FUZZY LOGIC CONTROLLED SOLAR MODULE FOR DRIVING THREE-PHASE

INDUCTION MOTOR

CHAN SOOI TAT

Thesis submitted in partial fulfilment of

the requirements for the award of the

Bachelor of Engineering (Hons) Mechatronics Engineering

Faculty of Manufacturing Engineering

UNIVERSITY MALAYSIA PAHANG

JUNE 2015

vi

ABSTRACT

Renewable Energy such as solar modules to generate electricity are getting

serious in modern life due to high consumption of electricity generation for applications

such as three-phase induction motor. The three-phase induction motor been used widely

for industrial applications and the speed to be controlled by variable frequency using

power electronics. Solar modules used to generate electricity to supply the three phase

induction motor but photovoltaic output is uncertain and complexity. The Fuzzy Logic

controller is one of controllers that can handle non-linear system and maximum power

of solar module. Fuzzy logic controller to be used for Maximum Power Point Tracking

(MPPT) in the non-linear system with fuzzy rules to control Pulse-Width Modulation

(PWM) for switching Metal Oxide Semiconductor Fast Emitted Transistor (MOSFET).

DC boost converter used to boost up photovoltaic voltage to desired output for supply

voltage source inverter which controlled by three-phase PWM generated by

microcontroller. IGBT switched Voltage source inverter produced alternating current

(AC) voltage from direct current (DC) source to control speed of three-phase induction

motor from boost converter output.

vii

ABSTRAK

Tenaga boleh diperbaharui seperti modul solar untuk menjana tenaga elektrik

yang semakin serius dalam kehidupan manusia kerana penggunaan yang tinggi

penjanaan elektrik untuk aplikasi seperti tiga fasa motor induksi. Motor aruhan tiga fasa

telah digunakan secara meluas untuk aplikasi industri dan kelajuan boleh dikawal

dengan frekuensi pembolehubah menggunakan elektronik kuasa. Modul solar boleh

digunakan untuk menjana elektrik untuk membekalkan motor aruhan tiga fasa tetapi

output solar adalah tidak menentu dan kerumitan. Pengawal Logik Fuzzy adalah salah

satu pengawal yang boleh mengendalikan sistem bukan linear dan kuasa maksimum

modul solar. Pengawal logik kabur boleh dihasilkan maksimum Kuasa Cahaya

Perikutan (MPPT) dalam sistem bukan stabil dengan set kabur untuk mengawal Pulse-

Lebar Moduklutor (PWM) untuk Transiktor besi perudaran cepat semikonductor

(MOSFET). DC penukar rangsangan boleh menaikkan voltan solar kepada output yang

dikehendaki untuk voltan bekalan sumber inverter yang dikawal oleh HPWMs

dihasilkan oleh pengawal mikro. Voltan sumber inverter boleh menghasilkan voltan seli

semasa memandu kelajuan dikawal tiga fasa motor aruhan daripada output rangsangan

penukar.

viii

TABLE OF CONTENTS

Page

EXAMINER’S DECLARATION ii

SUPERVISOR’S DECLARATION iii

STUDENT DECLARATION iv

ACKNOWLEDGEMENTS v

ABSTRACT vi

ABSTRAK vii

TABLE OF CONTENTS viii

LIST OF TABLES xi

LIST OF FIGURES xii

LIST OF SYMBOLS xv

LIST OF ABBREVIATIONS xvii

CHAPTER 1 INTRODUCTION

1.1 Background 1

1.2 Problem Statement 3

1.3 Objectives 3

1.4 Scopes of Project 3

CHAPTER 2 LITERATURE REVIEW

2.1 Solar Modules 5

2.1.1 Solar Cell Bypass Diodes 7

2.2 Maximum Power Point Tracking (MPPT) 7

2.3 DC Boost Converter 8

2.4 Fuzzy Logic Controller 10

2.5 MATLAB Simulink Fuzzy Logic Toolbox 13

2.6 Three Phase Voltage Source Inverter Using Insulated-Gate 14

Bipolar Transistor (IGBT)

