• 대한기계학회논문집B
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• 제36권12호
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• pp.1177-1183
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• 2012

본 연구의 목적은 형상계수와 태양추적장치를 이용하여 헬리오스타트에서 흡수기로 복사열전달이 최대화 될 수 있는 시스템을 개발하는 것이다. 헬리오스타트에서 타워 상단에 위치한 흡수기로의 열전달은 대부분 복사에 의해 일어나기 때문에, 복사 열전달에서 사용되는 형상계수를 헬리오스타트 제어에 이용하였다. 태양 추적 및 태양 위치 계산은 CdS 센서와 시뮬링크 프로그램을 이용하였다. 시뮬링크 프로그램을 이용하여 실시간으로 헬리오스타트, 흡수기, 태양 사이의 형상계수가 최대화되는 알고리즘을 적용함으로서, 헬리오스타트에서 흡수기로의 복사 열전달이 최대화 될 수 있도록 하였다. 또한 다양한 조건에 따른 헬리오스타트 제어에 필요한 각을 시뮬레이션 함으로서 각 조건에 필요한 각을 도출할 수 있었다.

• 한국태양에너지학회 논문집
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• 제34권3호
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• pp.21-29
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• 2014

Heliostat field control algorithm is the logics to operate the heliostat field of tower type solar thermal power plant and it could include various methodologies of how to control the heliostat field so as to optimize the energy collection efficiency as well as to reduce the system operating cost. This work, as the first part of the consecutive works, presents heliostat aiming mint allocation scheme which will be used in the heliostat field control algorithm for 200kW solar thermal power plant built in Daegu, Korea. We first discuss the structure of heliostat field control system required for the implementation of aiming scheme developed in this work. Then the methodologies to allocate the heliostat aiming points on the receiver are discussed. The simulated results show that the heliostat aiming point allocation scheme proposed in this work reduces the magnitude of peak heat flux on the receiver more than 40% from the case of which all the heliostats in the field aim at the center of receiver simultaneously. Also it shows that, when the proposed scheme is used, the degradation of heliostat field optical efficiency is relatively small from the maximal optical efficiency the heliostat field could have.

• 한국태양에너지학회 논문집
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• 제34권5호
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• pp.33-41
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• 2014

Heliostat field in a tower type solar thermal power plant is the sun tracking mirror system which affects the overall efficiency of solar thermal power plant most significantly while consumes a large amount of energy to operate it. Thus optimal operation of it is very crucial for maximizing the energy collection and, at the same time, for minimizing the operating cost. Heliostat field operational algorithm is the logics to control the heliostat field efficiently so as to optimize the heliostat field optical efficiency and to protect the system from damage as well as to reduce the energy consumption required to operate the field. This work presents the heliostat field operational algorithm developed for the heliostat field of 200kW solar thermal power plant built in Daegu, Korea. We first review the structure of heliostat field control system proposed in the previous work to provide the conceptual framework of how the algorithm developed in this work could be implemented. Then the methodologies to operate the heliostat field properly and efficiently, by defining and explaining the various operation modes, are discussed. A simulation, showing the heat flux distribution collected by the heliostat field at the receiver, is used to show the usefulness of proposed heliostat field operational algorithm.

• 한국태양에너지학회 논문집
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• 제29권1호
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• pp.50-57
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• 2009

Heliostat in the tower type solar thermal power plant is a mirror system tracking the sun's movement to collect the solar energy and it is the most important subsystem determining the efficiency of solar thermal power plant. Thus a good performance of it, which is mostly the accurate sun tracking performance under the various hazardous operating condition, is required. Heliostat control system is a system to manage the heliostat sun tracking movement and other operations. It also communicates with the master controller through the heliostat filed control system to receive and send the informations required to operate the heliostat as a part of the solar thermal power plant. This study presents a heliostat control system designed and developed for the 1MW solar thermal power plant. We first define the functionality of heliostat control system. Then sun tracking controller as well as the sun tracking algorithm satisfying the required functionality have been developed. We tested the developed heliostat control system and it showed a good performance in regulation of heliostat motion and communication.

• 제어로봇시스템학회논문지
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• 제16권7호
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• pp.711-719
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• 2010

Heliostat, as a concentrator reflecting the incident solar energy to the receiver located at the tower, is the most important system in the tower-type solar thermal power plant, since it determines the efficiency and performance of solar thermal plower plant. Thus, a good sun tracking ability as well as its good optical property are required. In this paper, we propose a method to compensate the heliostat sun tracking error. We first model the sun tracking error, which could be measured using BCS (Beam Characterization System), by multilayered neural network. Then the extended Kalman filter was employed to train the neural network. Finally the model is used to compensate the sun tracking errors. Simulated result shows that the method proposed in this paper improve the heliostat sun tracking performance dramatically. It also shows that the training of neural network by the extended Kalman filter provides faster convergence property, more accurate estimation and higher measurement noise rejection ability compared with the other training methods like gradient descent method.

• 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.