IRJET- Comparison between Ideal and Estimated PV Parameters using Evolutionary Algorithms

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 208
COMPARISON BETWEEN IDEAL AND ESTIMATED PV PARAMETERS
USING EVOLUTIONARY ALGORITHMS
Javad Rahmani1, Ehsan sadeghian2, Soheil Dolatiary3
1Graduate Student of Digital Electronics Engineering, Department of Islamic Azad University, Science and
Research BranchTehran, Iran
2,3Graduate Student of Electrical Engineering, Islamic Azad University Central Tehran Branch College, Tehran, Iran
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Abstract – Micro grids are now emerging from lab modules
sites into commercial markets, driven by technological
improvements, falling costs, a proven track record, and
growing recognition of their benefits. Their maincontribution
is to improve reliability and resilience of electrical grids, to
manage the addition of distributed cleanenergyresources like
wind and solar photovoltaic (PV) generation to reduce fossil
fuel emissions, and to provide electricity in areasnotserved by
centralized electrical infrastructure. In between, the
popularity of photovoltaic panel is not deniable. In this paper,
the ideal and practical electrical model of PV is being
discussed. Due to significant role of the PV parameters, they
are assessed by several heuristic methods and the results are
compared. Henceanoptimal methodforparameterestimation
is discussed that can be used in microgridsimulationstoreach
to an optimal quiescent working condition
Key Words: Micro grid,powersystem,PV panel,heuristic
optimization
1. INTRODUCTION
Over 99.9 per cent of Earth’s energy comes from the sun .In
addition to sunlight reaching the earth’s surface, the sun is
the source of all wind, hydro, and wave energy as well as
biomass and fossil fuels which originated from organic
matter and represents stored solar energy collected by
photosynthesis over many millions of years [1, 2]. Enough
direct sunlight reaches the earth to supply all of
humankind’s energy needs many times over [3, 4]. For
example, the yearly direct normal irradiation (DNI)
component of solar energy from just the Northern Cape
Province of South Africa is equivalent to more than six times
the worldwide annual energy consumption [5, 6]. The
necessity of energy management in a multi generation
source network to ensure a reliable and continuous power
supply, accelerates theintegrationofintermittentrenewable
energy resources in power grid [7, 8]. By associating a
variety of distributed energy sources and loads in a power
grid these issues are addressed in microgrd concept with
ability of islanding operation with the main grid [9, 10].
Economic, environmental, and electricity supply qualityand
reliability are impacted by deployments of micro grids.
Reducing carbon dioxide emissions related to energy
generation, guaranteeing a low cost of energy, and
maintaining high continuity and/orqualityof electricsupply
are drivers of coordination strategies in controllable local
grids [7, 11, 12].
AC
DC
DC
DC
DC
DC
DC Bus
Inverter
Grid
Fig. 1. Micro grid and renewable resources
Many analysts predict that, by the end of this decade,
distributed network energy costs will be competitive with
other conventional forms of generating electricity.
Government assistance can also contribute to the growth of
this procedure. Perhaps at first, it would seem that the cost
of generating electricity by solar power is more than
conventional, but policymakers will slowly conclude that
burning fossil fuels makes significant social costs, including
environmental pollution-related ones. How much the use of
the solar energy system can be welcomed by homeowners
depends on factors such as initial costs, maintenance costs,
government grants, electricity costs, and the price ofsalesof
surplus electricity [13, 14]. The ability to isolate the grid
from the main network also increases cybersecurity. The
reason is when an incident occurs, for anyreason,microgrid
can limit its consequences [15, 16]. If there is no physical
damage, a grid can continue to operate as long as it has
access to the energy source (fossil fuels, sun, or wind). Inthe
end, considering economic issues and technology
regulations, it is possible to expand the distributionnetwork
by using a series of adjacent grids [17, 18]. They share
energy close together with each other and the main grid, in
addition to maximizing the level of access, minimize thecost

