Improved MPPT Method for Increased Accuracy and Speed in PV Systems

A Major Project report on
IMPROVED MPPT METHOD TO INCREASE
ACCURACY AND SPEED IN PHOTO VOLTAIC
SYSTEM UNDER VARIABLES ATMOSPHERIC
CONDITIONS
Submitted to the
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY
HYDERABAD
In partial fulfillment of the requirement for the award of the degree
of
BACHELOR OF TECHNOLOGY
IN
Electrical & Electronics Engineering
Submitted By
S. VIJAY 16WJ1A02A3
P. SAICHARAN 16WJ1A0290
R. NAIMESH 15WJ1A0292
Under the Guidance of
B. SRAVAN KUMARM.Tech., Ph.D.
Assistant Professor
Department of Electrical & Electronics Engineering
GURU NANAK INSTITUTIONS TECHNICAL CAMPUS
(An Autonomous Institution, Accredited by NAAC A+ & NBA, Affiliated to JNTU
Hyderabad)
IBRAHIMPATNAM, R.R DISTRICT – 501506
2019-2020

i
GURU NANAK INSTITUTIONS TECHNICAL CAMPUS
(An Autonomous Institution, Accredited by NAAC A+ & NBA,Affiliated to JNTU
Hyderabad) IBRAHIMPATNAM, R.R District -501506.
Department of Electrical and Electronics Engineering
CERTIFICATE
This is to certify that the Technical Seminar report titled IMPROVED MPPT
METHOD TO INCREASE ACCURACY AND SPEED IN PHOTO VOLTAIC
SYSTEM UNDER VARIABLES ATMOSPHERIC CONDITIONS is being
submitted by S. VIJAY bearing Roll No. 16WJ1A02A3, & P. SAICHARAN bearing
Roll No. 16WJ1A0290 & R. NAIMESH bearing Roll No. 15WJ1A0292 of IV B.Tech
II Semester Electrical & Electronics Engineering are a bonafide record work carried
out by them. The results embodied in this report have not been submitted to any other
University for the award of any degree.
INTERNAL GUIDE PROJECT CO-ORDINATOR
B. SRAVAN KUMAR CH. SRISAILAM
M.Tech.,(Ph.D.) M. Tech.,(Ph.D.)
Assistant professor Assistant Professor
HEAD OF THE DEPARTMENT
Dr. K.Santhi
M.E,Ph.D.
Professor& Head

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ACKNOWLEDGEMENT
First and foremost, I express my sincere gratitude to my beloved Sri. Tavinder Singh
Kohli, Chairman, GNI and Sri. Gagandeep Sing Kohli, Vice Chairman, GNI who
has the visionary with a very good foresight and a wide angled in all encompassing
ideology.
I would like to acknowledge the positive involvement and support of my beloved
Managing Director Dr. H. S. Saini, Managing Director, GNI who is all my well-
wisher and helpful in my major project. I would like to thank Dr. M. Ramalinga
Reddy, Director, GNITC for providing facilities.
I would like to express my deep sense of gratitude to my Professor P.Parthasaaradhy,
Associate Director, GNITC for providing an opportunity to complete the Technical
seminar in the campus.
I would like to thank sincerely Dr. K. Santhi, Head of the Department-EEE for
guiding us in developing the requisite capabilities for taking up this Technical seminar.
I thank my project coordinator Mr. Ch.Srisailam, Assistant Professor providing
seamless support and right suggestions given in the development of the Technical
seminar.
I specially thank our internal guide B. Sravan Kumar, Assistant Professor for his
continuous suggestions and constant guidance in each and every stage of the Technical
seminar. I would also like to thank all my lecturers for supporting me in every possible
way whenever the need arose.
In All Sincerity,
S. VIJAY 16WJ1A02A3
P.SAICHARAN 16WJ1A0290

iii
R. NAIMESH 15WJ1A0292
CHAPTER NO. TITLE PAGE
NO.
ABSTRACT
LIST OF FIGURES
LIST OF SYMBOLS
LIST OF ABBREVIATIONS
LIST OF TABLES
I
v
vii
xi
xii
1. CHAPTER 1 : INTRODUCTION
1. GENERAL
2. SCOPE OF TE PROJECT
3. EXISTING SYSTEM
4. EXISTING SYSTEM TECHNIQUES
5. PROPOSED SYSTEM
6. PROPOSED SYSTEM TECHNIQUES
7. ADVANTAGES OF PROPOSED SYSTEM
1
1
2
2
3
4
5
2. CHAPTER 2 : PROJECT DESCRIPTION
2.1 GENERAL
2.2 MODULES NAME
2.3 MODULES DESCRIPTION
2.4 MODELING OF PROPOSED THEORY
3. CHAPTER 3 : SIMULATION THEORY
3.1 GENERAL
3.2 PSIM HISTORY

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3.3 SIMULINK
3.4 BUILDING THE MODEL
3.5 CIRCUIT SCHIMATIC DESIGN
3.6 SUB CIRCUIT
3.7 SIMULATION ISSUES
3.8 CONNECTING TO HARDWARE
3.9 APPLICATIONS
4. CHAPTER 4 : SIMULATION RESULTS
4.1 TECHNIQUES USED
4.2 TECHNIQUES DESCRIPTION
4.3 SIMULATION DESIGN OPEN LOOP
4.4 SIMULATION DESIGN CLOSED LOOP
5. CHAPTER 5 :
APPLICATIONS AND CONCLUSION
REFERENCES

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ABSTRACT
The changes in temperature and radiation cause visible
fluctuations in the output power produced by the photovoltaic
(PV) panels. It is essential to keep the output voltage of the PV
panel at the maximum power point (MPP) under varying
temperature and radiation conditions. In this study, a maximum
power point tracking (MPPT) method has been developed which
is based on mainly two parts: the first part is adapting calculation
block for the reference voltage point of MPPT and the second one
is Fuzzy Logic Controller (FLC) block to adjust the duty cycle of
PWM applied switch (Mosfet) of the DC-DC converter. In order
to evaluate the robustness of the proposed method,
Matlab/Simulink program has been used to compare with the
traditional methods which are Perturb & Observe (P&O),
Incremental Conductance (Inc. Cond.) and FLC methods under
variable atmospheric conditions. When the test results are
observed, it is clearly obtained that the proposed MPPT method
provides an increase in the tracking capability of MPP and at the
same time reduced steady state oscillations. The accuracy of the
proposed method is between 99.5% and 99.9%. In addition, the
time to capture MPP is 0.021 sec. It is about four times faster than
P&O and five times faster than for Inc. Cond. and, furthermore,
the proposed method has been compared with the conventional
FLC method and it has been observed that the proposed method

vi
is faster about 28% and also its efficiency is about 1% better than
flc method.

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LIST OF FIGURES
FIGURE
NO.
NAME OF THE FIGURE PAGE NO.
1 Single diode equivalent circuit of solar
cell
8
2 Boost converter circuit 11
3 Waveforms of buck boost converter 12
4 Solar cell 20
5 Roof top PV on half timbered house 27
6 Satellite image of topaz solar farm 29
7 Fuzzy interface system 42
8
9
10
11
Primary GUI tools
Ripping function
FIS editor
Updated membership function editor
Simulation design
Output waveformd
49
45
46
53
72
73

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LIST OF TABLE
TABLE
NO.
NAME OF THE TABLE PAGE NO.
1.1 Electrical characteristics of the used PV
channel
8
1.2
Rule base of fuzzy logic
57

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CHAPTER 1
INTRODUCTION
1.1 GENERAL
With the development of technology and the reduction of fossil fuels, PV power
generation has become very widespread throughout the world. PV panels do not include
moving parts and they are clean with low-cost and simple maintenance [1, 2]. However,
PV systems suffer from low efficiency due to the dependency of weather conditions
such as temperature and irradiance. In order to obtain maximum efficiency from PV
panels, maximum power point tracking (MPPT) methods are employed in PV systems
[3–7]. MPPT methods transfer the maximum power from the PV source to the load or
grid with adjusting the duty cycle of the DC-DC converter under variable weather
conditions. In the literature, there are extensive studies about MPPT methods. These
methods can be classified into direct, indirect and artificial intelligence (AI) based
methods [8]. Open circuit voltage (OCV) and short circuit current (SCC) methods are
known as indirect MPPT methods which require the work characteristics of PV panel
for many different environmental conditions. Tracking of the MPP of PV array at any
irradiance and cell temperature cannot have precise in the indirect methods [9, 10]. In
the second type of branch, there are more complicated MPPT algorithms named as
direct methods. Among them, perturb and observe (P&O), incremental conductance
(Inc. Cond.) are most used direct methods. P&O, also known as the hill climbing
method, is based on a comparison of the output power to the previous value by applying
perturbations to the reference voltage or current with a predetermined time [11–13]. If
the new output power value obtained by applying perturbations increases, the
perturbation must be applied in the same direction and in the other case, the perturbation
must be applied in the opposite direction. This process is iterated until the reference
voltage or current is equal to zero [12]. Because of the working principle of the P& O,
this method suffers from the oscillation problem. Also, P&O method can sometimes be
inadequate in suddenly changing weather conditions. Inc. Cond. method is a more
advanced method of the P&O in terms of tracking speed and accuracy. In Inc. Cond.
method, tracking of the MPP is performed by taking into consideration the
characteristic of the output power curve of the PV array with respect to the output
voltage.

