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CHAPTER-3
Design Aspects of DC-DC Boost Converter
in Solar PV System by MPPT Algorithm
45
CHAPTER-3
DESIGN ASPECTS OF DC-DC BOOST CONVERTER IN SOLAR PV
SYSTEM BY MPPT ALGORITHM
3.1 Introduction
In the recent past years, due to the energy crisis and environment pollution, the electrical
generation system using photovoltaic cells become more significant. Photovoltaic (PV)
power generation systems can substantially reduce environmental issues such as the green
house effect and air pollution. In a photovoltaic system the solar energy can be converted
directly into electrical energy. Photovoltaic cells are the basic component of a photovoltaic
system. Cells may be grouped in series and parallel to form a solar module. Again modules
may be grouped in series and parallel to form photovoltaic arrays. Cells are connected in
parallel to increase the output current and connected in series to increase the output voltage.
The major problem in PV power generation systems is that the amount of electric power
generated by PV module is always changing with weather conditions, i.e., irradiation.
Therefore, Maximum Power Point Tracking (MPPT) algorithms is implemented which has
led to the increase in the efficiency of operation of the solar modules.
3.2 Description
The main components of a photovoltaic system consist of PV panel, converter and the
controller to control the converter operation. Therefore the efficiency of a PV system depends
on the efficiency of its components i.e. the efficiency of the PV panel (8-15% in commercial
PV panels), the efficiency of the inverter (95-98 %), and the efficiency of the maximum
power point tracking (MPPT) algorithm (about 98%). For the improvement of the efficiency
of the PV panel suitable semiconducting material is required which depends on the
manufacturing technology used, whereas the efficiency of the converter can be increased by
using suitable control strategy for their operation. Due to non linear voltage-current
characteristic, the PV system should be operated at a point on the I-V curve where maximum
power is produced. This maximum power point depends on the temperature of the cell and on
the irradiance conditions. Both conditions change during the day and are also different
depending on the season of the year. Furthermore, irradiation can change rapidly due to
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changing atmospheric conditions such as clouds. Hence it is very important to track the MPP
accurately under all environmental conditions so as to obtain maximum power available.
In this chapter, advantages, shortcomings and execution of efficiency for two power-feedback
type MPPT methods, including perturbation & observation (P&O) and incremental
conductance (INC) methods are studied and compared. Matlab/Simulink is used in this paper
to implement the modeling and simulations tasks. The performance of a PV array system
depends on the the solar cell and array design as well as the operating conditions. The output
voltage, current and power of a PV array are the functions of solar irradiation level,
temperature and load current. Therefore in the design of PV arrays, the effects of these three
quantities must be considered so that any change in temperature and solar irradiation levels
should not adversely affect the PV array output power to the load or utility. To overcome the
effects of the variable temperature and solar irradiation on the output power of PV systems,
two control strategies have usually been applied:
a) Controlling the solar irradiation to the PV array, and
b) Controlling the electrical power output from the PV array.
In both the systems either electrical or thermal energy storage systems or auxiliary power
sources are incorporated which can supply electricity during the time of no solar irradiation.
Solar energy available to the PV systems is kept as high as possible either by rearranging the
solar cell configurations of PV arrays or by designing and controlling the position of sun
tracking solar collectors with respect to the changes in weather conditions. Consequently,
during the design process a simulation must be performed for the system analysis and
parameter settings. When a photovoltaic (PV) array is connected directly to the load, the
operating point of PV solar array is seldom at the maximum power point (MPP). The
Maximum Power Point Tracking (MPPT) combined with a dc-dc power converter allows a
PV generator to produce maximum continuous power, regardless of the environmental
conditions (solar radiation, temperature). There are various different algorithms of MPPT
control with different ways on implementation and performance. The best known MPPT
classic algorithms are perturbed-and observe (P&O) and incremental conductance (IC). These
algorithms are based on the same technology, regulating PV array voltage by adjusting the
optimal set point that represents the voltage at maximum power point (MPP).
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3.3 DC-DC Converter for Solar PV System
The DC-DC converter used to supply a regulated DC output with the given DC input. These
are widely used as an interface between the photovoltaic panel and the load in photovoltaic
generating systems. The load must be adjusted to match the current and voltage of the solar
panel so as to deliver maximum power. DC/DC converters are described as power electronic
switching circuits since they convert one form of voltage to other. These may be applicable
for conversion of different voltage levels.
