Çukurova university institute of natural and …

ÇUKUROVA UNIVERSITY INSTITUTE OF NATURAL AND APPLIED SCIENCES

MSc THESIS

Murat Mustafa SAVRUN

DESIGN AND SIMULATION OF THYRISTOR SWITCHING SYSTEM FOR REACTIVE POWER CONTROL AND MOTOR POWER EFFICIENCY CONTROL

DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING

ADANA, 2013


DESIGN AND SIMULATION OF THYRISTOR SWITCHING SYSTEM FOR

REACTIVE POWER CONTROL AND MOTOR POWER EFFICIENCY CONTROL


MSc THESIS

DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING We certify that the thesis titled above was reviewed and approved for the award of degree of the Master of Science by the board of jury on 06/08/2013.

................................................ ........................................................ ............................................................. Prof. Dr. Mehmet TÜMAY SUPERVISOR

Asst. Prof. Dr. M. Ugraş CUMA MEMBER

Assoc. Prof. Dr. Ramazan ÇOBAN MEMBER

This MSc Thesis is written at the Department of Institute of Natural And Applied Sciences of Çukurova University. Registration Number:

Prof. Dr. Mustafa GÖK Director Institute of Natural and Applied Sciences

Note: The usage of the presented specific declerations, tables, figures, and photographs either in this

thesis or in any other reference without citiation is subject to "The law of Arts and Intellectual Products" number of 5846 of Turkish Republic.

I

ABSTRACT

MSc THESIS

DESIGN AND SIMULATION OF THYRISTOR SWITCHING SYSTEM FOR REACTIVE POWER CONTROL AND MOTOR POWER

EFFICIENCY CONTROL



DEPARTMENT OF ELECTRICAL ELECTRONICS ENGINEERING Supervisor : Prof. Dr. Mehmet TÜMAY Year: 2013, Pages: 129 Jury : Prof. Dr. Mehmet TÜMAY : Asst. Prof. Dr. M. Uğraş CUMA : Assoc. Prof. Dr. Ramazan ÇOBAN

Nowadays, the majority of the increasing energy consumption is consumed by industrial organizations that are constantly growing and evolving. A large part of the energy used in industry is consumed by electric motors. As a result of work to be done on the efficiency of electric motors, large declines in energy consumption values can be established.

In this study, a new motor drive which is designed to provide efficiency in asynchronous motor that is the most commonly used industry is presented. The driver is modeled in PSCAD/EMTDC. Designed driver provides energy-efficiency in motors which operating at lightly loads. The proposed methods provide superior performance compared to conventional methods.

The aim of the thesis is that design and simulation a motor drive which provide energy efficiency in 7.5 kW induction motor. The performance and provided efficiency values of the proposed motor driver are investigated with different simulation studies by PSCAD program.

Key Words: Asynchronous Motor Control, Flux Control, Energy Efficiency, Stator

Voltage Control

II

ÖZ

YÜKSEK LİSANS TEZİ

REAKTİF GÜÇ KONTROLÜ VE MOTOR ENERJİ VERİMLİLİĞİ KONTROLÜ İÇİN TRİSTÖR ANAHTARLAMA SİSTEMİ TASARIMI VE

SİMÜLASYONU


ÇUKUROVA ÜNİVERSİTESİ FEN BİLİMLERİ ENSTİTÜSÜ

ELEKTRİK ELEKTRONİK MÜHENDİSLİĞİ ANABİLİM DALI

Supervisor : Prof. Dr. Mehmet TÜMAY Year: 2013, Pages: 129 Jury : Prof. Dr. Mehmet TÜMAY : Yrd. Doç. Dr. M. Uğraş CUMA : Doç. Dr. Ramazan ÇOBAN

Günümüzde artan enerji tüketiminin büyük çoğunluğuna sürekli olarak büyüyen ve gelişen endüstriyel kuruluşlar sahiptir. Endüstride kullanılan enerjinin büyük bir kısmı ise elektrik motorları tarafından tüketilmektedir. Bu nedenle de elektrik motorları üzerine yapılacak bir verimlilik çalışması sonucunda enerji tüketim değerlerinde büyük düşüşler sağlanabilecektir.

