Eduvest – Journal of Universal Studies
Volume 2 Number 8 , August 2022
1,617 http://eduvest.greenvest.co.id
Eduvest –Journal of Universal Studies
Volume 2 Number 8, August, 2022
p-ISSN 2775-3735-e-ISSN 2775-3727
MPPT PV MODELING WITH ANN USING MATLAB
SIMULINK
Bambang Dwinanto
Department of Electrical Engineering, Gunadarma University , Indonesia
Email: [email protected]c.id
A BSTRACT
Paper this presents the Matlab /Simulink model of PV module
and maximum system tracking point power (MPPT). Maximum
Power Point Tracking (MPPT) is something method for track
(track) the point power a source energy to produce power
maximum . one _ MPPT method that can applied to a
photovoltaic system is fractional open voltage method .
Common problems _ happen to the system source voltage
using photovoltaic is not suit Among load and power
generated . _ This thing occur due to source sourced voltage _
of photovoltaic is strongly influenced by the conditions
radiation and temperature around . For resolve Thing this so
required something battery for keep the power that will used
as source voltage . There are various methods and ways for
realize MPPT control , including perturb and observe,
incremental conductance, constant voltage, and parasitic
capacitance . Method the have many disadvantages , among
others , quite expensive and difficult implemented . There are
various method for realize MPPT controller , one of them is
with use network nerves imitation (ANN). MPPT ANN
technique effective increase speed tracking and upgrading
power output per day from one PV modules from 3.37 kW h to
3.75 kW h, i.e. percentage 11.28%. Enhancement power
output from PV prove tested advantages _ with techniques
that can reduce sufficient cost _ big of the generated kWh .
KEYWORDS
MPPT, Photovoltaic, JST, Matlab Simulink
This work is licensed under a Creative Commons
Attribution- ShareAlike 4.0 International
INTRODUCTION
Miniature Double Water Pump Design for Flood Management 1.618
The development of renewable energy that is growing fast lately this a lot of
domination with source of energy originating from wind , hydropower and photovoltaic.
These energy sources proven many help Public in fulfillment needs of renewable energy .
Photovoltaic technology is a lots of technology and can applied everywhere because _
source light the sun that can be seen almost ordered surface earth . Highly flexible
installation and installation techniques where could applied good on house nor land .
Common applications _ can our look at the source power on station outside space , vehicle
electricity and lighting road . [3][8]
Power ray the sun received by the earth outside the atmosphere is about
1300watt/m2. Efficiency conversion energy Sun Becomes energy electricity through PV
cells include low , a maximum of 20% in commercial PV cells [1]. one _ effort for increase
efficiency conversion energy photovoltaic cell is with use Maximum Power Point Tracking
(MPPT) method .
MPPT is something method / algorithm for track (track) the point work a source energy to
produce power maximum [2]. On condition load and condition different atmosphere ,
maximum output power _ PV cells occur at a value of current and voltage certain different
. _ With MPPT control is expected occur conversion energy max on various condition load
and condition atmosphere .
preferred PV system because low cost and flexibility _ compared with turbine wind
. The application of Photovoltaic is choice best in number big the cities where they could
with easy placed above _ housing and building roofs commercial and can integrated to in
structure other buildings like windows and walls with efficiency [5]. Generated power _
from system this could with easy integrated to network so that advantages existing energy
_ could managed and controlled .
For get power maximum from Photovoltaic can conducted with To do simulation
Suite filler battery use Simulink Matlab . On simulation this could designed and seen how
many score maximum possible _ in accordance desire .
RESEARCH METHOD
On research this for simulation use Simulink Matlab . Method used _ is make design
in Matlab simulink, then To do studies comparison with results measurement , Is occur
enhancement power system average output . Steps taken _ _ cover preliminary data
collection , design device , test device , then analysis comparison . Data used for study is
in the form of primary data. This data in the form of initial data , network test results data
nerves dummy , and result data simulation . These three data obtained from results
experiment . Initial data taken _ is the data of the output voltage and current of the solar
panel at various condition irradiation and temperature . Current and voltage data this then
processed for get power peak (P max and voltage maximum power point (
Vmpp
) .
Results of preliminary data analysis this then shared Becomes two . Mostly _ used
as input in testing network nerves imitation and the rest called with test data, used for test
network parameters nerves imitation result .
Test data obtained from testing parts system nor testing tool by whole . parts _ tested
system _ more formerly is a temperature sensor and a buck converter. Block diagram
system shown in Figure 10. Algorithm genetics used for practice network nerves imitation
.
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Figure 10. Block diagram system
Test data obtained from testing parts system nor testing tool by whole . Part part
tested system _ more formerly is a temperature sensor and a buck converter.
