Control algorithm of a heliostat in a solar power tower using the intelligence artificial systems to guide its motors Corresponding author. Abdelfettah ZEGHOUDI Tel: +213 552 99 88 20. E-mail address:
[email protected]
A.Zeghoudi, A. Takilalte, Y.Hamza Centre de Développement des Energies Renouvelables, CDER BP 62 Route de l'Observatoire, Bouzaréah, 16340, Algiers, Algeria
sunlight, or solar thermal energy, onto a small area. Electricity is generated when the concentrated light is converted to heat, which drives a heat engine connected to an electrical power generator [1], [2], [3]. A solar power tower consists of an array of dual-axis tracking reflectors (heliostats) that concentrate sunlight on a central receiver atop a tower; the receiver contains a fluid deposit, which can consist of sea water. The working fluid in the receiver is heated to 500–1000 °C and then used as a heat source for a power generation or energy storage system.[5] In general, the heliostat contributes around 50 % of the total cost of the system and the loss of annual energy [6]
Abstract—The main objective of this research is to use a control algorithm and computer capabilities developed in the field of Sun tracking to improve the effectiveness of its monitoring. In the first contribution a control algorithm of the heliostat was proposed; the aim of this algorithm is to direct the heliostat in its optimum position because in the solar field, each heliostat tracks the sun to minimize the cosine effect and reflect solar radiation incident to the target. The second part a method installed on an approach in the aim of ordering of the heliostat in a solar power tower using (PID) and artificial intelligence systems such as Neural Networks Controller (NNC) and Fuzzy Logic Controller (FLC) to minimize tracking error and increase the performance of Solar Power Plant. The tracking control system proposed in this paper uses astronomical formulas for determining the position of the heliostat.
The main component in a solar power tower is the heliostat fields and especially, the control part; the trajectories of the heliostat are generated by the two articulations controlled by two motors [7]. When the heliostat is equipped with a tracker system, the power output, then reaches its maximum [8], [9], [10]. most heliostat in operation now are controlled by computers or microcontrollers from the astronomical theory [9], [10].
The results of simulation and signal processing shown in this paper are reliable for sun tracking and the control of heliostats in a solar power tower plant.
Keywords—Sun tracking; Fuzzy Logic Controller; artificial intelligence systems; neural networks; heliostat; solar power tower plant; tracking error. I.
Modeling and simulation of motion of a heliostat is essential to have an idea about the trajectories of Azimuth and Elevation with a proposal for the control algorithm; regardless of the input parameters including geographic and temporal parameters with the heliostat position in the field. Also, a second control part of a universal motor to guide a heliostat using intelligence controllers such as neural networks, fuzzy logic and intelligent hybrid system.
INTRODUCTION
Solar energy is an important renewable energy getting more popular in many countries day by day [1]. The performance of solar power depends strongly on the solar field efficiency, which is related to the tracker design, the field layout, the tracking system and control system. Concentrated solar power systems generate solar power by using mirrors or lenses to concentrate a large area of
The rest of this paper is organized as follows: Section 2 gives a methodology with the control algorithm proposed
1
to guide the heliostat in a solar tower power Hybrid control system description of tracker solar. Section 3 shows the simulation results of this algorithm and motorization of the heliostat by a universal motor using an artificial intelligence controller. The last section is devoted to the conclusion.
e = arcsin(sinφsinδ + cosφcosδ cosΩ)
{ = II.
METHODOLOGY
The method proposed in this work allows controlling the orientation of the solar field of heliostat of a tower solar plant. The following figure shows the block diagram to determine both azimuth and elevation paths.
