Rev. Roum. Sci. Techn. Él ectrotechn. et Énerg. [602569]

Rev. Roum. Sci. Techn.– Él ectrotechn. et Énerg.
Vol. 61, 1, pp. 000–000, Bucarest, 2017

1 University of Khenchela and the laboratory of induction and propulsion systems of Batna, Algeria ([anonimizat])
2Laboratory of induction and propulsion systems, electrical engineering Dep, Universi ty of Batna2, Algeria ([anonimizat],
[anonimizat])
2University of Picardie Jules Verne, LTI (EA,3899), 13 av F. Mitterrand, 02880 Cuffies, France ([anonimizat])
3University of Brest, EA 4325 LBMS Rue de Kergoa t- CS 93837, 29238 Brest, France([anonimizat])

A NEW MODELING AND IMPLEMENTATION OF MAXIMUM
POWER POINT TRACKING FUZZY LOGIC ALGORITHM FOR SOLAR
PHOTOVOLTAIC SYSTEM
TAREK BOUTABBA1, SAID DRID2, LARBI CHRIFI ALAOUI2 AND MOHAMED BENBOUZID3
Key words: Photovoltaic, dc-dc converter, boost, modelisation, fuzzy logic.
In this paper, we present a modeling and im plementation of new control schemes for an isolated photovoltaic (PV) using a fuzzy
logic controller (FLC). The PV system is connected to a l oad through a DC / DC booster (boost) converter. The FLC controller
provides the appropriate duty cycle (D) to the DC / DC converter for the PV system to generate maximum power. Using FLC
Controller block in Simulink/Matlab environment simplify its implementation. However, all the parameters of the FLC blocks
are not accessible and can’t be modified without redesign it each time, causing the loss of considerable time to control our
system. To avoid these drawbacks and to simplify both the access and the plot of all blocks, a modelisation of FLC
membership’s functions became a necessity. Th e Simulation and experimental tests on a PV system shows that the FLC provides
a good track the maximum power point (MPP). Finally, we have evaluated the operation of the FLC on a real system consisting
of a photovoltaic panel (BP580) model and implement the cont rol strategy on a digital signal processor dSPACE DS1104 .

1. INTRODUCTION
Currently the photovoltaic solar energy is considered as
one of the most promising renewable energy sources
because of its high availability anywhere in the world and
in the absence of contamin ating effects. Operating a
photovoltaic cell or group of them to form a panel is
defined by its characteristic cu rves I-V and P-V, that show
how the element behaves under different working conditions. The MPP point is defined as the maximum
power where the power drawn from the PV cell is high. The
value of the maximum power (P
MPP) is obtained by
multiplying the voltage at the maximum power point (V MPP)
by the current at that point (I MPP) [1].
This paper develops a control of DC / DC converter
connected to an isolated photov oltaic system in order to a
load transfer to the maximu m possible power. Proper PV
system performances depends largely appropriate design
and control power conditioner (in our case DC / DC boost
converter) that allows the extr action of the highest possible
power of the PV generator. It implies that the PV generator
must be running permanently at its maximum power point
(MPP). Therefore, the good pe rformance of the controller
that performs the tracking of the maximum power point
(MPPT) becomes the key element in the successful
operation of a PV installation [2, 3].
The MPP tracking control is a challenge for photovoltaic
systems designers because the environmental conditions
(solar radiation and temperature) determine the amount of solar energy incident in the PV generator. Since these
environmental conditions they are continually changing
(natural change of radiation during the day, passing clouds, wind, rain, etc.), installation PV must be adapted to supply
the maximum power load possi ble for each new operating
condition[2]. The change in environmental conditions causes a change in I-V and P-V characteristic of the panels,
which they are strongly nonlinear. This nonlinearity makes
the use of some conventional tracking algorithms very difficult in certain operating points, which for example is
required the maximum power delivered.
Numerous methods have been developed in order to
track the maximum power poin t as the Extremum-Seeking
Control (ESC) or algorithm called "Perturbation and
observation "(P & O). The algorithm P & O, without doubt the most used in commercial systems (due to its simplicity
and easy implementation, no longer need radiation or
temperature measurements) [4 ]. If the power increases
mean that the operating point has moved toward the MPP,
thus the operating voltage must be changed in the same
direction. on the other hand, if the power extracted from the
photovoltaic generator decreases, then the operating point
has moved in the sense opposite to the location of the MPP and therefore. This algorithm ma y fail to track the point of
maximum since power, performance depends largely on the
choosing the perturbation size value used. The value of this disturbance is usually calculated by testing and / or
simulation tests [5]. In r ecent years, some control
techniques not conventional and artificial such us the fuzzy logic algorithm and also the neural networks (ANN) was
developed [5, 6]. The application of these algorithms varies
with their complexity and speed convergence of the maximum power point.
Usually in the FLC controller a large number of rules are
used in the configuration and setting to have a good performance, which require a high number of operations on
the system. Also, the use of the conventional FLC
controller block of Matlab limited us in the manner visualization and accessibility of the whole parts of the
Fuzzy logic structure (Fu zzification, Rules, and
Defuzzification). Where, the app lication of a control system
with these features, leads us to search practical solution that

