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PV MAXIMUM POWER POINT TRACKING BASED ON SINGLE-INPUT
FUZZY LOGIC CONTROLLER
MOHD HIDZIR BIN ABD KARIM
UNIVERSITI TEKNOLOGI MALAYSIA
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PV MAXIMUM POWER POINT TRACKING BASED ON SINGLE-INPUT
FUZZY LOGIC CONTROLLER
MOHD HIDZIR BIN ABD KARIM
A project report submitted in partial fulfilment of
the requirements for the award of the degree of
Master of Engineering (Electrical – Power)
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
JUNE 2014
iii
I dedicate this to all my beloved family members. Esspecially to my beloved mother,
Siti Hasnah Bahari and my father, Abd Karim Sudir.
iv
ACKNOWLEDGEMENT
Alhamdulillah, I am greatly indebted to Allah SWT on His mercy and
blessing for making this project success.
I would like to take this opportunity to express my deepest gratitude and
thanks to my project supervisor, Dr. Shahrin Md Ayob for his guidance assistance
and all konowledge that he has shared during the course of this project.
I sincerely thank to all the lecturers who has taught me for the lesson that has
been delivered. Not to forget, to my fellow postgraduate friends, thank you for
sharing useful ideas, information and moral support during the course of study.
Last but not least, I would like to express my appreciation and gratitude to my
parents, sisters and brother for all the supports and encouragement that they have
provided during my study.
Thank You.
Wassalam
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ABSTRACT
In this project, a new algorithm of maximum power point tracking (MPPT)
for photovoltaic is proposed. The algorithm is based on simpilified fuzzy logic
controller (FLC). The simplified FLC has one input, which is derived from the
signed distance method. Due to the single-input FLC, the number of rules and
tuning parameters are significantly reduced. Thus, simpler design and faster
computation are expected using the simplified FLC. The simplified FLC MPPT is
compared with conventional FLC MPPT. Both algorithms are implemented in
stand-alone PV system that uses boost-converter. The stand-alone PV system with
the algorithms are designed in MATLAB software and simulated. Result shows
that the simplified FLC has similiar performance with conventional FLC. Both
MPPT algorithms tracked maximum power point vary fast. Eventhough their
performance are same, simplified FLC present some advantages such as
simplicity of tuning of membership function, the control rule is built in one-
dimensional space, the computational complexity is reduced and faster ans so on.
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ABSTRAK
Di dalam projek ini, satu kaedah mengesan titik kuasa tertinggi (MPPT) untuk
sistem fotovoltadicadangkan. Kaedah ini adalah berdasarkan pada kawalan logik
kabur (FLC) yang dimudahkan. FLC yang dimudahkan mempunyai satu input yang
dihasilkan daripada kaedah ‘signed distance’. Hasilnya, rekaan yang lebih mudah
dan pengiraan yang cepat boleh didapati dengan menggunakan FLC yang
dimudahkan. MPPT FLC yang dimudahkan dibandingkan dengan FLC MPPT yang
biasa. Kedua-dua kaedah ini digunakan dalam sistem sistem berdiri sendiri fotovolta
yang menggunakan penukar galak. Sistem berdiri sendiri fotovolta dan kaedah-
kaedah tersebut direka menggunakan aplikasi MATLAB dan disimulasikan..
Keputusan menunjukkan kaedah FLC yang dimudahkan mempunyai pencapaian
yang sama dengan FLC yang biasa. Kedua-dua kaedah MPPT ini dapat mengesan
titik kuasa maksimum dengan cepat sekali. Walaupun pencapaian mereka sama,
MPPT FLC yang dimudahkan mempunyai beberapa kelebihan seperti mudah untuk
menala fungsi keahlian, jadual peraturan kawalan dibina dalam satu ruang dimensi
sahaja dan pengiraan yang kompleks dimudahkan dan cepat dan sebagainya.