of generating electricity. In the adjacent trenches that are
also set up and operated by the private sector, large power
plants can, in lieu of providing electricity to each consumer,
if necessary, the power required by a grid or in case of
emergency multi-grid connections. Hence, they can provide
part of the electricity required by the global network [19,
20]. However, the dilute nature of sunlight reaching earth
makes it relatively difficult to capture and convert into
usable power. There are two methods commonly used to
harvest solar energy for electricity. PV cells use
semiconductors to directly convert light into electricity
while solar thermal systems convert sunlight into thermal
energy to generate electricity by means of a heat engine in
much the same way as conventional power plants do. This
happens by usage of focusing optics and solar tracking
mechanisms to concentrate sunlight for producing high
temperature thermal energy. Fig.1 shows a classification
diagram of solar power in which a distinction is made
between PV and solar thermal systems.
Solar Power Plants
PhotovoltaicSolar Thermal
Non-
Concentrating
Concentrating
DC-AC Inverter
Thermal Energy Storage
Ranking CycleBrayton Cycle
Power Grid
Fig. 2 Classification diagram of solar power
The initial cost to make PVs is still high due to their low
returns, but with the increasing progression of
semiconductor technology, theirinitial pricesaredecreasing
and their returns are increasing. In between different
methods of optimization helped the gridoperatorstoexploit
the maximum benefit of the currently installed PVs. For
example, a new method for application in communication
circuit system is proposed in [21, 22] that it causes
increasing the efficiency, PAE, output power and gain in
which the author discusses about switching class of E-J and
switching class of AB-J respectively. Application of neural
network in power engineering as well as other branches of
science is well established as in [23, 24] neural networks
based models were successfully applied in external
radiotherapy to track the dynamic of respiratory system in
order to target tumors with high accuracy and prevent side
effects of extra radiation. [25, 26] represents a framework
for automatic segmentation of medical images of different
kinds of tumors using adaptive neuro-fuzzy inference
system. An Adaptive controller based on model reference
control is designed for tracking control of manipulator of
arm robots. Robust controller is used in [27, 28] to control
nonlinear systems. In case of robotics, In [29, 30] a sliding
mode controller is designed for spherical mobile robot t. A
grid resilient framework is utilized in [31-33]toenhance the
system outage recovery faced by extreme emergencies.
Dealing with a lot of parameters, in [34, 35] new methods to
reduce the dynamic model of power system is proposed.
Resilient cyber method to defend against attacks id
discussed in [36].The implemented optimizing engine helps
rapid recovery of the power grid outage in case of loss of
generators and is able to track the desired trajectory and
remain stability of the micro grid [19, 37]. In this research,
the parameters of the single-dihedral photovoltaic panel
model (5 parameters) and two diode (7 parameters) are
estimated using various meta-optimization optimization
algorithms. The photovoltaic panel is characterized by
experiments conductedattheRenewableEnergyLaboratory
of the University of Industrial Engineering and Advanced
Technology, Then using the practical results, the problem of
estimating the parameters in order to reduce the squared
error has become a minimization problem and been solved
with various optimization algorithms [38, 39]. In addition,
the model obtained from the experiments is compared with
the model derived from the panel data sheet. For this
purpose, the photovoltaic panel model obtained from the
panel data sheet is also obtained in standard conditions. In
the end, the two models are compared with each other.
1.1 PHOTOVOLTAIC PANEL MODELING
Photovoltaic panels can be modeled in two ways
A. Ideal single diode photovoltaic panel
pv,cell 0,cell
qV
I I -I [exp( )-1]
akT

(1)
This equation depends on the radiation perpendicular to
the panelsurfaceandpaneltemperatureandthephotovoltaic
panels used.
Ipv
Id
Rsh
Rs
Ish
I
V
G
Fig. 3 Electrical Photovoltaic Model
The constants pv,cellI
stands for the flow generated by the
collision light (directly related to the received radiation),