allows us to perform the calculation. In order to overcome
this drawbacks’ we chose to realize a modeling of a fuzzy
logic controller FLC algorithm for a maximum power tracking to access to all blocks. The proposed MPP
algorithm using FLC is presented in Fig 1. It is
experimentally validated using PV system and a system of control in real time based on the DSP model of dSPACE
DS1104. PV control system and data acquisition are
implemented using the same dSPACE software Control Desk.

Fig. 1 – Control system scheme
2. GENERAL SOLAR CELL MODEL
2.1 Conventional one diode electrical model
For the design of the photovoltaic panel model in
Matlab / Simulink, it was left of the equation that defines
the behavior of a solar cell [7, 8-17]:
0[ 1] (01)s
tVR I
Va s
pv
shVR III I eR
  
The photo generated current IPV depends on variations in
irradiance and temperature. They defined the following
parameters and variables: q=1.6021.10-19 C: elementary
charge, K=1.3810-23 J/K: Boltzmann constant, T:
temperature in degrees Kelvin, n: diode ideality factor, IL:
photogenerated current, Io: reverse saturation current of the
diode, V: voltage panel, I: current panel, Rs: Internal
resistance in series, Rsh: Internal resistance in parallel. The
photovoltaic panel arises from the need to adjust the levels of voltage and current of the PV generator to the
requirements of the electricity system which feeds. For this
photovoltaic cells are arranged in series or parallel depending on the needs. Equa tion (2) to characterize the
behavior of a photovoltaic array:
0[ 1] (02)s
spRIV
NN
snKT
sp
pp v
shRI V
NN
INI I eR





  


Where Ns is the number of cells in series and Np is the
number of cells in parallel.
The parameters that determine the functioning of a cell
are reflected in Figure 2; Are the short circuit current Isc, the
open circuit voltage Voc and the coordinates of the
maximum power point of photovoltaic array (MPP).
The mathematical model in (2) equation is
programming Matlab / Simulink includes and allows
current-voltage curves characteristics ( I-V) and Power-
Voltage ( P-V) for different values of irradiation and
temperature which are presented in Figure 2. From this variation we can observe the proportionality of the photo
current with the variation of irradiation. Fig. 2(b) show that
the terminal voltage increases with decreasing temperature [18].

0 5 10 15 20 25
Voltage Vpv (V)Power P pv (W)
010 20 30 40 50 60 70 80 90
0 5 10 15 20 25
Voltage Vpv (V)Power P pv (W)
010203040506070
(a) (b)
Fig. 2- Simulated waveforms showing th e effect of (a) Irradiation and (b)
temperature on P-V characteristics .
3. DC – DC BOOST CONVERTER
The DC-DC converter used to supply the DC link of
the installation and the boost type. Due to the inductance normally connected to the input terminals of the boost
converters, the PV panels behave as a current source. In this
way the converter has as input variable the drained current
of the photovoltaic panels and as output variable the DC
link voltage.
Due to the serially connected diode in the output
circuit, the power flow in the unidirectional boost
converter, that is, source-charge direction. Figure 2.6 shows
the basic topology of the boost converter using IGBT with
an anti-parallel connected diode.
The boost converters have the function of raising the
input voltage, in the proposed system; the output voltage
will always be fixed by the DC-AC converter (DC link) the
input voltage of the inverter may vary with the insulation of the photovoltaic panel.
Considering that DC-DC converters operate in the
Continuous Conduction Mode (CCM), it is assumed that the current in the input inductor L of the converter is always
greater than zero and the Behavior of the converter is
practically linear [9]. In order to increase the efficiency of
the system, a Maximum Power Point Tracker (MPPT) was
implemented to control the DC-DC converters. In this way
the controller varies the cyclic ratio of the converter to control the drained current and the voltage at the terminals
of the panel to follow the point of maximum power of the
characteristic curve.
This converter is introduced between the PV array and
the load. By using the averaging concept of the relationship
between the input–output voltages for continuous
conduction mode of dc-dc boost converter is given by:
1 (03)1o
inV
VD
Where, D = duty cycle. Since the duty ratio “ D” is
between 0 and 1 the output voltage must be higher than the input voltage in magn itude [10, 13].

