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i
COMPARISON STUDY OF MAXIMUM POWER
POINT TRACKER TECHNIQUES FOR PV SYSTEM
APPLIED TO UNIVERSAL MOTOR
A PROJECT REPORT
Submitted by
DHARMIGARI SHRI SURYA (312211105027)
DUVVURI VENKATA PAVAN KUMAR (312211105030)
KURAPATI VINUTHNA (312211105053)
in partial fulfillment for the award of the degree
of
BACHELOR OF ENGINEERING
in
ELECTRICAL AND ELECTRONICS ENGINEERING
SRI SIVASUBRAMANIYA NADAR COLLEGE OF ENGINEERING,
KALAVAKKAM
ANNA UNIVERSITY: CHENNAI 600025
APRIL 2015
ii
ANNA UNIVERSITY:CHENNAI 600025
BONAFIDE CERTIFICATE
Certified that this project report COMPARISON STUDY OF MAXIMUM
POWER POINT TRACKER TECHNIQUES FOR PV SYSTEM
APPLIED TO UNIVERSAL MOTOR is the bonafide work of
DHARMIGARI SHRI SURYA(312211105027),
DUVVURI VENKATA PAVAN KUMAR (312211105030),
KURAPATI VINUTHNA(312211105053), who carried out the project work
under my supervision.
DR. V.KAMARAJ Mr. M. PANDI KUMAR
HEAD OF THE DEPARTMENT GUIDE
Professor Assistant Professor
Department of Electrical Department of Electrical
andElectronics Engineering andElectronics Engineering
SSN College of Engineering, SSN College of Engineering,
Kalavakkam, Kalavakkam,
Chennai 603110 Chennai - 603110
iii
VIVA VOICE EXAMINATION
The Viva examination for the project work, Comparison study of Maximum
Power Point Tracker techniques for PV system applied to universal motor
submitted by
DHARMIGARI SHRI SURYA(312211105027),
DUVVURI VENKATA PAVAN KUMAR (312211105030),
KURAPATI VINUTHNA(312211105053)
held on ___________________
Internal Examiner External Examiner
iv
ACKNOWLEDGEMENT
I would like to express my gratitude to the below mentioned people who
have been instrumental in the completion of this project.
I express my deep respect to PADMA BHUSHAN Dr. SHIV NADAR,
Chairman, SSN Institutions for working on a great mission and vision and
providing excellent infrastructure.
I thank our President Ms. KALA VIJAYAKUMAR for providing the
required facilities and infrastructure which helped me to complete the project.
The Principal, Dr. S. SALIVAHANAN provided a great support to work. I
express my gratitude to him.
I would like to dedicate my sincere thanks to my guide Dr.
V.KAMARAJ Professor and Head, Department of Electrical and Electronics
Engineering, who has been kind, patient and provided great support. I express
my deep felt gratitude to him.
I would also like to thank our project coordinator Mr.M.PANDI
KUMAR, Assistant Professor, Department of Electrical and Electronics
Engineering, who showed their benevolence and helped us through tough
periods.
I extend my sincere thanks to the staff members and lab technicians,
Department of Electrical and Electronics Engineering for providing great
support at different times. I am grateful to them.
v
ABSTRACT
The need for renewable energy sources is on the rise because of the acute energy
crisis in the world today. India plans to produce 20 Gigawatts Solar power by
the year 2020, whereas we have only realized less than half a Gigawatt of our
potential as of March 2010. Solar energy is a vital untapped resource in a
tropical country like ours. Photovoltaic(PV) offers an environmentally friendly
source of electricity, which is however still relatively costly today. The main
hindrance for the penetration and reach of solar PV systems is their low
efficiency and high capital cost. The maximum power point tracking (MPPT) of
the PV output for all sunshine conditions is a key to keep the output power per
unit cost low for successful PV applications. In order for photovoltaic (PV)
systems to maximize their efficiency of power generation, it is crucial to locate
the maximum power point (MPP) in real time under realistic illumination
conditions. The current-voltage (I-V) characteristics of PV devices are
nonlinear, and the MPP may vary with intrinsic and environmental conditions.
Maximum power point tracking (MPPT) control is expected to seek the MPP
regardless of the device and ambient changes.These techniques vary in many
aspects as simplicity, digital or analogical implementation, sensor required,
convergence speed, range of effectiveness, implementation hardware,
popularity, cost and in other aspects. Our project aims on a comparitive study
between four most popular MPPT techniques which are Incremental
conductance algorithm, Perturb and Observe algorithm , Current sweep method,
vi
Short Circuit Current method and Open Circuit Voltage Method. Universal
motor will be used as load to the PV system. The prototyping PV system is
implemented with a boost DC-DC converter using Microcontroller and
MATLAB Simulink tools to execute the MPPT algorithms. Few comparisons
such as voltage, current and power output for each different combination have
been recorded. The above mentioned algorithms are implemented and tested
under different conditions and the test results are analyzed and compared. This
comparison helps in determining the optimal efficient technique and
significantly improves the tracking accuracy and speed of the MPPT control.
vii
TABLE OF CONTENTS
CHAPTER NO. TITLE PAGE NO.