Generally three basic types of converters are accountable as per their use. They either step up
by boosting voltage at output known as Boost converter or by stepping down by reducing
voltage known as Buck converters. There is another class of converters used for both
stepping up or down the voltage output described as Buck-Boost converters. Buck-Boost
converters reverse polarity of output voltage, as such they are sometimes known as inverters.
3.3.1 Boost Converter and its Mode of Operation
A simple boost converter consists of an inductor, a switch, a diode, and a capacitor. Figure
3.1 represents the circuit diagram of DC-DC boost convertor and figure 3.2 show the mode of
operation of boost converter.
Figure 3.1: Circuit diagram of boost converter
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Boost converter circuit operation can be divided into two phases. Phase 1 begins when the
switch SW is turned on at t = Ton. The input current which rises flows through inductor L and
switch SW. During this mode, energy is stored in the inductor.
Phase 2 begins when the switch is turned off at t = Toff. The energy stored in the inductor
causes its voltage to swap polarity and maintain current flow in the circuit, which is now
directed through inductor L diode D, capacitor C, load R, and the supply of Vin.
Figure 3.2: Circuit diagram of mode of operation of boost converter
The inductor current falls until the switch is turned on again in the next cycle. The reversing
of the inductor voltage polarity in phase 2 allows the Vout to be greater than Vin.
Where Vout is the output voltage, D is duty cycle, and Vin is the input voltage which in this
case will be the solar panel voltage.
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3.3.2 Waveforms
Figure 3.3 shows the different characteristics of boost converters. It shows the source
voltage, source current, inductor current, capacitor current with respect to time for a complete
duty cycle.
Figure 3.3: Waveforms of boost converter
It is assumed that the switch is made ON and OFF at a fixed frequency and let the period
corresponding to the switching frequency is T. Given that the duty cycle is D, the switch is on
for a period equal to DT, and the switch is off for a time interval equal to (1 - D)T. The
inductor current is continuous and is greater than zero.
3.3.3 Modeling of Boost Converter
The DC-DC boost converter circuit consists of Inductor (L), Diode (D), Capacitor (C), load
resistor (RL), the control switch(S). These components are connected in such a way with the
input voltage source (Vin) so as to step up the voltage. The duty cycle of the control switch
controls the output voltage of the boost converter. Hence by varying the ON time of the
switch, the output voltage can be varied. Thus, for the duty cycle “D” the average output
voltage can be calculated using
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Vo/Vin=1/(1-D) (3.1)
where Vin, Vo are the input and output voltage of the converter respectively and D is the duty
cycle of the control switch. In an ideal circuit, the output power of the converter is equal to
input power which yields.
Po = Pin (3.2)
i.e. VoIo=Vin Iin (3.3)
A. Selection of Inductor:
The inductor value of the Boost converter are calculated using
L=Vin/(fs∆IL) (3.4)
Where fs is the switching frequency and ∆IL is the input current ripple. Current ripple factor
(CRF) is the ratio between input current ripple and output current. For good estimation of
inductor value CRF should bound within 30%.
The current rating of inductor should be always higher than that of the maximum output
current ∆IL/Io=0.3 (3.5)
The current rating of inductor should be always higher than that of the maximum output
current.
B. Selection of Capacitor
The capacitor value can be obtained from
C = Iout/(fs∆Vo)D (3.6)
Where ∆Vo is the output voltage ripple which is usually considered as 5% of output voltage
which yields, ∆Vo/Vo= 5%.
3.3.4 Block diagram Model of Boost Converter
The DC-DC boost converter is designed for Vin= 12V,Vout = 20.99 V, Iout =1.05 amp and
Using these values the components values are calculated as follows L=200µH, C=50µF and
RL=20Ω. The block diagram is shown in figure 3.4.
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Figure 3.4: Block diagram model of boost converter
3.3.5 Simulation Results
The MATLAB simulation model for the above block diagram is given in figure 3.5. It consist
of one switch input voltage source, inductor, DC load and scopes to observe the output.
Figure 3.s shows the input voltage curve of boost converter whereas figure 3.7 shows the
simulation results of boost converter.
Figure 3.5: Simulink model of boost converter
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Figure 3.6: Input voltage curve of boost converter
It is observed from figure 3.6 that the input voltage is always constant for boost converter.
The output voltage and currents of boost convertor for this input is given below.