Initial data taken _ in the form of data voltage , current , temperature as well as
irradiation sun . From current and voltage obtained power solar panel output . Initial data
this then processed for obtain training data and test data for necessity training / testing
network nerves imitation use algorithm genetics .
Training and testing network nerves imitation use input in the form of voltage burden
zero (V
0
), temperature , and maximum power point voltage ( V
mpp
). Output training in
the form of MSE value ( mean squared error ). The more small MSE value is getting good
results his training . While testing parameters is MSE value and average error value . The
more small the more good .
Temperature sensor testing conducted for calibration of the sensor . Test results
compared with theory , then conducted linear regression for get equality characteristics
sensor output . Buck converter test done for get score make a loss voltage from converters.
The more small make a loss voltage , the more good . Test tool by whole conducted for get
score power solar panel output and value power load on various score resistance load .
This result then will compared with fractional open voltage method for get percentage
increase power
RESULTS AND DISCUSSION
I. tree Discussion / Theory
A lot of effort study about MPPT has been developed . in between study that among
others, Yusivar , and Tito [6] proposed technique MPPT based on PI controller with
feedback error for get it fast time tracking . Matsumoto et al . [7] illustrates the MPPT of a
system use boost converter for ultra -low input voltage . Askarzadeh and Rezazadeh [11]
proposed MPPT using optimization bird Mating - parameter based approach identification
. Destination from study this is needs will fast and accurate MPPT technique for followed
point power maximum PV modules . Paper this propose implementation device soft from
Miniature Double Water Pump Design for Flood Management 1.618
tracking point power maximum system cell solar . duty signal needed for determine PWM
duty ratio , because our want to control power with refers to the point power irradiation -
dependent maximum . _ The MPPT algorithm determines MPP with look for derivative dP
/ dV = 0. If power no change and voltage no change , derivative will Becomes zero and dot
will maximum .
A. PV System Modeling
Simulation proposed PV system has conducted using MPPT based network nerves
imitation . Tracker based on ANN has been used for identify score current ( I
mpp
) which
gives point power maximum . PV system can classified Becomes two group that is stand-
alone systems and grid -connected systems . On a stand-alone energy system power
generated solar _ customized with Request energy . Because of energy power generated
solar _ no could Fulfill needs energy on one time , then system storage additional ( battery
) is used . If the PV system is connected with source other power (diesel generator or wind
) then Thing this called with hybrid PV system . Stand-alone PV systems use battery for
keep energy . On system this could added a generator for power supporter or back-up
power. On system this inverter convert battery DC voltage Becomes AC voltage for
necessity electricity house stairs , will but for simple system is possible _ equipment for
use DC voltage so that no required the presence of inverters. In isolated areas , stand- alone
PV systems could Becomes very effective choice compared with using a generator made
from burn very expensive oil . However _ system this have a number of weakness among
them losses battery and facts that PV is usually operate not at the point efficient operation
[ 9][10].
Simulation simulated PV system using MPPT based network nerves imitation .
Also for destination comparison , Perturb and Observe (P&O) is also addressed . Tracker
based on ANN has been used for identifyvalue current ( Impp ) which gives point power
maximum .
Figure 1. Maximum value on Photovoltaic
From picture 1. it can be seen power maximum 852 Watts at 1000 W/m
2
B. Maximum Powerpoint Tracking
MPPT is a method for get power maximum from a source energy ( solar , wind , and
other energy) in various condition environment and conditions load . Point power
maximum at various condition lighting showed in figure 1. Lighting level highest shown
on the topmost curve , while level more lighting _ low shown in the curve below . Point
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power maximum from various condition lighting this connected with a line that is almost
vertical .
The techniques used _ in MPPT , among others, protrude and observe, incremental
conductance, fractional open-circuit voltage, fuzzy logic control, network nerves
duplication , ripple correlation control, current sweep, and so on [11]. Voltage maximum
power point ( Vmpp ), varies to temperature and intensity light sun . A DC–DC converter
can installed between the solar panel and the load . This converter used for maximize power
transfer from solar panels to load . DC–DC converter used depends from solar panel
specifications and installed loads . _
Converter One method enough simple is fractional open voltage method . In PV cells
made of from ingredient silicone voltage point power maximum ranged from 70 – 80% of
voltage Suite open . With decide cell from burden During a number of milliseconds by
periodic and measure
voltage Suite open , voltage optimal PV cells can determined with multiply voltage
Suite open with k factor of 0.75. With method this point power maximum no truly achieved
, however only approached course . Voltage Suite can also be open obtained with use pilot
PV cells , which have characteristics same with primary PV cell . With thereby no need
conducted disconnection power by periodic .
similar approach can also be conducted with use current connect short . Other
methods can use network nerves imitation . DC-DC converter controlled with use network
nerves imitation that has been trained (train) more first , so optimal voltage of PV cells can
estimated . Network input nerves imitation could in the form of temperature , irradiance ,
current connect short , and voltage Suite open [12].
