Comparative study and implementation of

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 461
COMPARATIVE STUDY AND IMPLEMENTATION OF
INCREMENTAL CONDUCTANCE METHOD AND PERTURB AND
OBSERVE METHOD WITH BUCK CONVERTER BY USING ARDUINO
Roshan Kini 1
, Geetha.Narayanan 2
, Aditya Dalvi 3
1
PG. Student, Electronics and telecomunication Department, Vidyalankar Institute of Technology, Maharashtra,India
2
Associate Professor,Electronics Department,Vidyalankar Institute of Technology,Maharashtra,India
3
UG. Student, Information Technology, Mumbai University, Maharashtra, India
Abstract
Maximum Power Point techniques (MPPT) of Photovoltaic array are used to continuously deliver highest possible power to the
load when variation in solar irradiation and temperature occur. This is achieved by many algorithms such as perturbs and
observer method, incremental conductance method, constant voltage method, fuzzy logic method, neural network etc. A DC/DC
converter (boost, buck, Cuk) serves the purpose of transferring maximum power from the solar PV module to the load. In this
project we compared perturb and observe method and incremental conductance method with DC-DC Buck converter by
implementing hardware using Arduino Duemilanove as a prototype board.
Key Words: MPPT, buck converter, solar voltage and current sensing, Incremental conductance method (INC),
Perturb and Observe method, Matlab real time graph, and Arduino Duemilanove, Lead acid battery.
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1. INTRODUCTION
Renewable energy is different from non-renewable energy
because renewable energy never runs out. It will never run
out because it comes from the wind, the sun, the ocean
waves, flowing rivers, and the heat that is deep inside the
earth. There will always be wind and sun, rivers and waves,
and the earth’s heat, so we will always be able to get energy
from them. Renewable energy also is more special because
it does not hurt the environment [22].
Solar energy i.e. energy from the sun provide consistent and
steady source of solar power throughout the year. As our
natural resources set to decline in the years to come, it’s
important for the whole world to move towards renewable
sources. The main benefit of solar energy that it can be
easily deployed by both home and business users as it does
not require any huge set up like in case of wind and
geothermal power stations. Solar energy not only benefits
individual owners, but also benefit environment as well.
Solar energy is non-polluting, clean, reliable and renewable
source of electricity. It does not pollute the air by releasing
harmful gases like carbon dioxide, nitrogen oxide and
sulphur oxide [20], [21].
2. MPPT METHODS
All MPPT methods follow the same Goal which is
maximizing the PV array output power by tracking the
maximum power on every operating condition as shown in
Fig -2.
A DC/DC converter (Boost, Buck, Cuck) serves the purpose
of transferring maximum power from the solar PV module
to the load. According to Maximum Power Transfer
theorem, it state that maximum power delivered from source
to a load when the load resistance is equal to the source
resistance. Thus, DC/DC converter is introduced between
the solar PV module and the load as Shown in Fig -1. By
changing the duty cycle (D), the load impedance as seen by
the source is varied and matched at the point of the peak
power with the source so as to transfer the maximum power.
The MPPT algorithm varies the duty cycle of a DC/DC
converter that ensures the output voltage of a PV panel
follow a reference voltage at any solar irradiance,
temperature and load conditions [2].
Fig -1: Block diagram of MPPT [20].

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Fig -2: I-V and P-V characteristics of solar panel.
There are many algorithms are present which are able to
track Maximum Power Point (MPP). Some of them are
simple, such as those based on voltage and current feedback,
some are more complicated, such as Neural Network (NN)
and Fuzzy Logic (FLC) and some MPPT method has
medium complexity such as perturb and observe method
(P&O) or the incremental conductance method (INC). These
methods also vary in complexity, sensor requirement, speed
of convergence, cost, range of operation, popularity, ability
to detect multiple local maxima, and their applications [4]
[17].
P&O and INC algorithms are in the center of consideration
because of their simplicity and ease of implementation.
1.1 Perturb & Observe Method. (P&O)
Fig -3: Flow chart of perturb and observe method [1].
Perturb & Observe (P&O) is the simplest method. In this
we use two sensor, that is the voltage sensor and current
sensor to sense the PV array voltage and current so the cost
of implementation is less and hence easy to implement. The
time complexity of this algorithm is very less but on
reaching very close to the MPP it doesn’t stop at the MPP
and keeps on perturbing on both the directions. When this
happens the algorithm has reached very close to the MPP
and we can set an appropriate error limit or can use a wait
function which ends up increasing the time complexity of
the algorithm [5], [6].
The P&O algorithms operate by periodically perturbing (i.e.
Incrementing or decrementing) the array terminal voltage or
current and comparing the PV output power with that of the
previous perturbation cycle.
Fig -4: Tracking wrong MPP by perturb and observe
method .
However the method does not take account of the rapid
change of irradiation level (due to which MPPT changes)
and considers it as a change in MPP due to perturbation and
ends up calculating the wrong MPP as shown in fig -4. To
avoid this problem we can use the incremental conductance
method [2].
1.2 Incremental Conductance Method. (INC)
The incremental conductance method is based on the fact
that the slope of the PV array power curve is zero at the
MPP, positive on the left of the MPP, and negative on the
right, as given by [17].
= 0, at MPP
=>0, left of MPP (1)
=<0, Right of MPP
=
( )
= I +V
( ) ( )
(2)
From (1) and (2), we get.
( )
+I/V = 0, at MPP
( )
+I/V>0, Left of MPP (3)
( )
+I/V<0, Right of MPP