3 Authors’ names

4. CONTROL STRATEGY OF
PHOTOVOLTAIC SYSTEM
Many research works have been carried out in
developing various MPPT control algorithms in which the
well-known algorithms are the perturbation and observation
(P&O) technique or the increm ental conductance technique.
This paper presents the application of Fuzzy Logic
Controller (FLC) as an intelligent MPPT method of a PV
system [6-11].
4.1 MPPT USING FUZZY LOGIC CONTROLLER
Controllers implemented with fuzzy logic have the
advantage of not requiring a mathematical model for the treatment of non-linearity of the system. The control
through fuzzy logic is do ne through three stages:
fuzzification, knowledge base and Defuzzification. As shown in Fig. 3, the output voltage of the PV module is fed
back to the block «fuzzification» which is converted to
fuzzy language. The block «mechanism inference» driven
by block "rule base", can then make decisions based on
fuzzy logic, which will be returned to the system by
“Defuzzification” block. This last block is responsible for
converting th e information of fuzzy language to a numeric
variable. This process provides an analog signal that will
control the duty cycle of the PWM converter, then varying
the maximum power point of the PV panel [15].

Fig. 3 – Components of a Fuzzy Logic Controller
The input variables to the FLC are the change in PV
array voltage ( ∆Vpv) and change in current ( ∆Ipv) whereas
the output of FLC is the duty cycle ( ∆D). The Classical
representation of the universe of discourse for the inputs
variables ( ∆Vpv) and (∆Ipv) and the output variable ∆D is
assigned in terms of their linguistics variables by using five
fuzzy subsets., and also their memberships functions for the
variable are represented respectively in figures 4, 5.
Based on this universe of discourse the modeling of the
MPPT Fuzzy logic will presented in the next section.

Fig. 4 – Memberships Functions of the 1st and 2nd Inputs respectively the
variables ( ∆Ipv) and(∆Vpv)

Fig. 5 – Membership Functions of the output Variable ( ∆D) 4.2 MODELISATION OF FUZZY LOGIC
CONTROLLER
The modelisation of FLC controller in Simulink
environment is based on the fundamental principle of the fuzzy logic, divided in thre e parts (Fuzzification, Fuzzy
rule, Defuzzification) fig.6.

Fig.6 – The model of FLC controller in Simulink environment
4.2.1 FUZZIFICATION
The fuzzification is the process which makes any
numerical quantity also call crisp in literature, fuzzy
quantity. It is therefore a f unction that ensures a certain
degree of imprecision to a numerical value, mapping the
physical value of a variable of a process in a standardized
universe of discourse [6]. In fuzzification phase the
controllers were created two in put’s variables: the Voltage
(ΔVpv) and the current ( ΔIpv) produced by the PV panels,
where their crisps universes are partitioned into five
different fuzzy subsets giving rise to total twenty-five
subsets in fuzzy output universe. Also, as output the duty
cycle variation ΔD have five different fuzzy subsets. For
partition of crisp universe, tr iangular membership function
has been used [7-9].
11 = min , ,0 (04)
21 32maxxx x x
xxx xx    
Where, x is the crisp variable and var1, var2 &
var3 are critical crisp poin ts corresponding to left
end, peak value & right e nd of the crisp universe.
4.2.2 DEFUZZIFICATION
In defuzzification, which is the reverse process of
fuzzification, the variable value output language, inferred
by the fuzzy rules, will be tran slated into an output value.
This value is what best represents the inferred fuzzy values
of the output linguistic variable, the distribution
possibilities [8].In times that require a numeric answer, the
fuzzy output set is transformed into a un ique value for the
defuzzification process, ie, the output value of linguistic
variable inferred by the fuzzy rules is translated into a
numerical value (crisp) that w ill act in the pr ocess in order
to regulate it. The term defu zzification is equivalent to
processing fuzzy-scale, corres ponding to a mapping of the
fuzzy control actions space and set on the universe of
discourse for the space not fu zzy or scalar actions. The
methods used are Center of Gravity (CoG) or Area of
Center (CoA), presented in fig.7. The CoA method

calculates the duty cycle variation ΔD output, by
determining the centroid of area composed which is the
fuzzy output function.
(05)ij ij
ijWZ
DW

