4th International Conference on Computer Integrated Manufacturing CIP’2007
03-04 November 2007
Modeling and Simulation of Photovoltaic Panel based
on Artificial Neural Networks and VHDL-Language
H. MEKKI, A. MELLIT, H.SALHI, and K.BELHOUT
Department of Electronics, Control laboratory, Faculty of Science Engineering
BLIDA University, USDB
BLIDA, Algeria
Mekkihamza@yahoo.fr, a.mellit@yahoo.co.uk, h_hassan@yahoo.com, bk_elec@yahoo.fr
Abstract- The Number of electronic applications using
artificial neural network-based solutions has increased
considerably in the last few years. However, their applications
in photovoltaic systems are very limited. This paper introduces
the preliminary result of the modeling and simulation of
photovoltaic panel based on neural network and VHDLlanguage. In fact, an experimental database of meteorological
data (irradiation, temperature) and output electrical
generation signals of the PV-panel (current and voltage) has
been used in this study. The inputs of the ANN-PV-panel are
the daily total irradiation and mean average temperature while
the outputs are the current and voltage generated from the
panel. Firstly, a dataset of 4x364 have been used for training
the network. Subsequently, the neural network (MLP)
corresponding to PV-panel is simulated using VHDL language
based on the saved weights and bias of the network. Simulation
results of the trained MLP-PV panel based on Matlab and
VHDL are presented. The proposed PV-panel model based
ANN and VHDL permit to evaluate the performance PV-panel
using only the environmental factors and involves less
computational efforts, and it can be used for predicting the
output electrical energy from the PV-panel.
I.
INTRODUCTION
Solar energy conversions has various advantages such as
short time duration of installation and long life of
exploitation, circuit simplicity, no need of moving part and
realize a salient, safe, not pollutant an renewable source of
electricity. The wide acceptance and utilization of the
photovoltaic (PV) generation of electric power depends on
reducing the cost of the power generated and improving the
energy efficiency of PV systems. In recent years, it has been
shown that artificial neural networks (ANN) have been
successfully employed in solving complex problems in
various fields of applications including pattern recognition,
identification, classification, speech, vision, prediction and
control systems [1]. Today ANNs can be trained to solve
problems that are difficult for conventional computers or
human beings. ANNs, overcome the limitations of the
conventional approaches by extracting the desired
information directly from the experimental (measured) data.
However, the application of the ANN in PV-systems are
very limited, in literature there are several models that are
proposed for modeling and simulation of PV-panel for
evaluating the performance and the power quality in the PVpanel [2],[3]. The performances of a PV module are strictly
dependent on the environmental factors such as irradiance
and cell temperature. These two parameters make the PV
module maximum power change and especially for PVintegrated power system dispatchers it is necessary to be able
to predict accurately the PV output [3]. It is very difficult to
develop an accurate PV-panel model based on analytical
models or numerical simulations; due to the influencing of
the environmental factors. In fact, some very recent works
were developed for modeling a PV-system based on ANN
[4],[10].
The main objective of this work is to use of the ANN and
VHDL-language for modeling and simulation PV-panel. The
proposed model allows us to evaluate the performance of the
PV-panel based only on the meteorological data such as
mean average temperature and daily total solar radiation.
This paper is organized as follows: the next section
introduces the PV-panel and presents the database used in
this study, section III provides the ANN architecture used for
modeling of the PV-panel, also, the same section describe
the simulation of the PV-panel based on ANN and VHDL.
Results and discussion are presented in section IV.
II.
PHOTOVOLTAIC PANEL AND DATABASE
The efficiency of solar energy conversion is related both
to the optimal design and to the maximum power extraction
from PV system. The PV-array used in this simulation
consists of 16 ITALSOLAR modules. Each module
comprises 30 square single crystal silicon cells. The total
peak power of each PV array is 720 We, the PV-array
voltage is 40 V (max) and the PV-array current is 20 A
(max). The area of PV generator is 6 m². The traditional I-V
characteristics of a solar array, when neglecting the internal
shunt resistance, are given by the following equation [2]:
  q
 