Bu çalışmada endüstride en çok kullanılan motor tipi olan asenkron motorda verimlilik sağlayabilmek için tasarlanmış olan yeni bir motor sürücü sunulmuştur. Sürücü PSCAD/EMTDC programı kullanılarak modellenmiştir. Tasarlanan sürücü düşük yüklerde çalışan motorlarda enerji verimliliği sağlamaktadır. Önerilen metot var olan metotlara göre üstün bir performans sağlamaktadır.

Tezin amacı 7,5 kW’lık bir asenkron motorda enerji verimliliği sağlayacak bir motor sürücüsü tasarlamak ve simülasyonunu yapmaktır. Önerilen motor sürücüsünün performansı ve sağladığı verimlilik değerleri PSCAD programında farklı simülasyon çalışmalarıyla incelenmiştir.

Anahtar Kelimeler: Asenkron Motor Kontrol, Akı Kontrol, Enerji Verimliliği,

Stator Gerilim Kontrol

III

ACKNOWLEDGEMENTS

The subject of this thesis was suggested by my supervisor, Prof. Dr. Mehmet

Tümay to whom I would like to express my heartfelt thanks for his supervision,

guidance, encouragements and extremely useful suggestions throughout this thesis. It

has been a great honor to have Prof. Dr. Mehmet Tümay as supervisor.

I would like to thank Assoc. Prof. Dr. K. Çağatay Bayındır for his extremely

valuable guidance and suggestions throughout in the thesis. His technical assistance

and personal advice have been invaluable.

I would also like to thank you to Asst. Prof. Dr. M. Uğraş Cuma for his

valuable advices and strong technical support. Their continuous support helped me to

finish this thesis.

I wish to thank Institute of Natural and Applied Science for financial supports

during the preparation of this thesis. I wish also to extend my acknowledgements to

all staff members in the department and my friends.

Finally, I would like to thank my family for their endless support and

encouragements.


IV

CONTENTS PAGE

ABSTRACT ............................................................................................................. I

ÖZ ........................................................................................................................................... II

ACKNOWLEDGEMENTS ............................................................................................... III

CONTENTS ......................................................................................................................... IV

LIST OF TABLES ............................................................................................................... VI

LIST OF FIGURES .......................................................................................................... VIII

LIST OF SYMBOLS........................................................................................................ XIV

LIST OF ABBREVATIONS .......................................................................................... XVI

1. INTRODUCTION ............................................................................................................. 1

1.1. Outline of Dissertation............................................................................................... 2

2. LITERATURE REVIEW ................................................................................................. 3

2.1. Introduction ................................................................................................... 3

2.2. Conclusion of Literature Review ................................................................. 25

3. ENERGY EFFICIENCY METHODS ON MOTORS ............................................... 27

3.1. Motor Starting Methods .......................................................................................... 28

3.1.1. Direct-On-Line Starting Method ......................................................... 29

3.1.2. Shunt Capacitance Method ................................................................. 30

3.1.3. Delta-Wye Starting Method ................................................................ 30

3.1.4. Autotransformer Method .................................................................... 31

3.1.5. Series Resistance or Reactance Method .............................................. 32

3.1.6. Soft Starter Method ............................................................................ 33

3.1.7. Variable Frequency Drive ................................................................... 34

3.2. High Efficiency Induction Motors ............................................................... 37

4. MOTOR DRIVER MODELING .................................................................................. 39

4.1. Mathematical Modeling............................................................................... 40

4.2. Power Circuit Model ................................................................................... 44

4.2.1. Motor Parameters .............................................................................. 45

4.2.2.Thyristor Block Design ...................................................................... 50

4.3. Proposed Fuzzy Logic Based Control System .............................................. 51

V

4.3.1. Torque Estimator Block Design ......................................................... 52

4.3.2. Firing Angle Generation Block Design .............................................. 53

4.3.3. FL Based Firing Angle Optimization Block Design ........................... 55

4.3.3.1. Fuzzy Logic System ................................................................ 55

4.3.3.1.(1). Rules ...................................................................... 56

4.3.3.1.(2). Fuzzy Inference Engine .......................................... 57