44.2.3 FUZZY RULES
The phases of fuzzification and defuzzification are
directly related. The acqu isition of numerical values,
normalized by the input variab les are generated discrete
control signals, the control variable. This relationship
between the inputs ΔIpv, ΔVpv and the output ΔD is
performed by fuzzy inference step. Fig. 8 illustrates the
connection between the input and the output of a controller.
In the fuzzy inference of this project, for the composition of
each control rule and the relationship between them used
the MAX-MIN inference tec hnique. The fuzzy method
applied to the modeling of controllers was proposed by
Mamdani. This method enabled prepares strictly linguistic
rules. All developed control acti ons are inserted in the rules
table fuzzy controllers [11]. This table was constructed
initially on the basis of sugges tions and for ty pical response
curves of a closed-loop syst em. They propose a controller
with two input variables and on e control variable, which are
associated with five membersh ip functions triangular, each
variable. Table-1 shown the fuzzy based rules used in this
paper. These rules are framed based on the logic that if the
quiescent point is far away from MPP. Five linguistic
variables have been used. Specifically, NB represents a
“Negative Big” value, NM is “Negative Middle,” ZE is
zero, PM is “Positive Middle,” and PB is “Positive Big.”.
Tabel 1 Fuzzy rules
ΔI
ΔV NB NM ZE PM PB
NB ZE PM PM PB PB
NM NS ZE PM PM PB
ZE NS NS ZE PM PM
PM NB NS NS ZE PM
PB NB NB NM NM ZE
5. REAL TIME IMPLEMENTATION OF
PROPOSED AND EXISTING MPPT
TECHNIQUES
5.1 Hardware details
Four panels mono-crystalline solar PV modules
(BP580) of specifications mentioned in Table 2, with boost
converter have been used for experimental analysis of
proposed and existing MPPT techniques.

TABLE II. PV module parameters at standard test conditions
Short circuit current Isc 4.7 A
Open circuit voltage Voc 22 V
Current at maximum power point I MPP 4.44 A
Voltage at maximum power point VMPP 18V
Number of cells in series N s 36
Pmax 80W
In this section we will ch eck the performance of the
FLC modeling driver that has been proposed in this work.
For this we are going to use the control system in real time
based on DS1104 of dSPACE. This control platform provides libraries to esta blish communication with the
MATLAB / SIMULINK environm ent. DSPACE libraries allow you to include I / O bl ocks that communicate with
SIMULINK diagrams so that th e input blocks allow you to
obtain signals from the actual system being controlled and the output blocks allow you to send signals to the system is
controlling.
SIMULINK diagrams can be converted to C code using
the Real-Time Workshop (RTW) Toolbox. This code C is
compiled and an executable is sent to the Digital Signal
Processor (DSP) integrated in the DS1104 card which is in
charge of executing the real-time control algorithm. The
software provided by dSPACE includes a graphical
interface called Control Desk that allows monitoring and control in real time and show the evaluation of the new
modeling of FLC MPPT. This soft ware also allows to save
the results of the experimental te sts that are done in files of
data that later can be processed and represented graphically
in the MATLAB environment.

Fig. 7.a – Simulink model of triangular membership function
Figure 7.a shows the block diagram of the hardware
configuration where you can s ee the PV panels, the DC /
DC converter, the DS1104 control platform and the
resistive load connected to the system. The capacitor C
provides a greater stability of the static opera ting point of
the photovoltaic generator.
Figure 7.b shows the SIMULINK block diagram of the
proposed control scheme that has been implemented on the
DS1104 real-time control board. Figure 8 shows the
photograph of the hardware used to perform the
experimental tests.
As can be seen in this di agram, the measured voltage
and current obtained directly from generator PV through
the channels C5 and C6, wher e the model of FLC MPP is
developed to generate the control output, which will be the
appropriate duty cycle D so that the DC / DC converter
provides a suitable value of the load resistance of the PV
That the PV works in the M PP. As can be seen in the
diagram the output of the MPPT FLC is sent to the block
DS1104SL DSP PWM, which is responsible for generating
of a PWM signal with the duty cycle sent to it by the MPPT
FLC.

5 Authors’ names

Fig. 7.b – Fuzzy Logic MPPT in MATLAB/SIMULINKTM environment
and its implementation in dSPACE 1104.

