In applications with low-energy conversion efficiency, maximizing the output power improves the efficiency. The maximum output power of a solar panel depends on the environmental conditions and load profile. In this paper, a method based on simultaneous use of two fuzzy controllers is developed in order to maximize the generated output power of a solar panel in a photovoltaic system: fuzzy-based sun tracking and maximum power point tracking. The sun tracking is performed by changing the solar panel orientation in horizontal and vertical directions by two DC motors properly designed. A DC-DC converter is employed to track the solar panel maximum power point. In addition, the proposed system has the capability of the extraction of solar panel
Nowadays, photovoltaic systems are rapidly expanding and have an increasing role in power-generation technology. Despite the fact that solar panels have high fabrication cost and low efficiency of energy conversion, they are power sources in photovoltaic systems. The reason is that they provide more secure power sources and pollution-free electric supplies. They generally have nonlinear
Figures
(a) and (b). Solar panel
Many papers have been published regarding more efficient use of a solar panel in photovoltaic systems. Some of them only have proposed maximum power point tracking systems to gain more generated output power [
In this research, a simultaneous combination of the two mentioned methods is implemented. Therefore, the solar panel is placed on its best orientation toward the sun, and its maximum output power will be attained. In this way, two separate controllers are required to perform the tasks of sun and maximum power point tracking. Fuzzy-based algorithms are selected for the mentioned controllers because of its high compatibility with nonlinear systems. The fuzzy theory based on fuzzy sets and fuzzy algorithms provides a general method of expressing linguistic rules so that they can be processed quickly. The maximum power point tracking fuzzy controller defines the proper duty cycle of a converter to track the maximum power point using
The rest of paper is organized as follows; Section
A block diagram of the proposed system is shown in Figure
The proposed system block diagram.
The system is able to measure, control, and monitor all parameters, which are needed to implement the mentioned fuzzy-based algorithms. A polycrystalline silicon solar panel with specifications indicated in Table
The solar panel specifications.
Specifications | Value | Dim |
---|---|---|
Weight | 6 | Kg |
Dimensions | mm3 | |
Ns | 13 | — |
Np | 5 | — |
Voc | 20.5 | V |
Isc | 2.98 | A |
Pmax | 45 | W |
A switching converter is required to maintain a solar cell’s operating point in its maximum power point. In this research, a flyback converter is utilized to fulfill the task. The advantage of using this kind of converter is the isolation of its input and output. In addition, fundamental criteria of choosing suitable topology for the required DC-DC converter in the mentioned application are the range of input and output voltage which requires a boost converter (why buck topology is not chosen), ability of controlling output power (why boost topology cannot be chosen is the existence of a direct path between its input and output), simplicity and no complexity (why push-pull topology is not chosen).
Figure
The detailed proposed system block diagram.
Figure
The used flyback topology.
The switch current and voltage waveforms, primary and secondary circuit currents.
Current
Figure main circuit of the flyback converter including T1, Q2, D7, C32, C33, snubber circuit including D8, R43, C31, switch driver (Q2) including Q1, Q3, R48, output filter including L2, C34, C35.
The flyback converter’s schematic.
When switch Q2 is on, energy is stored in the transformer’s inductor and then when the switch becomes off, by conducting the output diode this energy is transferred to the load. Therefore, the base of transformer’s calculation is the stored energy in its inductor which is computed as follows (with assumption of
An air gap is considered to prevent core saturation (in this way, small cores can be used). Since the air gap’s reluctance is much larger to the core’s reluctance, the core’s reluctance can be ignored in calculations.
Relation (
Now the inductance of transformer primary winding can be specified. For this purpose, the worst case should be considered which is minimum value of the input voltage and maximum load (in this case, the pulse width of PWM is maximum) again, by using the relation of stored energy in an inductor, we have
If
Considering core specification and the calculated value of air gap, the number of the primary winding turns is specified 22. To determine the number of the secondary coil turns, it is necessary to know the transformer turns ratio, which can be calculated as follows. If
In above relations,
By assuming
By considering a safe margin, fast diodes with 100 V forward voltage can be selected, but maximum power of the diode can be calculated as follows:
In computing the output capacitor, this point is needed to be considered that it should be able to provide load current when the input switch is off; therefore,
Drain voltage of MOSFET Q2 reaches the value of
When gate’s voltage of MOSFET Q2 becomes zero, the current is not immediately stopped and because of increasing of drain voltage to
The output filter should not allow the noises produced by switching reach the loads. Frequency of switching of the converter is 32 kHz so the filter’s elements should be chosen in a manner which reduces the amplitude of noise. Selected filter for the used converter is
frequency response of output filter.
In this section, the details of fuzzy-based maximum power point tracker and fuzzy-based sun tracker are fully described.