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1.2 SCOPE OF THE PROJECT
In literature, extensive studies have been carried out to mitigate the drawbacks of the
existing methods. To enhance the MPPT methods, Safari and Mekhilef [25] have
presented the simulation and hardware implementation of Inc. Cond. method used in
solar array power systems with a direct control method. In [26], a simpler fast-
converging maximum power point tracking technique has been proposed, which
reduces the control circuit complexity. Therefore, it is shown that the response of the
algorithm is four times faster than traditional Inc. Cond. methods. The proposed system
differs from the existing MPPT systems by eliminating the proportional-integral control
loop and investigating the effect of the simplifying the control circuit. In [27], a
modified Inc. Cond. algorithm has been proposed that responds correctly in the case of
increased solar irradiation level and shows zero oscillation in the power of the solar
module after MPP is tracking. Tey and Mekhilef [28] have proposed a modified Inc.
Cond. algorithm that is able to track the global MPP under environmental conditions
and load variations. This algorithm is introduced to adjust the duty cycle of the DC-DC
converter to ensure rapid MPPT operation. Inc. Cond. and P&O are experiencing the
problem of accuracy and speed in reaching the MPP in case of large changes in the
radiation. For this purpose, Radjai et al. [29,30] have estimated the duty cycle by using
fuzzy and got better results than fixed step Inc. Cond. and P&O. Kwan et al. [31] have
proposed an adaptive MPPT algorithm which used for adjusting the antecedents of FL
controller. They have utilized simple formulas instead of complex learning algorithms
to improve the tracking speed and stability according to the fixed FL antecedents. In
[32], a multi-fuzzy interference system is introduced to track the MPP of PV systems.
The analysis shows that the proposed FL controller takes less time to find the MPP,
mitigates the oscillation around the operating point and also reduces steady state error
comparing with the fuzzy logic controller and P&O by 1.29% and 1.76% respectively.
In other type of AI-based methods which are used in conjunction with optimization
algorithms is better result the tracking global maximum power point (GMPP). In
[33,34], PSO based MPPT algorithm has been introduced. Tey et al. [35] have
demonstrated an approach using an optimization algorithm called improved differential
evolution (DE) to track the GMPP. Unlike PSO, DE reduces the complexity in tuning

3
the required parameters to achieve accurate MPPT. Thus, tracking capability of the
GMPP have been increased and fast response to load changes.
1.3 EXISTING SYSTEM
MPPT methods These methods can be classified into direct, indirect and artificial
intelligence(AI) based methods Open circuit voltage (OCV) and short circuit current
(SCC) methods are known as indirect MPPT methods which require the work
characteristics of PV panel for many different environmental conditions.
DRAWBACKS:
Because of the working principle of the P&O, this method suffers from the oscillation
problem. Also, P&O method can sometimes be inadequate in suddenly changing
weather conditions. Inc. Cond. method is a more advanced method of the P&O in terms
of tracking speed and accuracy. In Inc. Cond. method, tracking of the MPP is performed
by taking into consideration the characteristic of the output power curve of the PV array
with respect to the output voltage
1.4 EXISTING SYSTEMS TECHNIQUE:
P&O method can sometimes be inadequate in suddenly changing weather
conditions.Inc. Cond. method is a more advanced method of the P&O in terms of
tracking speed and accuracy. In Inc. Cond. method, tracking of the MPP is performed
by taking into consideration the characteristic of the output power curve of the PV array
with respect to the output voltage
1.1 LITERATURE SURVEY:
TITLE: A Hybrid MPPT method for grid connected photovoltaic systems under
rapidly changing atmospheric conditions.
PUBLICATION: Electr Pow Syst Res 2017;152:194–210.
AUTHORS: Celik O, Teke A.

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The modest changes in operating current and voltage of photovoltaic (PV) panel due to
the temperature and radiation fluctuation constitute visible variations in the output
power. In this paper, a hybrid method to optimize the performance of the maximum
power point tracking (MPPT) controller for mitigating these variations and forcing the
system to operate on maximum power point (MPP) is developed. The presented Hybrid
MPPT method consists of two loops: (i) artificial neural network (ANN) based
reference point setting loop and (ii) perturbation and observation (P&O) based fine
tuning loop. To assess robustness of the proposed method, a comparison is performed
using the conventional P&O, incremental conductance (INC) and ANN based MPPT
methods under both rapidly changing radiation and partially shaded conditions by using
PSCAD/EMTDC program. The results obtained from the test cases explicitly
demonstrate that the presented MPPT method not only achieves an increase in speed of
MPP tracking, but also reduces the steady state oscillations and prevents the possibility
of the algorithm from confusing its perturbation direction. The system efficiency more
than 98.26%, 120 ms improvement in convergence speed and 1.16 V decrease in the
rate of overshoot are obtained with proposed Hybrid MPPT method under the rapidly
changing environmental conditions.
TITLE: Experimental verification of P&O MPPT algorithm with direct control based
on Fuzzy logic control using CUK converter.
PUBLICATION: Int T Electr Energy 2015;25:3492–508.
AUTHORS: Radjai T, Gaubert JP, Rahmani L, Mekhilef S.
The choice and design of a high efficient maximum power point tracking (MPPT)
algorithm is a necessity in the PV system design. Many approaches have been proposed
in literature, among them, the methods that are based on perturb and observe (P&O),
widely used in commercial products due their simplicity and ease of implementation.
In this paper, a new modified P&O (MPPT) method with adaptive duty cycle step size
using fuzzy logic controller is proposed. Both, simulation and experimental design are
provided in several aspects. The proposed and classical methods are developed and
tested successfully using a CUKDC–DC converter, which is connected to a SunTech
STP085B model. The proposed method is able to improve the dynamic response and
steady‐state performance of the PV systems simultaneously and effectively. In addition,

5
analysis and comparison with the conventional fixed step size P&O have been
presented.
TITLE: Fuzzy-logic-control approach of a modified hill-climbing method for
maximum power point in micro grid standalone photovoltaic system
PUBLICATION: . IEEE T Power Electr 2011; 26:1022–30.
AUTHORS: Alajmi BN, Ahmed KH, Finney SJ, Williams BW.
A new fuzzy-logic controller for maximum power point tracking of photovoltaic (PV)
systems is proposed. PV modeling is discussed. Conventional hill-climbing maximum
power-point tracker structures and features are investigated. The new controller
improves the hill-climbing search method by fuzzifying the rules of such techniques
and eliminates their drawbacks. Fuzzy-logic-based hill climbing offers fast and accurate
converging to the maximum operating point during steady-state and varying weather
conditions compared to conventional hill climbing. Simulation and experimentation
results are provided to demonstrate the validity of the proposed fuzzy-logic-based
controller.
1.5 PROPOSED SYSTEM
Artificial intelligence (AI) based methods such as fuzzy logic (FL), artificial neural
network (ANN), particle swarm optimization (PSO) and evolutionary algorithms (EA)
provide better results in terms of efficiency and tracking performance under suddenly
changing weather conditions. Also, AI-based MPPT controller has the best
performance in partial shading conditions.
1.6 PROPOSED SYSTEM TECHNIQUE
The adaptive calculation block produces a reference voltage for each MPP voltage,
(VMPP (ref)). The reference voltage and PV panel voltage are compared and then error
(VMPP (ref)−VPV) and the change rate of error (Δerror) are given to the FLC as an
input variable. The FLC generates a reference signal for a duty cycle of PWM which is
applied to the switch (MOSFET) of boost converter so that the PV panel is continuously
operated at the MPP.

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1.7 ADVANTAGES OF PROPOSED TECHNIQUE
• Tracking speed and accuracy.
• MPPT controller has the best performance in partial shading conditions
according to the other methods.
CHAPTER 2
PROJECT DESCRIPTION
2.1 GENERAL

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MPPT methods, AI methods used in conjunction with direct methods are proposed to
solve their
Individual drawbacks. These hybrid methods have high convergence speed and less
oscillation around MPPs. P&O and Inc. Cond. Methods are combined with the FL,
which are the commonly used structure in order to design membership functions of the
FLC and fuzzy rules easily [36]. Danandeh and Mousavi [37] and Punitha et al. [38]
have combined Inc. Cond. method and FL in a new structure in order to reduce fuzzy
rules and then it carries out easy of the implementation of MPPT controller. Al-Majidi
et al. [39] have designed a novel MPPT method to incorporate the advantages of the FL
and P&O algorithms. Reducing fuzzy rules ensure that the system fails to track MPP in
some operating conditions. Excessive fuzzy rules increase the accuracy of the MPPT
algorithm while decreasing the tracking speed of the algorithm. These two criteria must
be in balance for the FLC methods [40]. In this study, an improved MPPT method has
been developed using FLC. Unlike conventional FLC methods, a reference voltage
calculated based on temperature and radiation is used as the input of the FLC. On the
other hand, because the traditional FLC method performs hill climbing-based
measurements, its efficiency and speed are not as good as the proposed method. In
addition, by calculation of the reference voltage for MPP in the adapted calculation
block, the number of membership function in FLC is decreased, so that, the tracking
capability of the MPPT have increased and undesired oscillations at the MPP are
reduced. Simulation results are presented to demonstrate the efficiency of the proposed
MPPT method were compared to the traditional methods such as P&O, Inc. Cond. and
FLC. The block diagram of the designed system

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Fig. 1. Block diagram of the designed system.
2. PV panel model
The most popular model used to represent the PV cell consists of series and parallel
resistors connected to a single diode and a current source which is illustrated in Fig. 2
[41,42]. Rp represents the loss which small leakage current flow through the parallel
path (High-value order of kΩ). Rs represents the losses which are a loss of metal grid
(about 1 Ω), contacts and current collecting bus, diode represent a cross current which
associated with p-n junction semiconductor device [43,44].
Fig. Single diode equivalent circuit of the solar cell.

9
The electrical characteristics of PV arrays depend on environmental conditions. The
P-V and I-V characteristics of modeled PV array which include 10 panels in parallel
and 10 in series under variable environmental conditions are illustrated in Fig. 3. The
temperature values were changed between 20 °C and 80 °C while the characteristic
curves of the PV array obtained and the irradiance values were changed between 200
W/m2 and 1000 W/m2.
2.2 MODULES NAME

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• PHOTO VOLTAIC SYSTEM
• SMPS
• DC-DC CONVERTERS
• FUZZY LOGIC
2.3 MODULE DESCRIPTION
INTRODUCTION
This chapter gives an introduction to Switched Mode Power Supply (SMPS). The
requirements of a SMPS and various types of DC-DC converters (isolated and non-
isolated) are also discussed. The concept of resonance, quasi-resonance, hard switching
and soft switching are deliberated at full length. This chapter also discusses - identified
research gaps, research focus, contribution and organization of the thesis.
SWITCH MODE POWER SUPPLIES
Many analog and digital electronic systems require regulated DC power supplies. These
power supplies should adhere to certain requirements such as:
Regulated Output: The output voltage must remain constant within a specified range
for variations in input voltage and output load.
Isolation: The input and the output must be electrically isolated.
Multiple Outputs: Multi-output (positive and negative outputs) that may differ in
voltage and current ratings must be isolated from one another.
Reduction in power supply size, weight and improvements in efficiency are additional
requirements. Traditionally, linear power supplies were used. SMPS, as compared to
linear power supplies, are smaller and much more efficient due to advancements in
semiconductor technology. The cost comparison between linear and SMPS depends on
the power rating. High frequency transformer provides electrical isolation in SMPS.
1.3 DC-DC Converters
In general, switch mode converters can be either isolated or Non-isolated. By isolation,
it is implied as galvanic isolation so that there is no DC path from the input of the