4.3.3.1.(3). Fuzzification ........................................................... 58

4.3.3.1.(4). Defuzzification ....................................................... 58

4.3.3.2. Fuzzy Logic Controller Modeling ........................................... 60

4.3.3.2.(1). Fuzzification ........................................................... 61

4.3.3.2.(2). Decision Making ..................................................... 66

4.3.3.2.(3). Defuzzification ....................................................... 70

4.4. Over and Under Voltage Protection Unit ..................................................... 73

4.5. Over Current Protection Unit ....................................................................... 74

5. SIMULATION RESULTS ............................................................................................. 75

5.1. Proposed PSCAD Model and System Parameters ........................................ 75

5.2. Case 1: No Load .......................................................................................... 77

5.3. Case 2: 10% Loaded .................................................................................... 83

5.4. Case 3: 20% Loaded .................................................................................... 88

5.5. Case 4: 30% Loaded .................................................................................... 94

5.6. Case 5: 40% Loaded .................................................................................. 100

5.7. Case 6: 50% Loaded .................................................................................. 106

5.8. Case 7: Dynamic Performance .................................................................. 112

5.9. Case 8: Short Circuit Performance ............................................................. 114

6. CONCLUSION .............................................................................................................. 117

REFERENCES ................................................................................................................... 123

BIOGRAPHY ..................................................................................................................... 129

VI

LIST OF TABLES PAGE

Table 2.1. Measure Data (Hui et al., 2005) ............................................................ 13

Table 4.1. Codes of Torque Estimator Block ......................................................... 52

Table 4.2. Codes of Firing Angle Generation Block .............................................. 54

Table 4.3. Linguistic terms for the error, erate and Δα ........................................... 62

Table 4.4. Input Ranges of error and error_rate ..................................................... 64

Table 4.5. Membership Function Codes of error ................................................... 64

Table 4.6. Membership Function Codes of error_rate ............................................ 65

Table 4.7. Rule Base for the FL Controller ............................................................ 66

Table 4.8. FL Controller Decision Rules ............................................................... 68

Table 4.9. FL Rule Base Fuzzy Logic Controller .................................................. 69

Table 4.10. Fuzzy Decision Rule Table of Example ................................................ 70

Table 5.1. System Parameters ............................................................................... 75

Table 6.1. FL Power and Power Factor Efficiency .............................................. 121

VIII

LIST OF FIGURES PAGE

Figure 2.1. Iron Losses, Stator and Rotor Copper Losses and Total Losses for 20

HP motor running at 20 % load factor under variable voltages (Pitis

et al., 2008) ......................................................................................... 4

Figure 2.2. Power consumption of capacitor-start, capacitor-run, 2-hp single-

phase induction motor with and without controller (Fuchs et al.,

2002).................................................................................................... 5

Figure 2.3. Power factor (PFcontr) of controller and displacement factor

(DFmot) of capacitor-start, capacitor-run, 2-hp single-phase

induction motor with (PFcontr) and without (DFmot) controller

(Fuchs et al., 2002) ............................................................................... 6

Figure 2.4. Power consumption of 7.5-hp three-phase Y -connected three-phase

motor with and without controller (Fuchs et al., 2002) .......................... 6

Figure 2.5. Power factor of 7.5-hp three-phase Y -connected three-phase motor

with and without controller (Fuchs et al., 2002) .................................... 6

Figure 2.6. AC Voltage Controller Under Induction Motor Load (Eltamaly et al.,

2007).................................................................................................... 8

Figure 2.7. Controlling of Three-Phase Induction Motor Using MPAC and EAC

Strategies (Eltamaly et al., 2007) .......................................................... 8

Figure 2.8. Triac in Series for Induction Motor Voltage Control (Benbouzid et

al., 1996) ............................................................................................ 10

Figure 2.9. Voltage and Current Waveforms of Power Circuit (Benbouzid et al.,

1996).................................................................................................. 11

Figure 2.10. Thyristor Power Test Circuits (Paice, 1968) ....................................... 14

Figure 2.11. Matlab/Simulink Simulation Model Used to Generate Data for ANN

Training (Gastli et al., 2005) .............................................................. 17