I 0  I g  I sat exp 
(V0  I 0 Rs )  1
 
  AKT
(1)
4th International Conference on Computer Integrated Manufacturing CIP’2007
Where Io, Vo are the output current and output voltage of the
solar array. Ig is the generated current under a given
insolation. Isat is the reverse saturation current. q is the
charge of an electron. k is the Boltzmann constant. A is the
ideality factor for a P-N junction. T is the array temperature.
Rs and Rsh are the intrinsic series and shunt resistances of the
solar array.
40
400
T(C°)
30
H(KW h/day)
Output
layer
x1
y1
x2
y2
x3
y3
xn
yn
Fig. 2.a. Schematic diagram of a multi-layer feed-forward neural network.
200
10
0
100
200
Days
300
400
26
100
0
100
200
Days
300
400
Ipv(A)
16
25
xij
14
24
x1j
x1j
18
Vpv(V)
23
Hidden
ayers
300
20
0
Input
layer
03-04 November 2007
w1j
w1j
wij
yj
 
Activation function
Weights summation
12
0
100
200
Days
300
400
10
0
100
200
Days
300
400
Fig. 1. the database of T, H, Vpv and Ipv
“Fig. 1,” shows the different signal recorded form the
above PV-panel, there signals are the mean temperature, daily
irradiation. The output PV-current and PV-voltage generated.
III.
METHODOLOGY
Fig. 2.b. Information processing in a neural network unit.



y j  f   wij .xij   bj  .

