• Bulletin of the Korean Space Science Society
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• 2003.10a
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• pp.68-68
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• 2003

본 논문은 별 추적기의 여러 가지의 성능변수 중에 광행차가 성능에 미치는 영향을 연구하였다. 일반적으로 광행차는 별 추적기의 저주파오차로 작용하며, 별 추적기 좌표계에서 최대 27" 정도의 성능을 감소시킨다. 지구가 태양 주위를 공전함으로써 야기되는 광행차는 약 21"이며, 줄리안 데이트를 통해서 보정이 가능하며, 관성 좌표계에서 지구 저궤도 위성이 궤도운동을 함으로 야기되는 광행차 오차는 약 6" 이며, 궤도정보를 통해서 보정이 가능하다. 이를 보정하기 위해서, 보정 알고리즘을 구현하여 다목적 실용위성 자세제어계 성능해석 소프트웨어를 통해서 검증을 하였다.

• Journal of the Korean Solar Energy Society
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• v.29 no.6
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• pp.22-31
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• 2009

Canting is the optical alignment of mirror facets of heliostat such that the heliostat could focus the energy as a unit concentrator. Canting could improve the optical performance of heliostat and thus improves the efficiency of heliostat and ultimately improves the efficiency of the solar thermal power plant. This study discusses the effect of mirror canting, especially off-axis canting, used to compensate the sun tracking error caused by the heliostat geometrical errors. We first show that the canting could compensate the sun tracking error caused by the heliostat geometrical errors. Then we show that the proper canting time could exist, depending on the heliostat location. Finally we show how much the sun tracking performance could be improved by canting, by providing RMS sun tracking error. The limitation and caution of using canting to improve the sun tracking performance are also discussed.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.311-314
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• 2003

This paper describes a virtual instrument system with faulty sensor reconstruction mechanism based on personal computer. This system consists of sensor control board using 16bit RISC machine, error signal reconstruction algorithm based on principal component analysis and auto tunned GUI interface according to the attached sensors. USB module is used for fast communication between PC and sensor controller. To show the veridity of the proposed system, the proposed system was applied to the developed sun tracker with 8 solar sensors.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.502-508
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• 2012

Heliostat, as a concentrator to reflect the incident solar energy to the receiver, is the most important system in the tower-type solar thermal power plant since it determines the efficiency and ultimately the overall performance of solar thermal power plant. Thus, a good sun tracking ability as well as a good optical property of it are required. Heliostat sun tracking system uses usually an open loop control system. Thus the sun tracking error caused by heliostat's geometrical error, optical error and computational error cannot be compensated. Recently use of sun tracking error model to compensate the sun tracking error has been proposed, where the error model is obtained from the measured ones. This work is a development of heliostat sun tracking error measurement and compensation method using BCS (Beam Characterization System). We first developed an image processing system to measure the sun tracking error optically. Then the measured error is modeled in linear polynomial form and neural network form trained by the extended Kalman filter respectively. Finally error models are used to compensate the sun tracking error. We also developed the necessary image processing algorithms so that the heliostat optical properties such as maximum heat flux intensity, heat flux distribution and total reflected heat energy could be analyzed. Experimentally obtained data shows that the heliostat sun tracking accuracy could be dramatically improved using either linear polynomial type error model or neural network type error model. Neural network type error model is somewhat better in improving the sun tracking performance. Nevertheless, since the difference between two error models in compensation of sun tracking error is small, a linear error model is preferred in actual implementation due to its simplicity.