The Linear Model of a PV moduel

International Journal of Power Electronics and Drive System (IJPEDS)
Vol. 8, No. 2, June 2017, pp. 900~906
ISSN: 2088-8694, DOI: 10.11591/ijpeds.v8i2.pp900-906  900
Journal homepage: http://iaesjournal.com/online/index.php/IJPEDS
The Linear Model of a PV moduel
Mohamed Abd-El-Hakeem Mohamed
Faculty of Engineering, Electric Eng.,Al-Azhar University, Qena, Egypt
Article Info ABSTRACT
Article history:
Received Jan 8, 2017
Revised Mar 8, 2017
Accepted Mar 22, 2017
This paper proposes a new approach to determine a linear mathematical
model of a PV moduel based on an accurate nonlinear model. In this study,
electrical parameters at only one operating condition are calculated based on
an accurate model. Then, first-order Taylor series approximations apply on
the nonlinear model to estimate the proposed model at any operating
conditionts. The proposed method determines the number of iteration times.
This decreases calculation time and the speed of numerical convergence will
be increased. And, it is observed that owing to this method, the system
converged and the problem of failing to solve the system because of
inappropriate initial values is eliminated. The proposed model is requested in
order to allow photovoltaic plants simulations using low-cost computer
platforms. The effectiveness of the proposed model is demonstrated for
different temperature and irradiance values through conducting a comparison
between result of the proposed model and experimental results obtained from
the module data-sheet information.
Keyword:
Linearization
Modeling
Photovoltaic
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Mohamed Abd-El-Hakeem Mohamed,
Department of Electrical Engineering,
Al-Azhar University,
Faculty of Engineering, Electric Eng., Qena, Egypt.
Email: moh731411@yahoo.com
1. INTRODUCTION
The current-voltage characteristics of photovoltaic is an important role in solar industry because it
exactly reflects the module performance [1]. The one-diode model parameters of PV panels from a single I-V
curve is identified in [2] by converting nonlinear fitting to a linear system identification. Paper [3] presents
analytical solutions for the parameters of a five-parameter double-diode model of PV cells and modules
which only require the coordinates of three key points of the I–V curves, i.e., the open-circuit (0, Voc), the
short circuit (Isc, 0) and the maximum power point (MPP) (Im, Vm). These analytical solutions are
successfully used in Newton–Raphson numerical iterations to achieve convergence and obtain more accurate
solutions. Paper [4] presents a novel approach using the shuffled frog leaping algorithm (SFLA) to determine
the unknown parameters of the single diode PV model. The validity of the proposed PV model is verified by
the simulation results which are performed under different environmental conditions. However, some
disadvantages are also existed in the original algorithm, such as nonuniform initial population, slow
convergent rate, limitations in local searching ability and adaptive ability and premature convergence.
Paper [5] use particle swarm optimization (PSO) with inverse barrier constraint is proposed to determine the
unknown PV model parameter. Disadvantages of the basic particle swarm optimization algorithm that,the
method easily suffers from the partial optimism, which causes the less exact at the regulation of its speed and
the direction .Moreover, many evolutionary and swarm intelligence optimization techniqueshave been used
to solve this problem such as genetic algorithms (GA) [6]-[7], differential evolution (DE) [8], particle swarm
optimization (PSO) [9], simulated annealing (SA) algorithm [10], bacterial foraging (BF) algorithm [11],
harmony search algorithm (HSA) [12], and artificial bee colony (ABC)algorithm [13]. A LABVIEW
simulator for photovoltaic (PV) systems is presented in [15]-[16]. The