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converter to its output. In order to meet the requirements of various agencies, electronic
equipment operating from the AC power line needs at least one stage of isolated
conversion. Non-isolated converters are Buck, Boost and Buck-boost converters.
Isolated converters are Forward, Fly back, Half Bridge, Full Bridge and Push-pull
converters.
1.3.1 Non Isolated Converters
Buck, Boost and Buck Boost converters are basic converters, simple, with less
component count and least cost. The main drawback of these converters is that the
outputs are not isolated and hence are normally not preferred.
1.3.2 Isolated Converters
Isolation refers to the existence of an electrical barrier between the input and output of
a DC-DC converter. A separation between the applied input voltage and output voltage,
which is often user accessible is an essential requirement as mandated by safety
agencies and customers. An isolated DC-DC converter with an inbuilt high frequency
transformer in the topology provides a barrier that could withstand few tens of volts to
kilo volt ranges and hence are appropriate for medical applications also.
BOOST CONVERTER STEP-UP CONVERTER
The schematic in Fig. 6 shows the basic boost converter. This circuit is used when a
higher output voltage than input is required.
Boost Converter Circuit
While the transistor is ON Vx =Vin, and the OFF state the inductor current flows through
the diode giving Vx =Vo. For this analysis it is assumed that the inductor current always
remains flowing (continuous conduction). The voltage across the inductor is shown in
Fig. 7 and the average must be zero for the average current to remain in steady state

12
………… (18)
This can be rearranged as
………. (19)
And for a lossless circuit the power balance ensures
……….. (20)
Voltage and current waveforms (Boost Converter)
Since the duty ratio "D" is between 0 and 1 the output voltage must always be higher
than the input voltage in magnitude. The negative sign indicates a reversal of sense of
the output voltage.
BUCK-BOOST CONVERTER
Schematic for buck-boost converter
With continuous conduction for the Buck-Boost converter Vx =Vin when the
transistor is ON and Vx =Vo when the transistor is OFF. For zero net current change
over a period the average voltage across the inductor is zero.

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Waveforms for buck-boost converter
………….. (21)
Which gives the voltage ratio
………… (22)
And the corresponding current
……….. (23)
Since the duty ratio "D" is between 0 and 1 the output voltage can vary between
lower or higher than the input voltage in magnitude. The negative sign indicates a
reversal of sense of the output voltage.
CONVERTER COMPARISON
The voltage ratios achievable by the DC-DC converters are summarized in Fig.
10. Notice that only the buck converter shows a linear relationship between the control
(duty ratio) and output voltage. The buck-boost can reduce or increase the voltage ratio
with unit gain for a duty ratio of 50%.

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Comparison of Voltage ratio
BOOST CONVERTER:
A boost converter (step-up converter) is a power converter with an output DC
voltage greater than its input DC voltage. It is a class of switching-mode power supply
(SMPS) containing at least two semiconductor switches (a diode and a transistor) and
at least one energy storage element. Filters made of capacitors (sometimes in
combination with inductors) are normally added to the output of the converter to reduce
output voltage ripple.
Power can also come from DC sources such as batteries, solar panels, rectifiers and
DC generators. A process that changes one DC voltage to a different DC voltage is
called DC to DC conversion. A boost converter is a DC to DC converter with an output
voltage greater than the source voltage. A boost converter is sometimes called a step-
up converter since it “steps up” the source voltage. Since power (P = VI or P = UI in
Europe) must be conserved, the output current is lower than the source current.
A boost converter may also be referred to as a 'Joule thief'. This term is usually used
only with very low power battery applications, and is aimed at the ability of a boost
converter to 'steal' the remaining energy in a battery. This energy would otherwise be
wasted since a normal load wouldn't be able to handle the battery's low voltage.*

15
▪ This energy would otherwise remain untapped because in most low-frequency
applications, currents will not flow through a load without a significant difference
of potential between the two poles of the source (voltage.)
Block Diagram
The basic building blocks of a boost converter circuit are shown in Fig.
Fig. Block diagram
The voltage source provides the input DC voltage to the switch control, and to
the magnetic field storage element. The switch control directs the action of the
switching element, while the output rectifier and filter deliver an acceptable DC voltage
to the output.
Operating principle
The key principle that drives the boost converter is the tendency of an inductor
to resist changes in current. When being charged it acts as a load and absorbs energy
(somewhat like a resistor), when being discharged, it acts as an energy source
(somewhat like a battery). The voltage it produces during the discharge phase is related
to the rate of change of current, and not to the original charging voltage, thus allowing
different input and output voltages.
Fig: Boost converter schematic
Voltage
Source
Magnetic
Field Storage
Element
Switch
Control
Switching
Element
Output
Rectifier and
Filter

16
Fig. The two configurations of a boost converter, depending on the state of the switch
S.
The basic principle of a Boost converter consists of 2 distinct states (see figure ):
▪ in the On-state, the switch S (see figure) is closed, resulting in an increase in the
inductor current;
▪ In the Off-state, the switch is open and the only path offered to inductor current is
through the flyback diode D, the capacitor C and the load R. This result in
transferring the energy accumulated during the On-state into the capacitor.
The input current is the same as the inductor current as can be seen in figure. So it
is not discontinuous as in the buck converter and the requirements on the input filter
are relaxed compared to a buck converter.
Continuous mode
When a boost converter operates in continuous mode, the current through the
inductor (IL) never falls to zero. Figure shows the typical waveforms of currents and
voltages in a converter operating in this mode. The output voltage can be calculated as
follows, in the case of an ideal converter (i.e. using components with an ideal behavior)
operating in steady conditions:

17
Fig: Waveforms of current and voltage in a boost converter operating in continuous
mode.
During the On-state, the switch S is closed, which makes the input voltage (Vi)
appear across the inductor, which causes a change in current (IL) flowing through the
inductor during a time period (t) by the formula:
At the end of the On-state, the increase of IL is therefore:
D is the duty cycle. It represents the fraction of the commutation period T during which
the switch is on. Therefore D ranges between 0 (S is never on) and 1 (S is always on).
During the Off-state, the switch S is open, so the inductor current flows through the
load. If we consider zero voltage drop in the diode, and a capacitor large enough for its
voltage to remain constant, the evolution of IL is:
Therefore, the variation of IL during the Off-period is:
As we consider that the converter operates in steady-state conditions, the
amount of energy stored in each of its components has to be the same at the beginning
and at the end of a commutation cycle. In particular, the energy stored in the inductor
is given by:

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So, the inductor current has to be the same at the start and end of the
commutation cycle. This means the overall change in the current (the sum of the
changes) is zero:
Substituting and by their expressions yields:
This can be written as:
Which in turns reveals the duty cycle to be?
From the above expression it can be seen that the output voltage is always higher
than the input voltage (as the duty cycle goes from 0 to 1), and that it increases with D,
theoretically to infinity as D approaches 1. This is why this converter is sometimes
referred to as a step-up converter.
Discontinuous mode
In some cases, the amount of energy required by the load is small enough to be
transferred in a time smaller than the whole commutation period. In this case, the
current through the inductor falls to zero during part of the period. The only difference
in the principle described above is that the inductor is completely discharged at the end
of the commutation cycle (see waveforms in figure ). Although slight, the difference
has a strong effect on the output voltage equation. It can be calculated as follows:

19
Fig: Waveforms of current and voltage in a boost converter operating in discontinuous
mode.
As the inductor current at the beginning of the cycle is zero, its maximum
value (at t = DT) is
During the off-period, IL falls to zero after δT:
Using the two previous equations, δ is:
The load current Io is equal to the average diode current (ID). As can be seen on
figure 4, the diode current is equal to the inductor current during the off-state. Therefore
the output current can be written as:
Replacing ILmax and δ by their respective expressions yields:
Therefore, the output voltage gain can be written as flow:

20
Compared to the expression of the output voltage for the continuous mode, this
expression is much more complicated. Furthermore, in discontinuous operation, the
output voltage gain not only depends on the duty cycle, but also on the inductor value,
the input voltage, the switching frequency, and the output current.
APPLICATIONS:
Battery powered systems often stack cells in series to achieve higher voltage.
However, sufficient stacking of cells is not possible in many high voltage applications
due to lack of space. Boost converters can increase the voltage and reduce the number
of cells. Two battery-powered applications that use boost converters are hybrid electric
vehicles (HEV) and lighting systems.
The NHW20 model Toyota Prius HEV uses a 500 V motor. Without a boost
converter, the Prius would need nearly 417 cells to power the motor. However, a Prius
actually uses only 168 cells and boosts the battery voltage from 202 V to 500 V. Boost
converters also power devices at smaller scale applications, such as portable lighting
systems. A white LED typically requires 3.3 V to emit light, and a boost converter can
step up the voltage from a single 1.5 V alkaline cell to power the lamp. Boost converters
can also produce higher voltages to operate cold cathode fluorescent tubes (CCFL) in
devices such as LCD backlights and some flashlights.
PHOTO VOLTAIC SYSTEM
Photovoltaic (PV) is the name of a method of converting solar energy into direct
current electricity using semiconducting materials that exhibit the photovoltaic effect,
a phenomenon commonly studied in physics, photochemistry and electrochemistry.
A photovoltaic system employs solar panels composed of a number of solar cells to
supply usable solar power. The process is both physical and chemical in nature, as the
first step involves the photoelectric effect from which a second electro chemical
process take place involving crystallized atoms being ionized in a series, generating an
electric current.[1]
Power generation from solar PV has long been seen as a
clean sustainable[2]
energy technology which draws upon the planet’s most plentiful
and widely distributed renewable energy source – the sun. The direct conversion of
sunlight to electricity occurs without any moving parts or environmental emissions
during operation. It is well proven, as photovoltaic systems have now been used for
fifty years in specialized applications, and grid-connected PV systems have been in use

21
for over twenty years.[3]
They were first mass-produced in the year 2000, when German
environmentalists including Euro solar succeeded in obtaining government support for
the 100,000 roofs program.
Driven by advances in technology and increases in manufacturing scale and
sophistication, the cost of photo voltaic has declined steadily since the first solar cells
were manufactured,[3][5]
and the levelised cost of electricity from PV is competitive
with conventional electricity sources in an expanding list of geographic regions.[6]
Net
metering and financial incentives, such as preferential feed-in tariffs for solar-
generated electricity, have supported solar PV installations in many countries.[7]
With
current technology, photovoltaic recoups the energy needed to manufacture them in 1.5
to 2.5 years in Southern and Northern Europe, respectively.
Solar PV is now, after hydro and wind power, the third most important renewable
energy source in terms of globally installed capacity. More than 100 countries use solar
PV. Installations may be ground-mounted (and sometimes integrated with farming and
grazing) or built into the roof or walls of a building (either building-integrated
photovoltaic or simply rooftop).
In 2014, worldwide installed PV capacity increased to at least 177 gig watts (GW),
sufficient to supply 1 percent of global electricity. Due to the exponential growth of
photovoltaic, installations are rapidly approaching the 200 GW mark – about 40 times
the installed capacity of 2006.[9]
China, followed by Japan and the United States, is the
fastest growing market, while Germany remains the world's largest producer, with solar
contributing about 7 percent to its annual domestic electricity consumption.