2.7 Pulses-Width Modulation Generated By PIC Microcontrollers 16

ix

2.8 Induction Motor Control 18

CHAPTER 3 METHODOLOGY

3.1 Introduction 19

3.2 Project Flow Chart and Block Diagram 19

3.3 Fuzzy Logic Controlled Solar Module 21

3.4 Fuzzy Logic Algorithms 22

3.4.1 Fuzzy Inference System 23

3.5 Electrical Design – DC Boost Converter 26

3.5.1 Voltage Divider 27

3.5.2 Optocoupler Circuit 28

3.6 Fuzzy Logic Controller 29

3.6.1 IR2112 High Side and Low Side Driver 30

3.7 Voltage Source Inverter 31

3.7.1 Three Phase PWM Algorithms 31

3.7.2 Microcontroller Circuit 31

3.7.3 Optocoupler 32

3.7.4 IR2130 H-Bridge Driver 33

3.7.5 Insulated-Gate Bipolar Transistor (IGBT) Module 33

3.7.6 Induction Motor Circuit 34

3.8 Overall Project Circuit 35

CHAPTER 4 RESULT AND DISCUSSIONS

4.1 Introduction 38

4.2 Solar Module Characteristic 38

4.3 Voltage Divider 40

4.4 Fuzzy Logic Controller 41

4.5 Octocoupler and IR2112 Driver 44

4.6 DC Boost Converter 45

4.7 Three Phase PWM From Microcontroller 47

4.8 Octocoupler and IR2130 Motor Driver 49

4.9 IGBT Circuit 53

4.10 Three Phase Induction Motor 55

x

CHAPTER 5 CONCLUSION AND RECOMMENDATIONS

5.1 Introduction 57

5.2 Conclusion 57

5.3 Recommendations 57

REFERENCES 59

APPENDIX 61

A FUZZY LOGIC CODE 61

B THREE PHASE PWM CODE 65

C PROTOTYPE CIRCUIT’S PHOTOS 68

D DATASHEETS 70

E GANTT CHART 82

xi

LIST OF TABLES

Table No. Title Page

3.1 Specification data of MYS-60M/B3/CL-245 solar module 21

3.2 Fuzzy Rule Evaluation 24

3.3 Arduino Uno R3 Specification Data 30

4.1 Solar Cell Experiment 38

4.2 Comparison of Input and Output Voltage 40

4.3 MATLAB Model Simulation for Fuzzy Logic Controller 42

4.4 Data Collected from Controller by DC Power Supply 43

4.5 DC Boost Converter Output 46

4.6 Connected to Solar Module using Voltage Source Inverter 55

xii

LIST OF FIGURES

Figure No. Title Page

2.1 Equivalent Circuit of Solar Module 6

2.2 Current-voltage Characteristic PV Module 8

2.3 Power-Voltage Characteristic PV Module 8

2.4 DC-DC Boost Converter 9

2.5 The Linguistic Variable Range of Height 11

2.6 Process Mapping of Mandani-style Fuzzy Logic 13

2.7 Surface View of the Rule

14

2.8 Block Diagram in Matlab Simulink Fuzzy Logic Toolbox

14

2.9 Topology of Three-Phase Voltage Source Inverter

16

2.10 PIC18F4431 I/O Port Diagram

17

2.11 Different Duty Cycles in Three-Phase PWM Signals

18

3.1 Flow Chart of Project

20

3.2 Block Digram of Project

20

3.3 Data Collection of Solar Module

22

3.4 Fuzzy Logic Algorithm

23

3.5 Verror Membership Functions

24

3.6 Vcoe Membership Functions

25

3.7 Dutyratio as Output Variable

25

3.8 Surface Viewer

25

3.9 ISIS Proteus 7.0

27

3.10 DC-DC Boost Converter Design

27

3.11 Voltage divider

28

xiii

3.12 Optocoupler circuit 29

3.13 Arduino Uno R3

30

3.14 IR2112 High Side and Low Side Driver

31

3.15 Microcontroller Circuit

32

3.16 Optocoupler Circuit

33

3.17 Typical Circuit of IR 2130 Gate Driver

33

3.18 SKM 200GB123D Module

34

3.19 Induction Motor Circuit Diagram

35

3.20 Fuzzy Logic Controller, Optocoupler and Power supply

35

3.21 Boost Converter and DC Link Film Capacitor

36

3.22 Control Circuit of Voltage Source Inverter

36

3.23 IR2130 Gate Driver, Six IGBT Module and Induction

Motor

37

4.1 I-V Characteristic of Solar Module MYS-60M/B3/CL-245

39

4.2 P-V Characteristic of Solar Module MYS-60M/B3/CL-245

40

4.3 Voltage Divider

41

4.4(a) Fuzzy Logic Controller