IJPEDS ISSN: 2088-8694 
The Linear Model of a PV moduel (Mohamed Abd-El-Hakeem Mohamed)
901
All of the previously mentioned models suffer from high computational time due to their dependency on
complex transcendental implicit equations.
In this paper a five parameters extraction mainly based on a linearization method is presented. The
proposed method estimate the parameter of a pv without any the conversion problem. And also ,the number
of iteration times is determined . so the calculation time is decresed The predicted I-V and P-V curves are
compared with experimental data to conclude on the validity of the model and the followed procedure.
2. NON LINEAR MODEL OF PHOTOVOLTAIC MODULE
Figure 1.shows the equivalent circuit for a PV cell. The output current of the equivalent circuit, ,
can be expressed as a function of the PV cell’s voltage, [1]:
Figure 1. Equivalent circuit of a photovoltaic cell using
The single exponential module
( ) (1)
In the above equation, Vt is the junction thermal voltage:
(2)
Where k is the Boltzmann constant (1.38 x 10-23 J K-1), q is the electronic charge (1.602 x 10-19 C), T is the
cell temperature (K); A is the diode ideality factor, the series resistance (Ω) and is the shunt resistance
(Ω). ns is the number of cells connected in series. Equation (1) can be written for the three key-points of the
V-I characteristic:
= (3)
(4)
= (5)
An additional equation can be derived using the fact that is on the P-V characteristic of the panel, at the MPP,
the derivative of power with voltage is zero.
|
( )
( )
(6)
The fifth equation can be derived using the fact that is on the P-I characteristics of a PV system at the
maximum power point, the derivative of power with respect to current is zero.

 ISSN: 2088-8694
IJPEDS Vol. 8, No. 2, June 2017 : 900 – 906
902
|
( )
( )
( )
Equations. (3) and (5) can be inserted into Equation (4), which will take the form
( ) (8)
The first equations when constructing the model are the expressions of Io from Equation (3) and Iph from
Equation (5), in STC
F5=Io-( ) (9)
(10)
The effects of the environment, e.g. temperature and irradiance on the values of (Isc, Voc, Im, and Vm) are
include with differen methods [13]-[14].
3. LINEAR MODEL OF PHOTOVOLTAIC MODULE
The main objective of linearization is to transform and elemainte the nonlinearity model of a pv
moduel into a simple equivalent model . The linearized system of a pv moduel can be written
corresponding to equations (1,6-10) respectively as follow
( )
Where
[ ] ( )
( ) (13)
[ ] ( )
( ) (15)
[ ] ( )
( ) (17)
The Gauss–Jordan elimination method is a suitable technique for solving systems of linear equations
of any size. This method involves a sequence of operations on a system of linear equations to obtain at each
stage an equivalent system that is, a system having the same solution as the original system. The reduction is
complete when the original system has been transformed so that it is in a certain standard form from which
the solution can be easily read as follow.
( )
The constants CI,CG ,CT are defined in the appendix

903
4. LINEAR MODEL ESTIMATION ALGORITHM OF A PV PANEL
The estimation algorithm of linear model of a pv moduel for various temperature and irradiance
conditions, are described in the following steps:
a. Step 1: This step is executed one time only to determine the first operating point as discussed
in [17]. Newton-Raphson method is used to calculate the three unknown parameters (Rs, A,
and Rsh) of PV panel model using Equations. (6), (7) and (8) the other parameters ( ) are
calculated directly from Equations (9-10) respectively.
b. Step 2 : the number of iterations is determined exactly as follow
( ) ( )
(19)
Where are chosen according to accuracy requirement
c. Step 3 : the parameters of a pv model are calculated based on equations (17) as follow
( ) (20)
This step is repeated basen on : the number of iterations which are calculated in step2
d. Step 4: The Equation 18 is used for estimation of I-V curves of photovoltaic (PV) at various
environement conditions where.
( )
( )
5. RESULTS AND DISCUSSION
In this study, KC200GT (multicrystal) solar module is used to ensure the effectiveness of proposed
model. The typical electrical characteristics of these PV modules under the standard test conditions (STC)
(module temperature, 25 ◦C, AM 1.5 spectrum, irradiance 1000W/m2) are listed in Table 1.
Table 1.Shows the data obtained from the datasheet for KC200GT solar module at 25 ◦C, AM1.5,
and 1000 W/m2.
Parameter KC200GT solar
module
MaximumPower (Pmpp) 200 W
Maximum Power Voltage (Vmpp) 26.3 V
Maximum Power Current (Impp) 7.61 A
Open Circuit Voltage (Voc) 32.9 V
Short Circuit Current (Isc) 8.21 A
Temperature Coefficient of Voc(Kv) - 0.123V/oC
Temperature Coefficient of Isc (Ki) + 3.18 mA/oC
number of cells (ns) 54
According to the algorithm which is presented in Section 4, the first operating point is determined
as follow. These values are used in first time only. The validity of the proposed for the PV modules under
different environmental conditions. Figure 2 shows the I-V model curves based equation (18), and the data
sheet of the KC200GT PV module under different temperature conditions. It can be demonstrated that the I-V
curves of the proposed model coincide with the experimental data. This distinguishes the high accuracy of
the proposed PV model. The simulation results of the proposed model and the experimental data of this PV
module under different irradiation conditions are indicated in Figure 3. No deviations can be noticed between
the simulation and experimental results. This represents the verification of the validity of the linear PV model