22
Fig. Solar cells generate electricity directly from sunlight
Solar cells
Photovoltaic are best known as a method for generating electric power by using solar
cells to convert energy from the sun into a flow of electrons. The photovoltaic
effect refers to photons of light exciting electrons into a higher state of energy, allowing
them to act as charge carriers for an electric current. The photovoltaic effect was first
observed by Alexandre-Edmond Becquerel in 1839.[12][13]
The term photovoltaic
denotes the unbiased operating mode of a photodiode in which current through the
device is entirely due to the transduced light energy. Virtually all photovoltaic devices
are some type of photodiode.
Solar cells produce direct current electricity from sun light which can be used to power
equipment or to recharge a battery. The first practical application of photovoltaic was
to power orbiting satellites and other spacecraft, but today the majority of photovoltaic
modules are used for grid connected power generation. In this case an inverter is
required to convert the DC to AC. There is a smaller market for off-grid power for
remote dwellings, boats, recreational vehicles, electric cars, roadside emergency
telephones, remote sensing, and cathode rotation of pipelines.
Photovoltaic power generation employs solar panels composed of a number of solar
cells containing a photovoltaic material. Materials presently used for photovoltaic
include mono-crystalline silicon, polycrystalline silicon, amorphous silicon, cadmium

23
telluride, and copper indium gallium selenide/sulfide.[14]
Copper solar cables connect
modules (module cable), arrays (array cable), and sub-fields. Because of the growing
demand for renewable energy sources, the manufacturing of solar cells and
photovoltaic arrays has advanced considerably in recent years. Solar photovoltaic
power generation has long been seen as a clean energy technology which draws upon
the planet’s most plentiful and widely distributed renewable energy source – the sun.
The technology is “inherently elegant” in that the direct conversion of sunlight to
electricity occurs without any moving parts or environmental emissions during
operation. It is well proven, as photovoltaic systems have now been used for fifty years
in specialized applications, and grid-connected systems have been in use for over
twenty years.
Cells require protection from the environment and are usually packaged tightly behind
a glass sheet. When more power is required than a single cell can deliver, cells are
electrically connected together to form photovoltaic modules, or solar panels. A single
module is enough to power an emergency telephone, but for a house or a power plant
the modules must be arranged in multiples as arrays. Photovoltaic power capacity is
measured as maximum power output under standardized test conditions (STC) in "Wp"
(Watts peak).[18]
The actual power output at a particular point in time may be less than
or greater than this standardized, or "rated," value, depending on geographical location,
time of day, weather conditions, and other factors.[19]
Solar photovoltaic array capacity
factors are typically under 25%, which is lower than many other industrial sources of
electricity.
CURRENT DEVELOPMENTS
For best performance, terrestrial PV systems aim to maximize the time they face the
sun. Solar trackers achieve this by moving PV panels to follow the sun. The increase
can be by as much as 20% in winter and by as much as 50% in summer. Static mounted
systems can be optimized by analysis of the sun path. Panels are often set to latitude
tilt, an angle equal to the latitude, but performance can be improved by adjusting the
angle for summer or winter. Generally, as with other semiconductor devices,
temperatures above room temperature reduce the performance of photovoltaics. A
number of solar panels may also be mounted vertically above each other in a tower, if
the zenith distance of the Sun is greater than zero, and the tower can be turned

24
horizontally as a whole and each panels additionally around a horizontal axis. In such
a tower the panels can follow the Sun exactly. Such a device may be described as
ladder mounted on a turnable disk. Each step of that ladder is the middle axis of a
rectangular solar panel. In case the zenith distance of the Sun reaches zero, the "ladder"
may be rotated to the north or the south to avoid a solar panel producing a shadow on a
lower solar panel. Instead of an exactly vertical tower one can choose a tower with an
axis directed to the polar star, meaning that it is parallel to the rotation axis of the Earth.
In this case the angle between the axis and the Sun is always larger than 66 degrees.
During a day it is only necessary to turn the panels around this axis to follow the Sun.
Installations may be ground-mounted (and sometimes integrated with farming and
grazing)[22]
or built into the roof or walls of a building (building-integrated
photovoltaic). Another recent development involves the makeup of solar
cells. Perovskite is a very inexpensive material which is being used to replace the
expensive silicon which is still part of a standard PV cell build to this day. Michael
Graetzel, Director of the Laboratory of Photonics and Interfaces at EPFL says, “Today,
efficiency has peaked at 18 percent, but it's expected to get even higher in the
future.”[23]
This is a significant claim, as 20% efficiency is typical among solar panels
which use more expensive materials.
EFFICIENCY
Although it is important to have an efficient solar cell, it is not necessarily the efficient
solar cell that consumers will use. It is important to have efficient solar cells that are
the best value for the money. Efficiency of pv cells can be measured by calculating how
much they can convert sunlight into usable energy for human consumption. Maximum
efficiency of a solar photovoltaic cell is given by the following equation: η(maximum
efficiency)= P(maximum power output)/(E(S,γ)(incident radiation flux)*A(c)(Area of
collector)).[24]
If the area provided is limited, efficiency of the PV cell is important to
achieve the desired power output over a limited area. The most efficient solar cell so
far is a multi-junction concentrator solar cell with an efficiency of 43.5%[25]
produced
by Solar Junction in April 2011. The highest efficiencies achieved without
concentration include Sharp Corporation at 35.8% using a proprietary triple-junction
manufacturing technology in 2009,[26]
and Boeing Spectrolab (40.7% also using a
triple-layer design). The US company Sun Power produces cells that have an energy
conversion ratio of 19.5%, well above the market average of 12–18%. There have been

25
numerous attempts to cut down the costs of PV cells and modules to the point that will
be both competitive and efficient. This can be achieved by significantly increasing the
conversion efficiency of PV materials. In order to increase the efficiency of solar cells,
it is necessary to choose the semiconductor material with appropriate energy gap that
matches the solar spectrum. This will enhance their electrical, optical, and structural
properties. Choosing a better approach to get more effective charge collection is also
necessary to increase the efficiency. There are several groups of materials that fit into
different efficiency regimes. Ultrahigh-efficiency devices (η>30%)[28]
] are made by
using Ga As and GaInP2 semiconductors with multifunction tandem cells. High-
quality, single-crystal silicon materials are used to achieve high-efficiency cells
(η>20%).
Organic photovoltaic cells (OPVs) are also viable alternative that relieves energy
pressure and environmental problems from increasing combustion of fossil fuels.
Recent development of OPVs made a huge advancement of power conversion
efficiency from 3% to over 15%.[29]
To date, the highest reported power conversion
efficiency ranges from 6.7% to 8.94% for small molecule, 8.4%–10.6% for polymer
OPVs, and 7% to 15% for perovskite OPVs.[30]
Not only does recent development of
OPVs make them more efficient and low-cost, they also make it environmentally-
benign and renewable. Several companies have begun embedding power
optimizers into PV modules called smart modules. These modules perform maximum
power point tracking (MPPT) for each module individually, measure performance data
for monitoring, and provide additional safety. Such modules can also compensate for
shading effects, wherein a shadow falling across a section of a module causes the
electrical output of one or more strings of cells in the module to fall to zero, but not
having the output of the entire module fall to zero.
At the end of September 2013, IKEA announced that solar panel packages for houses
will be sold at 17 United Kingdom IKEA stores by the end of July 2014. The decision
followed a successful pilot project at the Lakeside IKEA store, whereby
one photovoltaic (PV) system was sold almost every day. The panels are manufactured
by the Chinese company Hanergy.
One of the major causes for the inefficiency of cells is overheating. The efficiency of a
solar cell declines by about 0.5% for every 1 degree Celsius increase in temperature.

26
This would mean that a 100 degree increase in surface temperature could decrease the
efficiency of a solar cell by about half. Self-cooling solar cells are a solution to this
problem. Rather than using energy to cool the surface, pyramid and cone shapes can be
formed from silica, and fastened to the surface of a solar panel. Doing so allows visible
light to reach the solar cells, but causes a deflection of infrared rays (which carry heat)
GROWTH
Solar photovoltaics is growing rapidly and worldwide installed capacity reached at least
177 gigawatts (GW) by the end of 2014. The total power output of the world’s PV
capacity in a calendar year is now beyond 200 billion kWh of electricity. This represents
1% of worldwide electricity demand. More than 100 countries use solar
PV.[10][34]
China, followed byJapan and the United States is now the fastest growing
market, while Germany remains the world's largest producer, contributing more than
7% to its national electricity demands.[10]
Photovoltaics is now, after hydro and wind
power, the third most important renewable energy source in terms of globally installed
capacity.[35]
Several market research and financial companies foresee record-breaking global
installation of more than 50 GW in 2015.[36][37][38][39]
China is predicted to take the lead
from Germany and to become the world's largest producer of PV power by installing
another targeted 17.8 GW in 2015.[40]
India is expected to install 1.8 GW, doubling its
annual installations.[38]
By 2018, worldwide photovoltaic capacity is projected to
doubled or even triple to 430 GW. Solar Power Europe (formerly known as EPIA) also
estimates that photovoltaics will meet 10% to 15% of Europe's energy demand in 2030.
The EPIA/Greenpeace Solar Generation Paradigm Shift Scenario (formerly called
Advanced Scenario) from 2010 shows that by the year 2030, 1,845 GW of PV systems
could be generating approximately 2,646 TWh/year of electricity around the world.
Combined with energy use efficiency improvements, this would represent the
electricity needs of more than 9% of the world's population. By 2050, over 20% of all
electricity could be provided by photovoltaics. Michael Liebreich, from Bloomberg
New Energy Finance, anticipates a tipping point for solar energy. The costs of power
from wind and solar are already below those of conventional electricity generation in
some parts of the world, as they have fallen sharply and will continue to do so. He also
asserts, that the electrical grid has been greatly expanded worldwide, and is ready to

