Minia Journal of Engineering and Technology, (MJET)
Vol. 32, No 1, January 2013
MODELING AND SIMULATION OF SMART MAXIMUM POWER
POINT TRACKER FOR PHOTOVOLTAIC SYSTEM
Abou-Hashema M. EL-Sayed
Electrical Engineering Dept., Minia University, Egypt
E-mail: abouhashema@mu.edu.eg
Abstract— The generated power from photovoltaic (PV) array depends on its terminal voltage
for each value of irradiation and temperature. There is an optimum value for the PV terminal
voltage for each irradiation and temperature. This optimum value corresponds to the maximum
power point at this condition. A maximum power point tracker (MPPT) is used to track this point at
any condition. This paper introduces a smart, fast and accurate technique for MPPT based on
Artificial Neural Network (ANN) to maximize the output power from PV system. The proposed
technique is modeled using MATLAB/Simulink program. The output power and energy from PV
system with ANN are compared with those obtained by the commonly used Perturb and Observe
(P&O) technique. MPPT using ANN shows superior performance with respect to the P&O
technique.
ً‫نمذجت ومحاكاة متتبع نقطت الطاقت القصىي لنظام كهروضىئ‬
ً‫ انطالت انًىنذة يٍ انخاليا انكهشوضىئيت حؼخًذ ػهً اندهذ انطشفً نهخاليا نكم ليًت يٍ اإلشؼاع انشًس‬: ً‫الملخص العرب‬
ً‫ هزِ انميًت انًثه‬.‫ يىخذ ليًت يثهً نهدهذ انطشفً نهخاليا انشًسيت نكم ليًت نإلشؼاع انشًسً ودسخت انسشاسة‬.‫ودسخت انسشاسة‬
‫ ويخخبغ‬.‫ ويسخخذو يخخبغ َمطت انطالت انمصىي نخخبغ هزِ انُمطت ػُذ أٌ ظشوف‬.‫حخىافك يغ َمطت انطالت انمصىي ػُذ هزِ انظشوف‬
ً‫َمطت انطالت انمصىي هى ػباسة ػٍ يىائى لذسة حياس يسخًشانزٌ يُظى إَخاج انطالت نضًاٌ انميًت انًثهً يٍ اندهذ نهسصىل ػه‬
‫ يمذو هزا انبسث حمُيت ركيت وسشيؼت ودليمت نًخخبغ َمطت انطالت انمصىي وانزي يؼخًذ‬.‫انطالت انمصىي يٍ انخاليا انكهشوضىئيت‬
‫ وانخمُيت انًمخشزت حًج ًَزخخها يغ َظاو انخاليا انكهشوضىئيت باسخخذاو‬.‫ػهً انشبكاث انؼصبيت االصطُاػيت نزيادة انطالت انُاحدت‬
‫ وحى يماسَت انطالت وانمذسة انًُخدت يٍ َظاو انخاليا انكهشوضىئيت باسخخذاو انخمُيت انًمخشزت‬.MATLAB/Simulin ‫بشَايح‬
‫ واحضر يٍ انًماسَت أٌ يخخبغ َمطت‬.(Perturb and Observe( ‫( يغ يثيالحها فً زال اسخخذاو حمُيت انخشىيش وانًشالبت‬ANN(
.‫انطالت انمصىي انًمخشذ باسخخذاو حمُيت انشبكاث انؼصبيت االصطُاػيت يخفىق فً األداء يماسَت باسخخذاو انخمُيت األخشي‬
Keywords—Photovoltaic System, MPPT, Perturb and Observe, Artificial Neural Network and
Boost Converter.
track the MPP increases the generated
energy from PV system about 30%. MPPT
must be fast to track the MPP effectively
and smart to avoid divergence in fast
changing irradiance or temperature.
The I-V curve of PV cell is an exponential
relationship between current and voltage,
and the maximum power point occurs at
the knee of this curve, where the resistance
is equal to the negative of the differential
resistance (V/I = -dV/dI). The MPPT
utilizes some type of control circuit or
logic to search for this point to extract the
maximum power available from a cell.
II. Model of the Proposed System
The proposed system includes PV module,
DC/DC boost converter and MPPT tracker
has been carried out using an ANN basedtracker to has been used for determine the
current at MPP for comparison purpose,
the Perturb and Observe (P&O) is also
I. Introduction
Photovoltaic
systems
has
many