(
sinØ.sinα−sinδ
)
cosØ.cosα sinØ.sinα−sinδ
−𝑎𝑟𝑐𝑠𝑖𝑛(
cosØ.cosα
(1)
Si ω < 0 ) Si ω < 0
(2)
The solar coordinates (α, γ) depend on the day of the year, time of day (considering the local solar time) and the latitude [14, 15]. B. Control algorithm In the solar field, each heliostat tracks the sun to minimize the cosine effect and reflect solar radiation incident to the target, and thus maximize the collection of solar energy through the positioning of the surface perpendicular to the bisecting the angle subtended by the Sun and the solar receiver. Figure 3 describes the orientation of a heliostat azimuth in the plan view. To direct the heliostat in this configuration, the normal is directed to the heliostat as bisects the angle between the incident ray and the reflected ray. The displacement of the normal and the incident beam during the day in the azimuth plane are carried out in the manner illustrated by figure 3
Fig. 1. block diagram to determine the position of heliostat
From the block diagram of Figure 1 can be obtained both azimuth and elevation plots heliostats in a solar tower, so it allows us to know the position of the heliostat any time of the day. mathematical expressions tracker formulas for AE method have already been obtained by "Stine" and "Harrigan" in 1985 [11] and "Chen et al" in 2001 [12].
During a morning at a time t1, incident beam t1 is reflected on the heliostat t1 with normal par1. At time t2, incident beam t2 follows the same path with the heliostat t2 and its associated normal part2.
A. Sun position There are different patterns to describe the position of the Sun in the sky. Because the heliostats track our star altazimutale method, we define the position of the Sun relative to a fixed point on the surface of the Earth using two angles: elevation “α” and azimuth “γ”. For installations in the northern hemisphere, we choose the following convention: negative azimuth morning (-90◦ for Sun in the East), null to solar noon (0◦ the South), and positive afternoon (90° in the West). The elevation α ranges from 0◦ for a Sun on the horizon until 90◦ for Sun at the zenith (see Fig.2). Fig. 3. Top view of a heliostat at two times (t1 and t2) during the morning.
The Heliostats are each provided with an automatic guidance system allowing them to continue the diurnal path of the sun so that they correctly project the reflected rays on the receiver all day.
In the following section, we propose a method to direct the heliostat in its optimum position. The following figure (fig 4 and fig 5) shows the two algorithms guidance to control both azimuth and elevation motors.
Fig. 2. Solar position algorithm parameter [13] the symbols of “ϴ”, “” and “e” are denoted as zenith angle, azimuth angle and elevation angle, respectively.
2
Fig. 4. Flow char azimuth motor orientation
Fig. 5. Flow char elevation motor orientation
the computer, in this case the wind speed exceeds the only one can make the heliostat has its rest position by -r pulses.
Abbreviation
Start time: Sunrise Stop time: Sunset Azimuth_c: Current azimuth Azimuth_n: Next azimuth Elevation_c: Current Elevation Elevation_n: Next Elevation EL +: elevation guidance upwards. EL -: elevation guidance down.
Next Azimuth = current Azimuth + 1 steep (all day)
If time > solar noon elevation + 1 steep
*Next elevation = Current
Else
*Next elevation = Current elevation - 1 steep
Counter (r = r + 1): this counter lets us know the heliostat position in degrees from the pulse numbers generated by 3
Move motor Azimuth +: the upward direction Move motor Azimuth -: the downward direction
Surveying sun time control will start to direct the heliostat azimuth and elevation until the sunset. Early in the day the algorithm load time of the day from the computer and then it will calculate both daytime trajectories. Every 5 seconds the loop will compare the value of azimuth and elevation, current with the next values. When the current value exceeds the following value that equals the present value over a step; orienting the heliostat with steep downstream. There is an exceptional case for elevation after solar noon, the heliostat is oriented downwards after the wait condition. Because the heliostat rises early in the day, reaches its highest point at 12 hours and decreasing until the end of the day.
21.8 21.75
Elevation[degree]
21.7 21.65 21.6 21.55 21.5 21.45 21.4 21.35 810.5
811
811.5
812
812.5
813
813.5
814
814.5
Time[hour]
The next section shows the simulation results. Fig. 7. Discretization of the azimuth presented in detail
These are the parameters and details of the heliostat and tower.
Figure 7 shows part of the discretization of the azimuth angle presented about 08: 00 am. The control algorithm generates the pulses in the positive direction throughout the day.
