Photovoltaic (PV) module behavior is not linear in nature with respect to environmental conditions and hence exhibitsnonlinear PV curves. There is only a single point in the nonlinear PV curve at which the power is maximum. Therefore,special methods have been proposed to track this maximum power point (MPP). This paper proposed an intelligent methodfor MPP tracking (MPPT) based on adaptive neuro-fuzzy inference system (ANFIS) controller. The proposed systemconsists of a PV module connected to a DC-DC isolated Ćuk converter and load. A MATLAB/SIMULINK-based MPPTmodel is built to test the behavior of the proposed method. The proposed method is tested under different weatherscenarios. Simulation results exhibit the successful tracking of the proposed method under all ambient conditions.Comparison of the tracking behavior of the proposed method with the perturb and observe method is also presented in thesimulation results. In addition, a 220 W prototype with the help of dSPACE 1104 data acquisition system is built andtested under practical weather conditions on a sunny day as well as on a cloudy day. Experimental results are presented toverify the effectiveness of the proposed method. These results exhibit satisfactory performance under different practicalweather conditions.
1. Introduction
Recently, the energy demand in the world is noticeably grow-ing due to the fast growth in the population and economy.Natural gas, coal, and crude oil are the main current fossilfuels, which are used to supply world energy. In the lateryears, irritation about energy crisis has been increased. Fossilfuels have been started to be gradually depleted. On the otherhand, concern about the fossil fuel exhaustion and otherenvironmental problems such as global warming caused byconventional power generation have been increased. It is aglobal challenge to generate a secure, available, and reliableenergy and at the same time reduce the greenhouse gas emis-sion [1]. One of the most effective and most suitable solutionto meet the worldwide energy requirements is the renewableenergy resources. Renewable energy can solve these problemssimultaneously since they are green, clean, environmentfriendly, and are sustainable.
There are many sources of renewable energy such as solarenergy and wind energy. Photovoltaic (PV) system has takena great attention and appears to be the most promisingrenewable energy source since it is a clean, maintenance-free,pollution-free, and not a noisy source [1, 2]. However, twoimportant factors limit the implementation of photovoltaicsystems: high installation cost and low efficiency of energyconversion [1]. The behavior of the PV module is nonlinearin nature and hence exhibits nonlinear PV curves. Thereexists only a unique point of maximum power in each PVcurve, which needs special techniques called maximumpower point tracking (MPPT) techniques to track it. There-fore, MPPT can be used to increase the system efficiency byfully utilizing the PV modules. Many methods have beenreported in the literature for tracking the maximum powerpoint [2]. Open circuit voltage method search for the MPPbased on the relationship between the open circuit voltageVoc and the voltage at maximum power VMPP of the PV
HindawiInternational Journal of PhotoenergyVolume 2017, Article ID 3106734, 15 pageshttps://doi.org/10.1155/2017/3106734
module under varying solar radiation and temperature levels.This relationship is almost linear where the voltage at maxi-mum power VMPP equals the open circuit voltage Voc multi-plied by a constant [3–5]. The constant depends on thecharacteristic of the PV module. The value of this factor isreported to be between 0.71 and 0.78 [4]. The common valueof this constant is about 0.76 (within ±2%) [5]. In order toimplement the open circuit voltage method, the PV modulesmust be interrupted with a certain frequency to measure theoutput voltage of the PV modules. Although this method issimple, choosing the value of the constant is difficult. Onthe other hand, the power losses are high due to frequentlyinterrupting the system [5].
Short circuit current method results from the fact that therelationship between the current at maximum power pointIMPP and the short circuit current of the PV module Isc isalmost linear where the current at maximum power IMPPapproximately equals the short circuit current Isc multipliedby a constant [6]. The constant depends on the characteristicof the PV module. The main drawback of the open circuitvoltage method and the short circuit current method is thepower losses due to measuring Voc and Isc, and the maxi-mum power point is never perfectly matched [6].