Fig .8 – The photovoltaic panel & th e hardware setup of the system.
6. RESULTS AND DISCUSSION
6.1 Response of MPPT Fuzzy Logic algorithm in front
Load change:
By the applying different Load to the system we obtain
the output voltage transient re sponse of boost converter.
Fig 9 and Fig 10 represents the I-V and P-V characteristic
and shows as the impact of th e load variation represented
by R1,2,3,4=[9.8, 7.6, 4.6, 2] Ω. Under this condition, the
algorithm of maximization gives rise to spiral shaped which represent the different po int of maximization of
photovoltaic system.
The modelisation of the MPPT FLC allow us to obtain
some of new results, where the classical FLC controller
block couldn’t brought us this information. Figures 10, 11
presents the outputs of five memberships functions of the
input’s current ΔI
pv and voltage ΔVpv and the output ΔD
variation when we applied à different load value.
Fig 12 and 13 represents respectively the variation of
power, voltage and current of the photovoltaic system under
load variation, both the outputs of the PV panel and the
boost converter are represented in these figures. This variation will involve a fast change of the optimal panel
voltage, and the MPPT algorithm reacts in a fast and
consistent behavior. Equipped with a boost converters and when the load is changed, the system try to find the
maximum power for each variation they with the both
MPPT FLC algorithms give th e appropriate duty D applied
to the controller an d PWM to make pulse of the boost's
switch, so that the actual powe r is increased to operation in
this point Fig 13. 0 5 10 15 20 25 30 35 40 45012345678910
Vin & Vout (V)Iout & Iin (A)

Iin-Vin
Iout-Vout

0 5 10 15 20 25 30 35 40 45050100150200250
Vin &Vout (V)Pin & Pout (W )

Pout-Vout
Pin-Vin

Fig. 9 – MPPT characteristics I-V and P-V with load variation
0 1 2 3 4 5 6 7 8 900.20.40.60.811.2
Membership Functions of the 1st Input Variable (DIpv)Membership Grades

Nm
ZZ
Pb
Pm
Nb

Fig. 10 – Output of membership function of the input current ΔIpv
modeled in Simulink
0 1 2 3 4 5 6 7 8 900.20.40.60.811.2
Membership Functions of the 2nd Input Variable (DVpv)Membershi p G rades

Nb
Nm
ZZ
Pb
Pm

0 1 2 3 4 5 6 7 8 900.20.40.60.811.2
Membership Functions of the output Variable (D)Membership Grades

Nm
Pm
Pb
Nb
ZZ

Fig. 11 – Outputs of memberships function of the inputs voltage ΔVpv and
duty cycle ΔD modeled in Simulink Pin- V in
Pout- V out Iin- V in
Iout- V out
NM
ZZ PM
PB
NBNB
NM PB
ZZPMPMZZ PB
NM NB

0 1 2 3 4 5 6 7 8 9050100150200250
Time (s)Pout &Pin (W)

Pout
Pin

0 1 2 3 4 5 6 7 8 9024681012
Time (s)Iin & Iout (A)

Iout
Iin

Fig. 12 – Responses of the power and the current for a load variation
0 1 2 3 4 5 6 7 8 915202530354045
Time (s)Vin & Vout (V)
Vout
Vin