C. Network Nerves Imitation
Network Nerves Imitation (ANN) is system processor information that has
characteristics similar with
network nerves biology . JST formed as generalization of mathematical models from
network nerves biology with assumptions that processing information happens to many
element simple (neurons), signal sent between neurons via liaisons , liaisons between
neurons has the weight to be strengthen or weaken signal , and for determine the output of
each neuron using function activation . ANN is determined by 3 things , pattern connection
between neurons ( architecture network ), method for determine weight link ( method /
algorithm learning / training ), as well as function activation .
For maximizing energy PV system , then need for extract available energy _ as much
possible from PV . So for operation PV system in MPP is done through the hour light sun
. The resulting output in the form of power from PV module change with voltage and
current operation on each score radiation and temperature . MPPT is used for track point
where dot power maximum happen .
Network nerve mock (ANN) is used in find right solution _ for non-linear system .
Network Engineering propagation come back is most extensive technique used in
engineering network nerves [13]. ANN customized with good for microcontroller . System
this have three layers : input , hidden , and output as shown in Fig. 2. Number of nodes in
each layer is standing variable _ alone . Input parameters are PV Array parameters such as
VOC
and Isc ,
atmospheric
data like radiation and temperature , or combination among them .
Output usually one or a number of signal reference . This can in the form of voltage , current
, power on MPP or signal duty cycle used for move converter power for operate on or close
to MPP. Link between all nodes worth . The link between nodes i and j is labeled with
symbol W
ij
. For identify MPP directly accurate .
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Figure 2. Construction from network nerves imitation
Network nerve bait advanced multilayer introduced in paper this . The number of
neurons in the input and output layers is two and one , respectively . Number of neurons in
layer hidden will determined with trial method [14] . The neurons in the input layer get
input signal from measurement radiation sun and temperature environment . Neurons in
layer hidden count the output use function sigmoid activation and pass it on to neurons in
the layer output . Nodes in the output layer provide identified current in MPP.
Trial data calculated using mathematical models
Figure 4. Error convergence on trial network nerves .
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Figure 5. Network nerve produce error moment tested with test data .
Output from ANN and target output on kth instant represented by { yi (k)} and {
di (k)}, where i = 1, 2,.. etc.
e
i
(K) = d
i
(K) - y
i
( 1)
The size error occurred _ are :
E(K) =
∑
[𝑒
𝑖
(𝐾)]
2
𝑁=1
𝑖=1
( 2)
Algorithm back-propagation used for minimize function E(k) in recursive with
renew weight from network . After test network _ with desired accuracy , a set of patterns
testing separated provided as input for JST for evaluate its performance . It need for JST
for could generalize situation from give test pattern and identify the optimal at points
operation .
JST has tested try with the value of the data obtained of mathematical models PV
modules . Big parameters is as following :
Speed parameter = 0.1 .
Amount iteration trial = 1000
Error value ( performance ) = 1𝑒
−5
D. DC-DC Converter Modeling
Input power for boost converter originated of connected PV array output to storage
battery . A converter riser voltage is DC to DC converter with output voltage over big from
voltage source . Sometimes is called an up- converter voltage due to '' increase '' the voltage
source . Simulink mathematical model from converter riser voltage discussed in [15][16].
Voltage transfer function from boost converter declared as following :
V
i
= V
b
(1 – D) (3)
Where :
V
i
is PV terminal terminal voltage
V
b
is voltage battery and
D is duty cycle
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Figure 6. Matlab / Simulink Model Design from PV module
Table 1. InGaP / InGaAs /Ge triple-junction PV parameters
Top sub-cell InGaP
InGaAs . sub-cell medium
Bottom sub-cell Ge
Eg (eV) at 298 K
Eg1 =1.976
Eg2 =1.519
Eg3 =0.744
Isc (mA)
Isc1= 6.7522
Isc2=7.7126
Isc3= 10,094
K (A/cm2 K4)
K1= 1.86 10
-9
K2 = 1,288 10
-8
K3= 10.5 10
-6
n
n1 =1.97
n3 = 1.75
n3 =1.96
️
2
2
2
️
7.5x10
-4
5.504x10
-4
4.774x10
-4
️
500
204
235
Calculated trial data _ with using mathematical models shown in Fig. 3. While
error convergence for the trial process shown in Fig. 4. After learn network nerves , is
important for test for ensure that that truly could predictable score desired output _ with
other input data that is not used in the learning process .