Figure 2.12. Torque Versus Speed Characteristics for Different Values of The

Thyristors Firing Angle (Gastli et al., 2005) ....................................... 17

Figure 2.13. Structure of ANFIS AC Voltage Controller (Ayyub, 2006) ................ 19

Figure 2.14. Simulation Model with ANFIS Controller (Ayyub, 2006) .................. 20

IX

Figure 2.15. Proposed ANFIS Based Soft Starter Strategy (Rajaji et al., 2008) ....... 22

Figure 2.16. The Schematic Block Diagram of Soft Starting System with Fuzzy

Adaptive Control (Dayong et al., 2011) .............................................. 23

Figure 3.1. Direct-On-Line Starter Circuit Diagram ............................................. 29

Figure 3.2. Soft Starter Circuit Diagram ............................................................... 34

Figure 3.3. VFD Circuit Diagram (Mistry et al., 2012) ......................................... 35

Figure 4.1. Equivalent Circuit of an Induction Motor ........................................... 41

Figure 4.2. Power Flow Diagram of an Induction Motor ...................................... 41

Figure 4.3. Typical Waveforms of Single-Phase Stator Voltage Controller with

an R-L Load. ...................................................................................... 44

Figure 4.4. Power Diagram of Proposed Motor Drive .......................................... 45

Figure 4.5. Squirrel Cage Induction Motor Model ................................................ 45

Figure 4.6. Squirrel Cage Induction Motor Parameter Setting .............................. 46

Figure 4.7. Modeled Motor a) Active Power, b) Reactive Power .......................... 47

Figure 4.8. Modeled Motor Phase a) Voltage Waveform b) Current Waveform.... 48

Figure 4.9. Modeled Motor a) No-Load Speed b) Full-Load Speed ...................... 49

Figure 4.10. Symmetrical AC Voltage Controller of Driver ................................... 50

Figure 4.11. Firing Sequence of Thyristors ............................................................ 51

Figure 4.12. The Whole of Proposed Fuzzy Logic Based Control System .............. 51

Figure 4.13. Firing Angle Generation Block .......................................................... 52

Figure 4.14. Firing Angle Generation Block .......................................................... 53

Figure 4.15. Torque versus Firing Angle Characteristics. ....................................... 54

Figure 4.16. Fuzzy Logic System ........................................................................... 56

Figure 4.17. Max-min Inference (Cuma, 2006) ...................................................... 58

Figure 4.18. Proposed FL Control System Block Diagram ..................................... 61

Figure 4.19. Proposed FL Control System Block Diagram of PSCAD ................... 61

Figure 4.20. Membership Function of Input 1 - error ............................................. 62

Figure 4.21. Membership Function of Input 2 - error_rate ...................................... 62

Figure 4.22. Input of error Signal ........................................................................... 69

Figure 4.23. Input of error_rate Signal ................................................................... 70

Figure 4.24. Firing Pulse Generation Block ........................................................... 72

X

Figure 4.25. Firing Pulses for Each Thyristors ....................................................... 72

Figure 4.26. Over Voltage Protection Unit ............................................................. 73

Figure 4.27. Under Voltage Protection Unit ........................................................... 74

Figure 4.28. Over Current Protection Unit ............................................................. 74

Figure 5.1. Circuit Diagram of Motor Driver ........................................................ 76

Figure 5.2. Motor Load Voltage With_Driver ...................................................... 77

Figure 5.3. Motor Load Voltage Without_Driver ................................................. 77

Figure 5.4. Motor Load Current With_Driver ....................................................... 78

Figure 5.5. Motor Load Current With_Driver in Large Scale ............................... 78

Figure 5.6. Motor Load Current Without_Driver .................................................. 79

Figure 5.7. Motor Input Power With_Driver ........................................................ 79

Figure 5.8. Motor Input Power Without_Driver ................................................... 80

Figure 5.9. Motor Input Reactive Power With_Driver .......................................... 80

Figure 5.10. Motor Input Reactive Power Without_Driver ..................................... 81

Figure 5.11. Speed of Motor With_Driver .............................................................. 82

Figure 5.12. Speed of Motor Without_Driver ......................................................... 82
