0 0.002 0.004 0.006 0.008 0.01-20
-10
0
10
20Voltage across Inductor
Time
Voltage
0 0.002 0.004 0.006 0.008 0.01-10
0
10
20
30Voltage across Load
Time
Voltage
0 0.002 0.004 0.006 0.008 0.01-2
0
2
4
6
8Capacitor Current
Time
Curr
ent
0 0.002 0.004 0.006 0.008 0.01-30
-20
-10
0
10Diode Current
Time
Curr
ent
Figure 3.7: Simulation results of boost converter
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The above results show that the voltage across the inductor remains within – 10V to +10V
during switching ON and OFF position. The load current remains constant after 0.002
seconds. The capacitor current remains stable within – 1A to +1A whereas the output voltage
of the boost converter is stable at 20A after 0.002 seconds. The diode (D1) current is 2A and
diode (D2) current is – 20A after 0.002 seconds.
The output voltage comes constant to 21.5 volts for various levels of inputs and current is
19.3 mA which is shown in figure 3.7. Whenever output voltage becomes more than 22 volts
the differential amplifier will generate a negative saturation level voltage signal which will
increase the duty cycle and so output would come down to 22 volts. And when output
becomes less than 22 volts the differential amplifier will generate a more positive level of
voltage signal which in turn would reduce the duty cycle and thereby increasing the voltage
to 22 volts. Thus how output voltage is kept constant. For the specified input variation, a
regulated dc output voltage of 21.5V has been obtained resulting in an efficiency of 95%.
3.4 Modelling of PV Array and Performance Enhancement by MPPT Algorithm
This section proposes modelling and simulation of photovoltaic model. Taking into account
the temperature and sun's irradiance, the PV array is modelled and its voltage current
characteristics and the power and voltage characteristics are simulated. This enables the
dynamics of PV system to be easily simulated and optimized. It is noticed that the output
characteristics of a PV array are influenced by the environmental factors and the conversion
efficiency is low. Therefore a maximum power tracking (MPPT) technique is needed to track
the peak power to maximize the produced energy. The maximum power point in the power -
voltage graph is identified by an algorithm called perturbation & observation (P&O) method
or Hill climbing. This algorithm will identify the suitable duty ratio in which the DC/DC
converter should be operated to maximize the power output.
3.5 MPPT Techniques Used in Solar PV System
The principal drawback of the PV Systems is their low efficiency. The typical efficiency of a
solar cell is around 8-15%. In case of the solar panels, it becomes hardly 30-40%. That means
the panels are capable to convert only 30-40% of the incoming solar irradiations into
electrical power. So the fundamental aim of this part of the thesis is to increase the efficiency.
There are several methods available, by which we can improve the efficiency by matching
the source and load properly. The Maximum Power Point Tracking (MPPT) is one such
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method, which has a huge importance in the era of Photovoltaic Technology. Now-a-days
this technique is vastly used to develop maximum possible power from a varying source
under a variable temperature and irradiance conditions. We know, the Maximum Power
Transfer Theorem tells that the output power of a circuit is maximum, when the Thevenin
impedance of a circuit i.e. the source impedance matches with the load impedance and
complex conjugate to it. So, MPPT problem is one kind of impedance matching problem.
Solar cells have a very complex relationship between solar irradiation, temperature and the
total resistance that develops a non-linear output efficiency which can be analyzed based on
the I-V curve. So the main function of MPPT is to sample the output of the cells and apply
the proper load to obtain the maximum power for any given location, time, season and
environmental conditions. The MPPT not only enables an increase in the power delivered
from the PV module to the load, but also enhances the operating lifetime of the PV system.
Various types of MPPT methods can be differentiated based on various features including the
types of sensors required, convergence speed, cost, range of effectiveness, implementation of
hardware requirements, popularity etc.
The operating characteristics of a solar cell consist of two regions i.e. the current source
region and the voltage source region.
In the current source region, the internal impedance of the solar cell is high and this region is
located on the left side of the current-voltage curve. The voltage source region, where the
internal impedance is low, is located on the right side of the current -voltage curve.
As per Maximum Power Transfer Theorem, Maximum Power is delivered to load when
source internal impedance matches load impedance. For determining MPP appropriate
Tracker is introduced between PV system and load. It is to be designed that gives good
performance, fast response, and less fluctuations. Since the efficiency of the PV is affected by
the panel’s irradiance and temperature which are stochastic and unpredictable.