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TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES ix
LIST OF FIGURES x
LIST OF ABBREVIATIONS xiii
1 INTRODUCTION 1
1.1 Backgroud 1
1.2 Objective of the Project 2
1.3 Scope of the Project 3
1.4 Problem Statement 5
1.5 Thesis Outline 5
2 LITERATURE REVIEW 7
2.1 Photovoltaic Model Characteristic 7
2.2 Conventional Fuzzy Logic Controller 11
2.3 Simplified Fuzzy Logic Controller 15
2.4 DC-DC Boost Converter 19
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3 METHODOLOGY 20
3.1 Introduction 20
3.2 Designing PV module in MATLAB 21
3.3 Designing DC-DC Boost Converter in 24
MATLAB
3.4 Designing Conventional FLC MPPT in 25
MATLAB
3.5 Designing Simplified FLC MPPT in MATLAB 30
3.6 Designing Stand-alone PV System 34
4 RESULT AND DISCUSSION 36
4.1 Introduction 36
4.2 Conventional FLC MPPT 36
4.3 Simplified FLC MPPT 40
4.4 Comparison Between Conventional FLC 44
And Simplified FLC
5 CONCLUSION 47
REFERENCES 49
ix
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Conventional FLC rule base [5] 13
2.2 Simplified FLC rule base [5] 17
3.1 Parameters of the PV module 23
3.2 Parameters of the boost converter 24
4.1 Maximum Power Point Tracking Time 44
x
LIST OF FIGURES
FIGURE NO. TITLE PAGE
1.1 A stand-alone PV system with MPPT 3
1.2 One diode model of PV 4
1.3 DC-DC boost converter 4
2.1 Photovoltaic array 7
2.2 An ideal and series resistance, Rs model of PV 8
2.3 P-V and I-V characteristic curve [12] 9
2.4 One diode model with Rs and Rp of PV [12] 9
2.5 Two-diode model of PV [12] 10
2.6 Fuzzy logic controller system [5] 11
2.7 Membership function for (a) input e, (b) input ∆e 12
and (c) output dD [5]
2.8 Mandani’s inference method with COA 14
defuzzification [16]
2.9 Stand-alone PV system using S-FLC [5] 15
2.10 Signed distance derivation 16
2.11 Membership functions for (a) ds and (b) dD [5] 18
2.12 Circuit of DC-DC boost converter 19
3.1 The flow of the methodology 20
3.2 One diode model of PV with Rs and Rpin MATLAB 21
3.3 A detailed PV array in MATLAB 22
3.4 A general PV array in MATLAB 22
3.5 A graph of insolation vs time for the photovoltaic 23
3.6 DC-DC boost converter in MATLAB 24
3.7 Conventional FLC MPPT in MATLAB 25
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3.8 Conventional FLC in FIS Editor in MATLAB 26
3.9 Membership function for input error 26
3.10 Membership function for input change of error 27
3.11 Membership function for output duty cycle step 27
3.12 Rules of interference 1-13 28
3.13 Rules of interference 14-27 28
3.14 Rules of interference 28-40 28
3.15 Rules of interference 41-49 29
3.16 Control surface of the conventional FLC 29
3.17 Simplified FLC MPPT in MATLAB 30
3.18 Simplified FLC in FIS Editor in MATLAB 31
3.19 Membership function for input signed distance 32
3.20 Membership function for output duty cycle step 32
3.21 Rules of interference of simplified FLC 33
3.22 Control surface of simplified FLC 33
3.23 Stand-alone PV system using conventional FLC 34
MPPT
3.24 Stand-alone PV system using simplified FLC MPPT 35
4.1 Output power of the stand-alone PV system 37
Using conventional FLC MPPT
4.2 Output power whan irradiance is set 1000W/m2 37
at t = 0
4.3 Output power when irradiance is changed from 38
1000W/m2 to 900W/m
2 at t = 40ms
4.4 Output power when irradiance is changed from 39
900W/m2 to 950W/m
2 at t = 60ms
4.5 Output power when irradiance is changed from 40
950W/m2 to 1000W/m
2 at t = 80ms
4.6 Output power of the stand-alone PV system 41
Using simplified FLC MPPT
4.7 Output power when irradiance is set 1000W/m2 42
at t = 0
4.8 Output power when irradiance is changed from 42
1000W/m2 to 900W/m
2 at t = 40ms
xii
4.9 Output power when irradiance is changed from 43
900W/m2 to 950W/m
2 at t = 60ms
4.10 Output power shwn irradiance is changed from 44
950W/m2 to 1000W/m
2 at t = 80ms
xiii
LIST OF ABBREVIATIONS
PV - Photovoltaic
MPPT - Maximum power point tracking
P & O - Perturb and observe
IC - Incremental conductance
HC - Hill climbing
FLC - Fuzzy logic controller
ANN - Artificial neural network
EA - Evolutionary algorithm
MPP - Maximum power point
SFLC - Simplified fuzzy logic controller
DC - Direct current
STC - Standard test conditions
PWM - Pulse width modulation
CFLC - Conventional fuzzy logic controller
1
CHAPTER 1
INTRODUCTION
1.1 Background
Energy demand is increasing every year due to increase in population and
some other reasons. However, amount of fossil energy is decreasing. This means
human cannot depend only on fossil energy to meet the increasing energy demand.