ABSTRACT iv
LIST OF TABLES ix
LIST OF FIGURES ix
1. INTRODUCTION 1
1.1 NEED FOR RENEWABLE ENERGY
1.2 DIFFERENT SOURCES OF RENEWABLE
ENERGY
1.2.1 WIND POWER
1.2.2 SMALL HYDRO POWER
1.2.3 BIOMASS
1.2.4 GEOTHERMAL
1.2.5 SOLAR POWER
1.3 LITERATURE REVIEW
1.4 OBJECTIVE
1.5 FUTURE SCOPE OF RENEWABLE
ENERGY RESOURCES
1.6 THESIS OUTLINE
2. MODELLING OF PV PANEL 10
2.1 PHOTOVOLTAIC CELL
2.2 PV CELL
2.3 PV ARRAY
2.4 PV MODELLING
2.4.1 NOMENCLATURE
2.4.2 INTRODUCTION
2.4.3 MATHEMATICAL MODEL OF
PHOTOVOLTAIC CELL
viii
2.4.4 REFERENCE MODEL
2.4.5 STEP BY STEP PROCEDURE FOR
SIMULINK MODELLING OF PV
MODULE
2.4.6 CONCLUSIONS
3. BOOST CONVERTER 28
3.1 MODE1 OPERATION OF BOOST
CONVERTER
3.2 MODE2 OPERATION OF BOOST
CONVERTER
3.3 MODELLING OF BOOST CONVERTER
USING MATLAB
3.4 DESIGN APPROACH OF PROPOSED
BOOST CONVERTER
4. MAXIMUM POWERPOINT TRACKING 33
4.1 AN OVERVIEW OF MAXIMUM POWER
POINT TRACKING
4.2 DIFFERENT MPPT TECHNIQUES
4.3 PERTURB AND OBSERVE
4.4 INCREMENTAL CONDUCTANCE
4.5 FRACTIONAL OPEN CIRCUIT VOLTAGE
4.6 FRACTIONAL SHORT CIRCUIT CURRENT
4.7 DETAILS OF PERTURB AND OBSERVE
ALGORITHM
4.7.1 MODELLING OF P&O ALGORITHM
4.7.2 COMPLETE MODEL OF PV PANEL
WITH MPPT
4.7.3 OUTPUT CHARACTERISTICS
4.8 DETAILS OF INCREMENTAL CONDUCTANCE
ALGORITHM
4.8.1 MODELLING OF INCREMENTAL
CONDUCTANCE ALGORITHM
4.8.2 COMPLETE MODEL OF PV PANEL
WITH MPPT
ix
4.8.3 OUTPUT CHARACTERISTICS
4.9 DETAILS OF SHORT CIRCUIT CURRENT
ALGORITHM
4.9.1 COMPLETE MODEL OF PV PANEL
WITH MPPT
4.9.2 OUTPUT CHARACTERISTICS
4.10 DETAILS OF OPEN CIRCUIT VOLTAGE
ALGORITHM
4.10.1 COMPLETE MODEL OF PV PANEL
WITH MPPT
4.10.2 OUTPUT CHARACTERISTICS
4.11 COMPARISON OF MPPT
5. HARDWARE IMPLEMENTATION 45
5.1 HARDWARE COMPONENTS
5.2 HARDWARE SETUP
5.3 SUB CIRCUITS
5.3.1 SUPPLY CIRCUIT
5.3.2 CONTROL CIRCUIT
5.3.3 OPTOCOUPLER CIRCUIT
5.3.4 POWER CIRCUIT
5.4 MATLAB INTERFACE WITH ARDUINO
FOR SERIAL COMMUNICATION
5.5 TESTING AND RESULT
6 CONCLUSION AND FUTURE WORK 51
7 REFERENCES 52
LIST OF TABLES
TABLE NO. TABLE NAME PAGENO.
2.1 ELECTRICAL CHARACTERISTICS 12
DATA OF SOLAR 36W PV MODULE
x
3.1 SPECIFICATIONS FOR BOOST 21
CONVERTER
4.1 COMPARISON OF MPPT ALGORITHM 43
5.1 HARDWARE COMPONENTS 45
LIST OF FIGURES
FIGURE NO. FIGURE NAME PAGENO.
1.1 MPPT TECHNIQUE WITH SOLAR CELL 9
2.1 DIFFERENT SOLAR MODULES 11
2.2 SCHEMATIC CROSS-SECTION OF A TYPICAL 11
SOLAR CELL
2.3 EQUIVALENT CIRCUIT OF PV CELL 12
2 SUBSYSTEM1
3 CIRCUIT UNDER SUBSYSTEM1
4 SUBSYSTEM2
5 CIRCUIT UNDER SUBSYSTEM2
6 SUBSYSTEM3
7 CIRCUIT UNDER SUBSYSTEM3
8 SUBSYSTEM4
9 CIRCUIT UNDER SUBSYSTEM4
10 SUBSYSTEM5
11 CIRCUIT UNDER SUBSYSTEM5
12 SUBSYSTEM6
13 CIRCUIT UNDER SUBSYSTEM6
14 INTERCONNECTION OF ALL 6
SUBSYSTEMS
15 SIMULINK MODELOF PV MODULE
16(a) INPUT TIME VARYING IRRADIATION
16(b) INPUT CONSTANT TEMPERATURE-25 C
16(c) OUTPUT I-V CHARACTERISTICS WITH
VARYING IRRADIATION
16(d) OUTPUT P-V CHARACTERISTICS
WITH VARYING IRRADIATION
17(a) INPUT TIME VARYING TEMPERATURE
17(b) OUTPUT I-V CHARACTERISTICS WITH
xi
VARYING TEMPERATURE
17(c) OUTPUT P-V CHARACTERISTICS
WITH VARYING TEMPERATURE
17(d) OUTPUT POWER VS TIME
3.1 CIRCUIT DIAGRAM OF A BOOST
CONVERTER 29
3.2 MODE 1 OPERATION OF THE BOOST
CONVERTER 29
3.3 MODE 2 OPERATION OF THE BOOST
CONVERTER 30
3.4 MODELLING OF Boost DC-DC
CONVERTER 30
4.1 FLOWCHART OF PERTURB & OBSERVE
ALGORITHM 37
4.2 MODELLING OF P&O ALGORITHM
4.3 COMPLETE MODEL OF PV PANEL WITH
MPPT 37
4.4 OUTPUT CHARACTERISTICS 37
4.5 IINCREMENTAL CONDUCTANCE MPPT
FLOW CHART 38
4.6 MODELLING OF INCREMENTAL
CONDUCTANCE ALGORITHM 38
4.7 COMPLETE MODEL OF PV PANEL
WITH MPPT 38
4.8 OUTPUT CHARACTERISTICS 38
4.9 FLOWCHART FOR SHORT CICUIT
CURRENT ALGORITHM 40
4.10 COMPLETE MODEL OF PV PANEL WITH
MPPT 40
4.11 OUTPUT CHARACTERISTICS 40
4.12 FLOWCHART FOR OPEN CIRCUIT
VOLTAGE ALGORITHM 41
4.13 COMPLETE MODEL OF PV PANEL WITH
MPPT 41
xii
4.14 OUTPUT CHARACTERISTICS 42
5.1 HARDWARE SETUP 45
5.1.1 WITHOUT BOOST CONVERTER 46
5.1.2 WITH BOOST CONVERTER
OPERATING 46
5.1.3 BOOST CONVERTER 46
5.1.4 GATE DRIVE CIRCUIT 47
5.1.5 ARDUINO 47
1
CHAPTER 1
INTRODUCTION
It's certainly clear that fossil fuels are mangling the climate and that the status
quo is unsustainable. There is now a broad scientific consensus that the world
needs to reduce greenhouse gas emissions more than 25 percent by 2020 -- and
more than 80 percent by 2050. The idea of harnessing the suns power has been
around for ages.
The basic process is simple. Solar collectors concentrate the sunlight that falls
on them and convert it to energy. Solar power is a feasible way to supplement
power in cities. In rural areas, where the cost of running power lines increases.