For this reason, it is not possible to connect the load directly to the PV to obtain the
maximum power, so it is necessary to include a balance of system (BOS). Typically this
BOS is a DC-DC converter to adjust the properties of the load. This converter has the
advantage of managing the power delivered to the load.
A DC/DC converter (step up/step down) serves the purpose of transferring maximum power
from the solar PV module to the load. A DC/DC converter acts as an interface between the
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load and the module. By changing the duty cycle 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. This acts as adjustment to match impedance of source & load. MPPT is
normally operated with the use of a DC-DC converter (step up or step down). The location of
the MPP is not known, but can be located, either through calculation models or by search
algorithms.
Figure.3.8: Block diagram of MPPT system
The typical block diagram of MPPT system considered for simulation study which is derived
from the concept of basic block diagram are given in figure 3.8 and 3.9 respectively.
Figure 3.9: Block Diagram of Typical MPPT System.
There are several MPPT method exists in order to maximizing the output power. The existing
methods are
a) Perturb and observation method.
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b) Incremental conductance method.
c) Parasitic capacitance method.
d) Voltage based peak power tracking method.
e) Current based peak power tracking method.
Among all the MPPT methods, Perturb & Observe (P&O) and Incremental Conductance (IC)
are most commonly used because of their simple implementation and lesser time to track the
maximum power point and also other economic reasons.
Table 3.1. Characteristics of different MPPT techniques
MPPT technique
Convergence Speed
Implementation complexity
Periodic Tuning
Sensed parameters
P & O Varies Low No Voltage
INC Varies Medium No Voltage, current
Fractional VOC Medium Low Yes Voltage
Fractional ISC Medium Medium Yes Current
Fuzzy Logic Fast High Yes Varies
3.5.1 Perturb and Observe MPPT Method
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. If the PV array operating voltage changes and power increases
(dP/dV > 0), the control system moves the PV array operating point in that direction;
otherwise the operating point is moved in the opposite direction. In the next perturbation
cycle the algorithm continues in the same way.
A common problem in P&O algorithms is that the array terminal voltage is perturbed every
MPPT cycle; therefore when the MPP is reached, the output power oscillates around the
maximum, resulting in power loss in the PV system. Perturb & Observe (P&O) is the
simplest method and is widely used. In this technique we generally use only one sensor, that
is the voltage sensor, to sense the PV module voltage and hence the cost of implementation is
less and hence easy to implement without any complexity.
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The time complexity of this algorithm is very less for calculating the maximum power but on
reaching very close to the Maximum Power Point (MPP) it doesn’t stop at the MPP and keeps
on perturbing on both the directions so for that reason it have multiple local maximum at the
very same point. First of all the algorithm which reads the value of the current and voltage
from the photovoltaic module from that power is calculated the value of voltage and power at
that instant is stored. Hence slight perturbation is added in the increasing direction. The next
values at the next instant are measured and power is again calculated. Hence, by adjusting the
maximum power duty cycle can be obtained based on it.
In certain situations like changing atmospheric conditions and change in irradiance the
maximum power point shifts from its normal operating point on the PV curve. In the next
iteration it changes its direction and goes away from the maximum power point and results in
multiple local maxima at the same point as shown in figure 3.10. So the maximum power
point deviates from its original position.
Figure 3.10: PV curve
3.5.1.1 Algorithm for Perturb and Observe Technique
a) Read the value of current and voltage from the solar PV module.
b) Power is calculated from the measured voltage and current.
c) The value of voltage and power at kth instant are stored.
d) Then next values at (k+1)th instant are measured again and power is calculated
from the measured values.
e) The power and voltage at (k+1)th instant are subtracted with the values from kth
instant.
f) In the power voltage curve of the solar PV module, it is inferred that in the right
hand side curve where the voltage is almost constant and the slope of power
voltage is negative (dP/dV<0) where as in the left hand side, the slope is positive
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(dP/dV>0).Therefore the right side of the curve is for the lower duty cycle (nearer
to zero) where as the left side curve is for the higher duty cycle (nearer to unity).
g) Depending on the sign of dP i.e. (P(k+1) - P(k)) and dV i.e. (V(k+1) -V(k)) after
subtraction the algorithm decides whether to increase the duty cycle or to reduce
the duty cycle.
The above algorithm is shown represented in a flow chart which is given in figure 3.11.