27
receive and distribute electricity from renewable sources. In addition, worldwide
electricity prices came under strong pressure from renewable energy sources, that are,
in part, enthusiastically embraced by consumers. Deutsche Bank sees a "second gold
rush" for the photovoltaic industry to come. Grid parity has already been reached in at
least 19 markets by January 2014. Photovoltaics will prevail beyond feed-in tariffs,
becoming more competitive as deployment increases and prices continue to fall. In
June 2014 Barclays downgraded bonds of U.S. utility companies. Barclays expects
more competition by a growing self-consumption due to a combination of decentralized
PV-systems and residential electricity storage. This could fundamentally change the
utility's business model and transform the system over the next ten years, as prices for
these systems are predicted to fall.
ENVIRONMENTAL IMPACTS OF PHOTOVOLTAIC TECHNOLOGIES
PV technologies have shown significant progress lately in terms of annual production
capacity and life cycle environmental performances, which necessitates the assessment
of environmental impacts of such technologies. The different PV technologies show
slight variations in the emissions when compared the emissions from conventional
energy technologies that replaced by the latest PV technologies.[47]
With the up scaling
of thin film module production for meeting future energy needs, there is a growing need
for conducting the life-cycle assessment (LCA) of such technologies to analyze the
future environmental impacts resulting from such technologies.[48]
The manufacturing
processes of solar cell involve the emissions of several toxic, flammable and explosive
chemicals. Lately, in the field of photovoltaic research, there has been a continual rise
in research and development efforts focused on reducing mass during cell manufacture.
Such efforts have resulted in reducing the thickness of solar cells and thus the next
generation solar cells are becoming thinner and eventually risks of exposure are reduced
nevertheless, all chemicals must be carefully handled to ensure minimal human and
environmental contact. The large scale deployment of such renewable energy
technologies could result in potential negative environmental implications. These
potential problems can pose serious challenges in promulgating such technologies to a
broad segment of consumers.
There are studies which have shown that the PV environmental impacts come mainly
from the production of the cells; operation and maintenance requirements and

28
associated impacts are relatively small. There has been a significant progress in the
published literature on LCA of thin film PV technologies. Research groups are applying
life-cycle assessment approach to emerging PV technologies in order to facilitate a
robust comparison of emerging next generation thin film photovoltaic technologies
competing with each other in the photovoltaic market. In a 2014 study,[47]
Collier et
al. conducted the LCA for CZTS and Zn3P2 PV technologies for the first time. In this
study, the cradle to gate environmental impacts from CZTS and Zn3P2 are assessed
and compared with those from current commercial PV technologies such as CdTe and
CIGS. The four impacts including Primary energy demand, global-warming potential,
freshwater use and eco-toxicity were primarily studied. For all four impacts studied,
CdTe and Zn3P2 performed better than CIGS and CZTS. In general, the contribution
of raw (absorber) material extraction and processing to the total impacts was low
compared with impacts coming from electricity consumption during manufacturing.
Therefore, to reduce environmental impact, future PV technology development should
focus more on the process improvement.[47]
Apart from conducting the LCA of
emerging PV technologies, there is a vital need to assess the energy payback period of
next generation PV technologies. Bhandari, Collier et al. (2015),[52]
conducted a
systematic review and a meta-analysis of the embedded energy, energy payback
time (EPBT), and energy return on energy invested metrics for the crystalline Si and
thin film PV technologies published in 2000–2013. Across different types of PV, the
variation in embedded energy was greater than the variation in efficiency and
performance ratio suggesting that the relative ranking of the EPBT of different PV
technology today and in the future depends primarily on their embedded energy and not
their efficiency.
APPLICATIONS
PHOTOVOLTAIC SYSTEMS
A photovoltaic system, or solar PV system is a power system designed to supply usable
solar power by means of photovoltaic. It consists of an arrangement of several
components, including solar panels to absorb and directly convert sunlight into
electricity, a solar inverter to change the electric current from DC to AC, as well as
mounting, cabling and other electrical accessories. PV systems range from small, roof-
top mounted or building-integrated systems with capacities from a few to several tens

29
of kilowatts, to large utility-scale power stations of hundreds of megawatts. Nowadays,
most PV systems are grid-connected, while stand-alone systems only account for a
small portion of the market.
ROOFTOP AND BUILDING INTEGRATED SYSTEMS
Photovoltaic arrays are often associated with buildings: either integrated into them,
mounted on them or mounted nearby on the ground. Rooftop PV systems are most often
retrofitted into existing buildings, usually mounted on top of the existing roof structure
or on the existing walls. Alternatively, an array can be located separately from the
building but connected by cable to supply power for the building. Building-integrated
photovoltaics (BIPV) are increasingly incorporated into the roof or walls of new
domestic and industrial buildings as a principal or ancillary source of electrical
power.[72]
Roof tiles with integrated PV cells are sometimes used as well. Provided
there is an open gap in which air can circulate, rooftop mounted solar panels can provide
a passive cooling effect on buildings during the day and also keep accumulated heat in
at night.[73]
Typically, residential rooftop systems have small capacities of around 5–
10 kW, while commercial rooftop systems often amount to several hundreds of
kilowatts. Although rooftop systems are much smaller than ground-mounted utility-
scale power plants, they account for most of the worldwide installed capacity
Fig. Rooftop PV on half-timbered house
CONCENTRATOR PHOTOVOLTAIC

30
Concentrator photovoltaic (CPV) is a photovoltaic technology that contrary to
conventional flat-plate PV systems uses lenses and curved mirrors to focus sunlight
onto small, but highly efficient, multi-junction (MJ) solar cells. In addition, CPV
systems often use solar trackers and sometimes a cooling system to further increase
their efficiency. Ongoing research and development is rapidly improving their
competitiveness in the utility-scale segment and in areas of high solar isolation.
PHOTOVOLTAIC THERMAL HYBRID SOLAR COLLECTOR
Photovoltaic thermal hybrid solar collector (PVT) are systems that convert solar
radiation into thermal and electrical energy. These systems combine a solar PV cell,
which converts sunlight into electricity, with a solar thermal collector, which captures
the remaining energy and removes waste heat from the PV module. The capture of both
electricity and heat allow these devices to have higher exergy and thus be more overall
energy efficient than solar PV or solar thermal alone.
POWER STATIONS
Many utility-scale solar farms have been constructed all over the world. As of 2015,
the 579-megawatt (MWAC) Solar Star is the world's largest photovoltaic power station,
followed by the Desert Sunlight Solar Farm and the Topaz Solar Farm, both with a
capacity of 550 MWAC, constructed by US-company First Solar, using CdTe modules,
a thin-film PV technology. All three power stations are located in the Californian
desert. Many solar farms around the world are integrated with agriculture and some use
innovative solar tracking systems that follow the sun's daily path across the sky to
generate more electricity than conventional fixed-mounted systems. There are no fuel
costs or emissions during operation of the power stations.

31
Satellite image of the Topaz Solar Farm
GRID-CONNECTED PHOTOVOLTAIC POWER SYSTEM
A grid-connected photovoltaic power system, or grid-connected PV system is
an electricity generating solar PV system that is connected to the utility grid. A grid-
connected PV system consist of solar panels, one or several inverters, a power
conditioning unit and grid connection equipment. They range from small residential
and commercial rooftop systems to large utility-scale solar power stations.
Unlike stand-alone power systems, a grid-connected system rarely includes
an integrated battery solution, as they are still very expensive. When conditions are
right, the grid-connected PV system supplies the excess power, beyond consumption
by the connected load, to the utility grid.
Operation
Residential, grid-connected rooftop systems which have a capacity less than 10
kilowatts can meet the load of most consumers.[2]
They can feed excess power to the
grid where it is consumed by other users. The feedback is done through a meter to
monitor power transferred. Photovoltaic wattage may be less than average
consumption, in which case the consumer will continue to purchase grid energy, but a
lesser amount than previously. If photovoltaic wattage substantially exceeds average
consumption, the energy produced by the panels will be much in excess of the demand.
In this case, the excess power can yield revenue by selling it to the grid. Depending on
their agreement with their local grid energy company, the consumer only needs to pay
the cost of electricity consumed less the value of electricity generated. This will be a

32
negative number if more electricity is generated than consumed.[3]
Additionally, in
some cases, cash incentives are paid from the grid operator to the consumer. Connection
of the photovoltaic power system can be done only through an interconnection
agreement between the consumer and the utility company. The agreement details the
various safety standards to be followed during the connection.
Features
Solar energy gathered by photovoltaic solar panels, intended for delivery to a power
grid, must be conditioned, or processed for use, by a grid-connected inverter.
Fundamentally, an inverter changes the DC input voltage from the PV to AC voltage
for the grid. This inverter sits between the solar array and the grid, draws energy from
each, and may be a large stand-alone unit or may be a collection of small inverters, each
physically attached to individual solar panels. See AC_Module. The inverter must
monitor grid voltage, waveform, and frequency. One reason for monitoring is if the grid
is dead or strays too far out of its nominal specifications, the inverter must not pass
along any solar energy. An inverter connected to a malfunctioning power line will
automatically disconnect in accordance with safety rules, for example UL1741, which
vary by jurisdiction. Another reason for the inverter monitoring the grid is because for
normal operation the inverter must synchronize with the grid waveform, and produce a
voltage slightly higher than the grid itself, in order for energy to smoothly flow outward
from the solar array.
Anti-islanding
Islanding is the condition in which a distributed generator continues to power a location
even though power from the electric utility grid is no longer present. Islanding can be
dangerous to utility workers, who may not realize that a circuit is still powered, even

33
though there is no power from the electrical grid. For that reason, distributed generators
must detect islanding and immediately stop producing power; this is referred to as anti-
islanding.
In the case of a utility blackout in a grid-connected PV system, the solar panels will
continue to deliver power as long as the sun is shining. In this case, the supply line
becomes an "island" with power surrounded by a "sea" of unpowered lines. For this
reason, solar inverters that are designed to supply power to the grid are generally
required to have automatic anti-islanding circuitry in them. In intentional islanding, the
generator disconnects from the grid, and forces the distributed generator to power the
local circuit. This is often used as a power backup system for buildings that normally
sell their power to the grid.
There are two types of anti-islanding control techniques:
• Passive: The voltage and/or the frequency change during the grid failure is
measured and a positive feedback loop is employed to push the voltage and /or
the frequency further away from its nominal value. Frequency or voltage may
not change if the load matches very well with the inverter output or the load has
a very high quality factor (reactive to real power ratio). So there exists
some Non Detection Zone (NDZ).
• Active: This method employs injecting some error in frequency or voltage.
When grid fails, the error accumulates and pushes the voltage and/or frequency
beyond the acceptable range.

