Photovoltaic system (PV) has nonlinear characteristics which are affected by changing the climate conditions and, in these characteristics, there is an operating point in which the maximum available power of PV is obtained. Fuzzy logic controller (FLC) is the artificial intelligent based maximum power point tracking (MPPT) method for obtaining the maximum power point (MPP). In this method, defining the logical rule and specific range of membership function has the significant effect on achieving the best and desirable results. This paper presents a detailed comparative survey of five general and main fuzzy logic subsets used for FLC technique in DC-DC boost converter. These rules and specific range of membership functions are implemented in the same system and the best fuzzy subset is obtained from the simulation results carried out in MATLAB. The proposed subset is able to track the maximum power point in minimum time with small oscillations and the highest system efficiency (95.7%). This investigation provides valuable results for all users who want to implement the reliable fuzzy logic subset for their works.
1. Introduction
Fossil fuel is a very common choice in many countries worldwide due to its large sources, but nowadays by increasing concerns about some issues such as fossil fuel storage, global warming, and skyrocketing oil costs, it is desirable to consider substitute possible energy source that has high productivity and low outpouring [1, 2].
All of the PV systems have some main problems affected by weather conditions such as dirt, changing irradiation, temperature, and other factors. The PV systems have two main characteristics, P-V and I-V, where P, V, and I are PV output power, voltage, and current, respectively. Changing the irradiation has the most effect on these characteristics.
The PV system has an operating point that can be specified by the crossing point between I-V curve of the PV panel and load line in I-V characteristic. The variation of some factors such as irradiation, temperature, and dust can change the operating point. There is single point in I-V and P-V curves of PV panel that power poses the maximum value and it is called maximum power point (MPP) [3]. In changing weather condition such as irradiation, the MPP controller should be capable of tracking MPP at minimum time in order to minimize the power loss.
In order to find the MPP, various methods have been proposed which can be classified in two general methods: conventional and soft computing methods. Conventional methods include perturb and observe (P&O), constant voltage (CV), and conductance increment (IC), and soft computing methods cover fuzzy logic controller, neural network predictor, genetic algorithm, and so on [4–12]. Every tracking control method has its advantages and disadvantages. One of the main factors for finding the best MPPT algorithm is that the MPP should be found by controller in the minimum time especially under changing condition. Another significant factor is that the controller can operate at this point with minimum oscillation. The conventional methods have drawbacks such as low tracking speed and also oscillation around MPP [13, 14]. In order to solve this problem, the artificial intelligent method such as fuzzy logic (FL) can be used which keeps strength in changing weather condition such as temperature and radiation. In fuzzy logic method the main points are defining a rule table and the related range of membership function. The rules are defined according to specific range of membership functions and vice versa. By changing the range of membership functions, the rules can be changed for obtaining the expected result and, also, by changing the rules, the range of membership functions should be changed.
In this paper, in Section 2 the PV system is explained and modeled by MATLAB in base of the KC200GT panel type. In Section 3 the DC-DC boost converter is explained. In Sections 4 and 5, fuzzy logic controller (FLC) and different subsets for this method are explained. Then, in Section 6, the results of simulations are analyzed. Finally, the last section concludes and discusses the results of simulations for studied fuzzy rules subsets.
2. Photovoltaic System2.1. Equivalent Model of Photovoltaic System
In Figure 1, the tantamount circuit for PV cell is shown that is relative to the incident radiation which is connected in parallel with shunt resistance (RSH) and diode. The internal series resistance (RS) can be considered for modeling the internal losses that are created by the flowing current and also connection between cells [15].
Equivalent circuit of a PV cell.
The PV efficiency is so sensitive to variation of the series resistance such that a small changing in value of RS has a big effect on PV characteristics such as power and voltage. On the other hand, the effect of shunt resistance on PV efficiency can be ignored because the PV efficiency is not responsive to changing of RSH, so the shunt resistance can be assumed to be almost infinite and it becomes open circuit. By these assumptions, the net current of a cell can be defined by (1) and I-V characteristic can be described according to
(1)I=IL-Io(exp(q(V+RS·I)n·K·T)-1).
In (1), I and V are the output solar cell current and voltage, respectively. Io and IL are the cell saturation and photocurrent, respectively. In this equation, there are some constant coefficients: K (=1.38 × 10^{−23} J/K) is a Boltzmann’s constant, q (=1.6 × 10^{−19} C) is an electron charge, n is the ideality factor that its value ranges are between 1 and 2, T is the cell’s working temperature, and RS is the series resistance that is explained before.