Solar power, a clean renewable resource with zero emission, has got tremendous
potential of energy which can be harnessed using a variety of devices. With
recent developments, solar energy systems are easily available for industrial and
domestic use with the added advantage of minimum maintenance. Solar energy
could be made financially viable with government tax incentives and rebates.
An exclusive solar generation system of capacity 250KWh per month would
cost around Rs. 20 lakhs, with present pricing and taxes (2013). Most of the
developed countries are switching over to solar energy as one of the prime
renewable energy source.
1.1 THE NEED FOR RENEWABLE ENERGY
Renewable energy is the energy which comes from natural resources such as
sunlight, wind, rain, tides and geothermal heat. These resources are renewable
and can be naturally replenished. Therefore, for all practical purposes, these
resources can be considered to be inexhaustible, unlike dwindling conventional
fossil fuels. The global energy crunch has provided a renewed impetus to the
2
growth and development of Clean and Renewable Energy sources. Clean
Development Mechanisms (CDMs) are being adopted by organizations all
across the globe. Apart from the rapidly decreasing reserves of fossil fuels in the
world, another major factor working against fossil fuels is the pollution
associated with their combustion. Contrastingly, renewable energy sources are k
known to be much cleaner and produce energy without the harmful effects of
pollution unlike their conventional counterparts.
3
1.2 DIFFERENT SOURCES OF RENEWABLE ENERGY
1.2.1 WIND POWER
Wind turbines can be used to harness the energy available in airflows. Current
day turbines range from around 600 kW to 5 MW of rated power. Since the
power output is a function of the cube of the wind speed, it increases rapidly
with an increase in available wind velocity. Recent advancements have led to
aerofoil wind turbines, which are more efficient due to a better aerodynamic
structure.
1.2.2 SMALL HYDROPOWER
Hydropower installations up to 10MW are considered as small hydropower and
counted as renewable energy sources. These involve converting the potential
energy of water stored in dams into usable electrical energy through the use of
water turbines. Run-of-the-river hydroelectricity aims to utilize the kinetic
energy of water without the need of building reservoirs or dams.
1.2.3 BIOMASS
Plants capture the energy of the sun through the process of photosynthesis. On
combustion, these plants release the trapped energy. This way, biomass works as
a natural battery to store the suns energy and yield it on requirement.
1.2.4 GEOTHERMAL
Geothermal energy is the thermal energy which is generated and stored within
4
the layers of the Earth. The gradient thus developed gives rise to a continuous
conduction of heat from the core to the surface of the earth. This gradient can be
utilized to heat water to produce superheated steam and use it to run steam
turbines to generate electricity. The main disadvantage of geothermal energy is
that it is usually limited to regions near tectonic plate boundaries, though recent
advancements have led to the propagation of this technology.
1.2.5 SOLAR POWER
The tapping of solar energy owes its origins to the British astronomer John
Herschel who famously used a solar thermal collector box to cook food during
an expedition to Africa. Solar energy can be utilized in two major ways. Firstly,
the captured heat can be used as solar thermal energy, with applications in space
heating. Another alternative is the conversion of incident solar radiation to
electrical energy, which is the most usable form of energy. This can be achieved
with the help of solar photovoltaic cells or with concentrating solar power
plants.
As the Photovoltaic module exhibits non-linear V-I Characteristics, which are
dependent on solar Insolation and environment factors, the development of an
accurate power electronic circuit oriented model is essential to simulate and
design the photovoltaic integrated system. In this paper, the design of PV
system using simple circuit model with detailed circuit modelling of PV module
using MATLAB/Simulink and the physical equations governing the PV module
is presented.
5
1.3 LITERATURE REVIEW
Studies show that a solar panel converts 21-40% of energy incident on it to
electrical energy. A Maximum Power Point Tracking algorithm is necessary to
increase the efficiency of the solar panel.
There are different techniques for MPPT such as Perturb and Observe (hill
climbing method), Incremental conductance, Fractional Short Circuit Current,
Fractional Open Circuit Voltage, Fuzzy Control, Neural Network Control etc.
Among all the methods Perturb and observe (P&O) and Incremental
conductance are most commonly used because of their simple implementation,
lesser time to track the MPP and several other economic reasons.
Under abruptly changing weather conditions (irradiance level) as MPP changes
continuously, P&O takes it as a change in MPP due to perturbation rather than
that of irradiance and sometimes ends up in calculating wrong MPP. However
this problem gets avoided in Incremental Conductance method as the algorithm
takes two samples of voltage and current to calculate MPP. However, instead of
higher efficiency the complexity of the algorithm is very high compared to the
previous one and hence the cost of implementation increases. So we have to
mitigate with a trade-off between complexity and efficiency.
It is seen that to get maximum efficiency we are getting which type of
converter. We are choosing here boost converter because it provide us more
voltage at output then input. We can also choose buck-boost converter but due
to our simplification and requirement we are selecting boost converter. It is
very simple to implement and has high efficiency both under stationary and
time varying atmospheric conditions.
6
N. Pandiarajan and Ranganath Muth, This paper presents a unique step-by-
step procedure for the simulation of photovoltaic modules with Matlab/
Simulink. One-diode equivalent circuit is employed in order to investigate I-V
and P-V characteristics of a typical 36 W solar module. The proposed model is
designed with a user-friendly icons and a dialog box like Simulink block
libraries [1].
Alpesh P. parekh, Bhavarty N. Vaidya and Chirag T. Patel, In this paper, the
design of PV system using simple circuit model with detailed circuit modelling
of PV module is presented. In this paper, Equivalent circuit of the PV module &
Simulink model for each equation has presented and complete circuit oriented
model has also presented [2].
Pandiarajan N, Ramaprabha R and Ranganath Muthu, Circuit model of
photovoltaic (PV) module is presented in this paper that can be used as a
common platform for the material scientists as well as power electronic circuit
designers to develop the better PV power plant. Detailed modeling procedure
for the circuit model with numerical dimensions is presented using power
system block set of MATLAB/ Simulink. The developed model is integrated
with DC-DC boost converter with closed loop control of maximum power point
tracking (MPPT) algorithm. The simulation results are validated with the
experimental set up [3].
P.Sathya, Dr.R.Natarajan, this paper presents the design and implementation
of high performance closed loop Boost converter for solar powered HBLED
lighting system. The proposed system consists of solar photovoltaic module, a
closed loop boost converter and LED lighting module. The closed loop boost
converter is used to convert a low level dc input voltage from solar PV module
7
to a high level dc voltage required for the load. To regulate the output of the
converter, closed loop voltage feedback technique is used. The feedback voltage
is compared with a reference voltage and a control signal is generated and
amplified. The amplified signal is fed to 555 Timer which in turn generates a
PWM signal which controls the switching of MOSFET. Thus by switching of
MOSFET it would try to keep output as constant. Initially the boost converter,
timer circuit, amplifier circuit and LED light circuits are designed, simulated
and finally implemented in printed circuit board. The simulation studies are
carried out in MULTISIM. The experimental results for solar PV and boost
converter obtained in both software and hardware was presented in this paper
[7].