applications as in space satellites and
orbital stations, solar vehicles, power
supply for loads in remote areas, and street
lighting systems. PV systems are
environmental friendly when compared to
the current level of CO2 emission
associated with conventional electricity
generation. A considerable utilization of
PV system as energy sources could reduce
substantially the emission of CO2 and other
pollutants that cause acid rain, smog and
other local environmental hazards.
The solar V-I characteristics is highly
nonlinear and change with irradiation and
temperature. In general, there is a unique
point on the V-I curve called the maximum
power point MPP at which the PV array
operates at maximum efficiency. Using the
maximum power point tracker MPPT to
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Minia Journal of Engineering and Technology, (MJET)
simulated [1-4]. The simulation of the
proposed system has been implemented
using MATLAB/Simulink program and
contains sub models explained in the
following sections:
A. PV module characteristics
  α V  
e0
ID  Io exp  D  1,α 
KT
  A  
ID
Rs
Figure 1 shows the equivalent circuit of
one-diode model of a solar cell, where
generated current depends on the
characteristic of material, irradiation and
cell temperature [5-8, 9, 10].
IC =Iph-ID-I
I
Iph
Rsh
VC
VD
Vol. 32, No 1, January 2013
Where:
IC
cell current (A).
Iph
light generated current (A).
Io
reverse saturation Current.
e0
charge of electron = 1.6 x 10-19
(coulomb)
K
Boltzmann constant (j/K).
A
dimensionless of the solar
panel.
T
cell temperature (K).
Rs, Rsh cell series and shunt resistance
(ohms).
Fig. 1. Equivalent circuit of solar cell.
Figure 2 shows the Simulink model of PV
module installed in EL- Owainate city of
The Arab Republic of Egypt as a case
study. The hourly radiations in ELOwainate on the horizontal and on the
tilted surface by angle equal 22.46º
southward are shown in Fig. 3.
Figure 4 and 5 show the P-V and V-I
characteristics of the tilted PV Module
using LA361K51S solar module [11] at
various hours from 8:00 AM to 5:00 PM
on October 15th.
Ipv
1
Rs
Radiaion
Ta
2
Isc/1000
G
Iph
N0 of series Cells
3
f (z)
Tr
Add
Product
Add1
Solve
f(z) = 0
z
Algebraic Constraint
VD
VC
cell voltage
ref.Temp
0.000318
Idiode
Ki
Io*(exp(u/Vt)-1)
Vdiode
VD/Rsh
-K-
Fig.2. Simulink model of PV module.
103
Ns
1
Vpv
Minia Journal of Engineering and Technology, (MJET)
Vol. 32, No 1, January 2013
1.1
60
Tilted radiation at angle= Latitude (22.46)
2
2
50 Ta=27 C, R=900.6 W/m , h=11
0.9
2
0.8
Ta=26.3 C, R=685.5 W/m , h=10
Horizontal radiation
Output power Watt
2
Radiation (KW/M )
X: 16.84
Y: 53.57
Ta=27.8 C, R=1042 W/m , h=12
1
0.7
0.6
0.5
0.4
40
X: 16.84
Y: 35.21
2
Ta=24.2 C, R=416.6 W/m , h=9
30
20
0.3
10
2
Ta=20.7 C, R=133.8 W/m , h=8
0.2
0.1
8
9
10
11
12
13
14
15
16
0
0
17
2
4
6
8
Hours
10
12
14
16
18
20
Fig. 3. Average daily hourly radiation
in EL- Owainate,
Egypt for October 15th.
Fig. 4. P-V characteristics using LA361K51S Solar
Module on October 15th.
4
2
Ta=27.8 C, R=1042 W/m , h=12
3.5
X: 16.84
Y: 3.181
2
Ta=27 C, R=900.6 W/m , h=11
Current ,A
3
2.5
2
X: 16.84
Y: 2.091
Ta=26.3 C, R=685.5 W/m , h=10
2
2
Ta=24.2 C, R=416.6 W/m , h=9
1.5
1
Ta=20.7 C, R=133.8 W/m 2, h=8