0 1 2 3 4 5 6 7 8 90.350.40.450.50.550.60.650.7
Time (s)Duty cycle

Fig. 13 – Responses of the voltage and the duty cycle for a load variation
7. CONCLUSION
This paper has presented the modeling and Control
technique of Photovoltaic System using both FLC algorithms to maximize the PV system output voltage,
aiming to operate at maximum power point.
The Modelisation of the MPPT FLC gives us the opportunity of extracting more information and results than
the conventional Fuzzy controller block in
Simulink/Matlab, that simplify the study and implementation of the FLC algorithm.
This algorithm robustness was noticeable in the estimation
of locate the maximum power point voltage range, the voltage range with a small step as a control voltage to the
corresponding instantaneous po wer, accurate and Features
simple nonlinear filtering powe r curve, after power is
relatively select the maximum power point and the
corresponding control voltage. Not only guarantees the tracking speed and improve the tracking accuracy; judging
by the state of mutation status tracking in front of weather
conditions change, avoiding MPPT algorithm "miscarriage
of justice" issue. The proposed control system is developed in the MATLAB / Simulink environment and real-time
implementation was performed used the DS1104 card
DSPACE.
REFERENCES
1. Bimal K. Bose, “Global Warming Energy: Environmental
Pollution and the Impact of Power Electronics”, IEEE
Industrial Electronics Magazine, pp.1-17, March 2010.
2. M. Masoum, “Design, Constructi on and Testing of a Voltage-
based Maximum Power Point Tracker (VMPPT) for Small
Satellite Power Supply”, 13th Annual AIAA/USU Conference
on Small Satellite.
3. Mohan, N., Undeland, T. e Robbi ns, W. P. “Power Electronics:
Converters, Applications and Design”, 2nd. edn, John Wiley e
Sons Inc(1995), .
4. Brunton, S.L.; Rowley, C.W.; Kulkarni, S.R.; Clarkson, C.
Maximum power point tracking for photovoltaic optimization
using ripple-based extremum seeking control. IEEE Trans. Power Electron. 2010, 25, 2531–2540.
5. Jiang, J.-A., Huang, T.-L., Hsio, Y.-T. e Chen, C.-H. (2005),
‘Maximum power tracking for photovoltaic power systems’, Tamkang Journal of Science and Engineering 8(2), 147–153.
6. Farhat, M., Flah, A., Sbita, L., (2014), Photovoltaic Maximum
Power Point Tracking Based on ANN Control, International Review on Modelling and Simulations, 7 (3), 474 – 480.
7. U. R. Yaragatti, A. N. Rajkiran, B. C.Shreesha, “A novel
method of fuzzy controlled maximum power point tracking in
photovoltaic system,” IEEE International Conference on Industrial Technology, 2005, pp 1421 – 1426.
8. Patcharaprakiti, N., Suttichai P., and Sriuthaisiriwong Y.,
(2005). Maximum Power Point Tr acking Using Adaptive Fuzzy
Logic Control for Grid Connected Photovoltaic System.
Renewable Energy 30 (11): 1771-1788.
9. M.S. Aït Cheikh, C. Larbes, G.F. Tchoketch Kebir and A.
Zerguerras, “Maximum power point tracking using a fuzzy
logic control scheme,” Revue des Energies Renouvelables,
2007, Vol. 10, N°3, pp 387 – 395.
10. G.Y. Ayvazyan1, G.H. Kirakosyan2, and A.H. Vardanyan1,
“Maximum Power Operation of PV System Using Fuzzy Logic
Control,” Armenian Journal of Physics, vol. 1, 2008, pp. 155-159.
11. El Hajjaji, M. BenAmmar, J. Bosche, M. Chaabene, and A.
Rabhi, “Integral Fuzzy Control for Photovoltaic Power Systems,” Sustainability in Energy and Buildings, Springer
Berlin Heidelberg, 2009, pp. 219-228.
12. N. Patcharaprakiti, S. Premrudeepreechacharn, Y.
Sriuthaisiriwong, “Maximum power point tracking using
adaptive fuzzy logic control for grid-connected photovoltaic
system,” Renewable Energy, 2005, Vol 3, No 11, pp. 1771-1788.
13. V. Di Dio, D. La Cascia, R. Miceli, “A Mathematical Model to
Determine the Electrical Energy Production in Photovoltaic Fields under Mismatch Effect”, Proceedings of the 978(1) IEEE, pp.46-51, August 2009.
14. A. Saadi and A. Moussi, “Optimation of Buck Boost Converter
By MPPT Technique With A Variable Reference Voltage Applied to Photovoltaic Water Pumping System Under
Variable Weather Conditions,” Asian Journal of Information
Technology 6, 2007.
15. N. Patcharaprakiti and S. Pr emrudeepreechacharn, “Maximum
PowerPoint Tracking Using Adap tive Fuzzy Logic Control for
Grid connected Photovoltaic System”, PESW2002, volume 1,
PP:372-377, 002.
16. Marcelo Gradella Villalva, Jona s Rafael Gazoli, and Ernesto
Ruppert Filho , “Comprehensive Approach to Modeling and
Simulation of Photovoltaic Arra ys”, IEEE Transactions on
Power Electronics, vol 24, no. 5, 2009, pp 1198-1208.
17. S. Drid, L.Chrifi-Alaoui, M.Ouriagli and P. Bussy, "Robust
Control of the Photovoltaic System with Improved Maximum
Power Point Tracking" Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER’14) EVER’2014, March 25-27, 2014, Monte-Carlo (Monaco) .
18. Boutabba Tarek, Drid said and M.E.H. Benbouzid, “Maximum
Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy “ANFIS”” 2013 Ecological Vehicles
and Renewable Energies (EVER), 8th International Conference
and Exhibition on Ecological Vehicles and Renewable Energies (EVER), Monaco and IEEE. Vin Vout Iin Iout Pin
Pout