In order to draw the I-V curve, (1) needs to be solved. This equation is nonlinear and, with the purpose of solving it, Newton Raphson’s method as a numerical method is used.
In (1), there are some subequations such as saturation current, photocurrent, and open circuit current equations that are dependent on temperature. These subequations can be expressed as follows:
(2)IL=IL(T1)+Ko(T-T1),IL(T1)=ISC(T1)GGref,Ko=ISC(T2)-ISC(T1)(T2-T1),Io=Io(T1)×(TT1)3/n·exp(q×Vg(T1)n·K·((1/T)-(1/T1))),Io(T1)=ISC(T1)(exp(q×VOC(T1)/n·K·T1)-1).
In these equations, G is the irradiation and the band gap energy is shown by Vg that its value is 1.12 eV for silicon. The “ref” subscript recognizes the standard test conditions (STC) expressed in the IEC 61215 international standard [16]. According to this standard, Tref and Gref are equal to 25°C (T1 in equations) and 1000 W/m^{2}, respectively. VOC and ISC in reference temperature are specified in PV panel data sheet.
As mentioned before, series resistance has a big effect on the PV characteristics. Gow and Manning first time defined (3) in order to calculate the value of series resistance (RS) [17]. This equation is obtained by differentiating (1) and evaluating it in open circuit conditions:
(3)RS=-[dVdIVOC+1XV](4)XV=Io(T1)·qn·k·T1·exp(q·VOC(T1)n·K·T1).
2.2. Electric Characteristic of Photovoltaic System
In this paper, simulation results are based on the KC200GT panel. In Table 1, the key specifications of this panel are shown according to datasheet [18].
Specifications of solar KC200GT at 1000 W/m^{2} and 25°C.
Parameters
Values
Power in maximum point, MPP
200 W
Voltage in maximum point, VMPP
26.3 V
Current in maximum point, IMPP
7.61 A
Open circuit voltage, VOC
32.9 V
Short circuit current, ISC
8.21 A
Temperature coefficient of VOC
−0.123 V/K
Temperature coefficient of ISC
0.0032 A/K
Number of cells per module
54
In the previous section, it was mentioned that I-V and P-V characteristics of the PV system depended on irradiance (G) and temperature. In Figures 2 and 3, the I-V and P-V curves of the KC200GT PV module in different irradiations and fixed temperature (25°C) are shown. As shown in Figure 3, the maximum power point is decreased by decreasing the irradiation. The maximum power in 1000 W/m^{2} irradiation is 200 watts.
I-V output characteristic with different irradiance.
P-V output characteristic with different irradiance.
3. DC-DC Boost Converter
The boost converter is a famous switched-mode converter where its produced output voltage is bigger than dc input voltage in extent. The ideal and simple form of this converter is shown in Figure 4 that is including switch and diode for switching the system.
The boost converter.
When the switch is ON (first subinterval) diode, capacitor, and load are connected to ground and the inductor is charged through the input voltage source (Vg). In this subinterval, load is supplied by capacitor and the inductor current is increased. When the switch is off (second subinterval), the load is supplied by inductor current and additionally recharges the capacitor.
In Figures 5 and 6, voltage and current of the boost converter inductor are shown. According to these two figures and also using the principles of voltage and ampere second balance [19], the voltage conversion ratio M(D) and the converter elements values are obtained. The voltage conversion ratio is defined as a proportion of the output voltage to input voltage of boost converter:
(5)M(D)=VVg=1(1-D),
where Vg and V are the input and output voltages of the boost converter and D is duty cycle that is defined as a ratio of the ON duration to the switching time period and it is adjusted by controller that in this case is fuzzy logic controller.
Voltage waveform of the boost converter’s inductor.
Current waveform of the boost converter’s inductor.
It can be noticed that when the boost converter is connected to PV panel, by increasing the duty cycle, the input voltage and current are decreased and increased, respectively, and it leads to shifting the operating point to the left side of the P-V curve of the PV panel. In a similar manner, by decreasing the duty cycle, the input voltage and current are increased and decreased, respectively, and it leads to shifting the operating point to the right side of the P-V curve of the PV panel.
4. Fuzzy Logic Controller (FLC)
Fuzzy logic (FL) is a strategy of processing degrees of truth instead of Boolean logic. The fuzzy logic rules were first proposed by Professor L. Zadeh in 1965 and can be implemented for the complex and unknown systems. The conventional methods are not satisfied for the system especially for nonlinear and complex systems and cannot obtain the desirable results. The FL systems are more flexible rather than classical and conventional methods and they are capable of modelling and approximating the nonlinear systems.