Renewable energy such as solar power, wind power, geothermal power and fuel cell
energy are being considered to meet the increasing energy demand. Renewable
energy is not only unlimited in source but also produce zero or very low of pollution
to the environment. Solar energy is one of popular renewable energy being used
today. The source of solar energy is unlimited and produces zero pollution to the
environment. Photovoltaic is used to convert the solar energy to electrical energy.
Improvement of the performance of photovoltaic is going on until today to make the
efficiency of the photovoltaic as high as possible.
Generally, there are two areas to improve the performance of solar
photovoltaic system which are photovoltaic manufacturing process and power
management strategy. An inappropriate power management strategy will result in
low efficiency in solar photovoltaic system. One of the power management strategies
being developed is the maximum power point tracking (MPPT). MPPT is a method
2
to find voltage and current of a photovoltaic module at which it will operate at
maximum power output under certain temperature and irradiance. MPPT methods
are categorized in two types which are conventional methods and intelligent
methods. Examples of conventional MPPT are perturb and observe method (P & O),
incremental conductance method (IC) and hill climbing method (HC). Examples of
intelligent MPPT are fuzzy logic control method (FLC), artificial neural network
method (ANN) and evolutionary algorithm method (EA). P & O method and IC
method are usual because there are easy and simple to be implemented [5]. However,
P & O method has two disadvantages which are power oscillation at maximum
power point (MPP) and divergence of MPP under rapid atmospheric change [2]. IC
method also has a problem of power oscillation when fast tracking of the maximum
power is desired [5]. Fast convergence to MPP and minimal oscillation about MPP
can be achieved using fuzzy logic control method [2]. However, conventional fuzzy
logic control method yields complex control rules. The conventional FLC presents
difficulty of modification and tuning of control rules. Due to this problem, author in
[6] simplified the inputs of FLC to one input which is known as simplified fuzzy
logic controller (S-FLC). The control rules are reduced, hence it is easier to modify
and tune the control rules.
1.2 Objective of the project
There are few objectives to be achieved in this project:
1. To propose a new algorithm for maximum power point tracking (MPPT)
based on simplified fuzzy logic (S-FLC).
2. To compare the performance between the proposed simplified fuzzy logic
controller (S-FLC) MPPT with conventional fuzzy logic controller MPPT.
The types of performance that will be compared are MPPT response time to
the steady state and MPPT response time to the changing of atmospheric
condition.
3
1.3 Scope of the project
A stand-alone typed of photovoltaic system will be used in this project.
Figure 1.1 shows a simple concept of a stand-alone photovoltaic system with MPPT.
Figure 1.1 A stand-alone PV system with MPPT
This project only involves simulation using software. MATLAB software
will be used for the simulation. All components in the stand-alone MPPT system as
shown in Figure 1.1 will be modeled using MATLAB.
One-diode model of photovoltaic will be used in the stand-alone system. This
model will be constructed in MATLAB. Figure 1.2 shows the one-diode model of
photovoltaic.