3.5.1.2 Flow Chart of Perturb and Observe MPPT Algorithm
Figure 3.11: Flow chart of Perturb and Observe MPPT algorithm
3.5.1.3 Simulink Model of Perturb and Observe MPPT Algorithm
To know the behaviour of Perturb and Observe MPPT Algorithm, a simulation model
is developed which is given in figure 3.12. The block diagram for this model is given in
figure 3.4
Measures of V(k) and I(k)
P(k) = V(k) × I(k)
∆P = P(k) – PO(k – 1)
Decrease Module Voltage
Updates V(k – 1) = V(k) P(k – 1) = P(k)
START
∆P > 0 Yes No
V(k) – V(k – 1)>0 V(k) – V(k – 1)>0 Yes Yes No No
Increase Module Voltage
Increase Module Voltage
Decrease Module Voltage
k = k+1
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Figure 3.12: Simulink model of Perturb and Observe MPPT algorithm
3.5.1.4 Simulation Results
1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
time t(in sec)
Du
ty c
yc
le(k
)
Figure 3.13: Simulink result of duty cycle of Perturb and Observe MPPT algorithm
Figure 3.13 shows the duty cycle of the boost converter which is obtained from the MPPT by
Perturb and Observe technique. Figure shows the ON and OFF period for the converter.
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3.5.1.5 Limitations of Perturb and Observe Algorithm
Figure 3.14: Curve showing wrong tracking of MPP by P&O algorithm under rapidly varying
irradiance
In a situation where the irradiance changes rapidly, the MPP also moves on the right hand
side of the curve. The algorithm takes it as a change due to perturbation and in the next
iteration it changes the direction of perturbation and hence goes away from the MPP as
shown in the figure 3.14. However, in this algorithm one sensor is used as voltage sensor, to
sense the PV array voltage and 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 in both the directions. When this
happens the algorithm has reached very close to the MPP and an appropriate error limit is set
or a wait function can be used which ends up increasing the time complexity of the algorithm.
3.5.2 Incremental Conductance (IC) Method
The disadvantage of the perturb and observe method to track the peak power under
fast varying atmospheric condition is overcome by IC method. The IC can determine that the
MPPT has reached the MPP and stop perturbing the operating point. If this condition is not
met, the direction in which the MPPT operating point must be perturbed & can be calculated
using the relationship between dl/dV and –I/V. This relationship is derived from the fact that
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dP/dV is negative when the MPPT is to the right of the MPP and positive when it is to the left
of the MPP.
We Know,
P= V × I (3.7)
or
or
or
But for maximum power the slope should be zero.
or
or (3.8)
In this equation, left hand side represents PV array’s incremental conductance, while right
hand side represents the opposite of its instantaneous conductance. It is clear that, at MPP,
these two values should be equal.
dI I
dV V> − with 0
dP
dV
>
(3.9)
And dI I
dV V< − with 0
dP
dV
<
Equation 3.9 decides the direction in which the operating point should move towards the
MPP. Under sudden changing condition of weather, the right direction is tracked in
incremental conductance algorithm, which is not possible in P&O algorithm and also the
point is not oscillated around the MPP as it is happened in case of P&O algorithm. Figure
3.15 shows the shifting of MPP under varying atmospheric condition and MPP is achieved
when dP/dV = 0.
This algorithm has advantages over P&O is that, it can determine when the MPPT has
reached the MPP, where P&O oscillates around the MPP. Also, incremental conductance can
track rapidly increasing and decreasing irradiance conditions with higher accuracy than
perturb and observe.
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Figure 3.15: PV characteristics showing MPP and operating points A and B.
One disadvantage of this algorithm is the increased complexity when compared to
P&O. The flowchart for incremental conductance algorithm is given in figure 3.16.
Figure 3.16: Flowchart for Incremental Conductance Algorithm.
Measure V(k) and I(k)
dI = I(k) – I(k – 1)
dV = V(k) – V(k – 1)
V(k) =V(k) + dV
V(k) =V(k) – dV
V(k) =V(k) + dV
V(k) =V(k) – dV
k=k+1
Start
∆V ≤ 0 Yes No
(dI/dV + I/V) ≤ 0 Error! No
No No
Yes
Yes
Error! (dI/dV + I/V) > 0
Yes
No Yes
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3.6 Simulation and Result:
The MPPT algorithm is simulated in MATLAB environment. The block diagram and the
simulation diagrams for the MPPT algorithm are given below. Figure 3.17 shows the block
diagram of PV System with MPPT Algorithm.
