Parameter Extraction of PV Module using NLS Algorithm with Experimental Validation

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 5, October 2017, pp. 2392~2400
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i5.pp2392-2400  2392
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
Parameter Extraction of PV Module using NLS Algorithm with
Experimental Validation
Alivarani Mohapatra1
, Byamakesh Nayak2
, K. B. Mohanty3
1,2
School of Electrical Engineering, KIIT University, Bhubaneswar, India
3
Dept. of Electrical Engineering, NIT Rourkela, Rourkela, India
Article Info ABSTRACT
Article history:
Received Dec 9, 2016
Revised Mar 27, 2017
Accepted May 11, 2017
Photovoltaic (PV) module parameters act an important task in PV system
design and simulation. Most popularly used single diode Rsh model has five
unknown electrical parameters such as series resistance (Rse), shunt
resistance (Rsh), diode quality factor (a), photo-generated current (Ipg) and
dark saturation current (Is) in the mathematical model of PV module. The PV
module output voltage and current relationship is represented by a
transcendental equation and is not possible to solve analytically. This paper
proposes nonlinear least square (NLS) technique to extract five unknown
parameters. The proposed technique is compared with other two popular
techniques available in the literature such as Villalva’s comprehensive
technique and modified Newton-Raphson (N-R) technique. Only two
parameters Rse and Rsh are estimated by Villalva’s technique, but all single
diode unknown electrical parameters can be estimated by the NLS technique.
The accuracy of different estimation techniques is compared in terms of
absolute percentage errors at MPP and is found the minimum for the
proposed technique. The elapsed time for parameter estimation for NLS
technique is minimum and much less compared to other two techniques.
Extracted parameters of polycrystalline ELDORA-40 PV panel by the
proposed technique have been validated through simulation and experimental
current-voltage (I-V) and power-voltage (P-V) characteristics.
Keywords:
Electrical characteristic
Maximum power point (MPP)
Nonlinear least square method
Parameter extraction
Polycrystalline ELDORA-40
PV panel
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Alivarani Mohapatra
School of Electrical Engineering
KIIT University, Bhubaneswar-24
Odisha, India
Email: aliva.priti@gmail.com
1. INTRODUCTION
Solar photovoltaic (PV) energy is becoming popular day by day, and a good deal of research is
going on PV generation because it needs less maintenance, simple to model and gives clean energy [1], [2].
The basic unit of a PV system is the PV cell which converts light energy into electrical energy. To meet the
load demand, many PV cells are joined in series and parallel fashion to make PV modules or panels [3]. For
large power application, many panels are again linked up in series-parallel fashion to make PV array.
Performance and efficiency of PV system are still below expectation, so a large amount of study is going on
to enhance the efficiency of the PV system. For extracting maximum power from the PV panel, the load line
must pass through the maximum power point (MPP). A converter with a proper MPP tracking (MPPT)
alogorithm is required for maximum power extraction from the panel. There is a significant impact of
environment on electrical characteristics of PV panel and MPP changes with a change in temperature and
irradiation. PV module has a unique operating point (Impp, Vmpp) where available power is maximum [4]–[8].
Maximum power extraction from the PV panel is achieved by connecting a converter to the PV system and
controlling the voltage and current with MPPT algorithm to track the MPP at all operating condition. In PV