XI


















Figure 5.45. Speed of Motor Without_Driver ....................................................... 100

Figure 5.46. Motor Load Voltage With_Driver .................................................... 101

Figure 5.47. Motor Load Voltage Without_Driver ............................................... 101

Figure 5.48. Motor Load Current With_Driver ..................................................... 102

Figure 5.49. Motor Load Current With_Driver in Large Scale ............................. 102

Figure 5.50. Motor Load Current Without_Driver ................................................ 103

Figure 5.51. Motor Input Power With_Driver ...................................................... 103

Figure 5.52. Motor Input Power Without_Driver ................................................. 104

Figure 5.53. Motor Input Reactive Power With_Driver ........................................ 104

Figure 5.54. Motor Input Reactive Power Without_Driver ................................... 105

Figure 5.55. Speed of Motor With_Driver ............................................................ 105


Figure 5.57. Motor Load Voltage With_Driver .................................................... 107

Figure 5.58. Motor Load Voltage Without_Driver ............................................... 107

XII

Figure 5.59. Motor Load Current With_Driver ..................................................... 108

Figure 5.60. Motor Load Current With_Driver in Large Scale ............................. 108

Figure 5.61. Motor Load Current Without_Driver ................................................ 109

Figure 5.62. Motor Input Power With_Driver ...................................................... 109

Figure 5.63. Motor Input Power Without_Driver ................................................. 110

Figure 5.64. Motor Input Reactive Power With_Driver ........................................ 110

Figure 5.65. Motor Input Reactive Power Without_Driver ................................... 111

Figure 5.66. Speed of Motor With_Driver ............................................................ 111


Figure 5.68. Dynamic Performance With_Driver ................................................. 113

Figure 5.69. Dynamic Performance Without_Driver ............................................ 113

Figure 5.70. Comparison of Power Consumption in Dynamic Performance ......... 114




Figure 6.1. Comparison of Load Voltage with and without Driver ..................... 118

Figure 6.2. Comparison of Load Current with and without Driver ...................... 119

Figure 6.3. Comparison of Load Current with and without Driver ...................... 119

Figure 6.4. Comparison of Motor Speeds with and without Driver ..................... 120

Figure 6.5. Comparison of Power Consumptions with and without Driver.......... 120

Figure 6.6. Comparison of Power Factors with and without Driver .................... 121