Vandana Khanna, Bijoy Kishore Das, Dinesh Bisht, A Simulation model for
simulation of a single solar cell and two solar cells in series has been developed
using Simelectronics (Matlab/Simulink) environment and was presented in this
paper. A solar cell block is available in simelectronics, which was used with
many other blocks to plot I-V and P-V characteristics under variations of
parameters considering one parameter variation at a time. The effect of variation
of parameters such as series resistance, Rs, shunt resistance Rsh, diode
parameters: diode saturation current, Is and ideality factor, N, could be seen on
the characteristics of a single solar cell. Effect of two environmental parameters
of temperature and irradiance variations could also be observed from simulated
characteristics. Matlab coding has been done to find the maximum power
output, Pm, and voltage at maximum power output, Vm, of a single solar cell
and two solar cells (in series) under different values of parameters. The Pmand
Vm values are tabulated here in this paper for variation of one parameter at a
time, considering the diode parameters: Is and N, resistances: series and shunt,
temperature and irradiance [5].
8
G. Venkateswarlu and Dr.P.Sangameswar Raju, The study of photovoltaic
systems in an efficient manner requires a precise knowledge of the IV and PV
characteristic curves of photovoltaic modules. A Simulation model for
simulation of a single solar cell and two solar cells in series has been developed
using Sim electronics (Mat lab /Simulink) environment and is presented here in
this paper. A solar cell block is available in simelectronics, which was used with
many other blocks to plot I-V and P-V characteristics under variations of
parameters considering one parameter variation at a time. Effect of two
environmental parameters of temperature and irradiance variations could also be
observed from simulated characteristics [4].
1.4 OBJECTIVE
The basic objective would be to study MPPT and successfully implement the
MPPT algorithms either in code form as well as using the Simulink/Simscape
model. Modelling of the solar cell in Simulink/Simscape and interfacing both
with the MPPT algorithm to obtain the maximum power point operation would
be of prime importance. After simulating our result with the help of
Simulink/Simscape we would like to implement it on hardware using Field
Programmable Gate Array (FPGA).
Fig.1.1 MPPT Technique with Solar Cell
9
1.5 FUTURE SCOPE OF RENEWABLE ENERGY RESOURCES
The current trend across developed economies tips the scale in favour of
Renewable Energy. For the last three years, the continents of North America and
Europe have embraced more renewable power capacity as compared to
conventional power capacity. Renewables accounted for 60% of the newly
installed power capacity in Europe in 2009 and nearly 20% of the annual power
production
1.6 THESIS OUTLINE
This thesis has been broadly divided into 7 chapters. The first one being the
introduction, chapter 2 is on photovoltaic effect and modelling of solar cell with
Matlab Simulink/Simscape and effect of load mismatching. In chapter 3 we will
study about Boost Converter. Chapter 4 is on maximum power point tracking
and study of the various algorithms. Chapter 5 will discuss about FPGA &
Hardware Implementation. Result and conclusion is discussed in chapter 6 & 7.
10
CHAPTER 2
MODELLING OF PV PANEL
2.1 PHOTOVOLTAIC CELL
A photovoltaic cell or photoelectric cell is a semiconductor device that converts
light to electrical energy by photovoltaic effect. If the energy of photon of light
is greater than the band gap then the electron is emitted and the flow of
electrons creates current.
However a photovoltaic cell is different from a photodiode. In a photodiode
light falls on n-channel of the semiconductor junction and gets converted into
current or voltage signal but a photovoltaic cell is always forward biased.
2.2 PV MODULE
Usually a number of PV modules are arranged in series and parallel to meet the
energy requirements. PV modules of different sizes are commercially available
(generally sized from 60W to 170W). For example, a typical small scale
desalination plant requires a few thousand watts of power
2.3 PV ARRAY
A PV array consists of several photovoltaic cells in series and parallel
connections. Series connections are responsible for increasing the voltage of the
module whereas the parallel connection is responsible for increasing the current
in the array.
11
Fig.2.1 Different Solar Modules
2.4 PV MODELLING
Typically a solar cell can be modelled by a current source and an inverted diode
connected in parallel to it. It has its own series and parallel resistance. Series
resistance is due to hindrance in the path of flow of electrons from n to p
junction and parallel resistance is due to the leakage current.
When irradiance hits the surface of solar PV cell, an electrical field is generated
inside the cell. As seen in Fig.3 this process separates positive and negative
charge carriers in an absorbing material (joining p-type and n-type). In the
presence of an electric field, these charges can produce a current that can be
used in an external circuit. This generated current depends on the intensity of
the incident radiation. The higher the level of light intensity, the more electrons
can be unleashed from the surface, the more current is generated.
12
Fig.2.2 Schematic Cross-Section of a Typical Solar Cell
The most important component that affects the accuracy of the simulation is the
PV cell model. Modelling of PV cell involves the estimation of the I-V and P-V
characteristics curves to emulate the real cell under various environmental
conditions. An ideal solar cell is modelled by a current source in parallel with a
diode. However no solar cell is ideal and thereby shunt and series resistances
are added to the model as shown in the Fig.4
Fig.2.3 Equivalent Circuit of PV Cell
The current source Ipv represents the cell photo current, Rsh and Rs are used to
represent the intrinsic series and shunt resistance of the cell respectively.
Usually the value of Rsh is very large and that of Rs is very small, hence they
may be neglected to simplify the analysis.
2.4.1.NOMENCLATURE
Vpv is output voltage of a PV module (V) Ipv is output current of a PV module
(A)
Tr is the reference temperature = 298 K
T is the module operating temperature in Kelvin
Iph is the light generated current in a PV module (A) Io is the PV module
saturation current (A)
A = B is an ideality factor = 1.6
k is Boltzman constant = 1.3805 10-23
J/K q is Electron charge = 1.6 10-19
C
Rs is the series resistance of a PV module
13
ISCr is the PV module short-circuit current at 25 oC and 1000W/m
2 = 2.55A
Ki is the short-circuit current temperature co-efficient at ISCr = 0.0017A / oC
is the PV module illumination (W/m2) = 1000W/m2
Ego is the band gap for silicon = 1.1 Ev
Ns is the number of cells connected in series Np is the number of cells connected
in parallel
2.4.2.INTRODUCTION
Among the renewable energy resources, the energy due to the photovoltaic (PV)
effect can be considered the most essential and prerequisite sustainable resource
because of the ubiquity, abundance, and sustainability of solar radiant energy.