42

4.4(b) LCD Display

42

4.5 Model Simulation of Fuzzy Logic Controller

43

4.6 Average Value of Duty Cycle from Controller

44

4.7(a) PWM from Octocoupler 4N25 (below) and IR2112 driver

(above)

45

4.7(b) Optocoupler and IR2112 Driver Circuit

45

4.8 DC voltage Output after pass through DC filter

46

4.9 DC-DC Boost Converter, Optocoupler, IR2112 Driver and

DC filter

47

xiv

4.10 PIC18F4431 microcontroller circuit

47

4.11(a) High Side Three Phase PWM Signal from Simulation

48

4.11(b) High Side Three Phase PWM Signal

48

4.12(a) Low Side Three Phase PWM Signal from Simulation

48

4.12(b) Low Side Three Phase PWM Signal

49

4.13 Pair mode of high side PWM (above) and low side PWM

(below)

49

4.14 Octocoupler Circuit

50

4.15(a) High Side PWM Signal from Octocouplers

50

4.15(b) Low Side PWM Signal from Octocouplers

51

4.16 IR2130 and Bootstrap Capacitor Circuit

51

4.17(a) High Side PWM signals from IR2130 Gate Driver

52

4.17(b) Low Side PWM signals from IR2130 Gate Driver

52

4.18 Dead time of Pair Mode PWM

53

4.19 Voltage source of Inverter

53

4.20 IR2130 H-Bridge Driver and IGBT modules

54

4.21 IGBT Line-to-Line Voltage from Vab

54

4.22 Delta connection to Three Phase Induction Motor

55

4.23 Rotational Speed of Three Phase Induction Motor using

Tachometer

56

xv

LIST OF SYMBOLS

D Duty Cycle

Verror Input Variables

Vcoe Input Variables

Vgs Gate Voltage

Vmpp Maximum power voltage

Voc Open circuit voltage

Impp Maximum power current

Isc Short circuit current

Pmax Maximum power

Vth Threshold voltage

Vin Voltage input from boost converter

Vout Output voltage from boost converter

Ipv Photovoltaic current

Iph Photovoltaic current

Vpv Photovoltaic voltage

HIN High input to gate driver

HO High side PWM output

LIN Low side PWM input

LO Low side PWM output

fs Frequency

Pd Diode power

Ton Turn on time

Toff Turn off time

xvi

∆𝑖𝐿 Current ripple

Lmin Minimum inductor

Cmin Minimum capacitor

Vab Voltage from line a-line b

VLL Line-to-line voltage

xvii

LIST OF ABBREVIATIONS

AC Alternative current

DC Direct current

FIS Fuzzy Inference system

FLC Fuzzy Logic Controller

IDE Integrated development environment

Verror Input variable of Fuzzy Logic

Vcoe Input variable of Fuzzy Logic

LCD Liquid-crystal display

LED Light-emitting diode

MOSFET Metal-oxide-semicondcutor field-effect transistor

BJT Bipolar Junction Transistor

MPPT Maximum power point tracking

PV Photovoltaic

PWM Pulse-width modulation

IGBT Insulated-Gate Bipolar Transistor

PID Proportional, integral and derivative

PIC Peripheral Interface Controller

COG Center of Gravity

1

CHAPTER 1

INTRODUCTION

This chapter will make brief discussion about background, problem statement,

objective and scope of the project.

1.1 BACKGROUND

In 1889, Nicole Tesla invented the AC phase induction motors while DC motors

were also used in that time. DC motor and AC motor have the same function of

converting electrical energy into mechanical energy but different power, construction

and control methods. Today, AC power systems are clearly dominated over DC systems

and induction motors are greatly increase used in many applications. Besides that, the

reason of DC brushed motors are replaced by AC brushless motors due to free

maintenance, cheap costs and no commutation. The speed of induction motor can be

controlled by variable frequency using power electronic circuits and microcontrollers.