Perturbation and observation (P&O) method is an alter-native method to obtain the maximum power point of thePVmodule. P&Omethod is thewidely used technique to trackMPP. It perturbs the operating point and observes the differ-ence in power. It measures the voltage and current and calcu-lates the power of the PVmodule. Then it perturbs the voltageto encounter the change direction. If power difference is posi-tive, the direction of perturbation remains the same; other-wise, it is reversed. However, this method suffers from slowtracking speed and high oscillations around MPP [2–5].Figure 1 shows the flowchart of the P&OMPPT algorithm.
Introducing a high efficient MPPT controller can help indecreasing the total cost of the PV systems since 7% of theinitial PV system cost is spent on the MPPT controller andinverter [7]. Therefore, researchers are focusing on propos-ing high efficient MPPT techniques. The use of intelligenttechniques has been increased over the last decade since theyare simple, deal with imprecise inputs, does not need anaccurate mathematical model, and can handle nonlinearity[8]. Artificial intelligence-based techniques such as the fuzzylogic controller (FLC), artificial neural networks (ANNs),and adaptive neuro-fuzzy inference systems (ANFIS) can beused as a controller to extract the maximum power that thePV modules capable of producing under changing weatherconditions. This is because they have the advantages suchas they are robust, relatively simple to design, and they donot require the knowledge of an exact model [8, 9].
The fuzzy logic controller was applied in designing differ-ent MPPT controllers [7, 8, 10–16]. They apply a set of lin-guistic rules to obtain the required duty cycle. The inputvariables of the fuzzy logic controller differ from one config-uration to another. In [11–14], the input variables to the FLCare the error (E) and the change in error (ΔE). The error canbe calculated as the change in the power to the change in thevoltage of the PV module ΔP/ΔV The output variablefrom the FLC is the duty cycle. In other configurations, the
change in the PV current instead of using the change in thePV voltage is used to calculate the error as in [15].
Noman et al. [17] proposed an algorithm-based FLC toachieve tracking the maximum power of the PV moduleunder changing the weather conditions. The inputs of theFLC are the change in the voltage of the PV module (ΔV)and the change in the power of the PVmodule (ΔP). The out-put from FLC is ΔU which corresponds to the modulationsignal, which is applied to the PWM modulator in order toproduce the switching pulses to drive the MOSFET of theDC-DC buck-boost converter.
Many configurations have proposed to use ANNs forMPPT purpose. As mentioned above, the PV module isnonlinear in nature. Therefore, ANNs can solve this nonlin-ear problem without the need for a mathematical model.Ocran et al. [18] proposed an MPPT-based ANNs for solarelectric vehicles. The input variables are the open circuit volt-age (Voc) and PV cell temperature (TPV) while the output var-iable is thevoltageatmaximumpowerpoint (VMPP).However,measuring the open circuit voltage needs interrupting thecircuit, and consequently, a power loss occurs. Ramaprabhaand Mathur [19] proposed a genetic algorithm-optimizedANNs for MPPT. The ANN was trained offline using GA-optimized data to obtain VMPP under changing weatherconditions. The main drawback of this configuration is theneed for solar radiation sensor, which increases the MPPTsystem cost.
Recently, much research has been devoted to use adaptiveneuro-fuzzy inference systems (ANFIS) for tracking the max-imum power of PV modules. ANFIS is actually fuzzy infer-ence system optimized by neural networks. In addition,ANFIS can generate the fuzzy rules automatically. VariousANFIS-based MPPT methods have been proposed to achieveMPPT [20–24]. The input variables and the output variablesare different from one configuration to another. The inputvariables in [20] are the change in the PV voltage (ΔVPV)and the change in the PV power (ΔPPV), while the outputvariable is the duty cycle. On the other hand, the input vari-ables to the ANFIS in [22] are the open circuit voltage (Voc)and the short circuit current (Isc), while the output variable isthe voltage at maximum power (VMPP). The disadvantage ofthis method is that the need for interrupting the PV system tomeasure the open circuit voltage and need to short circuit thePV module terminals to measure the short circuit current,which finally increases the system losses. Some other papersuse the solar radiation and temperature as input variables,and the output variable is the voltage at maximum power(VMPP) or the maximum power itself (PMPP) [21, 23, 24].However, measuring irradiance level by solar radiation sen-sor is not the exact solar radiation incident on the PVmodulesince the aging of the PV modules as well as the partial shad-ing is not taken into considerations. In addition, the solarradiation sensor is expensive which increases the MPPTtotal cost.