A error relative (ΔE) is used as criteria validation for network nervous and defined
as following :
E =
𝐼
𝑐𝑎𝑙
𝐼
𝑐𝑎𝑙
−𝐼
𝐽𝑆𝑇
( 4)
Where
I
cal
is calculated current _ based on equality math
I
JST
is current simulation on the network condition imitation
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Comparison Among percentage error expected output _ with results calculation
could seen in Figure.5.
Table.2 Specifications PV module parameter simulation
Characteristics
Maximum power
Short circuit current
Open circuit voltage
Current at MPP
Voltage at MPP
Figure 7. P–V and I–V characteristics of PV Module
on condition standard (800 W/m
2
and 20 C).
Figure 8a. curve power-voltage PV module on
Different radiation and temperature _ constant 25 C.
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Figure 8b. curve current-voltage PV module on
radiation different and temperature constant 25 C.
Figure 9.a Curve power-voltage PV module on
temperature and radiation constant 1000 W/m2.
Figure 9.b. curve current-voltage PV module on
temperature different and radiation constant 1000 W/m2.
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II. Simulation Results
A. Simulation PV module on MATLAB/Simulink
cell model triple-junction solar is implemented in MATLAB/Simulink as shown in
Figure 6. PV tested on the module made of from 20 cells triple- junction solar series and
provide power nominal maximum 480 W at 800 W/m2. Characteristics power photovoltaic
shown in Fig. 7. With quantity varied with level radiation sun and temperature . Tested PV
module in condition standard have the characteristics shown in Fig. 7. There is a point
unique to the curve called point power maximum , at which the PV module operates with
efficiency maximum and yield maximum power output .
With use effect radiation sun and temperature Becomes consideration , result
simulation power output characteristics voltage and characteristics current-voltage PV
model output below enhancement radiation the sun and at the temperature constant shown
in Fig. 8a and b. Strength point maximum is greatly affected with vary radiation sun . MPP
changed from 280 W to 570 W because radiation sun varied from 450 W/m2 to 1000 W/m2.
On the other hand, the I–V characteristics of cells could changed with vary solar radiation
before voltage output cell reach point where point power maximum occur after that a little
influenced by variation radiation sun . Picture. 9a and b are P–V characteristics and I –V
characteristics at different temperatures and radiation constant sun . _ MPP affected with
vary cell temperature . MPP value changed from 530 W to 612W when temperature
changed from 60 C to 10 C For radiation given sun , when _ temperature increases , I–V
characteristics remain constant until score voltage Suite open reach value at point where
power maximum happen . Characteristics I–V shifted to lower along with drop temperature
.
Figure 10. (a) Matlab /Simulink PV system model with MPPT ANN technique .
(b) Matlab /Simulink PV system model with MPPT P&Ontechnics .
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Figure 11. Power output from PV system with and without
use MPPT ANN technique
Figure 12. Power output from PV system using
MPPT ANN technique compared with MPPT P&O technique
B. Simulation PV system on MATLAB/Simulink
Simulation tested system _ has held using MATLAB/Simulink programs like shown
in Fig. 10 simulations conducted for learn influence PV system operating with MPPT ANN
technique on power output and energy . Fig.11 shows power output PV system as function
time with and without MPPT ANN technique . Figure 12 shows energy output from PV
system as function time for the proposed ANN MPPT compared with the MPPT technique
P&O . Clear from numbers that use proposed MPPT ANN technique, power and energy
output from more PV modules big from power and energy output in Thing use MPPT P&O
technique . Energy output per day from one PV module increase from 3.37 kW h to 3.75
kW h, that is percentage 11.28%.
CONCLUSION
Use Matlab /Simulink PV module for get efficiency cell triple-junction solar InGaP
/ InGaAs /Ge where the model being tested represent easy PV cells and arrays used on the
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simulation platform . Model pick up radiation sun and temperature cell as input parameter
and generate I–V and P–V characteristics in various conditions and also includes effect
from variation temperature at characteristics cell solar . Based on Network Nerves Imitation
technique tracker tested . Whole PV system with MPPT implemented in
MATLAB/Simulink. Power and energy yield from PV system with ANN compared with
what is obtained with common P&O techniques used .
Simulation results show that , with use MPPT ANN technique effective increase
speed tracking and upgrading power output per day from one PV modules from 3.37 kW h
to 3.75 kW h, i.e. percentage 11.28%. Enhancement power output from PV prove tested
advantages _ with techniques that can reduce sufficient cost _ big of the generated kWh .
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