44

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

46

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).

47

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

48

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.

49

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.

51

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

52

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

53

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

54

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

55

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.

56

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.

57

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

58

(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

59

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.

60

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

61

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.

67

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.

69

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.