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최영도,백자현,전동훈,박상호,최순호,김여진,허진,Choy, Youngdo,Baek, Jahyun,Jeon, Dong-Hoon,Park, Sang-Ho,Choi, Soonho,Kim, Yeojin,Hur, Jin 한국전력공사 2019 KEPCO Journal on electric power and energy Vol.5 No.3
In order to integrate large amounts of variable generation resources such as wind and solar reliably into power grids, accurate renewable energy forecasting is necessary. Since renewable energy generation output is heavily influenced by environmental variables, accurate forecasting of power generation requires meteorological data at the point where the plant is located. Therefore, a spatial approach is required to predict the meteorological variables at the interesting points. In this paper, we propose the meteorological variable prediction model for enhancing renewable generation output forecasting model. The proposed model is implemented by three geostatistical techniques: Ordinary kriging, Universal kriging and Co-kriging.
감쇠 구조물에 의한 토석류 토사체적 농도 변화에 관한 실험적 연구
최영도(Youngdo Choi),김성덕(Sungduk Kim),이호진(Hojin Lee) 한국방재안전학회 2023 한국방재안전학회 논문집 Vol.16 No.4
The purpose of this study is an experimental research to investigate the effectiveness of debris flow reduction structures when a debris flow disaster occurs on a steep slope. The control structure for debris flow took the form of baffle, and the soil deposition area and soil runout distance due to debris flow from the downstream were investigated according to the installation number of baffle and each specification. As the slope of the channel became steeper, the sediment deposition area and runout distance increased, and as the sediment volume concentration decreased, the sediment deposition area and runout distance increased. When the sediment concentration was low, differences appeared depending on the slope of the channel because the debris flow had a high liquid content. Overall, the larger the sediment volume concentration, the greater the decrease in sediment deposition area and soil runout distance. As the number of baffles increases, the soil deposition area and runout decrease, showing that the baffles have the ability to control debris flows. The results of this research will provide good information when installing attenuation or control structures when sediment disasters occur in steep slopes. 본 연구의 목적은 급경사지 사면에서 토석류 재해가 발생했을 때, 토석류 저감 구조물의 효과를 조사한 실험연구이다. 제어 구조물로는 베플 형태를 취하였고, 베플의 설치 기수와 각 제원에 따라서 하류에서의 토석류에 의한 토사퇴적면적과 토사도달거리를 조사한 것이다. 수로의 경사가 급할수록 토사의 퇴적면적과 도달거리가 증가하였고, 토사체적농도가 감소할수록 토사퇴적면적과 토사의 도달거리가 증가하였다. 토사농도가 작은 경우(Cv = 0.5)는 토석류의 액성이 크기 때문에 수로 경사에 따른 차이가 나타났고, 전반적으로 토사체적농도가 클수록 토사퇴적면적과 토사의 도달거리 감소율이 크게 나타났다. 베플의 수를 증가할수록 토사퇴적면적과 도달거리를 약 5~10% 이상 감소하는 것으로 나타남으로서 베플의 토석류 제어능력이 있음을 보여주고 있다. 본 연구의 결과는 급경사지에서 토사재해가 발생할 때 감쇠 또는 제어 구조물을 설치할 때 중요한 정보를 제공할 것이다.
CFD에 의한 산업용 교반기내의 비정상 유동특성에 관한 연구
이승엽(SeungYoub LEE),최영도(YoungDo CHOI),김정환(JeongHwan KIM),남청도(ChungDo NAM),이영호(YoungHo LEE) 대한기계학회 2007 대한기계학회 춘추학술대회 Vol.2007 No.10
The mixers are used in various industrial fields where it is necessary to mix two reactants in a short period of time. However, despite their widespread use, complex unsteady flow characteristics of industrial mixers has not been investigated systematically. This paper presents calculated results of the unsteady flow characteristics in a Industrial Mixer by CFD analysis. The results show that quite different flow patren if found between steady and unsteady calculation.
강성범(Sungbum Kang),고백경(Baekkyeong Ko),남수철(Suchul Nam),최영도(Youngdo Choi),김용학(Yonghak Kim),전동훈(Donghoon Jeon) 대한전기학회 2019 전기학회논문지 Vol.68 No.9
Recently, innovative techniques in artificial intelligence such as machine learning have emerged to efficiently process huge amounts of big data delivered from PMUs to WAMS. Through processing raw data and analyzing big data, It delivers highly useful and valuable system status information to system operators. The types of machine learning vary depending on the usage, but the CNN (Convolution Neural Network) model is mainly used for the post analysis and fault detection(classification) in the power system. In this paper, based on PMU big data, we study the power system fault classification model by using CNN Model. Using Convolution neural network model based on KERAS, the database for each fault type was built and supervised learning was conducted for the model. The constructed model was verified with test data and the validity of the model was verified by inputting the actual power system fault data for the trained model. As a result, developed model classified correctly for the actual fault.