The ANFIS-based MPPT techniques are very accuratebecause they track the maximum power point without theneed to interrupt the circuit or short circuit the PV moduleterminals or oscillating around MPP as conventional MPPTalgorithms. However, the proposed techniques in the
2 International Journal of Photoenergy
literature have some limitations since some of them needsolar radiation sensor, which is expensive and does not esti-mate the exact incident solar radiation as mentioned above.In addition, some of the proposed ANFIS-based MPPT tech-niques measure Voc and/or Isc which increase the systemlosses. This paper presents a new MPPT method based onANFIS to achieve maximum power point tracking. The pro-posed method depends on measuring the PV voltage (VPV),the PV current (IPV), and the PV cell temperature (TPV) asinput variables to the controller. It is known that there aresome areas in the characteristic chart of the PV module atwhich the PV curves are overlapped to each other, especiallyunder changing the temperature. These areas are located tothe left of MPP and in the intersection points of PV curves(under constant solar radiation and changing temperature).If the MPPT controller starts tracking in these areas, thecontroller may be confused to track the current PV curve,especially if the input variables to the controller are notenough to make the right decision. Therefore, putting thePV cell temperature as another input variable to the ANFIS,the controller will not be confused to determine which PVcurve where the measured temperature is belonging to,and hence the tracking will be more accurate and faster. Inaddition, the isolated Ćuk converter is used in this paperwhich is the first paper to use this type of converters. Theisolated Ćuk converter has many advantages compared tothe other isolated DC-DC converters as will be seen in latersections. The isolated Ćuk converter model and the perfor-mance of the ANFIS method are evaluated by MATLAB/SIMULINK environment. In addition, an experimental setup
was established to verify the proposed MPPT method practi-cally. DSPACE 1104 real-time data acquisition system is usedin the implementation of the MPPT hardware setup.
2. System Modeling
2.1. Modeling of Photovoltaic Module. PV module essentiallyconverts the incident light into electrical current when a loadis connected to its terminals. Modeling the PV modulerequires first to model the photovoltaic cell. The electricalequivalent circuit of the PV cell is shown in Figure 2. Asshown in this figure, the current source represents theamount of electron flow due to solar radiation incident onthe PV cell. The diode represents the PN junction of the PVcell. There are two resistances: series resistance and parallelresistance. Series resistance accounts for the losses in the cur-rent path due to the metal grid, contacts, and current collect-ing bus. On the other hand, the parallel resistance accountsfor the loss associated with a small leakage of current througha resistive path in parallel with the intrinsic device.
From Figure 2, the output current delivered to the loadcan be expressed as [25, 26]
I = IPV − IO eq V+IRS /nNsKTa − 1
ID
−VDRP
IRP
, 1
where I is the output current of the PVmodule (A); IPV is thecurrent source of the PVmodule by solar irradiance (A); ID is
Start
Measure V(n), I(n)
Calculate power P(n)
P(n) − P(n − 1) = 0
P(n) − P(n − 1) > 0
V(n) − V(n − 1) < 0 V(n) − V(n − 1) > 0
D = D − ΔD D = D + ΔD
Return
Yes
No Yes
YesNo YesNo
No
D = D − ΔD D = D + ΔD
Figure 1: The flowchart of the P&O algorithm.
3International Journal of Photoenergy
the diode current (A); IRPis the current flow to the parallel
resistance RP (A); Io is the reverse current of a diode (A);NS is the number of series cells in the PV module; n isthe ideality factor of the diode (n = 1~2); q is the electriccharge of an electron (1 6 × e−19c); k is the Boltzmann’sconstant (1 38 × 10−23 J/K); and T is the absolute temperatureof the solar cell (K).
The current generated by the incident solar radiation,also called short circuit current (Isc), at a given temperature(Ta) can be given as [25, 26]
IPV = Iscn 1 + a Ta − TnGGn
, 2
where Iscn is the short circuit current at normal conditions(Tn = 298K), Gn = 1000W/m2, Ta is the given temperature(K), a is the temperature coefficient of Isc, and G is the givensolar radiation (W/m2).