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The MPP can thus be tracked by comparing the
instantaneous conductance (I/V) to the incremental
conductance (I/V) as shown in the flow chart in Fig -5.
Fig -5: Flow chart of incremental conductance method [3].
The incremental conductance method also uses voltage and
current sensors to sense the output voltage and current of the
PV array. The left hand side is the instantaneous
conductance of the solar panel. When this instantaneous
conductance equals the conductance of the solar then MPP
is reached [19], [3].
3. SYSTEM DESCRIPTIONS
To Charge 12V lead acid battery by using solar panel
incorporating MPPT algorithm in Charger (DC-DC
Converter). We have to consider following parameters.
1.1 Solar panel or photovoltaic module.
A photovoltaic module is a packaged, connected assembly
of solar cells. A single solar module can produce only a
limited amount of power; most installations contain multiple
modules, but in this project connected two solar module
parallel. (All Electrical Parameters specified at STC: 25º C
cell temperature; 100m W/cm² irradiance ;)
Specifications of 1st solar panel:
Product Code: SPI1275
Max Power: 40W ± 5%
Max Power Voltage: 17V
Max Power Current: 2.26A
Open Circuit Voltage: 21V
Short Circuit Current: 2.49A
Weight: 6.8Kgs
Specifications of 2nd solar panel:
Product Code: SPI1220
Max Power: 10W ± 5%
Max Power Voltage: 17V
Max Power Current: 0.61A
Open Circuit Voltage: 21V
Short Circuit Current: 0.61A
Weight: 1.8Kgs
1.2 Microcontroller(Arduino Duemilanove -
ATmega 328 p)
Arduino Duemilanove is a single-board microcontroller,
consisting of an Atmel ATmega 328p along with various
other components that allow for easy programming and
access to various digital and analog input/output pins. The
ATmega 328p microcontroller is preloaded with the Arduino
boot loader, allowing this chip to be programmed in the
Arduino programming language.
The Arduino Duemilanove can be powered via the USB
connection or with an external power supply. External (non-
USB) power can come either from an AC-to-DC adapter
(wall-wart) or battery. The board can operate on an external
supply of 6 to 20 volts. If supplied with less than 7V,
however, the 5V pin may have less than 5V and the board
may be unstable. If supply is more than 12V, the voltage
regulator may overheat and damage the board. The
recommended range is 7 to 12 volts [10]. Therefore In this
project since the open circuit voltage of solar panel is 21 V
which is higher than the recommended voltage of Arduino a
voltage regulator IC7809 is used between solar panel and VIN
pin of Arduino.
1.3 Solar & Battery Voltage sensor.
In this project we have to constantly monitor the battery and
solar voltage. Unfortunately, sensor can only measure
voltages up to 5V. So a voltage divider circuit as shown in
Fig .4 is connected to reduce the voltage across analog input
pin of Arduino to measurable limit.
The values of resistance (R1 & R2) of a voltage divider
circuit are selected such that the output voltage across R2
(V2) is not more than 5V. Resistance for voltage divider
circuit is selected as R1=10K and R2=2.2K. The solar open
circuit voltage is 21V (which is maximum solar voltage)
therefore maximum voltage across R2 is 3.8V which is
acceptable for voltage sensing circuit as shown in Fig -6.
Before feeding this analog voltage into the Arduino, it
needed to go through some filtering to maintain an accurate
reading. V2 was first fed into a filtering capacitor (C1=0.1
μf) in order to smooth the output voltage and prevent noise
spikes in the signal which can result false readings. The
signal after filtering is fed to the Analog pin in the
microcontroller .It is later used to implement the Maximum
Power Point (MPP) algorithm [15], [18].
The Arduino AVR chip has 10 bit A to D resolution,
Therefore the AnalogRead () function is used to measure the
voltage as an integer between 0 and 1023. One integer’s
value is equal to 0.00488 V (5 V / 1024). The exact voltage
across solar panel can be calculated as