IJECE ISSN: 2088-8708 
Parameter Extraction of PV Module using NLS Algorithm with Experimental Validation (Alivarani
Mohapatra)
2393
power system, both PV panels and converters exhibit nonlinear and time-varying characteristics, which
result in challenging control problem [9], [10].
The arithmetical model of a PV panel is vital to know which is coupled to a power converter in a
PV system [11]. Unluckily all model parameters are not on hand in the data sheet provided by the
manufacturer. Manufacturer datasheet only provide few parameters like open circuit voltage (Voc,n), Short
circuit current (Isc,n), voltage and current at maximum power point (Vmpp,n, Impp,n), temperature coefficients
for voltage (Kv), current (KI) and power (Kp) at standard test condition (STC). STC refers to irradiation of
1000 W/m2
and temperature of 25◦
C. Therefore it is essential to estimate unknown parameters to model a
PV system accurately [12]. Different models of the PV module are classified as an ideal model, single diode
four parameters model and single diode five parameters model [13]. The most commonly used model is the
single diode five parameters model as shown in Figure 1. It has five unknown parameters as photo-
generated current (Ipg), saturation current (Is), diode quality factor (a), series resistance (Rse) and shunt
resistance (Rsh). According to the semiconductor theory, diode D represents the p-n junction of the PV
module and current Id stands for the escaping current through the p-n junction due to the diffusion
mechanism. Series resistance (Rse) represents the losses due to contact resistance and shunt resistance (Rsh)
represents the leakage current at the p-n junction of the PV module. There are different parameter extraction
techniques are available in literature with their merits and demerits [14]–[20]. In this paper, unknown
parameters are estimated by nonlinear least square (NLS) technique and experimentally validated with a
laboratory set up. The NLS technique is also compared with two known techniques such as Villalva’s
comprehensive technique and modified Newton-Raphson (N-R) technique [3], [21], [22]. The superiority of
the proposed NLS technique is established by comparing the absolute error of maximum power.
2. MODELLING OF PV PANEL
The current voltage relationship of a PV panel is expressed as:[14]
pv se pv pv se pv
pv pg s
s t sh
V R I V R I
I I -I exp 1
N V a R
   
    
   
(1)
Rse
Ipg
Id
Rsh
+
-
Ipv
Vpv
D
Figure 1.Popular Rsh model of PV panel
Vpv and Ipv represent the module output voltage and current respectively, and Vt is the thermal
voltage of the p-n junction The electrical properties of the PV panel depend upon the environmental factor
like solar insolation and temperature [23]. The dependency of photo-generated current and open-circuit
voltage of the PV module is represented by equations:
pg pg,n I
n
G
I I K dt
G
    (2)
oc oc,n vV V K dt  (3)
Where, Ipg,n and Voc,n are measured at a nominal condition of the temperature of 25o
C and irradiation
of 1000 W/m2
. The temperature difference (dt=T-Tn) is a difference between nominal and actual
temperature. G is the insolation at operating condition, and Gn is insolation at nominal condition. The
saturation current of the PV module at a particular environmental condition is represented by equation (4)
[3].
sc,n I
s
oc,n v
s t
I K dt
I
V K dt
exp 1
N V a


 
 
 
(4)

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Figure 2. I-V curve of ELDORA-40 PV panel and its three remarkable points
The simulated current-voltage (I-V) characteristic of the test module at STC is shown if Figure 2 with its
three remarkable points as Isc, Voc and Pmpp. Power extraction from the PV panel will be maximum when the
module is operated at its MPP.
3. EXTRACTION OF PV MODULE PARAMETERS
Construction of exact PV model is possible if all the electrical parameters of the module are to be
extracted accurately. Here an attempt has been taken to extract the model parameters accurately using only
the parameters of the manufacturer datasheet without any measurement. Three methods are discussed here
with their merits and demerits.
3.1. Starting of the methods
To estimate five unknown parameters, five equations whose dependencies upon the unknown
parameters is needed. Rewriting equation (1) at three remarkable points as short-circuit, open-circuit and
MPP gives three equations as (5), (6) and (7) respectively [22].
se sc se sc
sc pg s
s t sh
R I R I
I I -I exp 1
N V a R
  
    
   
(5)
oc oc
pg s
s t sh
V V
I =I exp 1
N V a R
  
   
   
(6)
mpp se m pp mpp se m pp
m pp pg s
s t sh
V R I V R I
I I I exp 1
N V a R
   
     
   
(7)
Two more equations are needed for solving five parameters model. Another two equations can be considered
knowing the fact that  mpp
dP
V V 0
dV
  and  sc
sh
dI 1
I I
dV R
  
mpp se mpps
s t s t sh
mpp mpp
mpp se mppse s se
s t s t sh
V R II 1
exp
N V a N V a R
I V
V R IR I R
1 exp
N V a N V a R
  