Regardless of the intermittency of sunlight, solar energy is widely available
and is free. Recently, photovoltaic system is recognized to be in the forefront in
renewable electric power generation. It can generate direct current electricity
without environmental impact and contamination when exposed to solar
radiation. Being a semiconductor device, the PV system is static, quiet, free of
moving parts, and has little operation and maintenance costs.
PV module represents the fundamental power conversion unit of a PV
generator system. The output characteristics of a PV module depend on the solar
insolation, the cell temperature and the output voltage of the PV module. Since
PV module has nonlinear characteristics, it is necessary to model it for the
design and simulation of maximum power point tracking (MPPT) for PV
system applications.
Mathematical modeling of PV module is being continuously updated to enable
researcher to have a better understanding of its working. [1]- [6]
In this paper, a step-by-step procedure for simulating PV module with
14
subsystem blocks, with user-friendly icons and dialog in the same way as
Matlab/ Simulink block libraries is developed. Section III presents the PV
module equivalent circuit and equations for Ipv, the output current from the PV
module. The reference model presented in section IV provides data for Solkar
make 36 W PV module for simulation. In section V, the step-by-step modeling
procedure of PV module is presented with simulation results. Finally, brief
conclusions are drawn in Section VI.
2.4.3.MATHEMATICAL MODEL FOR A PHOTOVOLTAIC MODULE
A solar cell is basically a p-n junction fabricated in a thin wafer of
semiconductor. The electromagnetic radiation of solar energy can be directly
converted to electricity through photovoltaic effect. Being exposed to the
sunlight, photons with energy greater then the band-gap energy of the
semiconductor creates some electron-hole pairs proportional to the incident
irradiation.
The current source Iph represents the cell photocurrent. Rsh and Rs are the
intrinsic shunt and series resistances of the cell, respectively. Usually the value
of Rsh is very large and that of Rs is very small, hence they may be neglected to
simplify the analysis.
PV cells are grouped in larger units called PV modules which are further
interconnected in a parallel-series configuration to form PV arrays.
The photovoltaic panel can be modeled mathematically as given in equations
(1)- (4) [3] [5].
Module photo-current:
I ph [I SCr K i (T 298)] * /1000 (1)
Module reverse saturation current - Irs:
15
I rs I SCr /[exp(qVOC / N S kAT ) 1] (2)
The module saturation current I0 varies with the cell
temperature, which is given by
T 3 q * Eg 0 1 1
I
0
I
rs
[ ] exp[ ] (3)
Tr Bk T
r T
2.4.4.REFERENCE MODEL
Solar make 36 W PV module is taken as the reference module for simulation
and the name-plate details are given in Table 1.
TABLE 2.1: ELECTRICAL CHARACTERISTICS DATA OF
SOLAR 36W PV MODULE
Rated Power 37.08 W
Voltage at Maximum power (Vmp) 16.56 V
Current at Maximum power ( Imp) 2.25 A
Open circuit voltage ( VOC) 21.24 V
Short circuit current ( ISCr) 2.55 A
Total number of cells in series (Ns) 36
Total number of cells in parallel (Np) 1
Note: The electrical specifications are under test conditions of irradiance of 1
kW/m2, spectrum of 1.5 air mass and cell temperature of 25
oC.
2.4.5.STEP BY STEP PROCEDURE FOR SIMULINK MODELING OF
PV MODULE
A model of PV module with moderate complexity that includes the temperature
16
independence of the photocurrent source, the saturation current of the diode,
and a series resistance is considered based on the Shockley diode
equation.Being illuminated with radiation of sunlight, PV cell converts part of
the photovoltaic potential directly into electricity with both I-V and P-V output
characteristics.Using the equations given in section III, simulink modeling is
done in the following steps
A. Step 1
Subsystem 1 is shown in Figure 1. This model converts the module operating
temperature given in degrees Celsius to Kelvin.
B. Step 2
Subsystem 2 is shown in Figure 4. This model takes following inputs.
Insolation/ Irradiation (G / 1000) 1 kW/ m2 = 1.
Module operating temperature TaK = 30 to 70oC
Module reference temperature TrK = 25oC.
17
Short circuit current (ISC) at reference temp. = 2.55A
Fig 4.SUBSYSTEM2
This model calculates the short circuit current ( ISC) at given operating
temperature. Figure 5 gives the circuit under subsystem
C. Step 3
Subsystem 3 is shown in Figure 6. This model takes short circuit current ISC at
reference temp. = 2.55A and Module reference temperature TrK = 25oC as
input.
18
Using equation 2, the reverse saturation current of the diode is calculated in
subsystem 3. Figure 7 gives the circuit under subsystem 3.
D. Step 4
Subsystem 4 is shown in Figure 8.
19
This model takes reverse saturation current Irs, Module reference temperature
TrK = 250 C and Module operating temperature TaK as input and calculates
module saturation current. Figure 9 gives the circuit under subsystem 4.
E. Step 5
Subsystem 5 is shown in Figure 10.
This model takes operating temperature in Kelvin TaK and calculates the
20
product NsAkT, the denominator of the exponential function in equation (4).
Figure 11 gives the circuit under subsystem 5.
F. Step 6
Subsystem 6 is shown in Figure 12.
This model executes the function given by the equation (4). The following
function equation is used.
IPV = u(3)-u(4)*(exp((u(2)*(u(1)+u(6)))/(u(5)))-1)
Figure 13 gives the circuit under subsystem 6.
21
G. Step 7
All above six models are interconnected as given in Figure 14.
Figure 14. Interconnection of all six subsystems
The final model is shown in Figure 15. The workspace is added to measure Ipv,
Vpv, Ppv in this model. The time tout is stored in workspace with scope model
can be used to plot graph.
22
The final model takes irradiation, operating temperature in Celsius and module
voltage as input and gives the output current Ipv and output voltage Vpv.
Matlab code for plotting XY graph is given below.
plot (Vpv,Ipv)
plot (Vpv, Ppv)
The code for plotting scope signals is
plot(tout,Ipv)
H. Performance Estimation
With the developed model, the PV module characteristic is estimated as follows.
(i) I-V and P-V characteristics under varying
irradiation with constant temperature are obtained as shown in Figures 16(a) to
16(d).
1. In Figure 16(a), the input irradiation is shown. Between 0 and 1 s, the
irradiation is 200W/m2, between 1 and 2 s it is 600 W/m, while from 2 s
onwards it is 1000W/m2.