The day : 50 (19 February) Latitude: 43.604 The receiver height: 100 m The height of the heliostat: 3 m The distance between the receiver and the tower: 168 m The facing angle: 0° The focal angle: 30° The terrain slope: 0°
32.2 Continous angle Discreet angle
X: 1200 Y: 32.16
32.1
32
Elevation[degree]
After the calculation of the sun position by astronomical formulas, the trajectories of heliostat were determined from Snell–Descartes law of refraction [16], [17].
31.9
31.8
31.7
31.6
31.5
The following figure (figures 6-8) shows the azimuth and elevation of paths all day, the simulation was given with definite site ordinated.
1120
1140
1160
1180
1200
1220
1240
1260
1280
Time
Fig. 8. discretization of the elevation presented in detail
Elevation[degree]
35
30
25
20
15
10 600
800
1000
1200
1400
1600
1800
1400
1600
1800
Time[Hour]
Azimuth[degree]
50
0
-50 600
800
1000
1200
Time[Hour]
When the Sun has shifted his thoughtful work on the receiver has also shifted the heliostat if not corrected. This correction is performed on the power tower each time the azimuth or elevation accuses an error reaching one definite level of the algorithm (eg 0.05 degree). This is a fixed angular correction, and not the impact on the receiver error. Thus, it reached angular threshold, an absolute correction 0.1 degree is performed in the opposite direction of movement in the task, the azimuth is always fixed in the same direction as the azimuth is growing in a day. For elevation, the correction can be performed in either direction since the Sun rises at first and down after solar noon. The azimuth and elevation angles are discretized every 0.1 degree during the day. This principle leads to a temporary advance of 0.05 degree.
Fig. 6. Heliostat’s movements (azimuth and elevation)—day 50
Figure 8 illustrates the discretization of the azimuth angle; we note that as time goes approaching to solar noon as the time between pulses will increase.
For a heliostat located in front of the target, the trajectories are symmetrical south as appears in the figure 6, the heliostat rises early in the day, reaches its highest point at 12 hours and decreases to the end of the day. This corresponds at the general form of racing the sun whose solar noon.
In this example, the heliostat keeps its optimal position between 11:20 ET 12:80 the control algorithm generates six pulses up before 12 o'clock and six down after 12:00.
4
C. Motorization of the heliostat by a universal motor using artificial intelligence controller
We use the control system by the neural network, proposed in [18]. And the fuzzy controller in [19]. In addition, a third PI auto-adjusting (F-PI) presented in [20]. A comparative study between controllers was realized, but in this case to guide a heliostat by a universal motor.
In this section, we will present a description, conception and configurations used in the simulation part for fuzzy, FPI and neural controllers for universal motor.
The figures 9-11 illustrate the diagram Simulink/Matlab used in this study
Fig. 9. Closed loop system using Neural Network Controller
Fig. 10. Closed loop system using Fuzzy logic Controller (FLC1, FLC2, FLC3)
Fig. 11. Closed loop system using F-PI controller
5
III.
and F-PI Controller in order to learn a little more about the design of controllers and determine the main characteristics of intelligent control.
RESULT
In this part, we are interested to demonstrate the results through the design and simulation of a Neural, fuzzy logic
Fig. 12. Comparison between regulators PID, Neural, fuzzy logic and F-PI Controller
1
FLC2
0.8
F-PI
0.6
NC
0.2 0 -0.2 1
-0.4
0.8 FLC2 0.6
-0.6 -0.8
ERRORS
ERRORS
0.4
F-PI 0.4
NC
0.2 0
-1 0
-0.2
1
1.05
0.5
1.1
1.15 Time (seconds)
1.2
1.25
1
1.5
Time (seconds) Fig. 13. Errors of three controllers
6
2
10
OUTPUTS
9 8
REF
7
F-PI NC
6
FLC2
5 4 3 2 1 0 0
0.5
1
1.5
2
2.5 Time (seconds)
3
3.5
2.6 Time (seconds)
2.8
4
4.5
5
9.5 9 8.5
OUTPUTS
8 7.5 7 6.5 6 5.5 2
2.2
2.4
3
3.2
Fig. 14. Comparison between regulators NC, FLC and F-PI at different stages
The error e, which calculate the difference between reference and actual value, it is commonly characterized into several quantities [21].
give good results. However, NC presents some fluctuations that can be harmful for the universal motor. However, we can see that FLC and F-PI and PID controller give results without fluctuations.