34
Fig. Diagram of a residential grid-connected PV system
ADVANTAGES
• A grid-connected photovoltaic power system will reduce the power bill as it is
possible to sell surplus electricity produced to the local electricity supplier.
• Grid-connected PV systems are comparatively easier to install as they do not
require a battery system.[1][6]
• Grid interconnection of photovoltaic (PV) power generation systems has the
advantage of effective utilization of generated power because there are no
storage losses involved.[7]
• A photovoltaic power system is carbon negative over its lifespan, as any energy
produced over and above that to build the panel initially offsets the need for
burning fossil fuels. Even though the sun doesn't always shine, any installation
gives a reasonably predictable average reduction in carbon consumption.
DISADVANTAGES
• Grid-connected PV can cause issues with voltage regulation. The traditional
grid operates under the assumption of one-way, or radial, flow. But electricity
injected into the grid increases voltage, and can drive levels outside the
acceptable bandwidth of ±5%.[8]
• Grid-connected PV can compromise power quality. PV’s intermittent nature
means rapid changes in voltage. This not only wears out voltage regulators due
to frequent adjusting, but also can result in voltage flicker.[9]

35
• Connecting to the grid poses many protection-related challenges. In addition to
islanding, as mentioned above, too high levels of grid-connected PV result in
problems like relay desensitization, nuisance tripping, interference with
automatic reclosers,
A PV system consists of a number of interconnected components designed to
accomplish a desired task, which may be to feed electricity into the main distribution
grid, to pump water from a well, to power a small calculator or one of many more
possible uses of solar-generated electricity. The design of the system depends on the
task it must perform and the location and other site conditions under which it must
operate. This section will consider the components of a PV system, variations in design
according to the purpose of the system, system sizing and aspects of system operation
and maintenance.
SYSTEM DESIGN
There are two main system configurations – stand-alone and grid-connected. As
its name implies, the stand-alone PV system operates independently of any other power
supply and it usually supplies electricity to a dedicated load or loads. It may include a
storage facility (e.g. battery bank) to allow electricity to be provided during the night
or at times of poor sunlight levels. Stand-alone systems are also often referred to as
autonomous systems since their operation is independent of other power sources. By
contrast, the grid-connected PV system operates in parallel with the conventional
electricity distribution system. It can be used to feed electricity into the grid distribution
system or to power loads which can also be fed from the grid.
It is also possible to add one or more alternative power supplies (e.g. diesel
generator, wind turbine) to the system to meet some of the load requirements. These
systems are then known as ‘hybrid’ systems.
Hybrid systems can be used in both stand-alone and grid-connected
applications but are more common in the former because, provided the power supplies
have been chosen to be complementary, they allow reduction of the storage requirement
without increased loss of load probability. Figures below illustrate the schematic
diagrams of the three main system types.

36
Fig.Schematic diagram of a stand-alone photovoltaic system.
Fig.Schematic diagram of grid-connected photovoltaic system.
Fig.Schematic diagram of hybrid system incorporating a photovoltaic array and a
motor generator (e.g. diesel or wind).
The PV array – characteristic is described by the following:
𝑖 𝑝𝑣 = 𝑛 𝑝 𝑖 𝑝ℎ − 𝑛 𝑝 𝑖 𝑟𝑠 [𝑒𝑥𝑝 (
𝑞
𝑘𝑇𝑐 𝐴
𝑣 𝑝𝑣
𝑛 𝑠
) − 1] (2)
In (2), is the unit charge, the Boltzman’s constant, the p-n junction ideality
factor, and Tc the cell temperature. Current irs
is the cell reverse saturation current, which varies with temperature according to
𝑖 𝑟𝑠 = 𝑖 𝑟𝑟 [
𝑇𝑐
𝑇 𝑟𝑒𝑓
]
3
𝑒𝑥𝑝 (
𝑞𝐸 𝐺
𝑘𝐴
[
1
𝑇 𝑟𝑒𝑓
−
1
𝑇𝑐
]) (3)

37
In (3), Tref is the cell reference temperature, the reverse saturation current at Tref.
and EG the band-gap energy of the cell. The PV current iph depends on the insolation
level and the cell temperature according to
𝑖 𝑝ℎ = 0.01[𝑖 𝑠𝑐𝑟 + 𝐾𝑣(𝑇𝑐 − 𝑇𝑟𝑒𝑓)]𝑆 (4)
In (4), iscr is the cell short-circuit current at the reference temperature and
radiation, Kv a temperature coefficient, and the insolation level in kW/m . The power
delivered by the PV array is calculated by multiplying both sides of (2) by vpv.
𝑃𝑃𝑉 = 𝑛 𝑝 𝑖 𝑝ℎ 𝑣 𝑝𝑣 − 𝑛 𝑝 𝑖 𝑟𝑠 𝑣 𝑝𝑣 [𝑒𝑥𝑝 (
𝑞
𝑘𝑇𝑐 𝐴
𝑣 𝑝𝑣
𝑛 𝑠
) − 1] (5)
Substituting iph from (3) in (4), Ppv becomes
𝑃𝑝𝑣 = 0.01𝑛 𝑝[𝑖 𝑠𝑐𝑟 + 𝐾𝑣(𝑇𝑐 − 𝑇𝑟𝑒𝑓)]𝑆𝑣 𝑝𝑣 0
−𝑛 𝑝 𝑖 𝑟𝑠 𝑣 𝑝𝑣 [𝑒𝑥𝑝 (
𝑞
𝑘𝑇𝑐 𝐴
𝑣 𝑝𝑣
𝑛 𝑠
) − 1] (6)
Based on (6), it is evident that the power delivered by the PV array is a function
of insolation level at any given temperature. Since the inverter employed in the PV
system of this paper is of current-source type, the power-versus-current characteristic
of the PV array has to be examined. Fig. 2 illustrates the power-versus-current
characteristic of the PV array based on the parameters listed in the Appendix for
insolation levels of 0.25, 0.5, and 1 kW/m . Fig. 2 shows that can be maximized by
control of ipv, based on an MPPT strategy [9].
Fig. 2. P–I characteristic of a PV array for s=0.25, 0.5, and 1 kW/m2
.
MAXIMUM POWER POINT TRACKING
Maximum Power Point Tracking, frequently referred to as MPPT, is an
electronic system that operates the Photovoltaic (PV) modules in a manner that allows
the modules to produce all
the power they are capable of. MPPT is not a mechanical tracking system that
“physically moves” the modules to make them point more directly at the sun. MPPT is

38
a fully electronic system that varies the electrical operating point of the modules so that
the modules are able to deliver maximum available power. Additional power harvested
from the modules is then made available as increased battery charge current. MPPT can
be used in conjunction with a mechanical tracking system, but the two systems are
completely different.
The problem considered by MPPT methods is to automatically find the voltage
VMPP or current IMPP at which a PV array delivers maximum power under a given
temperature and irradiance. In this section, commonly used MPPT methods are
introduced in an arbitrary order.
A. Fractional Open-Circuit Voltage
The method is based on the observation that, the ratio between array voltage at
maximum power VMPP to its open circuit voltage VOC is nearly constant.
This factor k1 has been reported to be between 0.71 and0.78. Once the constant k1 is
known, VMPP is computed by measuring VOC periodically. Although the
implementation of this method is simple and cheap, its tracking efficiency is relatively
low due to the utilization of inaccurate values of the constant k1 in the computation of
VMMP.
B. Fractional Short-Circuit Current
The method results from the fact that, the current at maximum power point IMPP is
approximately linearly related to the short circuit current ISC of the PV array.
Like in the fractional voltage method, k2is not constant. It is found to be between 0.78
and 0.92. The accuracy of the method and tracking efficiency depends on the accuracy
of K2and periodic measurement of short circuit current.
C. Perturb and Observe
In P&O method, the MPPT algorithm is based on the calculation of the PV
output power and the power change by sampling both the PV current and voltage. The
tracker operates by periodically incrementing or decrementing the solar array voltage.
If a given perturbation leads to an increase (decrease) in the output power of the PV,
then the sub sequent perturbation is generated in the same (opposite) direction. So,the

39
duty cycle of the dc chopper is changed and the process is repeated until the maximum
power point has been reached. Actually, the system oscillates about the MPP. Reducing
the perturbation step size can minimize the oscillation. However, small step size slows
down the MPPT. To solve this problem, a variable perturbation size that gets smaller
towards the MPP.
However, the P&O method can fail under rapidly changing atmospheric
conditions. Several research activities have been carried out to improve the traditional
Hill-climbing and P&O methods. A three-point weight comparison P&O method that
compares the actual power point to the two preceding points before a decision is made
about the perturbation sign. Reference proposes a two stage algorithm that offers faster
tracking in the first stage and finer tracking in the second stage.
D. Incremental Conductance
The method is based on the principle that the slope of the PV array power curve is zero
at the maximum power point.
(dP/dV) = 0. Since (P = VI), it yields:
The MPP can be tracked by comparing the instantaneous conductance (I/V) to
the incremental conductance (ΔI/ΔV).The algorithm increments or decrement the array
reference voltage until the condition of equation (4.a) is satisfied. Once the Maximum
power is reached, the operation of the PV array is maintained at this point. This method
requires high sampling rates and fast calculations of the power slope.
To understand how MPPT works, let’s first consider the operation of a
conventional (non-MPPT) charge controller. When a conventional controller is
charging a discharged battery, it simply connects the modules directly to the battery.
This forces the modules to operate at battery voltage, typically not the ideal operating
voltage at which the modules are able to produce their maximum available power. The
PV Module Power/Voltage/Current graph shows the traditional Current/Voltage curve
for a typical 75W module at standard test conditions of 25°C cell temperature and
1000W/m2 of insulation. This graph also shows PV module power delivered vs module
voltage. For the example shown, the conventional controller simply connects the
module to the battery and therefore forces the module to operate at 12V. By forcing the

40
75W module to operate at 12V the conventional controller artificially limits power
production to »53W.
Rather than simply connecting the module to the battery, the patented MPPT
system in a Solar Boost charge controller calculates the voltage at which the module is
able to produce maximum power. In this example the maximum power voltage of the
module (VMP) is 17V. The MPPT system then operates the modules at 17V to extract
the full 75W, regardless of present battery voltage. A high efficiency DC-to-DC power
converter converts the 17V module voltage at the controller input to battery voltage at
the output. If the whole system wiring and all was 100% efficient, battery charge current
in this example would be VMODULE ¸ VBATTERY x IMODULE, or 17V ¸ 12V x
4.45A = 6.30A. A charge current increase of 1.85A or 42% would be achieved by
harvesting module power that would have been left behind by a conventional controller
and turning it into useable charge current. But, nothing is 100% efficient and actual
charge current increase will be somewhat lower as some power is lost in wiring, fuses,
circuit breakers, and in the Solar Boost charge controller.
Actual charge current increase varies with operating conditions. As shown
above, the greater the difference between PV module maximum power voltage VMP
and battery voltage, the greater the charge current increase will be. Cooler PV module
cell temperatures tend to produce higher VMP and therefore greater charge current
increase. This is because VMP and available power increase as module cell temperature
decreases as shown in the PV Module Temperature Performance graph. Modules with
a 25°C VMP rating higher than 17V will also tend to produce more charge current
increase because the difference between actual VMP and battery voltage will be greater.