0.5
0
0
2
22
Module voltage
4
6
8
10
12
14
16
18
20
22
Module voltage
Fig. 5. I-V characteristics using LA361K51S solar module on October 15th.
the output voltage and current of PV array.
Vi =Vb (1− D).
where,
Vi
: Terminal voltage of PV module,
Vb
: Battery voltage.
D
: Duty cycle
Ii
:PV array current
B.
Mathematical model of boost
converter
The input to dc-dc converter is connected
to PV array and its output is connected to
battery storage. Fig.6 shows the Simulink
of mathematical model of boost converter
[12, 1, 13]. The inputs of this model are
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Minia Journal of Engineering and Technology, (MJET)
Vol. 32, No 1, January 2013
voltage
1
duty
1-D
Vi
Calculate 0 <= D <= 1
1-D
1
D
1
D
Add
1
output current
current
2
Ii
(1-D) Ii
Ii >= 0
Battery voltage
3
2
Pout
Vb
Fig.6. Simulink model of the boost converter.
perturb operation, the current and hence
the power are calculated and compared
with previous value to determine the
change of power ΔP (P-Pold). If ΔP>0, then
the operation continues in the same
direction of perturbation. Otherwise, the
operation reveres the perturbation direction
[1-4].
C. Perturb and Observe based-tracker
The P&O method is the most popular
MPPT algorithm due to its simplicity and
ease of implementation. Most MPPT
techniques attempt to find the PV optimum
voltage, Vmpp, or to find the current Impp
corresponding to the maximum power
point. The flowchart of the proposed P&O
based MPPT is shown in Fig. 7. After one
Start
Set Pold = 0; Iref = 2; Increment = -1,
IrefH = 4; IrefL = 0; DeltaI = 0.01;
Measure VPV, IPV
PPV= VPV* IPV
P>Pold
Yes
Increment =
-Increment
No
Iref=Iref+Increment*DeltaI;
Iref >IrefH
Yes
Iref =IrefH
No
Yes
Iref <IrefL
No
Pold = P, I=Iref
105
Iref =IrefL
Minia Journal of Engineering and Technology, (MJET)
Vol. 32, No 1, January 2013
Fig. 7. Flowchart of the proposed P&O MPPT algorithm.
provides the identified optimal current Impp
[14].
The Simulink model of the feed-forward
back-propagation ANN (2+6+1) is shown
in Fig. 8. ANN was trained with values
obtained from data of mathematical model
of PV module. The training parameters are
as follows:
 Learning rate parameter η =0.1;
 Number of training iterations=1000;
 Error goal (performance) =1e-7.
The convergence error for the training
process is shown in Fig. 9. After training
ANN in M-file and obtaining the optimum
ANN, it is used in Simulink proposed
system. The Weights and Biases for the
used 2+6+1 ANN are shown in the
following:
D. Artificial Neural Network Based
Tracker
A multilayer feed-forward neural network
is used in the proposed MPPT. The
network consists of an input layer, one
hidden layer and an output layer. The
numbers of neurons in the input and output
layers are two and one, respectively. The
number of neurons in the hidden layer
would be determined by trial and error.
The neurons in the input layer get their
input signal from the measurement of solar
radiation and ambient temperature. The
neurons in the hidden layer, receive data
from the input layer and calculate their
outputs using sigmoid activation function,
and pass them to the neurons in the output
layer. The node in the output layer
 -6.2999 