23
4. The P-V output characteristics of PV module with varying irradiation at
24
constant temperature are shown in Figure 16(d).
_ The above graphs are user friendly.
_ When the irradiation increases,
_ The current output increases
_ The voltage output also increases. This results in net increase in power output
with increase in irradiation at constant temperature.
(ii) I-V and P-V Characteristics under constant irradiation with varying
temperature are obtained in Figures 17(a) to 17(d).
1. In Figure 17(a) the time varying temperature signal is shown. Between 0 and
1 second, the temperature of 250C is applied and it is increased to 50 and 750C.
25
2. The I-V output characteristics of PV module with varying temperature at
constant irradiation of 1000W/m2 are shown in Figure 17(b).
3. The P-V output characteristics of PV module with varying temperature at
constant irradiation are shown in Figure 17(c).
4. The output power vs. time of PV module is shown in Figure 17(d). The
power output reduces with increase in temperature at constant irradiation.
26
_ When the operating temperature increases,
_ The current output increases marginally
_ But the voltage output decreases drastically
_ Results in net reduction in power output with rise in temperature
The results are verified and found matching with the manufacturers data sheet
output curves.
2.4.6. CONCLUSIONS
The step-by-step procedure for modeling the PV module is presented.
Thismathematical modeling procedure serves as an aid to induce more people
into photovoltaic research and gain a closer understanding of I-V and P-V
characteristics of PV module.
REFERENCES
[1] M.Veerachary,Power Tracking for Nonlinear PV Sources with Coupled
Inductor SEPIC Converter, IEEE Transactions on Aerospace and Electronic
Systems, vol. 41, No. 3, July 2005.
[2] I. H. Altas and A.M. Sharaf, A Photovoltaic Array Simulation Model for
Matlab-Simulink GUI Environment, IEEE, Clean Electrical Power,
International Conference on Clean Electrical Power (ICCEP '07), June 14-16,
27
2007, Ischia, Italy.
[3] S.Chowdhury, S.P.Chowdhury, G.A.Taylor, and Y.H.Song, Mathematical
Modeling and Performance Evaluation of a Stand-Alone Polycrystalline PV
Plant with MPPT Facility, IEEE Power and Energy Society General Meeting -
Conversion and Delivery of Electrical Energy in the 21st Century, July 20-24,
2008, Pittsburg, USA.
[4] Jee-Hoon Jung, and S. Ahmed, Model Construction of Single Crystalline
Photovoltaic Panels for Real-time Simulation, IEEE Energy Conversion
Congress & Expo, September 12-16, 2010, Atlanta, USA.
[5] S. Nema, R.K.Nema, and G.Agnihotri, Matlab / simulink based study of
photovoltaic cells / modules / array and their experimental verification,
International Journal of Energy and Environment, pp.487- 500, Volume 1, Issue
3, 2010.
28
CHAPTER 3
BOOST CONVERTER
A boost converter is designed to step up a fluctuating or variable input voltage
to a constant output voltage of 24 volts with input range of 6-23volts in. To
produce a constant output voltage feedback loop is used. The output voltage is
compared with a reference voltage and a PWM wave is generated, here Spartan
6 FPGA kit is used to generate PWM signal to control switching action.
A DC to DC converter is used to step up from 12V to 24V. The 12V input
voltage is from the battery storage equipment and the 24V output voltage serves
as the input of the inverter in solar electric system. In designing process, the
switching frequency, f is set at 20 kHz and the duty cycle, D is 50%.
Here we want to introduced an approach to design a boost converter for
photovoltaic (PV) system using microcontroller. The converter is designed to
step up solar panel voltage to a stable 24V output without storage elements such
as battery. It is controlled by a FPGA unit using voltage-feedback technique.
The output of the boost converter is tracked, measured continuously and the
values are sent to the microcontroller unit to produce pulse-width-modulation
(PWM) signal. The PWM signal is used to control the duty cycle of the boost
converter. Typical application of this boost converter is to provide DC power
supply for inverter either for grid-connected or standalone system. Simulation
and experimental results describe the performance of the proposed design.
Spartan 6 FPGA is used to perform tasks in the proposed design.
As stated in the introduction, the maximum power point tracking is basically a
load matching problem. In order to change the input resistance of the panel to
29
match the load resistance (by varying the duty cycle), a DC to DC converter is
required.
It has been studied that the efficiency of the DC to DC converter is maximum
for a buck converter, then for a buck-boost converter and minimum for a boost
converter but as we intend to use our system either for tying to a grid or for a
water pumping system which requires 230 Vat the output end, so we use a boost
converter.
Fig.3.1 Circuit Diagram of a Boost Converter
3.1. MODE 1 OPERATION OF THE BOOST CONVERTER When the switch is closed the inductor gets charged through the battery and
stores the energy.In this mode inductor current rises(exponentially) but for
simplicity we assume that the charging and the discharging of the inductor are
linear.The diode blocks the current flowing and so the load current remains
constant which is being supplied due to the discharging of the capacitor.
Fig.3.2 Mode 1 Operation of the Boost Converter
3.2. MODE 2 OPERATION OF THE BOOST CONVERTER In mode 2 the switch is open and so the diode becomes short circuited. The
energy stored in the inductor gets discharged through opposite polarities which
charge the capacitor. The load current remains constant throughout the
30
operation. The waveform for a boost converter are shown in figure.
Fig.3.3 Mode 2 Operation of the Boost Converter 3.3. MODELING OF BOOST CONVERTER USING MATLAB SIMSACPE
Fig.3.4 Modelling of Boost DC-DC Converter
3.4. DESIGN APPROACH OF PROPOSED BOOST CONVERTER Load Requirement: The load is a simple 4 x 4 LED panel and each row
containing 4 LED in a line would require a current of 10- 15 mA and thus total
of 60 mA to all four branches and thus having a resistance of 570. As each
LED gives a drop of 2.1 volts to become forward biased, so a minimum of 8.4
31
volts is required to glow 4 LED in series, for this a voltage of 24 V is required
to be supplied to LEDs. Thus the load requirement is 570 with 42 mA of total
current thus required voltage was 24 V. Since a potential divider is used whose total resistance is 1100 so total
equivalent resistance is Req = (1100) (570) = 375.Based on this load
requirement the other parameters would be calculated and the specifications are
tabulated in the following table.
Table 3.1 Specification for Boost Converter
S.No. Component Value
1 Inductor 290H 2 MOSFET 1N5408 IRF 840 3 Power Diode IN5408 4 Input Capacitor 470F 5 Output Capacitor 330 F 6 Resistive Load 50, 50W
Duty Cycle: The duty cycle can be found using the following relation-
D=1
Inductor value: The value of inductor is determined using the following relation Lmin=D (1-D
2)*R/2*Fs
An inductor is practically designed using the following parameters and is shown
in the figure 22.
