• 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.

• Journal of the Korean Solar Energy Society
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• v.29 no.3
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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.

• Journal of the Korean Solar Energy Society
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• v.29 no.2
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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.

• 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 Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.388-396
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• 2011

Sun tracking system is the most important subsystem in parabolic dish type solar thermal power plant, since it determines the amount of thermal energy to be collected, thus affects the efficiency of solar thermal power plant most significantly. Various types of sun tracking systems are currently used. Among them, use of photo sensors to located the sun(which is called sensor type) and use of astronomical algorithm to compute the sun position(which is called program type) are two of the mostly used methods. Recently some uses CCD sensor, like CCD camera, which is called image processing type sun tracking system. This work is concerned with the analysis of sun tracking performance of various types of sun tracking systems currently used in the parabolic dish type solar thermal power plant. We first developed a sun tracking error measurement system. Then, we evaluate the performance of five different types of sun tracking systems, sensor type, program type, hybrid type(use of sensor and computed sun position simultaneously), tracking error compensated program type and image processing type. Experimentally obtained data shows that the tracking error compensated program type sun tracking system is very effective and could provide a good sun tracking performance. Also the data obtained shows that the performance of sensor type sun tracking system is being affected by the cloud significantly, while the performance of a program type sun tracking system is being affected by the sun tracking system's mechanical and installation errors very much. Finally image processing type sun tracking system can provide accurate sun tracking performance, but costs more and requires more computational time.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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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.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.308-317
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• 2006

To control the tip position of a flexible-link manipulator, a neural network (NN) controller is proposed in this paper. The dynamics error used to construct NN controller is derived based on output redefinition approach. Without the filtered tracking error, the proposed NN controller can still guarantee the closed-loop system uniformly asymptotically stable as well as NN weights bounded. Furthermore, the tracking error of desired trajectory can converge to zero with the proposed controller. For comparison an NN controller with filtered tracking error is also designed for the flexible-link manipulator. Finally, simulation studies are carried out to verify the theoretic results.

• Journal of the Korean Solar Energy Society
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• v.36 no.6
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• pp.47-59
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• 2016

As the usage of sun tracking system in solar energy utilization facility increases, requirement of more accurate computation of sun position has also been increased. Accordingly, various algorithms to compute the sun position have been proposed in the literature and some of them insist that their algorithms guarantee less than 0.01 degree computational error. However, mostly, the true meaning of accuracy argued in their publication is not clearly explained. In addition to that, they do not clearly state under what condition the accuracy they proposed can be guaranteed. Such ambiguity may induce misunderstanding on the accuracy of the computed sun position and ultimately may make misguided notion on the actual sun tracking system's sun tracking accuracy. This work presents some comments related to the implementation of sun position computational algorithm for the sun tracking system. We first introduce the algorithms proposed in the literature. And then, from sun tracking system user's point of view, we explain the true meaning of accuracy of computed sun position. We also discuss how to select the proper algorithm for the actual implementation. We finally discuss how the input factors used in computation of sun position, like time, position etc, affect the computed sun position accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1913-1920
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• 2009

An intelligent accurate sun tracking controller for solar lighting system was developed. This controller can detect the faulty sensor and correct the error signal based on Principle Component Analysis theory. A fuzzy controller was developed to control the tracker by using the collected sensor signal for precise position control. Also a multiple range searching sensor module for sun tracking was designed. To show the validity of the developed system, some experiments in the field were illustrated.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.06a
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• pp.573-575
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• 2012

기존 RADAR 기반의 VTS에 AIS를 연계 집약하면서 예측 불가능한 데이터 전송률에도 동일선박으로부터의 AIS 및 RADAR 데이터는 상관관계를 유지하면서 물표에 대한 Tracking이 지속적으로 이루어져야 하지만 AIS 신호 Lost시 RADAR Tracking 자동 전환이 안 되는 경우가 많이 발생하고 있다. 또한 3개의 VTS 모니터 화면에 각각 다른 Scale과 다른 관제구역이 디스플레이 됨으로써 특히 모니터 가장자리 부근의 관제구역은 사각지대로 관제사의 집중도가 떨어질 수밖에 없다. 이러한 문제점들은 관제사의 Traffic Image구성 및 Situational Awareness를 방해하는 요소로 작용하며 사고의 개연성을 높이고 있다. 본 연구에서는 VTS 모니터상의 화면 재구성 방법을 통해서 관제사의 SA를 돕고, AIS-RADAR Tracking 알고리즘 보완을 통한 Target Tracking의 안정성을 확보하고, 교육 훈련을 통해서 AIS특성과 Error현상에 대한 관제사들이 충분히 이해하도록 하여 관제업무의 향상을 기하는 방안을 제시하였다.