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Sol. volts = ((R1+R2)/ R2)*0.00488 * solar Analog input [8],
[9].
Fig -6: Solar and battery voltage sensor.
1.4 Solar Current sensor
The current sensor played a significant role in the realization
of the MPPT in order to achieve the maximum output power
from the panel. I have used MAX4173H for the design since
it was used in Tim Nolan MPPT project which I referred for
this project. Information and description about MAX4173 is
provided in [14].
Fig -7: Circuit diagram of solar current sensor.
Current from the source flows through RSENSE to the load
as shown in Fig -7. So, VOUT= (GAIN) (RSENSE) (ILOAD)[22]
Where, GAIN = 100 for MAX4173H and RSENSE=0.005Ω,
Therefore, VOUT = (100) (0.005) (ILOAD) = (0.5) (ILOAD) [8].
1.5 MPPT charger (DC-DC Convertor)
Some analysis and design of buck converter are provided in
[9], [15] and a comparative study on different schemes of
switching converters is presented in the literature [33].
The components for the buck converter used in project were
selected as follows:
Input inductor L1 = 33 μH ;
Input Capacitor C1 = 35V, 100 uf Aluminum Electrolytic
Capacitors Switching
MOSFET = IRFZ44N
Diode = Schottky rectifier diode: A 1N5822
Output Capacitor C2 = 18V, 220 uf Aluminum Electrolyte
Capacitor
Resistive load = Lead acid battery
Switching frequency = 50 kHz;
Fig -8: Circuit synchronous buck converter [39].
Q1 is the main switching MOSFET for the buck converter
and Q2 is the synchronous switching MOSFET as shown in
Fig -8. The MOSFET are driven by IC2 which is an IR2104
MOSFET driver. The IR2104 takes the PWM signal from the
controller input on pin 2 and uses it to drive the switching
MOSFETs. The IR2104 can also be shut down with the
control from the processor on pin 3. Information and
description about IR2104 is provided in [13].
I connected diode between battery and DC to DC Converter
which eliminate the risk of current reversal from the battery
during night time, in case the battery is directly connected to
the panel.
1.6 Battery charging algorithm (Lead-Acid
Battery).
Since solar cells can only generate power at certain times of
the day, a storage element is required in all solar power
systems. The most common form of the energy storage for
the stand alone solar power system is battery technology.
The basic functions of the battery management are to control

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the charge/discharge of the battery, to protect the battery
from damage, to prolong the life of the battery, and to
maintain the battery in a state to fulfill the functional
requirements. Battery management systems for a solar
power system with lead acid battery are discussed in [11],
[12].
Fig -9: Charging diagram for a typical lead-acid battery[11].
Battery Specifications:
Product code: CP12170
Chemistry: Sealed Lead Acid
Voltage: 12V
Nominal Capacity: 18Ah
Weight (lbs): 12.6
Cycle Use: 14.5- 14.9V
Standby Use: 13.6- 13.8V
Initial Current: Less than 7.2A
I also added the battery management algorithm in my MPPT
project to keep the batteries from overcharging. This
algorithm has four states on, off, bulk and float. Hence the
maximum power point tracker can be in one of the following
four states:
Off State: When there is little or no power coming from the
solar panel, the device should go into an off state to protect
the battery from leaking back into the solar panel.
On State: When there is minimal power coming from the
solar panel, enough to power the system but not enough to
move to the next state, the system turns on and attempts to
deliver all of the power.
Bulk State: This is the main charging state where the MPPT
algorithms were most relevant. Here the maximum power
point was determined and the battery was charged
accordingly.
Float State: Once the battery reached a high enough voltage
and was close to fully charged, the controller then moved
into the float state. In this state, the goal was to maintain the
voltage level and compensate for self-discharge [7, 12].
1.7 Matlab interface with Arduino for serial
communication
MATLAB Support Package for Arduino (also referred to as
"Arduino-I/O Package") allows us to communicate with an
Arduino over a serial port. It consists of a MATLAB API on
the host computer and a server program that runs on the
Arduino. Together, they allow us to access transmit and
receive serial data from Arduino.
In this project we received data from Arduino such as solar
current reading (I), solar voltage reading (V), solar power
reading (P), time (in Sec) (T), battery voltage reading (Vb)
and develop a real time plot of I-V, P-V on Matlab to
monitor maximum power point of solar array And also plot
graph of V, P, I, Vb with respect to time to check variation
in this parameter.
4. TESTING AND RESULT.
1.1 Software Testing
The MPPT software and the algorithms that it encompasses
were tested using the test plan explained in Table 2.
Table -1: Software testing plan.
Step Procedure Expected Results Actual
Results
Status
1 Hard-code values
for current and
voltage
Should calculate the
correct value for
power
Complete
2 Write a data to
print to the screen
Data displayed on
Serial Monitor of
Arduino.
Complete
3 Hard-code values
for different
charging states
Algorithm will
change into different
states based on the
current and voltage
Complete
4 Take real time
reading of both
algorithms (P&O
and INC) on
Matlab for 100
second and plot
graph of I vs. V, P
vs. V and also plot
graph of P, V, I,
Vb with respect to
time.
Results of (P&O) and
INC method display
in Fig.10 to Fig.15.
Complete
5 Compare both
method (P&O and
INC) based on
result.
Result of comparison
of both method
display from Fig.35
to Fig.38
Complete