The structure of the fuzzy logic systems is based on the changing the control linguistic to form of the if-then in an automatic control system and a good knowledge and experience can be more useful instead of understanding a technical behavior and model of system [20–22].
As mentioned in previous sections, the conventional methods such as P&O have some drawbacks such as oscillation around the MPP and also take a long time to obtain the steady state, so in this paper the fuzzy logic control method is considered for maximum power point in order to evaluate the best structure of the interface and rules for obtaining the best fuzzy subset rules. The fuzzy logic diagram is shown in Figure 7 that is including two inputs and one output. The inputs of the FLC system are the error (E) and change of error (CE) that are defined by (6). The output of FL is duty cycle (D) that should operate to the boost converter [23, 24]. Consider
(6)E(K)=PPV(K)-PPV(K-1)VPV(K)-VPV(K-1),CE(K)=E(K)-E(K-1).
Fuzzy logic diagram.
VPV(k) and PPV(k) are the PV voltage and power, respectively, at instant k. If E will be positive it means that the operating point is in left side of the MPP and when it will be negative, the operating point is in right side of the MPP. The MPP will be obtained when E is equal to zero. The moving direction in I-V and P-V curves is specified by CE [25–28].
The fuzzy logic fundamentally consists of three steps: fuzzification, rule base and inference engine, and defuzzification.
4.1. Fuzzification
In process of fuzzification, all variables used to describe the control rules should be converted to linguistic fuzzy labels. These variables are demonstrated in different fuzzy levels: PB (positive big), PM (positive medium), PS (positive small), ZE (zero), NB (negative big), NM (negative medium), and NS (negative small). In this study, different fuzzification subsets with different levels and membership function ranges are considered.
4.2. Rule Base and Inference Engine
Rules base is if-then functions that are used for the fuzzified inputs in order to apply for the controlled parameters. Defining these rules is dependent on operation of the system and experience. In this study, different subsets include forty-nine, thirty-five, and twenty-five fuzzy control rules with different specific range of membership functions being considered. Fuzzy inference engine is the process of devising the logical decision based on the rules. The fuzzy rules should be transferred into fuzzy linguistic output. In this work, Mamadani’s fuzzy inference method has been used.
4.3. Defuzzification
In this stage, the output fuzzy data that is defined by the rules and inference engine should be converted to the numerical value by creating the union of the output from each rule. In this study, the center of gravity defuzzifier is used which is the common one.
5. Fuzzy Logic Subsets
As mentioned in previous section, implementing the fuzzy logic controller needs to have enough experience more than knowing the technical model of the system because in process of designing the fuzzy logic controller, rules and inference and also the range of membership functions are the important and crucial sections. Each fuzzy system has a subset rule that will be defined according to the specific range of membership function and vice versa [29–32]. In systems with the boost converter, different fuzzy logic subsets with different rules and range of membership functions have been used in order to obtain MPP. In each subset, range of membership function is defined according to the specific rule table and vice versa. In this paper, the main goal is to achieve the best fuzzy subset for obtaining MPP by comparative study of different common fuzzy subsets with considering main factors such as MPP’s reaching time, oscillation, steady state time, ripple, efficiency, and some other factors. In this work, different subsets (S1, S2, S3, S4, and S5) which include different rules and range of membership functions will be introduced and the best one will be selected.
For S1, S2, and S3, five subsets based on twenty-five rules have been used where their rules and membership functions are different. For S4, seven subsets based on forty-nine rules and, for S5, seven and five subsets based on thirty-five rules have been used. The triangular and trapezoid shaped membership functions have been used for all models. For the range of membership function, the oscillation of each signal has been checked and the best one is considered [33–35].
Figure 8 shows the fuzzy logic controller at which E and CE are the inputs and D is output of the controller.
Fuzzy logic controller (FLC).
5.1. First Subset (S1)
It is as shown in Table 2 and Figure 9.
The twenty-five fuzzy rules of the first fuzzy subset (S1).
E
CE
NB
NS
ZE
PS
PB
NB
ZE
ZE
PB
PB
PB
NS
ZE
ZE
PS
PS
PS
ZE
PS
ZE
ZE
ZE
NS
PS
NS
NS
ZE
ZE
ZE
PB
NB
NB
ZE
ZE
ZE
The membership functions of E, CE, and D for S1.
5.2. Second Subset (S2)
It is as shown in Table 3 and Figure 10.
The twenty-five fuzzy rules of the second fuzzy subset (S2).