Power electronic devices like controlling such as Insulated-Gate Bipolar Transistor

(IGBT) and microcontrollers can also make the electric drive system become cheaper

compared to DC motor.

The three-phase induction motor construction is smaller size compared to one-

phase induction motor with same power capacity. Single-phase induction motor cannot

do self-starting because of single-phase only produce single-phase field. The single-

phase induction motor cannot do self-starting because of single-phase motor produces

single magnetic field only. However, three-phase induction motor is the self-starting

motor. Three-phase induction motor usually uses for higher torque and higher power

2

rating compares to single-phase induction motor. The efficiency of three-phase

induction motor is higher than single-phase induction motor.

Nowadays, electric power plants have been using non-renewable resource such

as fossil fuel, coal and natural gas to generate huge power electricity for industries and

homes. Those resources have heavily damage to environment and it became less

available in recently year. The alternative energy such as solar energy can be used with

renewable solar technologies to replace some of them for cheaper energy and lower

environment impact. Besides that, factories in remote area which are off-grid electricity

generation from power plants are hard to obtain electricity for driving the factory.

Utility industries will not build distribution line for only few industries in remote areas

due to uneconomical outcomes. Industries use solar module to produce direct current by

convert solar energy into electrical energy as power supply to drive loads. The cheaper

energy will give benefits to industries in remote area in term of economical saving and

convenience.

The solar modules made from wafer-based crystalline silicon cells arrange in

arrays were exposured to sun irradiation and convert solar energy into electrical energy

due to photovoltaic effect. It has a sheet of glass cover the semiconductors and protect

them from any conditions. DC output power is converted by DC-DC converter to get

the desired output. Outputs from solar modules are unstable conditions and low

efficiency due to different range of sun radiation. The optimization of solar module can

be improved by using fuzzy logic controller of Maximum Power Point Tracking

(MPPT).

Fuzzy logic controller is a control system that using digital or discrete value in

the system that act like human brain. The input of non-linear systems which are

uncertain and confusing information can be presented indirect way using fuzzy set.

Despites PID controller is much better performance either linear system or non-linear

system when transfer function is known, PID controller is not so robust with non-linear

system that is uncertainty. Within this situation, the fuzzy logic controller can handle

the non-linear system by decreasing the possible uncertain effects. Fuzzy logic

controller can produced Maximum Power Point Tracking (MPPT) in the non-linear

3

system with fuzzy set to control Pulse-Width Modulation (PWM) for Metal Oxide

Semiconductor Fast Emitted Transistor (MOSFET) or Insulated Gate Bipolar Transistor

(IGBT).

1.2 PROBLEM STATEMENT

The output power of solar module is unstable and low efficiency due to sun

radiation and low efficiency rate of solar cells. Fuzzy logic controller optimizes power

output power of the solar module. The efficiency of three-phase induction motor is low

without control method and using Volt/Hz control to increase the efficiency of three-

phase induction motor.

1.3 OBJECTIVES

The objectives of this project are:

1. To optimize the output power of solar module by fuzzy logic controller

using microcontroller.

2. To control there-phase induction motor using voltage source inverter

switching by microcontroller generated PWM signal.

1.4 SCOPES OF PROJECT

Fuzzy Logic Controller programmed with Arduino microcontroller using C

language.

Using DC-DC boost converter to convert low DC input voltage to higher output

DC voltage.

Optocoupler of 4N25 will be used to prevent changing voltages and isolate

control circuit to power systems circuit.

Software of Picbasic Pro compiler will be used to generate programming code

and ProKit2 will be used to transfer the coding from MicroCode Studio into the

microcontroller to produce PWM signal.

4

Voltage source inverter used IGBT modules for generating AC output from DC

input. The AC output then will be used to drive the three phase induction motor.

Three-phase squirrel cage induction motor will be selected as the type of

induction motor in this project.