 1.4799 




 , B1=  -4.6540  ,



 2.1168 

 4.5185 
-0.0012 



0.0001 
 -0.0995
B2= [1.3032]
W2=[0.4376 -1.3937 -0.2736 0.1728 -0.2267 1.7505 ]
 4.3095
 -1.8936

W1=  2.4836

 -2.9865
 -3.8397

-1.4124
Radiation
KW/m^2
0.0030
-0.0009
0.0047
-0.0027
feed-forward back-propagation ANN (2+6+1)
0.9
p{1}
y {1}
2.742
25
temp, C
Fig. 9. Convergence error for the neural network
training process.
Fig.8. Simulink model of the neural
network.
presented. Output power and energy from
PV system are shown in Fig.11 and Fig.
12. It is clear from these figures that the
results from ANN and P&O are very close
to each other which prove the superiority
of the ANN technique. ANN technique is
faster than P&O one, because it does not
need a lot of mathematical manipulations
which makes it preferable in online
implementation. Fast, accurate technique
III. Simulation Results
The simulation of the proposed PV system
has been implemented using MATLAB
/Simulink program as shown in Fig. 10.
The simulation is carried out with the
realistic hourly data of the EL-Owainate
city - Egypt as a case study for various
times from 8:00 AM to 5:00 PM on
October 15th. The results of the two
techniques ANN and P&O have been
106
Minia Journal of Engineering and Technology, (MJET)
such as ANN can track the MPP
effectively and accurately even in harsh
weather. Hardware implementation of
ANN
technique
becomes
mature
Vol. 32, No 1, January 2013
technology with the availability of high
digital technology such as field
programmable gate array, FPGA.
Fig.10-a. MATLAB Simulink modeling of maximum power point tracker for photovoltaic
system using ANN.
Fig.10-b. MATLAB Simulink modeling of maximum power point tracker for photovoltaic
system using P&O algorithm.
60
50
400
ANN
P&O
ANN
P&O
350
300
Energy, Wh
Power, W
40
30
20
250
200
150
100
10
50
0
0
100
200
300
Time
400
0
500
0
100
200
300
400
500
Time
(9 min*60 sec)
(9 min*60 sec)
Fig. 11. The output power from PV system
using ANN and P&O algorithm.
Fig. 12. The output energy from PV system
using ANN and P&O algorithm.
107
Minia Journal of Engineering and Technology, (MJET)
Vol. 32, No 1, January 2013
Photovoltaic
Model
Using
MATLAB/SIMULINK,‖ Proceedings of
the World Congress on Engineering and
Computer Science 2008 WCECS 2008,
San Francisco, USA, Oct. 22 ‒ 24, 2008.
[6] H. I. Cho, S. M. Yeo, C. H. Kim, V.
Terzija, Z. M. Radojevic, ―A Steady State
Model of the Photovoltaic System in
EMTP, ‖ The International Conference on
Power Systems Transients (IPST2009),
Kyoto, Japan, June 3-6, 2009.
[7] Ramos Hernanz, Zamora Belver and
Zulueta Guerrero, ― Modeling of
Photovoltaic Module, ‖ International
Conference on Renewable Energies and
Power Quality (ICREPQ’10), Spain, 23rd
to 25th March, 2010.
[8] B.Chitti Babu, R. Sudharshan Kaarthik,
Nayan Kumar Dalei, R.Vigneshwaran,
Rabi Narayan Das, ―A Photovoltaic
Energy Conversion System for Water
Pumping Applications -Modeling and
Simulation, ‖ International symposium on
Photovoltaic Science and Technology,
Indian Institute of Technology, Kanpur, 13
Jan., 2010.
[9] Anna Rita Di Fazio and Mario Russo , ―
Photovoltaic generator modelling to
improve numerical robustness of EMT
simulation, ‖ Electric Power Systems
Research 83, 136– 143, 2012.
[10] M. Nabil, S.M. Allam, E.M. Rashad,
―Modeling and design considerations of
a photovoltaic energy source feeding a
synchronous reluctance motor suitable
for pumping systems, ‖ Ain Shams
Engineering Journal 3, 375–382, 2012.
[11]http://www.posharp.com/photovoltaic/sola
rpanel.aspx
[12] Ali M. Eltamaly, ―Modeling of Fuzzy
Logic Controller for Photovoltaic
Maximum Power Point Tracker,‖ Solar
Future 2010 Conf. Proc., Istanbul,
Turkey, pp. 4-9, Feb. 2010.
[13] Chokri Ben Salah and Mohamed Ouali , ―
Comparison of fuzzy logic and neural
network in maximum power point
tracker for PV systems , ‖ Electric Power
Systems Research 81, pp. 43–50, 2011.
[14] CAO Junyi, CAO Binggang and SUN
Xinghua, ―Artificial Neural Network
Maximum Power Point Tracker for Solar
Electric Vehicle,‖ Tsinghua science and
IV. Conclusion
Tracking of MPP needs fast and accurate
technique to follow the maximum power
point of PV module. An ANN based-tracker
can handle these requirements because of its
accuracy and fast performance. A simulation
of an ANN based-tracker is proposed in this
paper to track the maximum power available
from the PV module. Using Matlab Simulink
package the simulation shows identical
results for both two techniques. ANN
technique is fast because it does not need any
mathematical manipulations or computer
iteration as in P&O. This is which makes
ANN- based-tracker perfect option for on
line tracking of maximum power even in
harsh weather. The energy output from one
tilted PV Module on October 15 th from 8am
to 3pm by using ANN is 385 Wh, where it is
352.6 Wh using P&O algorithm.
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Vol. 32, No 1, January 2013