4
Figure 1.2 One diode model of PV [12]
The type of dc-dc converter that will be used in this project is boost DC-DC
converter. Figure 1.3 shows the circuit of DC-DC boost converter.
Figure 1.3 DC-DC boost converter [11]
The performance of the proposed simplified fuzzy logic controller MPPT will
be compared only with conventional fuzzy logic controller MPPT. The types of
performance that will be compared are MPPT response time to the steady state and
MPPT response time to the changing of atmospheric condition.
5
1.4 Problem Statement
The P & O method of maximum power point tracking (MPPT) and IC
method are simple and easy to be implemented [5]. However, P & O method yields
two weaknesses namely power oscillation at maximum power point (MPP) and
divergence of MPP under rapid atmospheric change [2]. IC method also yields the
same problem as P & O MPPT method [5]. Fuzzy logic controller method yields
some advantages such as fast convergence to MPP and minimal oscillation around
MPP [2]. However, there are complex control rules in conventional fuzzy logic
controller. Due to complex control rules, difficulty of modification and tuning of
control rules happen in conventional fuzzy logic controller. Due to this problem,
simplified fuzzy controller is introduced by author in [6] by simplifying the inputs of
conventional fuzzy logic controller to one input using signed distance method. In the
simplified fuzzy logic controller, the control rules are reduced, hence it is easier to
modify and tune the control rules.
1.5 Thesis Outline
This report consists of five chapters, which are from this chapter to
conclusion. Chapter 1 introduces the background of the research, objective of this
project, scope of project, problem statement and the overall thesis outline.
Chapter 2 focuses on literature reviews of this project based on journals and
other references. The literature reviews are about photovoltaic module characteristic,
conventional fuzzy logic controller, simplified fuzzy logic controller and dc-dc boost
converter.
6
Chapter 3 mainly discusses on the methodology of the project. Procedures of
designing PV model, dc-dc boost converter, conventional FLC MPPT, simplified
FLC MPPT and stand-alone PV system in MATLAB are elaborated in this chapter.
Chapter 4 presents the results and discussions of the project. The discussion
focused on the result of maximum power point tracking time of the conventional
FLC MPPT and simplified FLC MPPT. Comparison of the result of both MPPTs is
also discussed.
Chapter 5 concludes overall about the project based on the result. Some
recommendations for future works are stated.
49
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techniques of PV system for uniform insolation and partial shading
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2013, Pages 475-488.
[2] T. Esram and P. L. Chapman, “Comparison of Photovoltaic Array Maximum
Power Point Tracking Techniques,” IEEE Transaction on Energy Conversion,
Vol. 2, Issue 2, 2007, page 439-449.
[3] Pallavee Bhatnagar, R. K. Nema,” Maximum power point tracking control
techniques: State-of-the-art in photovoltaic applications”, Renewable and
Sustainable Energy Reviews, Volume 23, July 2013, Pages 224-241.
[4] Zainal Salam, Jubaer Ahmed, Benny S. Merugu,” The application of soft
computing methods for MPPT of PV system: A technological and status
review”, Applied Energy, Volume 107, July 2013, Pages 135-148.
[5] I Purnama, L Yu-Kang, C Huang-Jen, “A fuzzy control maximum power
point tracking photovoltaic system,” In: fuzzy systems (FUZZ), 2011 IEEE
international conference on, 2011, p. 2432–2439.
[6] B.J. Choi, S.W. Kwak, and B.K. Kim, “Design and Stability Analysis of
Single-Input Fuzzy Logic Controller,” IEEE Transactions on Systems, Man,
and Cybernetics, Part B: Cybernetics, April 2000, Volume 30, Issue 2, page
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[7] F. Taeed , Z. Salam and S. M. Ayob "Implementation of single input fuzzy
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PECon, pp.797 -802 2010.
50
[8] Ayob, S.M.; Azli, N.A.; Salam, Z., "DSP-based single input PI-Fuzzy
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[11] Daniel W. Hart,” Power Electronics”, Tata McGraw-Hill, 2011.
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