The reverse saturation current of diode (Io) at the normalconditions is given as [25, 26]
Ion =Iscn
qVocn/enNskTn − 1, 3
where Vocn is the open circuit voltage of the PV module atnormal conditions. The reverse saturation current at a givencell temperature (Ta) can be expressed as [26]
Io = IonTa
Tn
3/ne−qEg/nK 1/Ta−1/Tn 4
The PV module model number HIT-N220A01 is used inthis paper. The PV module parameters under the standardconditions (1000W/m2, 298K) are listed in Table 1. ThePV module is simulated using MATLAB software. Figure 3shows the simulated PV curves of the PV module underchanging solar radiation from 200W/m2 to 1000W/m2 with200W/m2 steps while keeping the temperature constant at
298K. On the other hand, Figure 4 shows the simulationresults of the PV curves of the PV module under changingtemperature while keeping the solar radiation constant at1000W/m2.
2.2. DC-DC Isolated Ćuk Converter. DC conversion hasgained the great importance in many applications, startingfrom low-power applications to high-power applications.Many DC-DC converter topologies have been developed.The well-known Ćuk converter is modified by inserting acurrent rectifier CR1 in series with the output resonantinductor Lr. In addition, a transformer operating at high fre-quency is inserted inside the DC-DC converter for isolationpurposes and for voltage amplification, if needed. The iso-lated Ćuk converter is shown in Figure 5. The isolated Ćukconverter has fantastic features such as low and limited volt-age stress on all switches over the entire duty ratio operatingrange from D=0 to D=1. Another feature is that isolatedĆuk converter eliminates both start-up and inrush currentproblems that isolated boost converter suffer from. In addi-tion, the isolated Ćuk converter efficiency is high comparedto the other isolated converters such as flyback converter,which suffers from low efficiency due to the voltage stresseson the output switch of the flyback converter, which couldbe many times higher than the regulated output voltageand hence finally leads to reduce the efficiency and
IPV
I
Rp
RsID
Load
Co
+
V
−
+
VD
−
IRP
Figure 2: Equivalent circuit of PV cell simulation.
Table 1: HIT-N220A01 PV module parameters.
Maximum power (Pmax) 220W
Voltage at Pmax (Vmp) 42.7V
Current at Pmax (Imp) 5.17A
Open circuit voltage (Voc) 52.3 V
Short circuit current (Isc) 5.65A
Temperature coefficient of Isc 1.98mA/°C
4 International Journal of Photoenergy
increase its cost. The isolated Ćuk converter has manyattractive features, for more information refer to [27, 28].
The basic principle of the isolated Ćuk converter is thatthe MOSFET and the diode CR1 are working together dur-ing on state interval of the MOSFET. Only diode CR2 isworking during MOSFET off state interval. The inductorvoltage waveform, inductor current waveform, and thecapacitor current waveform are shown in Figure 6. As shownin this figure, inductor voltage balance can be obtainedas [27, 28]
VgD + Vg −Vcr −Vo D′ = 0 5
During the full cycle in the resonance state, Vcr = 0;therefore,
VgD + Vg −Vo D′ = 0 6
From (6), the output-to-input voltage ratio can be given as
M =VoVg
=1
1 −D, 7
where D is the duty cycle.The above equation states that the output voltage will be
boosted by changing the duty ratio. The polarity of the out-put voltage is the same as that of the input voltage.
In order to design the main inductor value, from theinductor voltage waveform in Figure 6, we get
ΔiL =VgDTs
L, 8
where Ts is the switching interval.In addition, in order to design the output capacitor value,
Figure 3: P-V curves under changing the solar radiation.
0 10 20 30 40 50 600
50
100
150
200
250
V (V)
P (W
)
273 K283 K293 K
303 K313 K323 K
Figure 4: P-V curves under changing the temperature.
+ Vc1 −CR2
C0Vg S
L
IL
+V0−R
Lr
− Vc2 +
CR1
+
+
Vcr −
−
Cr1 Cr2
Figure 5: Isolated Ćuk Converter.