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Fig -10: Graph of solar power, current, voltage and battery
voltage with respect to time (P&O),
Date: 26/12/2013, Time: 2:45
Fig -11: Graph of solar current vs. solar voltage and solar
power vs. solar voltage (P&O), Date: 26/12/2013,
Time: 2:45
voltage with respect to time (INC),
Date: 26/12/2013, Time: 2:55
power vs. solar voltage (INC), Date: 26/12/2013,
Time: 2:55
voltage with respect to time (INC)
Date:27/12/2013,Time: 10:32
power vs. solar voltage (INC)
Date: 27/12/2013, Time: 10:32

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Fig -16: Annual report of temperature (2013):
The experimental test was recorded on 26th and 27th
December, 2013 in Mumbai, India. Perturb & observer and
Incremental conductance algorithms based MPPT
experiments are performed on these days to show the
robustness to the varying atmosphere and compare their
performances. We take real time reading on Matlab for 100
second and plot graph of I vs. V, P vs. V and also plot graph
of P, V, I, Vb with respect to time.
Results of perturb & observer method are shown in Fig -10
and Fig -11 in which Fig -10. has graph of solar power, solar
voltage, solar current, battery voltage with respect to time
from which we got instantaneous value of solar voltage, solar
current, solar power with respect to time and in Fig-11 shows
the graph of solar current versus solar voltage and solar
power versus solar voltage which shows maximum power
point generated by the solar panels when the solar charger
was running the perturb and observer algorithm in which
solar charger charges battery.
Similarly the Results of incremental conductance method are
shown in Fig -12 and Fig -13 in which Fig -12 has graph of
solar power, solar voltage, solar current, battery voltage with
respect to time from which we would get instantaneous value
of solar voltage, solar current, solar power with respect to
time and in Fig -13 shows the graph of solar current versus
solar voltage and solar power versus solar voltage which
shows maximum power point generated by the solar panels
when the solar charger was running the incremental
conductance algorithm in which solar charger charges
battery.
Fig -10 and Fig -13 represents graphs of maximum power
point tracker when battery is in bulk condition so it tracks the
maximum power point algorithm. Fig -14 and Fig -15
represents graphs of maximum power point tracker when
battery is in float condition. In this state we try and keep the
battery voltage at 14 volt by decreasing the pwm value.
Fig -17: Complete installation of MPPT tracker
Fig -18: MPPT tracker.
CONCLUSIONS
Using MPPT with solar panel installations has clear
advantages. The initial investment is smaller because
smaller panel wattage is required (very little potential power
is wasted), and adding correct battery-charging algorithms
will also decrease operating costs (batteries are protected
and last longer).
In this project we presents two MPPT algorithms
implemented on a synchronous Buck converter and compare
them on real time graph obtain in Matlab. From this we
come to know that Incremental conductance method has less
oscillation compare to perturb and observer method which
result in higher efficiency.
The maximum power point tracker works because there is a
difference between the solar panel’s MPP voltage and the

