뢰보호를 위한 규정 KEC의 접지방식에 따른 낙뢰 과전압 분석
표광진(Kuang-Jin Pyo),이택기 (Taeck-Kie Lee),김규호 (Kyu-Ho Kim),김진형(Jin-Hyung Kim),우정욱(Jung-Wook Woo) 대한전기학회 2020 전기학회논문지 Vol.69 No.4
Standard for lightning protection of wind turbine KEC has information on grounding to prevent damage of wind power plant due to lightning strikes. In the case of KEC, grounding is performed using equipotential bonding to eliminate the potential difference due to the abnormal overvoltage of wind power generation facility, and the resistance of the grounding is also limited to below a certain level. This analysis includes the contents of lightning overvoltage generated by direct lightning strikes and lightning-induced with equipotential bonding according to KEC standards. Although there is a fine potential difference due to the equipotential bonding conductor, compared with independent ground, common ground is very effective in eliminating potential difference, and the use of softening among common ground methods is more useful for lightning overvoltage reduction effect.
일조 시간과 일조량 데이터를 고려한 LSTM 기반 태양광 발전량 예측
안연주(Yeon-Ju An),이택기(Taeck-Kie Lee),김규호(Kyu-Ho Kim) 대한전기학회 2021 전기학회논문지 Vol.70 No.8
This paper presents a method to predict the photovoltaic power generation using daylight and solar radiation data. Keras based long short-term memory(LSTM) model, a deep learning library, is used to predict the photovoltaic power generation and compared with a simple machine learning model. Based on the annual power generation, the weather parameters are selected with the highest correlation such as sunshine time and solar radiation. The prediction of Keras based LSTM model is superior to the prediction of the photovoltaic power generation using the simple machine learning model. This is because the probabilistic characteristics of actual variables are considered forecasting with actual weather parameters in the prediction of photovoltaic power generation.