41
A highly discharged battery will also increase charge current since battery voltage is
lower, and output to the battery during MPPT could be thought of as being “constant
power”.
FUZZY LOGIC
In recent years, the number and variety of applications of fuzzy logic have increased
significantly. The applications range from consumer products such as cameras,
camcorders, washing machines, and microwave ovens to industrial process control,
medical instrumentation, decision-support systems, and portfolio selection.
To understand why use of fuzzy logic has grown, you must first understand what
is meant by fuzzy logic.Fuzzy logic has two different meanings. In a narrow sense,
fuzzy logic is a logical system, which is an extension of multivalve logic. However, in
a wider sense fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a
theory which relates to classes of objects with unsharp boundaries in which membership
is a matter of degree. In this perspective, fuzzy logic in its narrow sense is a branch of
fl. Even in its more narrow definition, fuzzy logic differs both in concept and substance
from traditional multivalve logical systems.
WHAT IS FUZZY LOGIC?
Fuzzy logic is all about the relative importance of precision: How important is
it to be exactly right when a rough answer will do?
You can use Fuzzy Logic Toolbox software with MATLAB technical
computing software as a tool for solving problems with fuzzy logic. Fuzzy logic is a
fascinating area of research because it does a good job of trading off between
significance and precision—something that humans have been managing for a very
long time.
In this sense, fuzzy logic is both old and new because, although the modern and
methodical science of fuzzy logic is still young, the concept of fuzzy logic relies on
age-old skills of human reasoning.
WHY USE FUZZY LOGIC?

42
Fuzzy logic is a convenient way to map an input space to an output space. Mapping
input to output is the starting point for everything. Consider the following examples:
• With information about how good your service was at a restaurant, a fuzzy logic
system can tell you what the tip should be.
• With your specification of how hot you want the water, a fuzzy logic system
can adjust the faucet valve to the right setting.
• With information about how far away the subject of your photograph is, a fuzzy
logic system can focus the lens for you.
• With information about how fast the car is going and how hard the motor is
working, a fuzzy logic system can shift gears for you.
To determine the appropriate amount of tip requires mapping inputs to the
appropriate outputs. Between the input and the output, the preceding figure shows a
black box that can contain any number of things: fuzzy systems, linear systems, expert
systems, neural networks, differential equations, interpolated multidimensional lookup
tables, or even a spiritual advisor, just to name a few of the possible options. Clearly
the list could go on and on.
Of the dozens of ways to make the black box work, it turns out that fuzzy is
often the very best way. Why should that be? As Lotfi Zadeh, who is considered to be
the father of fuzzy logic, once remarked: "In almost every case you can build the same
product without fuzzy logic, but fuzzy is faster and cheaper.".
WHEN NOT TO USE FUZZY LOGIC?
Fuzzy logic is not a cure-all. When should you not use fuzzy logic? The safest
statement is the first one made in this introduction: fuzzy logic is a convenient way to
map an input space to an output space. If you find it's not convenient, try something
else. If a simpler solution already exists, use it. Fuzzy logic is the codification of
common sense — use common sense when you implement it and you will probably
make the right decision. Many controllers, for example, do a fine job without using
fuzzy logic.

43
However, if you take the time to become familiar with fuzzy logic, you'll see it
can be a very powerful tool for dealing quickly and efficiently with imprecision and
nonlinearity.
WHAT CAN FUZZY LOGIC TOOLBOX SOFTWARE DO?
You can create and edit fuzzy inference systems with Fuzzy Logic Toolbox
software. You can create these systems using graphical tools or command-line
functions, or you can generate them automatically using either clustering or adaptive
neuro-fuzzy techniques.
If you have access to Simulink software, you can easily test your fuzzy system
in a block diagram simulation environment.
The toolbox also lets you run your own stand-alone C programs directly. This
is made possible by a stand-alone Fuzzy Inference Engine that reads the fuzzy systems
saved from a matlab session. You can customize the stand-alone engine to build fuzzy
inference into your own code. All provided code is ansi compliant.
Because of the integrated nature of the matlab environment, you can create your
own tools to customize the toolbox or harness it with another toolbox, such as the
Control System Toolbox, Neural Network Toolbox, or Optimization Toolbox software.
FUZZY LOGIC TOOL BOX:
The Fuzzy Logic Toolbox extends the MATLAB technical computing
environment with tools for designing systems based on fuzzy logic. Graphical user
interfaces (GUIs) guide you through the steps of fuzzy inference system design.
Functions are provided for many common fuzzy logic methods, including fuzzy
clustering and adaptive neuro fuzzy learning.
The toolbox lets you model complex system behaviors using simple logic rules
and then implements these rules in a fuzzy inference system. You can use the toolbox
as a standalone fuzzy inference engine. Alternatively, you can use fuzzy inference
blocks in simulink and simulate the fuzzy systems within a comprehensive model of
the entire dynamic system.

44
WORKING WITH THE FUZZY LOGIC TOOLBOX:
The Fuzzy Logic Toolbox provides GUIs to let you perform classical fuzzy
system development and pattern recognition. Using the toolbox, you can develop and
analyze fuzzy inference systems, develop adaptive neuro fuzzy inference systems, and
perform fuzzy clustering. In addition, the toolbox provides a fuzzy controller block that
you can use in Simulink to model and simulate a fuzzy logic control system. From
Simulink, you can generate C code for use in embedded applications that include fuzzy
logic.
BUILDING A FUZZY INFERENCE SYSTEM:
Fuzzy inference is a method that interprets the values in the input vector and,
based on user defined rules, assigns values to the output vector. Using the GUI editors
and viewers in the Fuzzy Logic Toolbox, you can build the rules set, define the
membership functions, and analyze the behavior of a fuzzy inference system (FIS). The
following editors and viewers are provided.
fig fuzzy interference system

45
KEY FEATURES:
■ Specialized GUIs for building fuzzy inference systems and viewing and analyzing
results
■ Membership functions for creating fuzzy inference systems
■ Support for AND, OR, and NOT logic in user-defined rules
■ Standard Mamdani and Sugeno-type fuzzy inference systems
■ Automated membership function shaping through neuro adaptive and fuzzy
clustering learning techniques
■ Ability to embed a fuzzy inference system in a Simulink model
■ Ability to generate embeddable C code or stand-alone executable fuzzy inference
engines.
In this section we'll be building a simple tipping example using the graphical
user interface (GUI) tools provided by the Fuzzy Logic Toolbox. Although it's possible
to use the Fuzzy Logic Toolbox by working strictly from the command line, in general
it's much easier to build a system graphically. There are five primary GUI tools for
building, editing, and observing fuzzy inference systems in the Fuzzy Logic Toolbox.
The Fuzzy Inference System or FIS Editor, the Membership Function Editor, the Rule
Editor, the Rule Viewer, and the Surface Viewer. These GUIs are dynamically linked,
in that changes you make to the FIS using one of them, can affect what you see on any
of the other open GUIs. You can have any or all of them open for any given system.
These are shown in Fig.

46
fig. The Primary GUI Tools of the Fuzzy Logic Toolbox
The FIS Editor handles the high level issues for the system: How many input
and output variables? What are their names? The Fuzzy Logic Toolbox doesn't limit
the number of inputs. However, the number of inputs may be limited by the available
memory of your machine. If the number of inputs is too large, or the number of
membership functions is too big, then it may also be difficult to analyze the FIS using
the other GUI tools.
The Membership Function Editor is used to define the shapes of all the
membership functions associated with each variable. The Rule Editor is for editing the
list of rules that defines the behavior of the system.
The Rule Viewer and the Surface Viewer are used for looking at, as opposed to
editing, the FIS. They are strictly read-only tools. The Rule Viewer is a matlab-based
display of the fuzzy inference diagram shown at the end of the last section. Used as a
diagnostic, it can show (for example) which rules are active, or how individual
membership function shapes are influencing the results. The Surface Viewer is used to
display the dependency of one of the outputs on any one or two of the inputs that is, it
generates and plots an output surface map for the system.

47
The five primary GUIs can all interact and exchange information. Any one of
them can read and write both to the workspace and to the disk (the read-only viewers
can still exchange plots with the workspace and/or the disk). For any fuzzy inference
system, any or all of these five GUIs may be open. If more than one of these editors is
open for a single system, the various GUI windows are aware of the existence of the
others, and will, if necessary, update related windows. Thus if the names of the
membership functions are changed using the Membership Function Editor, those
changes are reflected in the rules shown in the Rule Editor. The editors for any number
of different FIS systems may be open simultaneously. The FIS Editor, the Membership
Function Editor, and the Rule Editor can all read and modify the FIS data, but the Rule
Viewer and the Surface Viewer do not modify the FIS data in any way.
We'll start with a basic description of a two-input, one-output tipping problem.
The Basic Tipping Problem. Given a number between 0 and 10 that represents the
quality of service at a restaurant (where 10 is excellent), and another number between
0 and 10 that represents the quality of the food at that restaurant (again, 10 is excellent),
what should the tip be?
The starting point is to write down the three golden rules of tipping, based on years of
personal experience in restaurants.
1. If the service is poor or the food is rancid, then tip is cheap.
2. If the service is good, then tip is average.
3. If the service is excellent or the food is delicious, then tip is generous.
We'll assume that an average tip is 15%, a generous tip is 25%, and a cheap tip
is 5%. It's also useful to have a vague idea of what the tipping function should look like.
A simple tipping function is shown as in Fig.2. Obviously the numbers and the shape
of the curve are subject to local traditions, cultural bias, and so on, but the three rules
are pretty universal. Now we know the rules, and we have an idea of what the output
should look like. Let's begin working with the GUI tools to construct a fuzzy inference
system for this decision process.

48
fig The Tipping Function
THE FIS EDITOR:
The following discussion walks you through building a new fuzzy inference
system from scratch. If you want to save time and follow along quickly, you can load
the already built system by typing fuzzy tipper This will load the FIS associated with
the file tipper.fis (the .fis is implied) and launch the FIS Editor. However, if you load
the pre-built system, you will not be building rules and constructing membership
functions.
The FIS Editor displays general information about a fuzzy inference system.
There's a simple diagram as shown in Fig.3 that shows the names of each input variable
on the left, and those of each output variable on the right. The sample membership
functions shown in the boxes are just icons and do not depict the actual shapes of the
membership functions.
Below the diagram is the name of the system and the type of inference used.
The default, Madman-type inference, is what we'll continue to use for this example.
Another slightly different type of inference, called Surgeon-type inference, is also
available.
Below the name of the fuzzy inference system, on the left side of the figure, are
the pop-up menus that allow you to modify the various pieces of the inference process.
On the right side at the bottom of the figure is the area that displays the name of an
input or output variable, its associated membership function type, and its range. The
latter two fields are specified only after the membership functions have been. Below
that region are the Help and Close buttons that call up online help and close the window,
respectively. At the bottom is a status line that relays information about the system.