0,cellI reverse saturation current or diode leakage current,q
electron charge, K is Boltzmann's constant, T bond
temperature and an is ideal constant of diode respectively
[40, 41].
A. Practical photovoltaic panel modeling
The practical characteristics of the photovoltaic panel by(1)
do not adequately describe the features of the experimental
photovoltaic panel [42, 43]. In order to express the practical
characteristics of the photovoltaic panel, the addition of
other components requires equation (2). The experimental
model of the photovoltaic panel is as follows:
s s
pv 0
t p
V R I V R I
I I I [exp( ) 1]
V a R
 
    (2)
In which pvI is photovoltaic current , 0I diode saturation
current ,T temperature of photovoltaic panel, sN number of
cell series, pN the number of parallel cells, sR series
equivalentresistance, pR parallel equivalent resistanceanda
is the ideal coefficient of the panel. The photovoltaic and
diode saturation flows are calculated according to the
number of series and parallel cells in the panel using the
following formulas:
pv pv,cell pI (I ) (N )  (3)
0 0,cell pI (I ) (N )  (4)
With the photovoltaic panel temperature and the number of
series cells present in the panel, the photovoltaic panel
temperature is obtained [44]. Therefore, (2) has five
unknown parameters. These parameters are the
photovoltaic current, the diode saturation current, the
equivalent series resistance, the equivalent parallel
resistance, and the ideal coefficient of the panel [45]. All of
these five parameters depend on the environmental factors
affecting the panel, which are the most important factors of
radiation and temperature [46, 47].
B. Two diode photovoltaic panel
By adding a diode to a single-dihedral photovoltaic panel
model, a two-dihedral photovoltaic panel model (Fig. 3) is
obtained [48, 49]. This model involves the effectoftheopen-
ended loss in the space between the two links by adding an
additional diode in the flow relation. This model increases
the accuracy considerably, but also makes the calculation
even more complicated. The equivalent circuit of the
photovoltaic panel of the two diode is in Fig.3.
Ipv
Id1
Rsh
Rs
Ish
I
V
G
Id2
Figure. 4. Two diode photo voltaic cell model
s s s
pv 01 02
t 1 t 2 p
V R I V R I V R I
I I I [exp( ) 1] I [exp( ) 1]
V a V a R
  
     
(5)
In which 0I is diode saturation current , tV temperature of
photovoltaicpanel, sN numberofseriescells, pN thenumber
of parallel cells, sR series equivalent resistance, pR parallel
equivalent resistance and a is the ideal coefficient of the
diodes is. In this equation, as the single-diodemodel withthe
photovoltaic panel temperature and the number of series
cells in the panel and, the temperature of the photovoltaic
panel can be obtained, but still, seven parameters are
unknown. These parameters are the photovoltaic current,
the diodes saturation current, the equivalent series
resistance, the equivalent parallel resistance, and the ideal
coefficients of the diodes respectively.
2. IMPROVEMENT OF PHOTOVOLTAIC PANEL MODEL
The model presented in the previous section for the
radiation, the specified temperature is obtained,andthefive
parameters in that model are stable: the three parameters,
namely series and parallel resistors, and the coefficient of
the ideality of the diodes in the panel [50]. Two other
parameters, are the photovoltaic flow of the panel and the
saturation current, are modified by the use of (3) and (4). In
these formulas, the photovoltaic panels are mainly effective
from radiation and effective saturation flow from the panel
temperature. This model does not respond appropriately to
different solar conditions and temperatures, while changing
at least four parameters of the five parameters are expected
by different weather conditions [51, 52].
In this paper, an analytical model ofa photovoltaicpanel that
is capable of representing the characteristics of the
photovoltaic panel characteristic for different weather
conditions has been used. Equivalent circuit parameters are
obtained by solving a set of equations using the information
in the panel data sheet under standard conditions and with
the test and error method [53]. The superiority of this
method is not using a particular method to solve the
equation or using optimization algorithms, which makes it
easier to implement this model in control devices such as
microcontrollers. Also, by using the test and error method
and the lack of use of approximation in equations, the more
accurate answers are obtained, which is another advantage
of this method.