5

CHAPTER 2

LITERATURE REVIEW

2.1 SOLAR MODULES

Solar module or Photovoltaic module is allowing light to pass while protecting

the semiconductor wafer by a sheet of transparent glass on the sun-facing side

(Wikipedian, 2010). Example, the MYS-48P has tempered 3.2mm/4 mm solar gas. One

of the solar modules is silicon crystalline based solar cell is popular in the market

because the silicon crystalline based solar modules are low manufacturing cost and non

–toxic materials used in the final product. There are four types of silicon crystalline

solar cells which are single crystal, mulitcrystalline, ribbon, and silicon thin film (Y.S.

Tsuo, T.H Wang, T.F Ciszek, 1999).

Several types of solar modules efficiencies are measured under room

temperature, 25 °C with spectrum 1000W/m2 which is silicon crystalline solar module,

polycrystalline solar module, thin-film polycrystalline solar module and amorphous

silicon solar module. The efficiency of silicon crystalline solar cell is 22.9% ± 0.6 and

silicon multicystalline solar module efficiency is 17.3% ± 0.5. The efficiency of

amorphous silicon solar module is 13.8% ± 0.5 and thin-film polycrystalline solar

module efficiency is 8.2% ± 0.2 (Martin A. Green, Keith Emery, Yoshihiro Hishikawa

and Wihelm Warta, 2010).

The one diode model equivalent circuit of solar module is shown as figure 2.1.

The simplest form of equation for solar module is about output current, where the Iph is

photocurrent and Id is diode current that is shown as below. When there is parasitic

6

effect of solar module, it will reduce the efficiency of solar module and equation 2.3 is

shown about effect parasitic on output current (Smitha Krishnamurthy, 2006).

Figure 2.1: Equivalent Circuit of Solar Module

Source: Smitha Krishnamurthy, 2006

𝐼 = 𝐼𝑝ℎ − 𝐼𝑑 (2.1)

𝐼 = 𝐼𝑝ℎ − 𝐼𝑠 {exp (𝑞 𝑉𝑑

𝑛 𝐾 𝑇) − 1} (2.2)

𝐼 = 𝐼𝑝ℎ − 𝐼𝑠 {𝑒𝑥𝑝 (𝑞 [𝑉 + 𝐼 𝑅𝑠]

𝑛 𝐾 𝑇) − 1} − (

𝑉 + 𝐼 𝑅𝑠

𝑅𝑠ℎ) (2.3)

For this project, the MYS-60M/B3/CL-245 mono-cyrstalline photovoltaic

module is chosen and distinguished by 245 watt of solar cells with efficiency up to

15.35% high output per square meter of module area. Vmpp and Impp can reach up to

29.702 V and 8.107 A. It also has 25 years long life span of power output and noise free

(Malaysian Solar Resources, 2012).

7

2.1.1 SOLAR CELL BYPASS DIODES

Bypass diodes in solar cells has purposed of protect against high spot damage

when it is used in different weather condition. The diode voltage drop is because of

internal resistance in operation and power loss. For solar module bypass diode must

always higher breakdown voltage than the open circuit voltage (Voc). The damage on

bypass diode will occurs when the output voltage is higher than breakdown voltage

from power dissipation. The equation 2.4 is shown as below as power dissipation which

consists of breakdown voltage and current at breakdown operation.

𝑃𝑑 = 𝑉𝐵𝑅 × 𝐼𝐵𝑅 (2.4)

The monocrystalline silicon solar module commonly will used schottky

rectifiers as bypass diodes. Schottky rectifiers has characteristic of low forward voltage,

better efficiency and current density but consider reverse leakage current will increase

when exposure to the sun which may be damage to the diode. The reverse leakage

current is related to the reverse biased voltage (Vishay Document Number 89398,

2011).

2.2 MAXIMUM POWER POINT TRACKING (MPPT)

The photovoltaic module operation can be defined by getting 2 types of

characteristic of a PV module which are current-voltage characteristic and power-

voltage characteristic. We need to overcome the unstable of solar module, we can use

MPPT controller which consists of DC-DC converter to control operating voltage (M.S

Ait Cheikh, C.Labers, G.F Tchnoketch Kebir and A.Zerguerras, 2007).