5International Journal of Photoenergy
According to the above equations, the design parametersof the isolated Ćuk converter are shown in Table 2.
3. The Proposed ANFIS-Based MPPT Method
In this paper, a new ANFIS-based MPPTmethod is proposedto achieve tracking the maximum power of the PV moduleunder changing the weather conditions. The proposed inputvariables are the PV voltage (VPV), PV current (IPV), and thePV cell temperature (TPV). The output variable is the dutycycle, which is used to control the DC-DC isolated Ćuk con-verter in order to keep tracking maximum power. Since themodeling of the conventional FLC is based on trial and error,the probability of obtaining the optimal performance is low.Therefore, obtaining membership functions and fuzzy rulescan be done through learning using ANFIS. The trained datashould be collected first. The following steps were executed inthis paper to obtain the trained data:
(i) The system was simulated under different solarradiation and temperature conditions by using con-ventional MPPT algorithms.
(ii) The data were collected and manipulated using aMATLAB code, which is built for this purpose toget the desired data.
(iii) The manipulated data were then shuffled. Theresults data are then filtered again to obtain onlythe unique rows in collecting data. Finally, 60% ofthe resulting data are used for training and theremaining 40% are divided equally between testingand checking data.
The input trained data to the ANFIS are VPV, IPV, andTPV. Figure 7 is the overall proposed ANFIS model structure,which is a five-layer network. Figure 8 is the membershipfunction of the PV voltage (VPV) generated by ANFIS.Figure 9 is the generated membership function of the PV cur-rent (IPV) while Figure 10 is the generated membership func-tions of the PV cell temperature (TPV). Each input has threegeneralized bell-shaped-type membership function namedlow, medium, and high. The output is the duty cycle, whichis compared with the sawtooth signal in order to generatethe suitable gate pulses for the Ćuk converter. The PV voltagevaries from 36.49V to 45.14V, which is the area of the volt-age at maximum power under different ambient conditions.On the other hand, the PV current varies from 2A to5.599A, which is also the area of the current at maximumpower under different ambient conditions. In addition, thePV cell temperature varies between 298K and 323K. Themembership functions are generated by the ANFIS controllerbased on the prior knowledge obtained from the trainingdataset as described earlier. The membership function’sshape varies during the training stage, and the final shapeobtained after the completion of the training is shown inFigures 8, 9, and 10.
The rule base depicts the relationship and mappingbetween the input and output membership functions. Oneparticular situation is shown in Figure 11 when VPV is39.5V, IPV is 3.51A, and TPV is 303K. All the rules canbe accessed by moving the red slider shown in Figure 11.Moving the slider results the output duty cycle of theANFIS appearing in the last column of Figure 11, which isused to drive the MOSFET of the isolated Ćuk converter.As an example, rule 4 can be read as if VPV is low, IPV ismedium and TPV is low and then the duty cycle to achieveMPP is 0.33.
4. Experimental Setup
An experimental implementation setup was established toverify the performance of the proposed method practically.Figure 12 shows the schematic diagram of the hardware setupwith the dSPACE 1104 board. Figure 13 shows the hardwaresetup of the MPPT system. In the hardware setup, one PVmodule model number HIT-N220A01 is connected to theDC-DC isolated Ćuk converter and then to the load. The iso-lated Ćuk parameters and the other parameters used in thehardware setup are listed in Table 2. The MOSFET type isIRFB4229 while the diode type is BYV32-200. Data acquisi-tion and the control system are implemented by usingdSPACE 1104 software and digital signal processor card onPC, respectively. In order to start the implementation, thePV voltage, the PV current, and the PV cell temperature mustbe initially measured. In this system, the PV voltage is mea-sured by using the voltage divider while the PV current ismeasured by using hall effect current sensor model numberLTS 25-NP. The measured signals are used to feed the ADCchannels of the dSPACE board. On the other hand, the PVcell temperature is measured by using 10KΩ negative tem-perature coefficient thermistor because it is inexpensive andsimple. The thermistor is connected in series with a 10KΩ
−
−
VL
IC0
Vg
Vg −Vcr −VC0
t
t
IL
IL
V0
V0
R
R
Figure 6: Waveforms of inductor voltage and capacitor current.