  
  
  
   
   
(8)
s se sc
s t s t sh
sh se s se sc se
s t s t sh
I R I 1
exp
N V a N V a R1
R R I R I R
1 exp
N V a N V a R
 
 
 
 
  
 
(9)
0 5 10 15 20
0
0.5
1
1.5
2
2.5
Voltage (V)
Current(A)
Voltage
Source
region
Power region
Current Source region
MPPI
sc
V
oc

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Some literature uses  oc
se
dI 1
V V
dV R
   as one equation but it has some disadvantages as discussed in
[24]. Now five equations are available to extract five unknown parameters using equations (5)-(9). Further
improving the model the photo generated current may be written as equation (10) by neglecting saturation
current sI in (5). Rsh,min as given in equation (11) represents the minimum value of Rsh taken as initial value
of parallel resistance [3].
sh se
pg,n sc,n
sh
R R
I I
R

 (10)
mpp oc mpp
sh,min
sc mpp mpp
V V V
R
I I I

 

(11)
3.2. Review of Villalva’s iterative technique and mocified N-R technique
3.2.1. Villalva’s iterative technique
This method intends to adjust Rse and Rsh values such that with one pair of {Rse, Rsh}, the maximum
power as given in the datasheet (Pmpp,curve= Vmpp*Impp) must be equal to Pmpp, model. The iterative method
begins with Rse=0 and Rsh= Rsh,min. The adjustment of Rse and Rsh continues until Pmpp,curve= Pmpp,model.solving
(12). Figure 3 represents the flow diagram of Villalva’s technique. The main drawback of this technique is
that the diode quality factor ‘a’ chosen arbitrarily between1 a 2  before iteration starts. Only two
parameters Rse and Rsh are extracted by this method and Ipg and Is values are calculated as per equation (2),
(10) and (4). The extracted parameter values highly dependent on chosen tolerance and increment on Rse
value in the parameter extraction process.
 mpp mpp se mpp
sh
mpp se mpp
mpp pg mpp s mpp s
s t
V V R I
R
V R I
V I V I exp V I Pmax,model
N V a


   
    
   
(12)
Inputs :datasheet parameters,
T,G,a=1.5,Rse=0,Rsh=Rsh,min,Eq(11)
Set, tol=e-5, ɛpmax>tol
ɛpmax>tol ?
Y
for 0 ≤V≤Voc,n
Solve Eq. (1),(2),(4),(10), (12)
Calculate P=V*I
Find Pmax,model
Calculate
Ɛpmax=║Pmax,model-Pmax,curve║
Increment Rse
Display Rse,
Rsh
N
Figure 3. Flow diagram for Villalva’s iterative method
3.2.2. Modified N-R technique
To mitigate slow convergence of N-R technique, the modified N-R technique has been developed to
solve nonlinear equations with multiple roots. This technique is utilized for estimation of unknown

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parameters X=[Rse, Rsh, a. Ipg, Is] by considering five nonlinear equations (5)-(9) using fsolve command in
MATLAB. Chosen initial parameters play a vital role in accurate estimation of parameters.
4. PROPOSED NLS TECHNIQUE
4.1. Starting of the technique
To estimate unknown parameters of the polycrystalline Eldora-40 PV test module, equations (5)-(9) rewritten
as [21].
  se sc se sc
1 pg s sc
s t sh
R I R I
0 I I 1 I
N V a R
f x exp
  
       
   
(13)
  s
sh
oc oc
pg
s t
V V
0 I 1 I
N V a R
2f x exp
  
      
   
(14)
  mpp se mpp mpp se mpp
pg mpp
s t
s
sh
V R I V R I
0 I I 1 I
N V a R
3f x exp
   
       
   
(15)
 
mpp mpp
s t s t
mpp mpp
mpp se mppse se
s t s
ses
sh
t
s
sh
V II 1
N aV N aV R
0 V I
V R IR I R
1
N aV N aV
R
R
4
exp
f x
exp
  
  
    
  
   
   