 i

f ( x) 
1
1  e x


(2)
(3)
where yj, is the output of the processing unit wjj, the
synaptic weight coefficient of the i-th input of the
processing unit bj, is the bias.
A) Multi-layer Perceptron (MLP) network
Back-propagation (BP) has been widely adopted as a
successful learning rule to find the appropriate values of the
weights for NNs. The MLP consists of various layers: an
input and output ones between which lie one or several
hidden ones whose outputs are not observable. These layers
are based upon some processing unit (neurons)
interconnected by means of feed-forward pondered links
(figure 2-a) [11]. All these processing units carry out the
same operation (figure 2-b): i.e. the sum of their weighed
inputs (See “(2)”). Then they apply the result to a non-linear
function named activation function and generally based upon
the sigmoid function (see “( 3)”).
B) MLP-Based modeling and simulation of PV-panel
“Fig. 3,” shows the MLP configuration proposed for
modeling and simulation of the PV-panel, the input of this
model are the daily total solar radiation (H) and the mean
average temperature (T) while the output are the generated
current (Ipv) and voltage (Vpv). The input and the output are
fixed initially however the number of hidden layers and the
neurons within these layers are optimized during the learning
process based on the good performance of MSE. A database
of 365*5 patterns for each signals are divided in two parts, a
dataset of 365*4 (4-years) patterns used for training the
proposed MLP-PV panel model and a dataset of 365*4 (1year) patterns used for testing and validation of the model. A
soft computing program has been implemented for MLP-PV
panel based on the LM-algorithm, the Matlab Ver 7.1b are
used in this simulation.
4th International Conference on Computer Integrated Manufacturing CIP’2007
1
03-04 November 2007
¡
17
Ipv
H
2
16
2
Vpv
T
15
14
3
3
13
12
2-neurons
Input layer
9
7
7-neurons
1st hidden
layer
2-neurons
Output layer
11
10
0
50
100
150
9-neurons
2nd hidden
layer
200
250
300
350
400
(a)
2 6
2 5 .5
Fig. 3. MLP-PV panel model
2 5
2 4 .5
2 4
2 3 .5
2 3
0
5 0
1 0 0
1 5 0
2 0 0
2 5 0
3 0 0
3 5 0
4 0 0
(b)
Fig. 5. Comparison between Measured and estimated output signal of
a. The current, b. the voltage
Fig. 4. Neural processor architecture
C) VHDL-implementation of the PV-panel
Once the MLP-PV panel model is optimized, the weights
and bias are saved in order to be used for implementation of
the MLP based on VHDL language. The structural
description of a neuron with VHDL allows during the
compiling step to specify generically some characteristics
like the input numbers and the data size, and even to modify
the type of some arithmetic operators like the adder or the
multiplier and eventually the activation function unit if one
wants to modify the activation function. The neuron
computes the product of its inputs, with the corresponding
synaptic weights, which are memorized in an ROM, and then
the results are added. The result is presented to a comparison
unit designed to represent an appropriate sigmoid function.
Then the output of this comparison unit is used as a
controller for a multiplexing unit, which delivers they output
of the neuron “Fig. 4,” [11].
IV.
RESULTS AND DISCUSSION
Firstly, we present the results of MLP-PV panel model
based on Matlab simulation. “Fig. 5,” shows a comparison
between measured and estimated output generating current
and voltage of the PV-panel.
It should be noted there is a very good agreement between
measured and estimated signal (Ipv, Vpv) in addition the
coefficient of determination R2 is 97% which is very
satisfactory. In order to simulate the PV-panel model on
VHDL language, the data was coded on 18 bits in fixedpoint [12].
Function activation is very difficult in its known expression
Tansig) “(3)”. In order to simplify function expression, it
was linearized on several intervals [Ci, Ci+1] and its value is
evaluated using two constants (ai and bi) corresponding to
this intervals [13] “( 4 )”and “(5)”.
f ( xi )  a i .xi  bi For xi  [ci , ci 1 ] .
f (x) = 1
for x > 3.
(4)
(5)
“Fig. 6-a,” presents the result of the activation function
simulation. The obtained results by MODELSIM has been
tested and compared with Matlab, which gives a good
accurate result See “Fig. 6.b,”.
4th International Conference on Computer Integrated Manufacturing CIP’2007
03-04 November 2007
26
25.5
25
24.5
24
23.5
Fig. 6-a. VHDL Simulation of the activation function
23
0
50
100
150
200
250
300
350
400
Fig. 8.a. comparison between MATLAB and VHDL
estimation of a voltage
17
16
15
14
13
12
11
Fig. 6-b. Comparaison between activation function calculated by
Matlab and simulated by MODELSIM (VHDL)
The network is composed of several elements of basis that
are the neurons; this network is presented in “Fig. 7.a,”.
The simulation result based on VHDL is presented in
“Fig. 7.a,”, where X1 is the mean average temperature, X2 is
the daily total solar radiation, Y1 is the output voltage and
Y2 is the output current from the PV-panel.
10
0
50
100
150
200
250
300
350
400
(b)
Fig. 8.b. Comparison between MATLAB and VHDL
estimation of a current
The MODELSIM has been used for simulating the
proposed architecture based on the VHDL, once the
architecture is simulated. “Fig. 8,” shows a comparison
between Matlab and VHDL (ModelSim) estimation. the next
step consists the implementation of this architecture on
FPGA hardware.
ACKNOWLEDGMENT
Fig. 7.a. Global architecture of the neural network
In this paper, a PV-panel has been modeled and
simulated based on ANN and VHDL-language. The
accuracy and the generalization of the neural model in the
system prediction are demonstrated by comparing test results
with actual data. The advantage of the proposed model that it
can be used for estimating the performance of PV-panel and
it is able to predict the output electrical energy generation
from the PV-panel based only on the environmental data. In
addition, the using of the VHDL language for PV-panel
simulation has been demonstrated. The modelsim has been
used for simulate this network. Actually, it remains the
implementation of this architecture on a FPGA hardware,
that will be our objective in the future paper.
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Fig. 7.b. Simulation results based on VHDL
4th International Conference on Computer Integrated Manufacturing CIP’2007
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