Table 2: Isolated Ćuk converter parameters.
Main inductor L 0.5mH
Resonance inductor Lr 1.5 μH
Resonance capacitor Cr1 =Cr2 20μF
Capacitance CPV 300μF
Output capacitance Co 33μF
Switching frequency 30KHz
Resistive load 35Ω
6 International Journal of Photoenergy
resistor to form a voltage divider fed by a 5V voltage regula-tor. The voltage across the thermistor is used to feed the ADCchannel of the dSPACE board. Inside SIMULINK, the lowpass filters are used to remove undesired switching noises.The output signal of the MPPT algorithm is then applied tothe PWM block, which is used to generate the requiredswitching signal to drive the MOSFET. The PWM generatedsignal from the dSPACE is connected to the MOSFET of theĆuk converter via optocoupler model number PS9505 asshown in Figure 12.
5. Simulation and Experimental Results
5.1. Simulation Results. The simulation is achieved usingMATLAB/SIMULINK environment. The model used forsimulation is shown in Figure 14. As shown in this figure,the input variables to the FLC are VPV, IPV, and TPV. Theoutput variable from the FLC is the duty cycle which is thencompared with the sawtooth carrier signal. The result pulsesare used to drive the MOSFET.
To verify the performance of the proposedMPPTmethod,three scenarios are applied to the proposed ANFIS-basedMPPTmethod: constant weather condition scenario, dynamicweather condition scenario, and load variation scenario. Forcomparison purposes, the ANFIS-based MPPT method iscompared with the conventional P&O MPPT method.
The PVmodule parameters used for simulation are shownin Table 1 while the other system parameters are shown inTable 2. The proposed ANFIS-based MPPT method is testedunder the following scenarios.
5.1.1. Constant Weather Condition Scenario. A steadyweather condition is realized by putting the solar radiationat 1000W/m2 and putting the PV temperature at 298K.
VPV
IPV
TPV
Input Input mf Rule Output mf Output
D
Figure 7: The proposed ANFIS model structure.
1
0.5
Low Medium
Input variable VPV
High
037 38 39 40 41 42 43 44 45
Figure 8: The membership function of the input variable (VPV).
1
0.5
02 2.5 3 3.5 4 4.5 5 5.5
Low Medium High
Input variable IPV
Figure 9: The membership function of the input variable (IPV).
1
0.5
0300 305 310 315 320
Low Medium High
Input variable TPV
Figure 10: The membership function of the input variable (TPV).
7International Journal of Photoenergy
The ANFIS-based MPPT method succeeds in tracking themaximum power under the steady weather conditions asshown in Figure 15. In order to verify the effectiveness and
the accuracy of the ANFIS-based MPPT method, its perfor-mance is compared with the conventional perturb andobserve MPPT methods with two step sizes, 0.01 and 0.015.
VPV = 39.5 IPV = 3.51 TPV = 303 D = 0.33123456789
101112131415161718192021222324252627
Figure 11: Rule base of ANFIS controller.
V0
Temperaturein K
Solar radiation(W/m2)
+
+
39 K
120 K
Current sensor
PV moduleHIT-N220A01
dSPACE 1104 board
Vcc
OptocouplerPS9505
ComputerANFIS-based
MPPT algorithm
+ Vc1 −CR2
C0+
VPV
−
S
L
RL35 ohms
Lr
− Vc2 +
CR1
IPV
TPV
CPV
Cr1Cr2
1 UF550 ohms
Figure 12: Schematic diagram of the hardware setup.
8 International Journal of Photoenergy
F
A
B
C
D
E
PV moduleHIT-N220A01
Figure 13: The hardware setup of the system. (a) DC-DC isolated Ćuk converter. (b) MOSFET model number IRFB4229 and its heat sink.(c) Resistive load bank. (d) Sensor board. (e) dSPACE 1104 board. (f) Input circuit breaker connected to the PV module.