(16)
 
se sc
s t s t sh
se se sc se
s t s t s
s
sh
h
s
I R I 1
N aV N V a R 1
0
RR I R I R
1
N aV N V a R
5
exp
f x
exp
 
 
   
 
  
 
(17)
To reduce the computational complication and for reduction of iterations, NLS algorithm modifies
multi-objective function to a single objective function. The initial parameter values, lower and upper limit
constraints are chosen with its physical significance. The flow algorithm for NLS technique used for
estimation of unknown parameters is shown in Figure 4. To solve five nonlinear equations by this approach,
lsqnonlin command in MATLAB is used. The objective function f(x) is chosen as
  2 2 2 2 2
x 1 2 3 4 5min f(x) f (x) f (x) f (x) f (x) f (x)     considering upper and lower limit constraints.
4.2. Initialization
Since the accuracy of estimation depends very well upon the initial parameter values, an intelligent
estimate of initial condition is very much essential for the solution to converge to the solution. From the
available literature [3][25] the initial values of different parameters are as follows.
 Ipg could be approximated by Isc.
 Is is determined using equation (4).
 Quality factor ‘a’ of the diode is chosen as1 a 2  .
 Rsh is a large value approximated according to (11).
 Rse is very small taken as 0, or in the range of milliohms.
In most cases, convergence occurs with the aforementioned initialization. If it fails, initialization
needs to be done randomly considering the range of each parameter values. The lower and upper bound of
search must be chosen carefully, keeping in mind the minimum and maximum values of the parameters.
5. RESULTS AND DISCUSSION
Unknown parameters are estimated by NLS technique and obtained results are compared with two
popular techniques like Villalva’s comprehensive technique and modified N-R technique. Module

IJECE ISSN: 2088-8708 
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characteristics are drawn at STC and compared as shown in Figure 5 and Figure 6. Extracted parameter
values are validated by conducting experiment on ELDORA-40 PV panel. Figure 5 (a) shows the I-V
characteristic of the ELDORA-40 PV panel and the result shows the characteristics match the datasheet
remarkable points such as, Isc, Voc, Vmpp, and Impp. Similarly Figure 5 (b) shows the P-V characteristics of the
ELDORA-40 PV panel and result shows the good agreement between the datasheet curve and simulated
curve using the extracted parameters. Table1 represents the datasheet of the test module at STC.
Start
Inputs: datasheet parameters
Initialize the unknown parameters,
Set lower and upper bounds of the
unknown parameters
Solve system of nonlinear equations:
Eqs.(13)-(17)
Function converge
to a solution?
Y
Output:
Rse,Rsh,a,Ipg,Is
N
End
Figure 4. Fowchart for nonlinear least square method of parameter extraction
Table 1. Datasheet of the test module ELDORA-40 at STC
Voc,n(V) Isc,n(A) Vmpp,n(V) Impp,n(A) Ns Kv(V/o
C) KI(A/o
C)
21.8 2.4 17.2 2.20 36 -0.32% 0.04%
Table 2. Comparison of extracted parameters of the test module at STC
Parameters Villalva’s technique
(Method-A)
Modified N-R technique
(Method-B)
NLS technique
(Method-C)
Rse ( Ω) 0.493 0.53 0.58
Rsh (Ω) 939.35 466.31 704.24
a 1.5 (fixed) 1.41 1.4
Ipg (A) 2.4 2.402 2.4
Is (A) 3.62e-7 1.1e-7 1.1e-7
Time of computation (S) 3.8 1.2 0.34
Absolute error of maximum power in % 0.43 0.24 0.07
The extracted parameter values are tabulated in Table 2 for ELDORA-40 PV module defined at
STC. The extracted parameter values are compared and it is seen that parameter values are in good agreement
with the datasheet characteristics. The absolute error of maximum power is calculated and seen that it is
minimum for NLS technique. The time of computation for parameter extraction is less in NLS technique as
compared to other two techniques.