 ISSN: 2088-8694
904
Table 2. Identification of first operating point
Parameter
V =Voc, I=0 (arbitry)
T= at 25 ◦C, and G=1000 W/m2 (arbitry)
A=1.5611
Rsh =2.8860e+06
Rs =2.5241e-01
determined using
Newton-Raphson
based on equations(6-8)
1.4352e-06
=4.8
determined using
equations(9-10)
Figure 2. Voltage current characteristics of at
different temperature
Figure 3. Voltage current characteristics at different
irradiation
To evaluate accuracy of the proposed model, the corresponding normalized root mean square error
percentage [nRMSE(%)] are calculated at different conditions and it compared with [nRMSE(%) of an
accurate which are given in [18]-[19]. Table 3 gives the corresponding normalized root mean square error
percentage [nRMSE(%)] calculated by
√ ∑ ( )
√ ∑ ( )
where Ei is the estimated value, and Ti is true values obtained from data sheet. Table 2 gives the evaluated
nRMSE(%) from Ref. [19] and evaluated nRMSE(%) corresponding to the theoretical 1-5 curves
(Figure 2-3) it can be seen from this table II, the linear model has presented the best accuracy under different
conditions. In additional to, the number of tunable parameters are lowered.
Table 2. E NRMSE(%) of the different PV models for KC200GT moduel
G=1000
T=25
G=600
T=25
G=200
T=25
NRMSE(% OF MODELE IN [18] 6.35 [18] 4.36 [18] 6.55 [18]
NRMSE(% OF MODELE IN [19] 1.12 [18] 2.15 [18] 1.29 [18]
NRMSE(% OF THIS WORK 0.47 0.57 0.66
Table 3 gives the evaluated nRMSE(%) corresponding to the theoretical 1–5 curves based linear
model and experimental data for KC200GT at different temperatures. It can be observed that the theoretical
1–5 curves are sufficiently accurate for the experimental data. This proves the validity of the proposed
parameter identification technique for PV modules.

905
Table 2. NRMSE(%) of the linear model at different temperature
G T NRMSE(%)
1000 25 0.47
1000 50 0.39
1000 75 0.69
6. CONCLUSION
The target of this study is to obtain an linear PV model which plays an important role in linear
control approach and simulation studies of the PV power systems. The mathematical model of the PV
module is a nonlinear I-V characteristic that includes several unknown parameters because of the limited
information provided by the PV manufacturers. So, a linear model of photovoltaic panels has been developed
and implemented. From the present analysis, one can draw the following main conclusions:
1. The proposed method estimate the parameter of a pv without any the conversion problem .
2. The calculated (I-V) curves based on proposed model are in good agreement with the experimental
data of KC200GT module for different effects of the environment (temperature and irradiance).
Also, the maximumvalue of corresponding normalized root mean square error percentage
[nRMSE(%)]less than 1%
3. The proposed model can be used for linear control approach and simulate large-scale PV systems
with low-cost computer platforms
APPENDIX
By simplification of equations (13-17), CI,CG ,CT can be defined as follow
CI=
CT=
where
( ) ,c4= ,
( ) , (( ) ) ( )
(( ) )
( )
( ) , ( )
+( ) ( )
+
+( )
C13= ( ),C14=( )
(
( ) ( )
)

 ISSN: 2088-8694
906
( )
(
( )
)
(
( )
)
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