+ Vc1 −CR2
C0CPV
S
L
+V0
−
RLr
− Vc2 +
CR1
v+−
VPV
i+−
IPV
[VPV]
[IPV]
Temperature
Irradiation
[T]
[G]
Fuzzy logiccontroller
with ruleviewer
>=D
VPV
IPV
TPV
Cr1 Cr2
Figure 14: ANFIS-based MPPT system used for simulation.
9International Journal of Photoenergy
As shown in Figure 15, the proposed method tracked themaximum power faster than the P&O method. In addition,the oscillations of the proposed method around maximumpower point are lower compared to the P&O method at bothstep sizes. The percentage tracking efficiency for the MPPTcan be given as
η =t2t1PMPP dt
t2t1PidealMPP dt
10
According to (10), by putting t1 = 0 and t2 = end simulation time, the tracking efficiency of the proposed ANFIS-based MPPT method is calculated as 98.87%. On the otherhand, the tracking efficiency of the P&O MPPT method at0.01 step size is 97.34% while the tracking efficiency at
0.015 step size is 92.71%. In both cases, the tracking efficiencyof the proposed method is higher.
5.1.2. Dynamic Weather Condition Scenario. In order to ver-ify the accuracy of the proposed method under dynamicweather conditions, it is tested under the weather conditionsshown in Figure 16. As shown in this figure, the solar radia-tion is kept constant at 400W/m2 until 0.02 sec. The solarradiation is then assumed to be changed as a ramp functionwith positive slope from 0.02 sec to 0.05 sec to account forchanging the solar radiation in the sunrise periods. The irra-diance is then changed as a unit step function to account forchanging the solar radiation rapidly. On the other hand, thetemperature is kept constant at 293K until 0.03 sec. Then itis increased as a slope function. It is then stepped up to334°F and then stepped down to 284K.
The proposed method tracked the maximum powereffectively and accurately under this dynamic weather condi-tions as shown in Figure 17. The proposed method is faster intracking the MPP compared to the P&O MPPT method atboth step sizes, 0.01 and 0.015. In addition, the oscillationsaround MPP are lower than the oscillations caused by theP&O MPPT method at both step sizes. According to (10),the tracking efficiency of the proposed method under thisdynamic weather conditions is about 98.34%, which is higherthan the tracking efficiency of the P&O MPPT at both stepsizes. The tracking efficiency of the P&O MPPT at 0.01 stepsize is 96.93% while the tracking efficiency at 0.015 step sizeis 93.89%.
5.1.3. Load Variation Scenario. To extra prove the effective-ness and robustness of the proposed MPPT method, the pro-posed method is tested under load variations. The load ischanged as shown in Figure 18 while keeping the weatherconditions constant at 1000W/m2 and 298°F. As shown inthis figure, the load is kept constant at 35Ω unit 0.03 sec, at
which the load is reduced to 17.5Ω. The load is then againchanged to 35Ω at 0.06 sec. Figure 19 shows the maximumpower tracked by the proposed ANFIS-based MPPT as wellas the power tracked by the P&O method. As shown in thisfigure, the proposed method tracked the maximum poweraccurately under changing load. It is clear that the ANFIS-based MPPT method is robust under changing loads com-pared to the performance of the P&O method.
From above simulation results, Table 3 concludes thecomparison between the proposed ANFIS-based MPPTmethod and the P&O MPPT method. Both methods needvoltage and current measurements of the PV module. OnlyANFIS-based MPPT method needs temperature measure-ments as an extra input. It is evident from simulation resultsthat the tracking speed of the ANFIS-based MPPT method isfaster. In addition, oscillations around MPP are smaller com-pared to the P&O MPPT method since P&O MPPT methodsuffers from high oscillations around MPP, which depend onstep size. On the other hand, ANFIS-based MPPT method ismore robust compared to P&O method. It is also evident
from simulation results that the tracking efficiency of theANFIS-based MPPT method is higher than the efficiency ofthe P&O MPPT method.
5.2. Experimental Results. To verify the function and theperformance of the proposed ANFIS-based MPPT method,
a 220W prototype is built and the proposed method isexperimentally tested with the help of dSPACE 1104 dataacquisition system. In order to show the performance ofthe ANFIS-based method, the method is tested on a sunnyday as well as on a cloudy day to prove the effective trackingperformance under all weather conditions.