TABLE 1. PERFORMANCE INDEXES OF PID, FLC, NNC, AND F-PI CONTROLLERS. FLC1
FLC2
PID
FLC3
F-PI
NC
ITSE
0.0009
0.0002
0.0049
0.0004
0.0001
0.0015
ITAE
0.0066
0.0016
0.0019
0.0032
0.0062
0.0155
IAE
0.0596
0.0278
0.0437
0.0386
0.0317
0.0579
ISE
0.0359
0.0179
0.0248
0.0245
0.0130
0.0157
Similarly, Table 1 shows the performance indexes of fuzzy, F-PI and neural controllers. The calculated values of the IAE, ISE, ITAE and ITSE shown in Table 1 confirm the simulation results and values of the figures 12 and 14. IV.
CONCLUSION
This work shows an example of application and simulation of artificial intelligence control device based on models of fuzzy logic and neural network for controlling a heliostat in the solar power tower. The first part of this work propose a control algorithm to guide a heliostat in its optimal position in both directions (azimuth and elevation) on the heliostatic fields.
The results of the analysis with a comparison of the six controllers are presented in figure 12 and figure 14 with details. Figure 14 shows the comparison result between the six controllers for a given scenario and figure 13 shows the error between the reference and simulated data of the best controllers. From these figures, we can note that F-PI controller gives the best results compared to other methods. From this figure, it is clearly shown that F-PI and FLC2
The second part of this paper is a study of a sample application of artificial intelligence controller based on the neural networks, F-PI and fuzzy logic models. In the design of intelligent controllers, it is more important to know how the system works as it is evident in the dynamic case, a set 7
[11] W.B. Stine et R.W. Harrigan, Solar Energy Fundamentals and Design with Computer Applications. John Wiley, New York, Pages 135-262, 1985.
of rules can be created in the case of fuzzy logic controller and learning neural network model to achieve the desired results. At first stage, simulation of different configurations of neural and fuzzy controllers has been tested. At the second stage, a comparative phase is needed in order to get the appropriate model.
[12] Y.T. Chen et al, Non-imaging focusing heliostat, Solar Energy, Volume 71(3), Pages 155-164, 2001. [13] Yusie Rizala, Sunu Hasta Wibowoa , Feriyadia. Application of solar position algorithm for sun-tracking system, International Conference on Sustainable Energy Engineering and Application [ICSEEA 2012]. Energy Procedia 32 ( 2013 ) 160 – 165
Comparing the three controllers fuzzy and F-PI with neural. We can note that the FLC and F-PI, it show better response to step changes and very close to zero (Table 1 and figures 12-14). The fuzzy logic controller is better, according to the ITAE and IAE parameter. Even the auto-adjustable (F-PI) controller gives the best results (ISE, ITSE) for controlling the universal motor and guide the solar tracker.
[14] Robert Walraven. Calculating the position of the sun. Solar Energy, 20(5) :393 – 397, 1978. [15] J.A. Duffie and W.A. Beckmann. Solar Engineering of Thermal Processes. John Wiley & Sons Inc, 1991 [16] Snell's Law and the Index of Refraction, Fall 2004, Lab O3:
The results presented in this study are very encouraging because from the configuration model of neural network and combination of a classic control system PI and fuzzy logic it will be possible to further increase system performance.
http://www.colorado.edu/physics/phys1140/phys1140_sp05/Experiments /O3Fall04.pdf [17] https://en.wikipedia.org/wiki/Snell%27s_law [18] a.Zeghoudi, a.Chermitti. Speed Control of a DC Motor for the Orientation of a Heliostat in a Solar Tower Power Plant using Artificial Intelligence Systems (FLC and NC), Research Journal of Applied Sciences, Engineering and Technology 10(5): 570 580, 2015 .ISSN: 20407459; e-ISSN: 2040-7467.
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