The failure of this method is to create a new equation for the
equivalent photovoltaic panel in a different condition than
standard conditions [51, 54]. In the sense of the equation
used in standard conditions, the equation of ordinary
photovoltaic panel is adapted. In addition, the use of panel
data values in standard conditions, which rarely happens,
reduces the accuracy of the model. In any case, it is possible
to use this model for different weather conditions on other
models. The proposed model of the photovoltaic panel is
represented by the following equation [19, 55]:
G ref s G( [V K(T T )] IR )/ nT
G G pv o G
G ref s
sh
I( ,T) I (T) I ( ,T)(e 1)
[V K(T T )] IR
R
    
     
   

(6)
In this equation, radiation (G) is entered using the following
equation:
G
ref
G
G
  (7)
In which refG the radiation in standard conditions is 1000
watts per square meter( 2
W
m
). The pvI in (6) is obtained by
the following formula:
pv pv,n II (I K T)   (8)
Therefore, the photovoltaic flow is equal to the previous
formula and the other parametersobtainedaredifferent.Kis
a temperature coefficient that is an additional parameter
than the previous formula. In this formula, series and
parallel resistors are taken for each radiation and constant
temperature [48].
In the previous chapter, the parameters were obtained by
numerical methods [56, 57]. In this section, the parameters
are obtained by an adaptive test and error method. This
method is more suitable than other methods because of the
lack of numerical methods and approximations [58]. In this
method, the data of three points of short circuit, open circuit
and maximum power, as well as the characteristic slope of
the current voltage at short points and open circuit are used
[42].
A. Calculating Parameters
In the method used the values of short circuit and open
circuit and maximum power in the data sheet of the
photovoltaic panel and the curve slope of the voltage
characteristic of the short circuit and open circuit obtained
from [36] are used.
• Short circuit point sc,refV 0,I I 
sc,ref s refI R nT sc,ref s
sc,ref L,ref o,ref
sh
I R
I I I (e 1)
R
    (10)
• Short circuit slope point
sc,ref s ref
sc,ref s ref
sc,ref
I R nT
o,ref ref sh
I R nTV 0
shos o,ref ref shI I
(I nT )e 1 RdI 1
dV R1 R ((I nT )e 1 R )


   
 
(11)
• Open circuit point
oc,refV V ,I 0 
oc,ref refV nT oc,ref
L,ref o,ref
sh
V
0 I I (e 1)
R
    (12)
• Open circuit gradient
oc,ref ref
oc,ref ref
oc,ref
V nT
o,ref ref sh
V nTV V
sos o,ref ref shI 0
(I nT )e 1 RdI 1
dV R1 R ((I nT )e 1 R )


   
 
Maximum power point ( mp,ref mp,refV V ,I I  ) (13)
mp,ref mp,ref s ref(V I R nT ) mp,ref mp,ref s
mp,ref L,ref o,ref
sh
V I R
I I I (e 1)
R
 
   
The formulas obtained from these parameters are
approximated. Therefore, in this paper, these methods are
directly used in the test and error method based on the
change of n and sR parameters [50].
I. PARAMETER ESTIMATION ALGORITHM
The following algorithm is used to estimate the parameters:
1. Determine the initial values of the parameters n
and sR
2. Insert values L,refI and shR using:
L,ref sc,refI I
sh shoR R
3. Calculation I0 using (4).
4. n is calculated using (6) and is compared withthe
amount specified in step (1).
5. The steps 3 and 4 are repeated to obtain the n
value according to the desired accuracy.
6. sR is calculated based on n from (7), is
compared with assumed value, and will change.
7. Steps 3,4 and 5 will be repeated.
sR Re-calculated by (7) and compared to the previousvalue
and changed accordingly.
This process continues to reach the correct value. In this
way, the system is solved by not simplifyingany equationsin
a non-approximate form. Very simple programs that can be
used to estimate these parameters in various control
methods implement this process [59]. Matlab software has
been used to run the algorithm. The results obtained by this
method provide accurate and high-speedresults.Buttheuse
of the initial values in the photovoltaic panel data sheet due