XIV

LIST OF SYMBOLS

α : Firing Angle

φ : Phase Angle

θ : Conduction Angle

γ : Holdoff Angle

BRK : Breaker

Eload : Load Voltage

f : Fundamental Supply Frequency

IA : Phase A Current

IA_RM : Phase A RMS Current

IB : Phase B Current

IB_RMS : Phase B RMS Current

IC : Phase C Current

IC_RMS : Phase C RMS Current

Iload : Load Current

Iref : References Current

Pcore : Stator Core Loss

PF&W : Friction and Windage Losses

Pin : Input Power

Pmisc : Stray Losses

Pout : Output Power

Pr_core : Rotor Core Loss

PRCL : Rotor Copper Loss

PSCL : Stator Copper Loss

Qin : Input Reactive Power

R1 : Stator Resistance

R2 : Rotor Resistance

S : Select

Speed : Motor Speed

T : Torque

XV

t : Time

Te : Electrical Torque

TL : Load Torque

Tm : Mechanical Torque

tn : Time Instant

Tref : Reference Torque

Trq : Electrical Torque of Motor

TrqRMS : RMS Value of Torque

U : Voltage

VA : Phase A Voltage

VA_RMS : Phase A RMS Voltage

VB : Phase B Voltage

VB_RMS : Phase B RMS Voltage

VC : Phase C Voltage

VC_RMS : Phase C RMS Voltage

Vref : References Voltage

w : Angular Frequency

wm : Motor Speed

wref : Reference Speed

XVI

LIST OF ABBREVATIONS

AC : Alternative Current

ANFIS : Adaptive Neuro Inference System

ANN : Artifical Neural Network

ASD : Adjustable Speed Drive

BN : Big Negative

BP : Big Positive

COG : Centre of Gravity

DC : Direct Current

DOL : Direct On-Line

DS : Decision

EAC : Extinction Angle Control

FL : Fuzzy Logic

FLBEC : Fuzzy Logic Based Efficiency Controller

FLC : Fuzzy Logic Control

FP : Firing Pulse

HM : Height Method

HP : Horse Power

IGBT : Insulated Gate Bipolar Transistor

IM : Induction Machine

LD : Large Decrease

LI : Large Increase

MD : Medium Decrease

Mem.V. : Membership Value

MFs : Membership Function

MI : Medium Increase

MN : Medium Negative

MOM : Mean of Maximum

MP : Medium Positive

MPAC : Modified Phase Angle Control

XVII

N : Neutral

PAC : Phase Angle Control

PF : Power Factor

PSCAD/EMTDC : Power System Computer Aided Design /

Electromagnetic Transient DC Program

PSIM : Power Simulation

PWM : Pulse Width Modulation

SCR : Silicon Controlled Rectifier

SD : Small Decrease

SI : Small Increase

SN : Small Negative

SP : Small Positive

TEFC : Totally Enclosed, Fan Cooled

THD : Total Harmonic Distortion

VFD : Variable Frequency Drive

VVCF : Variable Voltage-Constant Frequency

Z : Zero

1. INTRODUCTION Murat Mustafa SAVRUN

1

1. INTRODUCTION

In globalizing world, the energy consumption is increasing at an exponential

rate due to the exponential growth of world population, industrial developments and

the world economy. In parallel with this rapidly increase in energy consumption, the

fossil energy sources will be exhausted in the near future that has been revealed by

scientific studies. Considering these situations, the world has to seek alternative

solutions. In Turkey, as in many countries of the world, the importance given to the

use of renewable energy sources and energy efficiency studies is increasing day by

day. Within this operation, new policies and regulations are determined. Incentive

mechanisms are created for the use of renewable energy sources and the legal

changes are being made for the energy efficiency studies. The world aims to use the

clean energy as more efficient.

The industrial sector is the largest users of energy around the world.

Industrial motor uses a major fraction of total industrial energy uses (Saidur, 2010).

The induction motor is very popular in industrial production due to its simple

structure, reliability, low cost, convenient maintenance and long life (Li, Liu 2009).

So, these devices are widely used as an electric drives in various industrial

applications, such as pumps, fans, compressors, conveyors, spindles, to name just a

few, as well as in main powered home appliances (Patil et al., 2009).

Due to the vast majority of the energy is consumed by the electric machines

in industrial organizations; as a result of work to be done on the efficiency of electric

motors, large reductions in energy consumption values can be established. In this

context, many efficiency studies were carried out for many years. These efficiency

methods are variety starting methods, motor drive methods, use of high efficient

motors etc.

As a result, the purpose of this study is to design a motor driver that provide

energy efficiency in induction motors and is to simulate in PSCAD software.

1. INTRODUCTION Murat Mustafa SAVRUN

2

1.1. Outline of Dissertation

In Chapter 2, previous studies are mentioned about thyristor controlled motor

drivers which are studied by different researchers. Also, suitable models are prepared

for comparison with available studies in the literature.

In Chapter 3, energy efficiency methods which are used nowadays are

explained.

In Chapter 4, the power circuit configuration and control methods of driver

are described in detail. Fuzzy logic based firing angle determination and protection

units are also elaborated.

In Chapter 5, simulation results for different case studies are given and

evaluated. In these cases, the performance of driver for different loads is analyzed;

dynamic response of driver during variable load change is also investigated.

In Chapter 6, the important conclusions of the study and author’s

recommendations for future work are explained.

Finally, related references used in the thesis and biographical information of

the author are presented.

2. LITERATURE REVIEW Murat Mustafa SAVRUN

3

2. LITERATURE REVIEW

2.1. Introduction

Different studies have been made and are available in the literature about

motor power efficiency control. Also, the subject of stator voltage control by using

thyristors is not investigated directly in some studies. However, it is known that

stator voltage control is an important factor for the energy efficiency in induction

motors. Hence, these studies are also mentioned in the literature review. Commonly,

numerical and experimental studies are made.