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battery’s charging voltage. The IV curves for an actual solar
panel show that the MPP voltage goes down as the
temperature of the solar panel goes up, this means that the
solar panels Maximum power point voltage is lower as the
panel temperature rises. On the other hand, if the
temperature of the solar panel is low and the battery is
mostly discharged, the maximum power point tracker will
show higher power gains. My experience with maximum
Power point Tracking has shown that large power gains are
possible only under ideal circumstances. If the solar panels
are cool, the batteries mostly discharged and voltage drops
in the system are low, maximum power point tracker gets
higher efficiency should occur. Under other conditions the
maximum power point tracker efficiency will be lower,
especially if the solar panels are being used in hot conditions
In this project we presents two MPPT algorithms
implemented on a synchronous Buck converter and compare
them on real time graph obtain in Matlab. From this we
come to know that Incremental conductance method has less
oscillation compare to perturb and observer method which
result in higher efficiency.
REFERENCES
[1] W. Xiao, W. G. Dunford, "A modified adaptive hill
climbing MPPT method for photovoltaic power
systems," in Proc. IEEE PESC, 2004, pp. 1957-1963.
[2] JAW-KUEN SHIAU, Member, IEEE DER-MING MA
Design of a Solar Power Management System for an
Experimental UAV I.
[3] Safari A, Mekhilef S, “Simulation and Hardware
Implementation of Incremental Conductance MPPT
with Direct Control Method Using Cuk Converter”,
IEEE Trans,March 2010.
[4] Trishan Esram, Patrick L. Chapman, “Comparison of
Photovoltaic Array Maximum Power Point Tracking
Techniques”, IEEE Trans. on Energy Conversion, vol.
22,no. 2, pp.439-449, June 2007.
[5] Tat Luat Nguyen, Kay-Soon Low, "A Global Maximum
Power Point Tracking Scheme Employing DIRECT
Search Algorithm for Photovoltaic Systems," IEEE
Transactions on Industrial Electronics, vol. 57, no. 10,
pp. 3456-3467, Oct. 2010.
[6] K.H.Hussein, I.Muta, T.Hoshino and M.osakada
“Maximum Power Point Tracking: an Algorithm for
Rapidly Chancing Atmospheric Conditions”, IEE Proc.-
Gener. Transm.Distrib., vol. 142, no.1, pp. 59-64,
January, 1995.
[7] Texas Instruments, “Synchronous Switch-Mode Battery
Charge Controller for Solar Power with Maximum
Power Point Tracking,” Texas Instruments, July 2010.
[Online]. Available
[8] [online] http://www.timnolan.com
[9] Mihnea Rosu-Hamzescu, Sergiu Oprea, “Practical
Guide to Implementing Solar Panel MPPT Algorithms,”
Microchip Technology Inc.
[10][online]http://arduino.cc/en/Main/arduinoBoardDuemil
anove
[11][online]http://batteryuniversity.com/
[12][online]IsidorBuchmann:http://batteryuniversity.com/le
arn/article/charging_the_lead_acid_battery
[13][online]http://www.irf.com/productinfo/datasheets/data/
ir2104.pdf
[14][online]http://www.maximintegrated.com/datasheet/ind
ex.mvp/id/1971
[15]Microchip Technology Inc. “Buck Converter Design
Example”
[16]Anuradha Kar, Asim Kar “A New Maximum Power
Point Tracking Algorithm for PV Modules under Partial
Shading and Rapidly Varying Illumination”, IEEE
Trans, University of Calcutta, Kolkata, India, 2009.
[17]Zhou Yan, Liu Fei, Yin Jinjun, Duan Shanxu, “Study
on realizing MPPT by improved incremental
conductance method with variable step-size”, ICIEA,
IEEE Conference on Industrial Electronics and
applications, pp. 547~550, June.
[18]Dr. Nirmal-Kumar Nair,Yen-Jung Mark Tung, Dr.
Aiguo Patrick Hu,"Evaluation of Micro Controller
Based MaximumPower Point Tracking Methods", Dept.
of Electrical andComputer Engineering University of
Auckland, New Zeland,2006.
[19]Divya Teja Reddy Challa,
Raghavendar,Implementation of Incremental
Conductance MPPT with Direct Control Method Using
Cuk Converter. International Journal of Modern
Engineering Research (IJMER), Vol.2, Issue.6, Nov-
Dec. 2012 pp-4491-4496 ISSN: 2249-6645
[20]Solar Power (Book) - T Harko
[21]Harnessing Solar Power (Book) - K Zweibel – 1990
[22]REN21 Renewables 2010 Global Status Report
BIOGRAPHIES
Roshan kini is perusing M.E at
Vidyalankar Institute of
Technology. He is interested in
Power Electronics, Power systems
and Renewable energy systems
Prof.Geetha Narayanan has
completed her Btech in Electrical
and Electronics Engineering from
Govt Engineering College Trichur
Kerala,India in 1991 and M-Tech in
Power Electronics from Regional
Engineering College Calicut(NIT
Calicut) Kerala ,India in 1994.She
is currently working as Associate
Professor in Electronics
Engonerring at Vidyalankar
Institute of Technology

_______________________________________________________________________________________
Aditya S. Dalvi, Department of
Information Technology,
Mumbai University, Mumbai,
Maharashtra, India.
adityad504@gmail.com

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