to non-experimental informationreducestheaccuracyofthe
model. The parameters calculated for the regular
commercial solar panel which are specified in the standard
conditions are depicted in Table 1.
Table -1: Parameters calculated for the solar panel
under standard conditions
shR sR n oI pvI
131.7225(Ω) 0.27742(Ω) 0.002438(Ω) 3.7e-
11(A)
5.311(A)
The only remaining unobstructed parameter K is the
temperature coefficient obtained by the curve values of the
voltage characteristic current in the panel data sheet. This
coefficient is used to move the curve of the voltage
characteristic curve to better position the curves. For a
favorable temperature *
T different than refT , this
parameter is obtained from the following equation [53, 60]:
*
mp mp
* *
mp ref
V V
K
I (T T )



(14)
Which *
mpV voltageand *
mpI currentatoptimal temperatures
are presented in the panel data sheet.
It is more appropriate to use radiation data of 1000 watts
per square meter and a temperature of 75 degrees that is
present in the data sheet of the curve of the current voltage
curve. To do this, you can turn curve information into
numbers using the Plot digitizer software [61, 62].
3. VALIDATION OF THE MODEL
This chapter uses the Newton algorithm to validate the
previous chapter. Due to the complexity of the formula, the
speed of the Newton algorithm decreases, but it still has the
right precision. For radiation of 1000 W / m2 and at 25 ° C,
the curve of the characteristic current-voltage and voltage-
power characteristics of the panel in standard conditions,
are shown in Fig .5 and Fig.6 respectively.
As can be seen, the results for a given temperature and
radiation are not quite as good as the results of the previous
chapter, and the curves are somewhat biased [42, 63].
However, being able to operate in different environmental
conditions is very suitable, in contrasttowhichlowaccuracy
can be neglected. It should be noted, however,thattheuse of
panel data information is one of the factors reducing model
accuracy.
0 2 4 6 8 10 12 14 16 18 20
0
1
2
3
4
5
6
I-V curve
V [V]
I[A]
Model
Exprimental data at STC
Chart -1: Comparision between current-voltage
characteristics with ideal and optimized parameters
0 2 4 6 8 10 12 14 16 18 20
0
10
20
30
40
50
60
70
80
P-V curve
V [V]
P[W]
Model
Exprimental data at STC
Chart -2: Comparison between power-voltage
characteristics with ideal and optimized parameters
0 2 4 6 8 10 12 14 16 18 20
0
1
2
3
4
5
6
I-V curve
V [V]
I[A]
(R=1000,T=25)(R=1000,T=25)(R=1000,T=25)(R=1000,T=25)(R=1000,T=25)
Model
Exprimenta data at STC
Chart -3: Comparison of with ideal and optimized voltage-
current profiles in different weather conditions

0 2 4 6 8 10 12 14 16 18 20
0
10
20
30
40
50
60
70
80
P-V curve
V [V]
P[W]
(R=1000,T=25)(R=1000,T=25)(R=1000,T=25)(R=1000,T=25)(R=1000,T=25)Model
Exprimenta data at STC
Chart -3: Comparison of with ideal and optimized voltage-
power profiles in different weather conditions
Modeling and Validation for different conditions are
performed in Fig.7 and Fig.8. It is obvious that the resulting
optimized model is accurate with proper accuracy for
different atmospheric conditions. Hence, with the slightest
neglect of precision, we can use the model for different
atmospheric conditions and different radiation and
temperatures.
4. CONCLUSION
The costs of solar photovoltaic generation are rapidly
reducing, to the point that they are closing in on cost parity
with traditional electricity sources. Due to high penetration
of PV panels having an accurate electrical model of it is
necessary for micro grid simulation. Though first ideal
mathematical model of a PV is discussed and then based on
multiple heuristic methods, the optimized parameters are
calculated. The proposed model for the photovoltaic panel
has been improved and is capableof modelinga photovoltaic
panel in different radiation and temperatures, and provides
a good performance for designers and planners of
photovoltaic systems. The model presented for different
atmospheric conditionsisusedandvalidatedforinformation
obtained from field experiments, which results in the
acceptable accuracy of this model.
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