5.2.1. Sunny Day. Steady weather conditions practically meantesting the proposed method on a sunny day. Figure 20 showsthe changing solar radiation and the PV cell temperature,which are measured from 9:23AM until 3:00 PM on a sunnyday. Figure 21 shows the maximum power tracked by usingthe proposed method. The PV voltage at MPP, PV currentat MPP, and the duty cycle measured from the output ofthe FLC are also shown in Figure 21. As shown in this figure,the proposed method has tracked the maximum power suc-cessfully and accurately.
5.2.2. Cloudy Day. Having a depth investigation on thebehavior of the proposed MPPT system under dynamicweather conditions, the proposed method is tested underthe weather conditions shown in Figure 22. As shown inFigure 22, the changing solar radiation and the PV cell tem-perature are measured from 12:56PM until 3:00 PM on acloudy day. The proposed method is tested under thisweather condition. Variations of the tracked power, PV volt-age at MPP, PV current at MPP, and the duty cycle are shown
Figure 19: MPP tracking performance under load changes.
Table 3: Comparison of proposed MPPT and P&O MPPTmethods.
Proposed ANFIS-basedMPPT method
Conventional P&OMPPT method
PV currentmeasurements
Yes Yes
PV voltagemeasurements
Yes Yes
PV temperaturemeasurements
Yes No
Tracking speed High Depends on step size
PV paneldependency
Yes No
Robustness High Medium
Oscillationsaround MPP
Less Depends on step size
Trackingefficiency
High Medium
12 International Journal of Photoenergy
in Figure 23. As shown in this figure, the proposed methodhas tracked the maximum power effectively and accuratelyunder dynamic solar radiation.
6. Conclusion
Photovoltaic model using MATLAB/SIMULINK and thedesign of appropriate DC-DC isolated Ćuk converter witha maximum power point tracking facility are presented inthis paper. ANFIS-based MPPT method is proposed in thispaper and simulated using MATLAB/SIMULINK environ-ment. The proposed method is tested under disturbancesin the weather conditions to show its tracking performance.
The tracking behavior shows that the proposed MPPTmethod successfully and accurately tracked the maximumpower under all scenarios with higher efficiency and loweroscillations around MPP compared to the conventionalP&O MPPT method. The comparison of the proposedmethod and conventional P&O MPPT method is also pre-sented in the simulation results. The experimental imple-mentation of the proposed ANFIS-based MPPT method isdone in this paper. Data acquisition and the control of theproposed ANFIS-based MPPT method are done bydSPACE 1104 software and digital signal processor cardon PC, respectively. The experimental results show howthe proposed method tracked the MPP effectively and
0 1 20
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0.2 0.4 0.6 0.8 1.2 1.4 1.6 1.8×104
Figure 20: Changing the solar radiation and PV temperature on a sunny day. From 9:23AM to 3:00 PM.
0100200
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0 1 20.2 0.6 0.8Time (sec)
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0 1 20.2 0.6 0.8Time (sec)
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0 1 20.2 0.6 0.8Time (sec)
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Figure 21: Experimentally tracking behavior of the ANFIS MPPT. From 9:23AM to 3:00 PM.
13International Journal of Photoenergy
accurately with fast response and low oscillations. TheANFIS-based MPPT method is well functioning underpractical steady and dynamicweather condition. Thismethodis effectively capable of improving maximum power trackingof PV modules.
Conflicts of Interest
The authors assure that the authors of this manuscript donot have any interest in dSPACE 1104 board and its soft-ware. The authors used dSPACE 1104 for research pur-pose only without any relations with the manufactureror dealer.
Acknowledgments
The authors would like to acknowledge the support providedby the Deanship of Scientific Research at King SaudUniversity,through the Research Centre at the College of Engineering.
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0 1000 2000 3000 4000 5000 6000 70000
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0 1000 2000 3000 4000 5000 6000 7000303
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318
323
Time (sec)
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PV ce
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Figure 23: Experimentally tracking behavior of the ANFIS MPPT. From 12:56 PM to 3:00 PM.
14 International Journal of Photoenergy
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