• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.645-652
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• 2011

The present study investigated the tracking system of a reflector to trace the movement of sun. The system was designed to minimize the error between the vertical vector of reflector and the position of sun. The proposed system was able to collect the sun lights at a point as a useful source of light energy and transmit the collected light to a remote area through optical fibers. Also the study successfully solved the controller design problem due to the complexity of modeling of the sun tracking system using a fuzzy logic controller which mimics human reasoning.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.3
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• pp.220-227
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• 2019

The satellites in orbit use a sun reference vector from solar model based the ephemeris. To get the ephemeris, we use DE-Series, an ephemeris developed by the Jet Propulsion Laboratory (JPL), or the reference vector generation formula proposed by Vallado. The DE-Series provides the numerical coefficients of Chebyshev polynomials, which have the advantage of high precision, but there is a computational burden on the satellite. The Vallado's method has low accuracy, although the sun vector can be easily obtained through the sun vector generation equation. In this paper, we have developed a program to provide the Chebyshev polynomial coefficients to obtain the sun position coordinates in the inertial coordinate system. The proposed method can improve the accuracy compared to the conventional method and can be used for high - performance, high - precision nano satellite missions.

• Aerospace Engineering and Technology
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• v.4 no.1
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• pp.66-76
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• 2005

In this study, star visibility analysis of a star tracker is performed by using a statistical apprach. The probability of the Sun and the Earth proximity, the solar array masking probability, and the solar array blinding probability by the Sun light are obtained from the arbitrary chosen satellite positions as a function of a line of sight vector of the star tracker in several satellite attitude modes. This analysis demonstrates that the optimized star tracker accomodations can be determined to be an elevation angle -40o and two azimuth angles $-35^{circ}$ and $-150^{circ}$.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.129.1-129.1
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• 2012

서포트 벡터 머신(Support Vector Machine, SVM)과 인공신경망 모형(Neural Network, NN)을 사용하여 태양 양성자 현상(Solar proton event, SPE)의 플럭스 세기를 예측해 보았다. 이번 연구에서는 1976년부터 2011년까지 10MeV이상의 에너지를 가진 입자가 10개 cm-1 sec-1 ster -1 이상 입사할 경우를 태양 양성자 현상으로 정의한 NOAA의 태양 고에너지 입자 리스트와 GOE위성의 X-ray 플레어 데이터를 사용하였다. 여기에서 C, M, X 등급의 플레어와 관련있는 178개 이벤트를 모델의 훈련을 위한 데이터(training data) 89개와 예측을 위한 데이터(prediction data) 89개로 구분하였다. 플러스 세기의 예측을 위하여, 우리는 로그 플레어 세기, 플레어 발생위치, Rise time(플레어 시작시간부터 최대값까지의 시간)을 모델 입력인자로 사용하였다. 그 결과 예측된 로그 플럭스 세기와 관측된 로그 플럭스 세기 사이의 상관계수는 SVM과 NN에서 각각 0.32와 0.39의 값을 얻었다. 또한 두 값 사이의 평균 제곱근 오차(Root mean square error)는 SVM에서 1.17, NN에서는 0.82로 나왔다. 예측된 플럭스 세기와 관측된 플럭스 세기의 차이를 계산해 본 결과, 오차 범위가 1이하인 경우가 SVM에서는 약 68%이고 NN에서는 약 80%의 분포를 보였다. 이러한 결과로부터 우리는 NN모델이 SVM모델보다 플럭스 세기를 잘 예측하는 것을 알 수 있었다.

• The Bulletin of The Korean Astronomical Society
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• v.20
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• pp.17.1-17.1
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• 1995

• The Bulletin of The Korean Astronomical Society
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• v.20
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• pp.27-28
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• 1995

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.102-108
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• 2010

The photovoltaic system changes solar energy into DC by solar cell and this DC is inverted into AC which is used in general houses by inverter. Recently, the use of power of the photovoltaic system is increased. Therefore, the study of 3 phase solar system to transmit large power is very important. This paper proposes a method that finds simply faults and diagnoses the switch open faults of 3-phase pulse width modulation (PWM) inverter of grid-connected photovoltaic system. The proposed method in $\alpha\beta$ plane uses the patterns of trajectory image as the characteristic parameters and differenciates a normal state and open states of switches. Then, the result is made into tree. The tree is composed of 21 fault patterns and the parameters to classify faults are a shape, a trajectory area, a distributed angle, and a typical vector angle. The result shows that the proposed method diagnosed fault diagnoses, classified correctly them, and made a pattern tree by fault patterns.

• Environmental and Resource Economics Review
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• v.28 no.4
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• pp.467-495
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• 2019

Uncertainty of renewable energy such as photovoltaic(PV) power is detrimental to the flexibility of the power system. Therefore, precise prediction of PV power generation is important to make the power system stable. The purpose of this study is to forecast PV power generation using meteorological data including particulate matter(PM). In this study, PV power generation is predicted by support vector machine using RBF kernel function based on machine learning. Comparing the forecasting performances by including or excluding PM variable in predictor variables, we find that the forecasting model considering PM is better. Forecasting models considering PM variable show error reduction of 1.43%, 3.60%, and 3.88% in forecasting power generation between 6am~8pm, between 12pm~2pm, and at 1pm, respectively. Especially, the accuracy of the forecasting model including PM variable is increased in daytime when PV power generation is high.

• Aerospace Engineering and Technology
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• v.10 no.2
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• pp.87-93
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• 2011

FSSA(Fine Sun Sensor Assembly) is the important sensor for satellite attitude control. FSSA measures the direction of the sun's rays and determines whether the satellite is in the eclipse or not. FSSA for GEO Satellite is also used to acquire the attitude error information in the attitude control reference frame and acquire the Sun direction during transfer orbit or mission Process. This paper shows the configuration of Fine Sun Sensor for LEO and GEO Satellite and their principle of operation that angle measurement is obtained by using the transfer function which is the ratio of the difference between output currents of Solar Cell to the sum of all output currents.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.514-517
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• 2017

In solar photovoltaic systems, power generation is greatly affected by the weather conditions, so it is essential to predict solar energy for stable load operation. Therefore, data on weather conditions are needed as inputs to machine learning algorithms for solar energy prediction. In this paper, we use 15 kinds of weather data such as the precipitation accumulated during the 3 hours of the surface, upward and downward longwave radiation average, upward and downward shortwave radiation average, the temperature during the past 3 hours at 2 m above from the ground and temperature from the ground surface as input data to the algorithm. We analyzed the statistical characteristics and correlations of weather data and extracted the downward and upward shortwave radiation averages as a major elements of a feature vector with high correlation of 70% or more with solar energy.