Formula for inductor design, L = (d2n2) / (l + 0.45d)
Required dimensions of inductor Coil length, l= 8.1 cm
Diameter, d= 6.3 cm Inductance value required, L= 151 H Number of turns, n=64
32
Where L is inductance in micro Henrys, d
is coil diameter in meters, l is coil length in meters, and n is
number of turns
Capacitor value: The value of capacitor is determined from the following equation
C=D/Fs*R*Vr Where C is the minimum value of capacitance,
D is duty cycle, R is output resistance, Fs is switching frequency, and Vr is output voltage ripple factor.
33
CHAPTER 4
MAXIMUM POWER POINT TRACKING ALGORITHM
4.1. AN OVERVIEW OF MAXIMUM POWER POINT TRACKING
A typical solar panel converts only 30 to 40 percent of the incident solar
irradiation into electrical energy. Maximum power point tracking technique is
used to improve the efficiency of the solar panel. According to Maximum
Power Transfer theorem, the power output of a circuit is maximum when the
Thevenin impedance of the circuit (source impedance) matches with the load
impedance. Hence our problem of tracking the maximum power point reduces
to an impedance matching problem. In the source side we are using a boost
convertor connected to a solar pan el in order to enhance the output voltage so
that it can be used for different applications like motor load. By changing the
duty cycle of the boost converter appropriately we can match the source
impedance with that of the load impedance.
4.2. DIFFERENT MPPT TECHNIQUES There are different techniques used to track the maximum power point. Few of
the most popular techniques are:
1) Perturb and Observe (hill climbing method)
2) Incremental Conductance method 3) Fractional short circuit current 4) Fractional open circuit voltage
4.3 PERTURB & OBSERVE
Perturb & Observe (P&O) is the simplest method. In this we use only one
sensor, that is the voltage sensor, to sense the PV array voltage and so the cost
34
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 doesnt 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. 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.
To avoid this problem we can use incremental conductance method.
4.4. INCREMENTAL CONDUCTANCE
Incremental conductance method uses two voltage and current sensors to sense
the output voltage and current of the PV array. At MPP the slope of the PV
curve is 0.
(dP/dV)MPP=d(VI)/dV
0=I+VdI/dVMPP
dI/dVMPP = - I/V
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. Here we are sensing both the voltage and current simultaneously.
Hence the error due to change in irradiance is eliminated. However the
complexity and the cost of implementation increases. As we go down the list of
algorithms the complexity and the cost of implementation goes on increasing
which may be suitable for a highly complicated system. This is the reason that
Perturb and Observe and Incremental Conductance method are the most widely
used algorithms. Owing to its simplicity of implementation we have chosen the
Perturb & Observe algorithm for our study among the two.
35
4.5. FRACTIONAL OPEN CIRCUIT VOLTAGE
The near linear relationship between VMPP and VOC of the PV array, under varying
irradiance and temperature levels, has given rise to the fractional VOC method.
VMPP = k1 Voc where k1 is a constant of proportionality. Since k1 is dependent on the
characteristics of the PV array being used, it usually has to be computed
beforehand by empirically determining VMPP and VOC for the specific PV array at
different irradiance and temperature levels. The factor k1 has been reported to be
between 0.71 and 0.78. Once k1 is known, VMPP can be computed with VOC
measured periodically by momentarily shutting down the power converter.
However, this incurs some disadvantages, including temporary loss of power.
4.6. FRACTIONAL SHORT CIRCUIT CURRENT Fractional ISC results from the fact that, under varying atmospheric conditions, IMPP
is approximately linearly related to the ISC of the PV array.
IMPP =k2 Isc
Where k2 is a proportionality constant. Just like in the fractional VOC technique, k2
has to be determined according to the PV array in use. The constant k2 is generally
found to be between 0.78 and 0.92. Measuring ISC during operation is problematic.
An additional switch usually has to be added to the power converter to periodically
short the PV array so that ISC can be measured using a current sensor.
4.7. DETAILS OF PERTURB & OBSERVE ALGORITHM
The Perturb & Observe algorithm states that when the operating voltage of
the PV panel is perturbed by a small increment,if the resulting change in power P is
positive,then we are going in the dir of perturbationection of MPP and we keep on
36
perturbing in the same direction.If P is negative,we are going away from the
direction of MPP and the sign of perturbation supplied has to be changed.
The flowchart for the P&O algorithm is shown in the figure
Fig.4.1 Flowchart Of Perturb & Observe Algorithm
4.7.1. MODELLING OF P&O ALGORITHM
Fig.4.2 Modelling of P&O Algorithm
37
4.7.2. COMPLETE MODEL OF PV PANEL WITH MPPT
Fig.4.3 Complete Model of PV Panel With MPPT
4.7.3 OUTPUT CHARACTERISTICS
FIG 4.4 OUTPUT CHARACTERISTICS
38
4.8 DETAILS OF INCREMENTAL CONDUCTANCE ALGORITHM
The flowchart for the Incremental Conductance algorithm is shown in the figure
Fig-4.5:Incremental conductance MPPT Flow chart
39
4.8.1. MODELLING OF INCREMENTAL CONDUCTANCE
ALGORITHM
Fig.4.6 Modelling of Incremental Conductance Algorithm
4.8.2. COMPLETE MODEL OF PV PANEL WITH MPPT
Fig.4.7 Complete Model of PV Panel With MPPT
40
4.8.3 OUTPUT CHARACTERISTICS
PV CELL OUTPUT INC CONDUCTANCE OUTPUT
FIG 4.8 OUTPUT CHARACTERISTICS
4.9 DETAILS OF SHORT CIRCUIT CURRENT ALGORITHM
The flowchart for the Short Circuit Current algorithm is shown in the figure
FIG 4.9 FLOWCHART FOR SHORT CICUIT CURRENT ALGORITHM
41
4.9.1. COMPLETE MODEL OF PV PANEL WITH MPPT(SHORT CIRCUIT
CURRENT)
Fig.4.10 Complete Model of PV Panel With MPPT
4.9.2 OUTPUT CHARACTERISTICS
PV CELL OUTPUT SHORT CIRCUIT CURRENT OUTPUT
FIG 4.11 OUTPUT CHARACTERISTICS
42
4.9DETAILS OF FRACTIONAL OPEN CIRCUIT VOLTAGE ALGORITHM
The flowchart for the Open Circuit Voltage algorithm is shown in the figure
FIG 4.12 FLOWCHART FOR OPEN CIRCUIT VOLTAGE ALGORITHM
4.9.1. COMPLETE MODEL OF PV PANEL WITH MPPT(OPEN CIRCUIT
VOLTAGE)
Fig.4.13 Complete Model of PV Panel With MPPT
43
4.9.2 OUTPUT CHARACTERISTICS:
PV CELL OUTPUT OPEN CIRCUIT VOLTAGE OUTPUT
FIG 4.14 OUTPUT CHARACTERISTICS
4.10 COMPARISON OF MPPT ALGORITHMS:
Comparing different parameters, it is evident that each algorithm is suited for
different purposes. Below table shows a comparison between these algorithms.