49
To start this system from scratch, type fuzzy at the mat lab prompt. The generic
untitled FIS Editor opens, with one input, labeled input1, and one output, labeled
output1. For this example, we will construct a two-input, one output system, so go to
the Edit menu and select Add input. A second yellow box labeled input2 will appear.
The two inputs we will have in our example are service and food. Our one output is tip.
Fig The FIS Editor
We'd like to change the variable names to reflect that, though:

50
fig The updated FIS Editor
THE MEMBERSHIP FUNCTION EDITOR:
The Membership Function Editor shares some features with the FIS Editor. In
fact, all of the five basic GUI tools have similar menu options, status lines, and Help
and Close buttons. The Membership Function Editor is the tool that lets you display
and edit all of the membership functions associated with all of the input and output
variables for the entire fuzzy inference system. Fig.6 shows the Membership Function
Editor. When you open the Membership Function Editor to work on a fuzzy inference
system that does not already exist in the workspace, there is not yet any membership
functions associated with the variables that you have just defined with the FIS Editor
On the upper left side of the graph area in the Membership Function Editor is a
"Variable Palette" that lets you set the membership functions for a given variable. To
set up your membership functions associated with an input or an output variable for the
FIS, select an FIS variable in this region by clicking on it.
Next select the Edit pull-down menu, and choose Add MFs.... A new window
will appear, which allows you to select both the membership function type and the
number of membership functions associated with the selected variable. In the lower
right corner of the window are the controls that let you change the name, type, and
parameters (shape), of the membership function, once it has been selected.
The membership functions from the current variable are displayed in the main
graph. These membership functions can be manipulated in two ways. You can first use
the mouse to select a particular membership function associated with a given variable
quality, (such as poor, for the variable, service), and then drag the membership function
from side to side. This will affect the mathematical description of the quality associated
with that membership function for a given variable. The selected membership function
can also be tagged for dilation or contraction by clicking on the small square drag points
on the membership function, and then dragging the function with the mouse toward the
outside, for dilation, or toward the inside, for contraction. This will change the
parameters associated with that membership function.
Below the Variable Palette is some information about the type and name of the
current variable. There is a text field in this region that lets you change the limits of the

51
current variable's range (universe of discourse) and another that lets you set the limits
of the current plot (which has no real effect on the system).
The process of specifying the input membership functions for this two input tipper
problem is as follows:
• Select the input variable, service, by double-clicking on it. Set both the Range
and the Display Range to the vector [0 10].
• Select Add MFs... from the Edit menu. A window pops open as shown in Fig.
fig. Add MFs… Window

52
fig. The updated Membership Function Editor
Now that the variables have been named, and the membership functions have
appropriate shapes and names, you're ready to write down the rules. To call up the Rule
Editor, go to the View menu and select Edit rules..., or type ruleedit at the command
line. The Rule Editor window pops open as shown in Fig
THE RULE EDITOR:
Constructing rules using the graphical Rule Editor interface is fairly self-
evident. Based on the descriptions of the input and output variables defined with the
FIS Editor, the Rule Editor allows you to construct the rule statements automatically,
by clicking on and selecting one item in each input variable box, one item in each output
box, and one connection item. Choosing none as one of the variable qualities will
exclude that variable from a given rule.

53
Choosing not under any variable name will negate the associated quality. Rules
may be changed, deleted, or added, by clicking on the appropriate button.
The Rule Editor also has some familiar landmarks, similar to those in the FIS
Editor and the Membership Function Editor, including the menu bar and the status line.
The Format pop-up menu is available from the Options pull-down menu from the top
menu bar -- this is used to set the format for the display. Similarly, Language can be set
from under Options as well. The Help button will bring up a MATLAB Help window.
Fig 6.9. The Rule Editor
To insert the first rule in the Rule Editor, select the following:
Poor under the variable service
• Rancid under the variable food
• The radio button, or, in the Connection block
• Cheap, under the output variable, tip.
The resulting rule is
1. If (service is poor) or (food is rancid) then (tip is cheap) (1)
The numbers in the parentheses represent weights that can be applied to each
rule if desired. You can specify the weights by typing in a desired number between zero
and one under the Weight setting. If you do not specify them, the weights are assumed
to be unity (1).
Follow a similar procedure to insert the second and third rules in the Rule Editor to get

54
1. If (service is poor) or (food is rancid) then (tip is cheap) (1)
2. If (service is good) then (tip is average) (1)
3. If (service is excellent) or (food is delicious) then (tip is generous) (1)
To change a rule, first click on the rule to be changed. Next make the desired changes
to that rule, and then click on Change rule. For example, to change the first rule to
1. If (service not poor) or (food not rancid) then (tip is not cheap) (1) click not under
each variable, and then click Change rule.
The Format pop-up menu from the Options menu indicates that you're looking
at the verbose form of the rules. Try changing it to symbolic. You will see
1. (Service==poor) => (tip=cheap) (1)
2. (Service==good) => (tip=average) (1)
3. (Service==excellent) => (tip=generous) (1)
There is not much difference in the display really, but it's slightly more language
neutral, since it doesn't depend on terms like "if" and "then." If you change the format
to indexed, you'll see an extremely compressed version of the rules that has squeezed
all the language out.
1, 1 (1) : 1
2, 2 (1): 1
3, 3 (1): 1
This is the version that the machine deals with. The first column in this structure
corresponds to the input variable, the second column corresponds to the output variable,
the third column displays the weight applied to each rule, and the fourth column is
shorthand that indicates whether this is an OR (2) rule or an AND (1) rule. The numbers
in the first two columns refer to the index number of the membership function.
A literal interpretation of rule 1 is: "if input 1 is MF1 (the first membership
function associated with input 1) then output 1 should be MF1 (the first membership
function associated with output 1) with the weight 1." Since there is only one input for

55
this system, the AND connective implied by the 1 in the last column is of no
consequence.
The symbolic format doesn't bother with the terms, if, then, and so on. The
indexed format doesn't even bother with the names of your variables. Obviously the
functionality of your system doesn't depend on how well you have named your variables
and membership functions. The whole point of naming variables descriptively is, as
always, making the system easier for you to interpret. Thus, unless you have some
special purpose in mind, it will probably be easier for you to stick with the verbose
format.
At this point, the fuzzy inference system has been completely defined, in that
the variables, membership functions, and the rules necessary to calculate tips are in
place. It would be nice, at this point, to look at a fuzzy inference diagram like the one
presented at the end of the previous section and verify that everything is behaving the
way we think it should. This is exactly the purpose of the Rule Viewer, the next of the
GUI tools we'll look at. From the View menu, select View rules....
PROPOSED SYSTEM CONFIGURATION AND MODELING
The adaptive calculation block produces a reference voltage for each MPP voltage,
(VMPP (ref)). The reference voltage and PV panel voltage are compared and then error

56
(VMPP (ref)−VPV) and the change rate of error (Δerror) are given to the FLC as an
input variable. The FLC generates a reference signal for a duty cycle of PWM which is
applied to the switch (MOSFET) of boost converter so that the PV panel is continuously
operated at the MPP. The reference voltage calculation is derived from the (11–14). In
PV modules, the variation of current and voltage values (Impp, Vmpp, Ioc, and Voc)
according to different irradiance and temperature
are presented in the following equations
where K represents the temperature coefficient (K=−0.32398), VMPP is voltage at
MPP, a represents a thermal coefficient
(a=1.17 * 10−1, K * a=μVOC=−38 mV/°C), G represents irradiance
(W/m2), GSTC represents irradiance at standard test condition (1000 W/
m2), T represents ambient temperature (°C), TSTC represent temperature
at standard test condition (25 °C), b=0.0005 [15], D represents the
irradiance effect on VMPP it is a very small coefficient 0 < D < 1
(D=4 * 10−5 estimated for this study according to given equations).
The effect of temperature and irradiance on MPP voltage
The flowchart of the adaptive calculation block of the reference
voltage for the proposed MPPT method is shown in Fig.
The method works as the following principle.

58
If the temperature value equal to TSTC (25 °C) and the irradiance
value is not equal to GSTC (1000 W/m2). The Vref is calculated according
to (11) and sent to the FLC. • If the irradiance value is equal to GSTC (1000 W/m2)
and the temperature
value is not equal to TSTC (25 °C). The Vref is calculated
according to (11) and sent to the FLC. • If the temperature and irradiance are not equal
to TSTC (25 °C) and
GSTC (1000 W/m2) then the Vref is calculated as the sum of the values
obtained in (11) and (13) which is given as (14) and sent to the FLC. • If the temperature
is equal to TSTC (25 °C) and the irradiance is equal
to GSTC (1000 W/m2), then the Vref is calculated as VMPP (STC) and
sent to the FLC.

59
DESIGN CONSTRAINTS
Fuzzy:
(a) Inputs: Inputs must be clearly defined for FL process, for this study first input is
an error which is the difference between VMPP (ref) and VPV, the other input is a
change rate of error (Δerror).
(b) Fuzzification: The success of study depends on the correct operating of this
process. The crisp variables of inputs Error (Fig. 8(a)) and Δerror (Fig. 8(b)) are
converted into fuzzy variables which are identified the membership functions in fuzzy
data set. Each variable is the member of subset according to the degree of
membership (μ), the membership degree varies between 0 (non-member) 1 (full
member). The fuzzy dataset consists of NB (Negative Big), NS
(Negative Small), ZE (Zero), PS (Positive Small) and PB (Positive Big) [46–49].
(c) Inference System: In this stage, the IF-THEN rules establish the connections
between inputs and outputs in terms of membership functions. For this study, the
inference engine created 5x5 rule table
as shown in Table 2 and fuzzy inference system realized by Mamdani method because
of easy to understand and most suited for human instinct [47].

60
(d) Defuzzification: In this study, for Defuzzification the center of area method was
used which is known (COA) method. In this method,
Fig. (a). Error (Vref − Vpv), (b). Change of error (Δerror), (c). Duty (regulating duty).
CHAPTER 3
SIMULATION THEORY
3.1 GENERAL

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