Pitis et al. (2008) investigated power saving obtained from supply voltage

variation on squirrel cage induction motors. They have prepared a practical guidance

about the nature of design and application engineering trade-offs that may be used to

optimize motor efficiency to match a low load output shaft power condition and

thereby achieve power savings by reducing the losses in squirrel cage induction

motors. The study was done using a typical 20 hp (15kW), TEFC (totally enclosed,

fan cooled), 4 poles, 480 V, energy-efficient motor. This motor type is a common

squirrel cage induction motor used in various industry applications and sometimes

found operating at low loads for long periods of time. A squirrel cage induction

motor design program was used to simulate the motor performance characteristics for

low values of motor loading under various supply voltages.

After the simulation studies, the results indicate that the total power losses are

decreasing with load and reach a minimum value by reducing the supply voltage.

When reducing the supply voltage even further, the total losses increase again due to

the effect of increasing copper losses. This parabolic characteristic is shown in

Figure 2.1.

Power savings can be obtained as a result of voltage supply variation on low

voltage squirrel cage induction motors. Motor performance simulations were

conducted on a 20 hp general purpose energy-efficient motor at various motor shaft

loads and under various power supply voltages using specialized motor design

software. Minimum power losses for motors operating under low loads can be


4

achieved by manipulating the motor’s magnetic loads, which traditionally are

considered constant. The study found that for this particular motor design, power

savings of up to 2% and up to 20% of the total input power at motor loadings of 0.5

and 0.1 respectively can be achieved by varying the supply voltage. The findings of

this study suggest a method of evaluating possible power savings for any SCIM prior

to installing such a control device.

Figure 2.1. Iron Losses, Stator and Rotor Copper Losses and Total Losses for 20 HP

motor running at 20 % load factor under variable voltages (Pitis et al., 2008)

Blaabjerg et al. (1997) studied and described results of an experimental

evaluation of seven commercial soft starters used with three motors of different

power ratings. Several performance indicators have been measured and compared to

illustrate operation of soft-starters and assess their energy-saving capabilities. They

emphasize that the squirrel-cage induction motors can be equipped with power

electronic soft-starters that alleviate the starting stresses and improve efficiency of

the drive system with light loads. With motor loads higher than 50% of the rated

load, systems with soft-starters tend to have the efficiency reduced in comparison

with that of the motor alone. The highest low-load efficiency gains are obtained in

small-size motors. Soft-starters improve the input power factor. Soft-starters reduce

the stator current and developed torque at starting, allowing reduction of the power


5

rating of the supply system and increasing life expectancy of the drive. The drop in

motor speed due to soft-starters is not significant. Energy savings affected by soft-

starters are small and the simple payback periods are long. Therefore, the decision on

use of soft starters should not be based on the anticipated energy cost savings alone.

Fuchs et al. (2002) studied efficiency improvement of induction motor with

thyristor controller. The contribution of their paper is the integrated treatment of

load, induction motor, controller, and (pole) transformer. This leads to the following

important conclusions. Thyristor controllers directly or indirectly sensing the torque

of an induction motor can significantly reduce power consumption below half-rated

load (see in Figures 2.2., 2.3., 2.4., 2.5.). However, power savings disappear at above

half-rated load, and the drive losses increase because of the inherent losses of

controllers. Savings are more pronounced for single-phase installations than for

three-phase motors since the percentage loss of single-phase motors is higher than

those of their three-phase counterparts. Some of the controllers employ a “soft-start”

feature that provides for ramping up the voltage when starting, thereby reducing the

peak starting current and eliminating the necessity for additional reduced-voltage or

mechanical starters.