ERRORS P AND O INCREMENTAL
CONDUCTANCE
SHORT
CIRCUIT
CURRENT
OPEN
CIRCUIT
VOLTAGE
MEAN
ABSOLUTE
PERCENTAGE
ERROR
0.093780528
0.00442
0.098105
0.578062
MEAN
PERCENTAGE
ERROR
9.3780528
0.442
9.8105
57.8
MEAN
ABSOLUTE
ERROR
0.1
0.002264999
0.133459
1.74725082
TABLE 4.1 COMPARISON OF MPPT ALGORITHMS
44
Conclusion from the table 4.1
Due to minimum error in Incremental Conductance,Incremental Conductance is
the Optimum Algorithm.
4.11 COMPLETE MODEL OF PV PANEL WITH MPPT(INCREMENTAL
CONDUCTANCE) APPLIED TO UNIVERSAL MOTOR
45
CHAPTER 5
HARDWARE IMPLEMENTATION
5.1 Hardware Components
The Components used for the project are listed in the Table 5.1
Table 5.1 Hardware Components
COMPONENT NAME SPECIFICATION
Capacitor 470 F ,50 V
Inductor ecore 22 SWG (0.5mH)
Mosfet IRF 840
Diode IN4007 , 3A
Transformer 220/12 V
Optocoupler IC TLP 250
Resistors 460,1k,1.2 k
Universal motor 240v,60Hz,,50-1000W
Adruino
46
5.2 Hardware Setup
The entire Hardware Setup for the project is Shown in the Figure 5.1
Figure 5.1 Hardware SetupFIG 5.1.1 Without Boost Converter
Fig 5.1.2With Boost Converter Operating
47
Fig 5.1.3 BOOST CONVERTER
Fig 5.1.4 Gate Drive Circuit
Fig 5.1.5 Arduino
48
5.3 Sub Circuits
The entire hardware setup consists of the following sub circuits:
1. Supply Circuit
2. Control Circuit
3. Optocoupler Circuit
4. Power Circuit
5.3.1 Supply Circuit
The AC input supply is stepped down to 12 V from 230 V using a step down
transformer. Using a diode bridge rectifier AC voltage is converted to DC voltage.
5.3.2 Control Circuit
The control circuit is used to produce the pulses to trigger the MOSFET of
the boost converter.
5.3.3 Optocoupler Circuit
The optocoupler circuit is used to isolate the Power Circuit from Control
Circuit. The supply of 12 V is given to the pin 5 and the output of the optocoupler
is taken from pin 4 and it is used to trigger the MOSFET switch of the boost
converter.
5.3.4 Power Circuit
The power circuit consists of consists of the boost converter whose
output voltage greater than the input voltage depending on the boost mode of
operation.
49
5.4 Matlab interface with Arduino for serial communication:
MATLAB Support Package for Arduino(also referred 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.
5.5 TESTING AND RESULT.
The experimental test was recorded in Chennai, 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.
50
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.
51
CHAPTER 6
CONCLUSION AND FUTURE WORK
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 present four MPPT algorithms implemented on a synchronous
Boost converter and compare them on real time graph obtain in Matlab. From this
we come to know that Incremental conductance method has less oscillation.The
maximum power point tracker works because there is a difference between the
solar panels MPP voltage and the batterys 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
52
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1.Arun KumarVerma, Bhim Singh and S.C Kaushik, An Isolated Solar Power
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Conference on Recent Advances in Computational Technique in Electrical
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2.Comparison of MPPT Algorithms for DC-DC Converters Based PV Systems by
A.Pradeep Kumar Yadav,S.Thirumaliah,G.Haritha International Journal of
Advanced Research in Electrical, Electronics and Instrumentation Engineering
Vol. 1, Issue 1, July 2012
3.Eftichios Koutroulis and Freder Blaabjerg , (2012), A New Technique
for Tracking the Global Maximum Power Point of PV Arrays Operating
Under Partial-Shading Conditions , IEEE Journal of Photovoltaics , Vol. 2 , No. 2,
pp. 184-190.
4.G. Acciari, D. Graci, and A. La Scala, (2011), Higher PV module efficiency
by a novel CBS bypass, IEEE Trans. Power Electron, Vol. 26, No. 5, pp.
13331336.
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53
6.International Conference on Microelectronics, Communication and Renewable
Energy (ICMiCR-2013)
Experimental Implementation of Micro-controller based MPPT for Solar PV
System Ahmed Bin-Halab,Adel Abdennour,Hussein Mashlay in-Halabi Adel
Abdennour Hussein Mashaly Department of Electrical Engineering ,King Saud
University ,Riyadh, Saudi Arabia.
7.Mihnea Rosu-Hamzescu, Sergiu Opera Practical Guide to Implementing
Solar Panel MPPT Algorithms"
8.M. Miyatake, M. Veerachary, F. Toriumi, N. Fujii, and H. Ko, (2011)
Maximum power point tracking of multiple photovoltaic arrays: A PSO
approach, IEEE Trans. Aerosp. Electron. Syst., Vol. 47, No. 1, pp. 367380.
9.M. Berrera, A. Dolara, R. Faranda and S. Leva, Experimental test of seven
widely-adopted MPPT algorithms, 2009 IEEE Bucharest Power Tech Conference,
June 28th - July 2nd, Bucharest, Romania.
10.N.Pandiarajan and Ranganath Muth Mathematical Modeling of
Photovoltaic Module with Simulink in 2011 1st International Conference on
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11. N. Femia, G. Petrone, G. Spagnuolo and M. Vitelli, (2005)
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54
12.Nevzat Onat, Recent Developments inMaximumPower Point Tracking
Technologies for Photovoltaic Systems, Hindawi Publishing Corporation
International Journal of Photoenergy Volume 2010, Article ID 245316, 11 pages.
13.Pandiarajan N, Ramaprabha R and Ranganath Muthu Application Of
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14.P.Sathya, Dr.R.Natarajan Design and Implementation of 12V/24V Closed
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