Maximum power point tracking can be carried out by adjusting the duty cycle of the flyback converter. Fuzzy logic is used as one of the several methods to track the maximum power point of a solar panel because of its good stability and quick response [
(a) Membership function plots of E, (b) Membership function plots of dE, (c) Membership function plots of converter duty cycle.
25 fuzzy rules are used as stated in Table
Maximum power point tracking fuzzy rule base.
dE | |||||
NB | NS | PS | PB | ||
NB | NB | NB | NB | ||
NS | NS | NS | NS | ||
NS | PS | ||||
PS | PS | PS | PS | ||
PB | PB | PB | PB |
Power versus current curve of a solar panel.
Range of membership functions is specified based on the panel’s specification, relations (
As it was mentioned, several sun-tracking systems have been proposed and implemented [
Figure
The proposed sun-tracking sensor.
Sun tracking fuzzy rule base.
Sun sensor | |||||||
Duty cycle | NB | NM | NS | PS | PM | PB |
(a), (b). Membership function of sun-tracking subsets.
Calibration of the designed sensor is necessary for tracking the sun position accurately. A controllable lighting system is considered and four solar cells are calibrated. Light is radiated on each solar cell and the value of
Voltage variation of J2 cell.
Sun sensor1 | Sun sensor2 | |
---|---|---|
0.5 | 313 | 517 |
0.4 | 313 | 517 |
0.3 | 327 | 517 |
0.2 | 390 | 517 |
0.1 | 450 | 517 |
0 | 514 | 517 |
The range of the input and output membership functions in the fuzzy sun tracker is specified based on mathematic relation (
Solar panel
Figure
The proposed nonlinear electronic load.
Figures
(a) and (b) A typical extracted solar panel
Based on the established setup, different tests were executed. First, only the fuzzy-based MPPT was applied to the system for 100 minutes. Solar panel
The Variations of solar panel operating point.
The test was performed in the following conditions: temperature = 36.8°C and solar Irradiance = 830 W/m2. It is observed that the fuzzy-based maximum power point tracking algorithm reached its maximum level after 0.57 seconds, which is a reasonable rate.
solar panel maximum output power.
It is observed that the fuzzy-based controller has tracked the maximum power point during the test period. The decreasing trend of the figure at the end of the test period is because of the sun movement. To overcome the defect, the adjustment of the solar panel orientation should be added to the system. The second test was applying only the fuzzy-based sun tracking to the system for 100 minutes. Figure
Solar panel output power.
(a) and (b). Solar irradiance and temperature variations.
The algorithm was tested in the condition that the angle between solar panel and sun radiation was 80°. A pyranometer and a digital thermometer with 20 seconds intervals measured solar irradiance and panel temperature online. As it can be observed from Figure
Figures
Solar panel output power.
(a) and (b) Solar irradiance and temperature variations.
In order to compare the implemented technique with other techniques of maximum power point tracking, incremental conductance and perturbation & observation techniques are executed and compared with the fuzzy technique. Figure
The incremental conductance algorithm.
Figure
The perturbation and observation algorithm.
Other experiments are executed regarding combination of sun and maximum power point tracking, which are expressed as follows. Figure
Panel’s power variations during Simultaneous execution of the two mentioned algorithms.
So that the system, in 250 s, reached the situation which the sun direction is completely perpendicular to the panel and 28 W power delivered. That amount of power is equal to the maximum generated power, which can be delivered in temperature about 26°C and irradiance about 600 W/m2.
In Figure
Output power variations during sun tracking.
Another experiment is executed to show the importance of sun tacking which its result is presented in Figure
Long-term sun tracking and MPP Tracking.
A similar experiment was performed to show the speed of sun-tracking. In the experiment, sun tracking algorithm for 5 min and maximum power point tracking algorithm for 10 min was successively executed. Figure
Reaction of system to fast movement of the sun position.
Three methods to maximize the output power of a solar panel were employed in this research. Fuzzy-based maximum power point tracking was the first technique. It is observed that by use of the technique, 23 W was approximately obtained during the measurement time, which is about 51 percents of the nominal output power. In the second method, fuzzy-based sun tracking was applied and it is observed that 11 W was approximately attained during the measurement period, which is about 24.5 percent of the nominal output power. The result is expected because sun tracking was only employed without maximum power point tracking and so a small amount of the nominal power was obtained from the solar panel. Finally, the combination of fuzzy-based maximum power point tracking and fuzzy-based sun tracking was used to maximize the output power. It can be seen that by stimulus use of those techniques, the output power can reached the amount of 35 W, which is about 78 percents of the nominal output power. The panel was not in its nominal conditions, it is the reason why it could not reach its nominal output power. It was shown that the mixture of the two techniques yields the maximum output power delivery. This achievement can also lead to a reduction of the size, weight, and cost of solar panels in photovoltaic systems.