Figure 3.17: Block diagram Model of PV System with MPPT Algorithm.
Figure 3.18 shows simulation model of PV System with MPPT Algorithm.The MATLAB
simulation model of the PV system with MPPT algorithm is shown in figure below. The
simulation model is based on the block diagram model shown above. It consists of two sub
systems that is PV panel and MPPT controller, a boost converter and several scopes to show
the simulation results.
Figure 3.18: Simulation Model of PV System with MPPT Algorithm.
The simulation diagram for solar PV cell is shown in figure 3.19, which is a sub system of
PV panel in the main simulation diagram given in figure 3.18.
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Figure 3.19: Simulation Diagram of Solar PV Cell
There are several MPPT algorithm available in literature whereas, in this thesis two type of
MPPT controller algorithm is used for simulation study that is IC algorithm and P&O
algorithm. The simulation model of MPPT controller (which is a subsystem for the main
simulation model given in figure 3.18) based on IC algorithm is shown in figure 3.20. This
model developed based on the flowchart which is given in figure 3.16.
Figure 3.20: Simulation Model of IC algorithm.
The simulation model of MPPT controller (which is a subsystem for the main simulation
model given in figure 3.18) based on P&O algorithm is shown in figure 3.21. This model
developed based on the flowchart which is given in figure 3.11. The sub sytems areshown in
subsequent figures.
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The comparison result of output voltage, current and power for the above two MPPT
controller algorithm is given in section 3.7.
Figure 3.21: Simulation Model of P&O Algorithm.
Figure 3.22: Simulation of Load Current.
Figure 3.22 represent the subsystem of the main Simulink diagram for load current
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Figure 3.23: Simulation of Shunt Current.
Figure 3.23 represent the subsystem of the main Simulink diagram for shunt current
Figure 3.24: Simulation of Diode Current.
Figure 3.24 represent the subsystem of the main Simulink diagram for diode current
Figure 3.25: Simulation of Photo Current.
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The subsystem of the main Simulink diagram for photo current is given in figure 3.25
Figure 3.26: Simulation of Diode Leakage Current.
The subsystem of the main Simulink diagram for diode leakage current is given in figure 3.26
Figure 3.27: Simulation of Reverse Saturation Current.
The subsystem of the main Simulink diagram for reverse saturation current is given in figure
3.27
3.7 Simulation Results
The IGBT is used in boost converters and the GATE signal for triggering the IGBT is given
in figure 3.28.
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Figure 3.28: Gating Signal in IC Method.
The simulated results of output voltage, current and power are shown below. To understand
the behaviour of P&O and IC methods, the simulated results of both the method are plotted in
same plot for comparison purpose.
Figure 3.29: Output Voltage in P&O and IC Method.
Figure 3.30: Output Current in P&O and IC Method.
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Figure 3.31: Output Power in P&O and IC Method.
From the figure 3.29 to 3.31 it is observed that in MPPT controller based on P&O algorithm
gives better result that is more voltage, current and power compare to IC algorithm. After the
initial transient period the P&O algorithm gives the smooth variation of the outputs.
The results show that the best MPPT technique is the modified P&O method. The logic
turned out to be effective in both the situations which always provides the highest efficiency.
P&O technique shows its limit in the response to the irradiance variation at low irradiance
level. The IC technique has efficiency lower than the P&O techniques, but its response time
is quite independent to the irradiation values and its efficiency increase with the irradiance
level. This technique can be a good alternative to the P&O techniques in applications
characterized by high, fast and continuous radiance variations, e.g. the PV applications in
transportation systems.
3.8 Summary:
In this chapter, the photovoltaic system with DC-DC boost converter and maximum power
point controller has been designed and constant voltage of 21.5V is maintained at the output
side of the converter. For the specified input variation, a regulated dc output voltage of 21.5V
has been obtained resulting in an efficiency of 95%. It is concluded that Perturb & Observe
method has better efficiency compared to Incremental Conductance method at low power. In
this case, Perturb & Observe method gave an increase of 2.6% in voltage, 5.3% increase in
current and 7% increase in power at low power output, but is inefficient in case of sudden
change in irradiance level. From the modelling of boost converter, it was also observed that
the output voltage of the boost converter increases along with the increase in duty cycle.