E
CE
NB
NS
ZE
PS
PB
NB
ZE
ZE
PB
PB
PB
NS
ZE
ZE
PS
PS
PS
ZE
PS
ZE
ZE
ZE
NS
PS
NS
NS
ZE
ZE
ZE
PB
NB
NB
ZE
ZE
ZE
The membership functions of E, CE, and D for S2.
5.3. Third Subset (S3)
It is as shown in Table 4 and Figure 11.
The twenty-five fuzzy rules of the third fuzzy subset (S3).
E
CE
NB
NS
ZE
PS
PB
NB
PB
PB
PB
PS
ZE
NS
PB
PB
PS
ZE
NS
ZE
PB
PS
ZE
NS
NB
PS
PS
ZE
NS
NB
NB
PB
ZE
NS
NB
NB
NB
The membership functions of E, CE, and D for S3.
5.4. Fourth Subset (S4)
It is as shown in Table 5 and Figure 12.
The forty-nine fuzzy rules of the fourth fuzzy subset (S4).
E
CE
NB
NM
NS
ZE
PS
PM
PB
NB
ZE
ZE
ZE
NB
NB
NB
NB
NM
ZE
ZE
ZE
NM
NM
NM
NM
NS
NS
ZE
ZE
NS
NS
NS
NS
ZE
NM
NS
ZE
ZE
ZE
PS
PM
PS
PM
PS
PS
PS
ZE
ZE
ZE
PM
PM
PM
PM
ZE
ZE
ZE
ZE
PB
PB
PB
PB
ZE
ZE
ZE
ZE
The membership functions of E, CE, and D for S4.
5.5. Fifth Subset (S5)
It is as shown in Table 6 and Figure 13.
The thirty-five fuzzy rules of the fifth fuzzy subset (S5).
E
CE
NB
NS
ZE
PS
PB
NB
PB
PB
PS
PB
PB
NM
PB
PS
ZE
PS
PB
NS
PB
PS
PS
PS
PB
ZE
NS
NS
ZE
PS
PS
PS
NB
NS
NS
NS
NB
PM
NB
NS
ZE
NS
NB
PB
NB
NB
NS
NB
NB
The membership functions of E, CE, and D for S5.
6. Simulation Result
In this work, the simulations are carried out by MATLAB/Simulink to validate the performance of the system. The source of the system is PV that is modeled by script in MATLAB. The five studied subsets are implemented in control unit for the same system and performance of the system is considered for evaluation of the different fuzzy logic subsets. In Figure 14, the configuration of the system includes PV, boost converter, and MPPT controller and input filter is shown. The input capacitance of the converter, Cin, is 2000 μF, the inductance, L, is 25 μH, the resistance of inductor, RL, is 0.02 Ω, the output capacitance, Cout, is 450 μF, and the load resistance, R, is 20 Ω. The switching frequency of the system is 25 kHz.
Configuration of the system.
For investigation and analyzing behavior of the system, there are some important factors which will be considered such as maximum power point reaching time, efficiency, and ripple in input and output voltage and power. The simulation values for these factors at irradiation of 1000 W/m^{2} and 25°C are summarized in Tables 7 and 8. These factors are defined as follows:
T1 is the maximum point reaching time (Sec);
TS is the maximum point reaching steady state time (Sec);
Pave is the average power (W);
Pripp is the ripple in power in time T1 (W);
Pripp_S is the ripple in power in time Ts (W);
Vave is the average voltage (V);
Vripp is the ripple in voltage in time T1 (V);
Vripp_S is the ripple in voltage in time Ts (V);
Iave is the average current (A);
Iripp is the ripple in current at steady state time (A);
ηT is the efficiency (%).
The simulation values for PV variables.
PavePV
T1PPV
TSPPV
PrippPV
Pripp_SPV
VavePV
T1VPV
TSVPV
VrippPV
Vripp_SPV
S1
200
0.0085
0.021
0.3
0
26.14
0.0085
0.021
0.52
0.12
S2
200
0.0085
0.01
1.2
0
26.09
0.0085
0.015
0.96
0.04
S3
199.3
0.0085
0.015
4.2
1.4
25.82
0.0085
0.015
1.76
1.03
S4
200
0.022
0.022
0
0
26.08
0.022
0.022
0.07
0.05
S5
199
0.0085
0.015
5.4
1.9
25.64
0.0085
0.015
1.76
0.96
The simulation values for boost converter variables.