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(a)
(b)
Figure 5. (a) I-V (b) P-V characteristic of the test module at STC
5.1. Validation of the model
To validate the extracted parameter values, the characteristic curve of ELDORA-40 PV panel has
been drawn using the extracted parameter values. The test setup is placed inside the laboratory, and artificial
irradiation is used with the help of halogen light as shown in Figure 6. The current and voltage values are
measured from zero load to full load condition at 25◦
C of temperature and 220W/m2
of irradiation. Parameters
extracted by NLS method has been used for simulation of the P-V and I-V curves of the PV panel at the
above mentioned temperature and irradiation. Figure.7 (a) and Figure 7 (b) shows good agreement between
the simulated and experimental characteristics, using the parameters extracted by the proposed NLS method.
The absolute percentage error of maximum power is calculated and is found less than one percent. The I-V
characteristic shown in Figure 7 (a) shows the simulated and experimental curve matching at almost all
points including the three remarkable points. Similarly, P-V characteristics shown in Figure 7 (b) also show
good agreement between simulated and experimental results.
Figure 6. Test set-up of ELDORA-40 PV panel

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(a)
(b)
Figure 7. (a) I-V (b) P-V characteristic of the test module using the parameters extracted by NLS technique
6. CONCLUSION
In this paper, NLS technique is proposed for parameter extraction of PV module and is compared
with other two well known techniques. All five unknown parameters of ELDORA-40 PV module extracted
using three different techniques and compared. The characteristics of the PV module show that proposed
NLS technique gives the best result with less computational complication compared to other two techniques.
It is seen that the proposed technique not only give good results at STC but also at other temperature and
irradiation. The absolute error of maximum power is compared with all the techniques and it is observed that
the value is very less for NLS technique. The absolute error of maximum power is also calculated taking the
experimental characteristics and simulated one at a temperature of 25o
C and irradiation of 220 W/m2
and it is
found less than one percent. From the experimental results, it is observed that the unknown parameter
extracted by the proposed NLS technique is correct and it validates the characteristic of the datasheet at all
the three remarkable points with less error.
REFERENCES
[1] M. Taghi, S. Moslehpour, and M. Shamlo, “Economical load distribution in power networks that include hybrid
solar power plants,” Electr. Power Syst. Res., vol. 78, pp. 1147–1152, 2008.
[2] M. Louzazni, E. H. Aroudam, and H. Yatimi, “Modeling and Simulation of A Solar Power Source for a Clean
Energy without Pollution,” Int. J. Electr. Comput. Eng., vol. 3, no. 4, pp. 568–576, 2013.
[3] M. G. Villalva, J. R. Gazoli, and E. R. Filho, “Comprehensive Approach to Modeling and Simulation of
Photovoltaic Arrays,” IEEE Trans. Power Electron., vol. 24, no. 5, pp. 1198–1208, May 2009.
[4] A. Pallavee Bhatnagar and B. R. K. Nema, “Conventional and global maximum power point tracking techniques
in photovoltaic applications: A review,” Journal of Renewable and Sustainable Energy, vol. 5, no. 3. American
Institute of PhysicsAIP, p. 32701, May-2013.
[5] T. Esram and P. L. Chapman, “Comparison of Photovoltaic Array Maximum Power Point Tracking
Techniques,” IEEE Trans. Energy Convers., vol. 22, no. 2, pp. 439–449, 2007.
[6] A. Mohapatra, B. Nayak, and K. B. Mohanty, “Current based novel adaptive P&O MPPT algorithm for
photovoltaic system considering sudden change in the irradiance,” in 2014 IEEE International Conference on
Power Electronics, Drives and Energy Systems, PEDES 2014, 2014.
[7] A Jusoh, T Sutikno, TK Guan, S Mekhilef, “A Review on favourable maximum power point tracking systems in
solar energy application,” TELKOMNIKA Telecommunication Computing Electronics and Control., vol. 12, no.
1, pp. 6-22, 2014.
[8] K. T. Ahmed, M. Datta, and N. Mohammad, “A Novel Two Switch Non-inverting Buck-Boost Converter based
Maximum Power Point Tracking System,” Int. J. Electr. Comput. Eng., vol. 3, no. 4, pp. 467–477, 2013.

