Internal Code: IAH204
Although solar energy is considered to be in a developed phase, the literature available tends to focus on the basic important topics: materials selection for panel, control schemes for harnessing the most energy, number of axis to maximize the efficiency, and tracking options available.
1. Gan, GY, 2014. Research on Solar Tracking Composite Control. Tokyo The goals of the authors is to explain a genetics based algorithm to improve the control applied to heliostats in solar-thermal plants. There are two basis methods for controlling the movement of the panels, the theoretical sun-movement dependant, where the rotation of the panel depends on the Kepler´s equations, and the sunlight-measurement dependant, where the closed loop circuit uses sunlight sensors to provide feedback to a microcontroller and this one controls the rotation of the panel. Authors say that the first one lacks on precision and self-control because the movement is “fixed” meaning that the system is relying on equations that may not take into account the weather conditions, i.e. if there are clouds or it´s raining and the second one requires a lot of initial costs since several photodiodes or sensors are to be installed in each panel to make it light-dependable.
The authors suggest that there has to be an algorithm that can overcome those inefficiencies and explain the basis of the assumption (based on genetics) by saying that the solar thermal power plant is a population where “heliostat angles survive on through collection compared with individuals, reproduction, and development”. The amount of the energy that the tower is absorbing is the factor that affect the evolution of populations, the most-optimum focusing heliostat angle survives and GA (genetics algorithm) eliminates the others.
In the design process, for this paper they come up with two improved control schemes. The first is the theoretical combined with the genetic algorithm (what they call Dichotomy combined with local area algorithm-D- LAGA) and the second one is the fit control scheme combined with genetic algorithm (GA-OFCS). The fit control scheme is an approach where each heliostat fitness is described as the energy being supplied to the tower from that specific device making it “the strongest one”. The developing stage of these algorithms includes some equations and theoretical approaches, but the report lacks on programming and construction details, what microprocessor was used, which sensors were placed and where, etc. As a conclusion, the paper shows that the efficiency of D-LAGA increases about 34.8% and GA-OFCS increases approximately 31.5% compared to fixed position generation. Statistics say that original composite control method efficiency increases about 33% to 35% compared to fixed-type power generation, what be assumed as that the original composite control is enough efficiency improvement but the fact that just some heliostats need sunlight sensors make the current paper a very good option for controlling the devices from the financial viewpoint.
1. Initialization of the system.
2. Calculation of the required sun angles for vertical and horizontal axis.
3. Set the time of the blocks in the PLC according to the required position.
4. Sending command from the yearly and weekly timers to the motors.
4*. At the sunrise, start the horizontal tracking by sending the signal to the motor.
5. At the sunset, the motors receive a signal to go to the position required for the next day.
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