PaveBoost
TSPBoost
PrippBoost
VaveBoost
TSVBoost
VrippBoost
IaveBoost
TSIBoost
IrippBoost
ηT
S1
190.8
0.04
1.6
61.77
0.04
0.26
3.08
0.031
0.013
95.4%
S2
191.35
0.04
0.7
61.86
0.04
0.1
3.09
0.032
0.005
95.7%
S3
177.3
0.037
10.8
59.43
0.037
1.85
2.97
0.035
0.09
89%
S4
190.2
0.05
1.1
61.6
0.05
0.19
3.08
0.036
0.009
95.1%
S5
176.1
0.035
10.8
59.23
0.035
1.75
2.96
0.035
0.093
88.52%
In Figure 15, the PV available maximum power at irradiation of 1000 W/m^{2} and 25°C for different fuzzy subsets is shown. In all subsets, the maximum power point is obtained after a small stilling time for S1, S2, and S4 with the average value of 200 watts that is exact value according to data sheet, but, for S3 and S5, the values are 199.3 and 199 watts, respectively. As shown in Figure 15 and Table 7, the MPP is obtained at 0.0085 (T1PPV) second for S1, S2, S3, and S5, but, for S4, it is 0.022 second. Other than that, S2 has the lower steady state time for obtaining MPP. The important point is that, in T1P, there are oscillations (PrippPV) in power for S1, S2, S3, and S5 with values of 0.3, 1.2, 4.2, and 5.4 watts, respectively, that can lead to some losses, but, for S4, the power oscillation is zero from the first time that MPP is obtained. The power ripple at steady state time of MPP is zero for S1, S2, and S4, but, for S3 and S5, the values are 1.4 and 1.9 watts, respectively.
PV power at irradiation of 1000 W/m^{2} and 25°C.
The PV available maximum voltage at 1000 W/m^{2} and 25°C for different subsets is shown in Figure 16. According to this figure and Table 7, the time for reaching the maximum point of voltage for S4 is more than other subsets but this subset has the minimum ripple voltage value in T1V and approximately in TSV.
PV voltage at irradiation of 1000 W/m^{2} and 25°C.
In Figures 17 and 18, the output power and voltage of the system are shown. According to these figures and Table 8, S1, S2, and S4 have much more average power in comparison with S3 and S5 as the main reason is bigger oscillations in power and voltage. S2 has the minimum ripple in power and voltage that are 0.7 watt and 0.1 volt, respectively.
Boost output power at irradiation of 1000 W/m^{2} and 25°C.
Boost output voltage at irradiation of 1000 W/m^{2} and 25°C.
The output currents for all subsets are shown in Figure 19. According to this figure and Table 8, S2 has the maximum average value and also the minimum ripple in current. S4 is the next subset that has maximum average current value and the minimum ripple in current.
Boost output current at irradiation of 1000 W/m^{2} and 25°C.
Another important factor for selecting the best fuzzy subset is the efficiency of the system that is defined by ratio of the output power to input power of the system. Among the discussed subsets, S2 has the maximum efficiency with 95.7% and S5 has the minimum one with 88.52%.
In Figures 20 and 21, the bar graphs of this comparative study are plotted that is beneficial for better general survey. According to these graphs and Tables 7 and 8, S2 and S4 have the better performance among all subsets. In comparison between S2 and S4, the maximum power point reaching time for S2 is less than S4 that is so important for controller to track the MPP. The ripple values of power and voltage are important when the MPP is obtained so that the minimum ripple value makes a significant reduction in switching and power losses. According to Table 7, S4 and S2 have the minimum ripple values in MPP’s reaching time, respectively. So, considering all above analyzed factors, it can be concluded that S2 is the best fuzzy subset which can track MPP in minimum time and low oscillation with high efficiency.
The bar graph for PV variables.
The bar graph for boost converter variables.
7. Conclusion
This paper presents a detailed comparative survey of five general fuzzy logic subsets that are used for fuzzy logic (FL) based on maximum power point tracking technique. In this work, simulation has been done in MATLAB. The main objective of this work was to obtain the most efficient fuzzy subset among the general FL subsets used for FLC in boost converter applications. The obtained results show that the rules and range of membership functions are so significant for implementing the fuzzy logic controller in the main system. By considering the main factors for different subsets in same conditions, it is observed that second and fourth subsets have better performance in comparison with other subsets. The second subset (S2) has minimum MPP’s reaching time and zero ripple value for power at steady state time with maximum efficiency. The fourth subset (S4) has the minimum ripple in voltage and current at reaching time of MPP but it needs much more time for reaching the MPP. Therefore, considering all above analyzed factors, it can be concluded that S2 is the best fuzzy subset which can track MPP in minimum time and low oscillation with high efficiency.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
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