 ISSN: 2088-8708
2400
[9] W. Xiao, W. G. Dunford, P. R. Palmer, and A. Capel, “Regulation of photovoltaic voltage,” IEEE Trans. Ind.
Electron., vol. 54, no. 3, pp. 1365–1374, 2007.
[10] M. G. Villalva, T. G. de Siqueira, and E. Ruppert, “Voltage regulation of photovoltaic arrays: small-signal
analysis and control design,” IET Power Electron., vol. 3, no. 6, pp. 869–880, 2010.
[11] M. G. Villalva, J. R. Gazoli, and E. F. Ruppert, “Modeling and Circuit-Based Simulation of Photovoltaic
Arrays,” Brazilian J. Power Electron., vol. 14, no. 1, pp. 1244–1254, 2009.
[12] K. Azizi and A. Ghaffari, “Parameter estimation of photovoltaic panels and a model-based maximum power
point tracking algorithm,” J. Renew. Sustain. Energy, vol. 8, no. 4, p. 43504, Jul. 2016.
[13] Y. Mahmoud, W. Xiao, and H. H. Zeineldin, “A Simple Approach to Modeling and Simulation of Photovoltaic
Modules,” Sustain. Energy, IEEE Trans., vol. 3, no. 1, pp. 185–186, 2012.
[14] V. Chin, Z. Salam, and K. Ishaque, “Cell modelling and model parameters estimation techniques for
photovoltaic simulator application: A review,” Appl. Energy, vol. 154, pp. 500–519, 2015.
[15] Y. A. Mahmoud, W. Xiao, and H. H. Zeineldin, “A parameterization approach for enhancing PV model
accuracy,” IEEE Trans. Ind. Electron., vol. 60, no. 12, pp. 5708–5716, 2013.
[16] D. F. Alam, D. A. Yousri, and M. B. Eteiba, “Flower Pollination Algorithm based solar PV parameter
estimation,” Energy Convers. Manag., vol. 101, pp. 410–422, 2015.
[17] Y. Mahmoud and E. F. El-Saadany, “Fast power-peaks estimator for partially shaded PV systems,” IEEE Trans.
Energy Convers., vol. 31, no. 1, pp. 206–217, 2016.
[18] O. Hachana, K. E. Hemsas, G. M. Tina, and C. Ventura, “Comparison of different metaheuristic algorithms for
parameter identification of photovoltaic cell/module,” J. Renew. Sustain. Energy, vol. 5, no. 5, p. 53122, Sep.
2013.
[19] B. Subudhi and R. Pradhan, “A comparative study on solar array parameter extraction methods,” Int. J. Renew.
Energy Technol., vol. 3, no. 3, pp. 295–315, 2012.
[20] Y. Mahmoud and E. F. El-Saadany, “A Photovoltaic Model With Reduced Computational Time,” IEEE Trans.
Ind. Electron., vol. 62, no. 6, pp. 3534–3544, 2015.
[21] B. K. Nayak, A. Mohapatra, and K. B. Mohanty, “Parameters estimation of photovoltaic module using nonlinear
least square algorithm: A comparative study,” in 2013 Annual IEEE India Conference, INDICON 2013, 2013.
[22] D. Sera, R. Teodorescu, and P. Rodriguez, “PV panel model based on datasheet values,” 2007 IEEE Int. Symp.
Ind. Electron., pp. 2392–2396, Jun. 2007.
[23] G. Farivar and B. Asaei, “A new approach for solar module temperature estimation using the simple diode
model,” IEEE Trans. Energy Convers., vol. 26, no. 4, pp. 1118–1126, 2011.
[24] B. Nayak, A. Mohapatra, and B. Misra, “Non Linear IV Curve Of PV Module: Impacts On MPPT And
Parameters Estimation,” Int. J. Eng. Res. Technol., vol. 1, no. 8, pp. 1–8, 2012.
[25] A. Chatterjee, A. Keyhani, and D. Kapoor, “Identification of photovoltaic source models,” IEEE Trans. Energy
Convers., vol. 26, no. 3, pp. 883–889, Sep. 2011.

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