8

Figure 2.2: Current-voltage Characteristic PV Module

Source: M.S Ait Cheikh, C.Labers, G.F Tchnoketch Kebir and A.Zerguerras, 2007

Figure 2.3: Power-Voltage Characteristic PV Module

Source: M.S Ait Cheikh, C.Labers, G.F Tchnoketch Kebir and A.Zerguerras, 2007

2.3 DC BOOST CONVERTER

A boost converter usually is used for modifying levels of voltage. Boost

converters have a MOSFET that receives control signal from controller and then the

same of a switch, it become on or off. The boost converters can be used in solar module

act like switching modules between solar module and the load by letting MPPT control.

9

Figure 2.4 indicates boost converter with a PWM comparator (A.Gheibi, S.M.A

Mohammadi and M.M Farsangi, 2014).

The PWM module can able to control between command voltage with control

signal after compare with saw tooth output signal to make PWM control signal as gate

signal for power device such as MOSFET transistor. The boost converter can alter input

resistance larger than Rmpp and due to the fact the photovoltaic resistance is higher than

Rmpp resistance in high radiations (1000 W/m2), and considering that during day time

tracking photovoltaic resistance is usually higher than Rmpp resistance, therefore the

boost converter is used more often for tracking purposes. (A.Gheibi, S.M.A

Mohammadi and M.M Farsangi, 2014).

𝐼𝐿 = 𝐼𝑜 [ 1

1 − 𝑑−

𝑅𝐿

2𝐿𝑓𝑑(1 − 𝑑)] (2.5)

Where,

IL = Minimum inductor current

Io = Output current

RL = Resistance of load

f = Switching frequency

Figure 2.4: DC-DC Boost Converter

10

Inductor current must be zero when steady-state operation, which the formula

can give in conducting ratio of Vo and Vi:

𝑉𝑜

𝑉𝑖𝑛=

1

1 − 𝑑 (2.6)

Where d is a duty cycle (Dylan Dah-Chuan Lu, David Ki-Wai Cheng and Yin-

Shu Lee, 2003).

2.4 FUZZY LOGIC CONTROLLER

Fuzzy logic is been built when 1965 by Lotfi A. Zadeh which defines it is

control logic which using fuzzy concept to represent common sense and decision

selective than the binary logic system. It can able to provide an effective way of get

suitable results from complex data which different in real world. The fuzzy logic

controller (FLC) is defined as linguistic control rules using human language which is

related by fuzzy rules as inference and implications. A fuzzy logic controller is popular

for complexity analysis by some methods or when input variables of information are

uncertainly or inexactly (Chuen Chien Lee, 1990).

Compare to Boolean logic which is 2 simple value set, fuzzy logic is not crisp

number but multi-valued logic. By using different types of methods, it had deals with

degrees of truth and degrees of membership. The degree must take in range of 0 to 1.

Fuzzy logic uses the real number of logical values between 0 (completely false) and 1

(completely true).

It means that element that belong to fuzzy set have certain degree of

membership. So, the matter to deal in fuzzy logic can be nearly true or nearly false to

any degree from range of crisp number (Micheal Negnevitsky, 2011).

The range of linguistic variables can be set as the fuzzy set theory. A linguistic

variable is represent as fuzzy variables when fuzzy inference. As figure 3, the linguistic

values of height are short, average and tall which can apply to practical. The height is

11

the universe of discourse that acts like natural language. The height range of fuzzy logic

control can be seen in figure 2.5 below. The hedge is acts as break down continuums

into fuzzy intervals.

Figure 2.5: The Linguistic Variable Range of Height

Source: C.Y Won, D.H Kim, S.C Kim, W.S Kim, H.S Kim,2008

Fuzzy rule acts like human knowledge to analysis of complex system which was

published by Lotfi Zadeh in 1973. Fuzzy set can determine the linguistic values by

universe of discourses. Fuzzy rules can be defined as a conditional statement in the form

as shown as below (Micheal Negnevitsky, 2011).

IF X is A

THEN Y is B

Where,

X and Y = linguistic variables

A and B = linguistic values

Fuzzy inference can be described as process of letting input variables using

theory of fuzzy set to get suitable output. This is shown as figure 2.6 as below. One of