• 한국태양에너지학회 논문집
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• 제29권6호
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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.

• 한국태양에너지학회 논문집
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• 제29권2호
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• pp.39-46
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• 2009

Heliostat, as a mirror system tracking the sun's movement, is the most important subsystem determining the efficiency of solar thermal power plant. Thus the accurate sun tracking performance under the various hazardous operating condition, is required. This study presents a methodology of development of the solar ray tracing technique and the application of it in the analysis of sun tracking error due to the heliostat geometrical errors. The geometrical errors considered here are the azimuth axis tilting error and the elevation axis tilting error. We first analyze the geometry of solar ray reflected from the heliostat. Then the point on the receiver, where the solar ray reflected from the heliostat is landed, is computed and compared with the original intended point, which represents the sun tracking error. The result obtained shows that the effect of geometrical error on the sun tracking performance is varying with time(season) and the heliostat location. It also shows that the heliostat located near the solar tower has larger sun tracking error than that of the heliostat located farther.

• 한국태양에너지학회 논문집
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• 제29권3호
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• pp.1-11
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• 2009

Heliostat sun tracking accuracy could be the most important requirement in solar thermal power plant, since it determines the overall efficiency of power plant. This study presents the effect of geometrical errors on the heliostat sun tracking performance. The geometrical errors considered here are the mirror canting error, encoder reference error, heliostat position error. pivot offset and tilt error, gear backlash and mass unbalanced effect error. We first investigate the effect of each individual geometrical error on the sun tracking accuracy. Then, the sun tracking error caused by the combination of individual geometrical error is computed and analyzed. The results obtained using the solar ray tracing technique shows that the sun tracking error due to the geometrical error is varying almost randomly. It also shows that the mirror canting error is the most significant error source, while the encoder reference error and gear backlash are second and the third dominant source of errors.

• 제어로봇시스템학회논문지
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• 제18권5호
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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.