Figure 2.2. Power consumption of capacitor-start, capacitor-run, 2-hp single-phase

induction motor with and without controller (Fuchs et al., 2002)


6

Figure 2.3. Power factor (PFcontr) of controller and displacement factor (DFmot) of

capacitor-start, capacitor-run, 2-hp single-phase induction motor with (PFcontr) and without (DFmot) controller (Fuchs et al., 2002)

Figure 2.4. Power consumption of 7.5-hp three-phase Y -connected three-phase

motor with and without controller (Fuchs et al., 2002)

Figure 2.5. Power factor of 7.5-hp three-phase Y -connected three-phase motor with

and without controller (Fuchs et al., 2002)


7

Mohan (1980) investigated that improvement in energy efficiency of

induction motors by means of voltage control. His paper shows that in a lightly

loaded induction machine, a large amount of energy is wasted which can be

conserved by voltage control. By way of tests on a 1/3 hp motor, where the voltage is

controlled by actual circuits, the following conclusions are arrived at: At no-load, the

voltage control can reduce the power consumption by as much as 25% of the rated

full-load output. Power savings are possible at all loads. Savings decrease with the

increasing load on the motor. Except at absolutely no-load, more than 50% reduction

in voltage tends to stall the motor. This could be treated as the limit on voltage

reduction though a smaller value may be adequate depending on the motor load

cycle. During loaded conditions, the reduction in voltage beyond a certain value

causes the efficiency to decrease and the motor current to increase. The motor

efficiency and current seem to reach their extreme at the same value of motor

voltage. The power factor keeps on improving with the voltage reduction. This

suggests that the motor current could be used as the control signal for adjusting the

motor voltage. At moderate loads, operation at optimum voltage also results in a

substantial improvement in power factor.

Eltamaly et al. (2007) have studied three voltage control strategies for three-

phase ac voltage regulator. These strategies depend on varying the stator ac voltage

to control the speed of three-phase induction motor. These strategies are phase angle

control (PAC), extinction angle control (EAC), and modified phase angle control

(MPAC). The first control strategy is carried out using three back-to-back thyristors

connected in series with motor terminals (see in Figure 2.6.). The other two

techniques are used with converter having six bidirectional switches (see in Figure

2.7.). So, the essence of this paper is to evaluate the performance of three-phase

induction motor controlled by thyristors converter in phase angle control, (PAC), by

new power semiconductor switches in modified phase angle control, (MPAC), and

extinction angle control (EAC) at different operating conditions.


8

Figure 2.6. AC Voltage Controller Under Induction Motor Load (Eltamaly et al.,

2007)

Figure 2.7. Controlling of Three-Phase Induction Motor Using MPAC and EAC

Strategies (Eltamaly et al., 2007)

Three control strategies have been analyzed for speed control of three-phase

induction motors. The simulation results for the different control strategies have been

carried out using PSIM software. The simulation results reveal that all switching

strategies give high level of harmonics in supply and motor currents. The motor

speed variation with motor voltage is same in MPAC and EAC but slightly different

in PAC control strategies. The efficiency of PAC is the highest in high speed and

drops down the efficiencies of the other two techniques in the lower speeds. EAC

control strategy gives better power factor than the other two techniques. MPAC has

the best THD in the supply and motor currents.


9

Alolah et al. (2006) have analyzed and simulated a stator voltage control of

three phase induction motor by using thyristors. The stator voltage has been

controlled by phase angle control of three-phase supply. The effect of harmonic

distortion in the line voltage and current have been studied and shown. The study

reveals that, this method is suitable for applications requiring a low torque and low

speeds. The power factor, efficiency and torque capability of the motor drop

dramatically for lower voltages. Firing requirements and limits of control have also

been studied. The speed control of the induction motor is achieved by controlling the

firing angle. The relation between the speed of the motor and the firing angle

depends on the mode of operation. So, the speed control system has to identify the

mode of operation to send a correct value of firing angle to switches otherwise the

system will get out of control.

Kai et al. (2005) has aimed at the variable-voltage controlled induction motor,

analyzed the steady-state performance of variable-voltage thyristor controlled

induction motor system, and presented the mathematic model and circuit model of

such system. Power factor, as a significant variable to measure the efficiency of

motor operation, is particularly studied in the paper, and the control strategy based on

the studies of power factor and slip is given. The simulation curves and real waves

are shown in the result. Finally, the transient performance of induction motor is

mentioned. The analysis and results reveal that such system based on the special

arithmetic is available.

Representation of a non-sinusoidal voltage supplied induction motor has now

been fully developed. The analysis of thyristor controlled induction motors has tuned

to the tools of mathematics deduction, circuit calculation and simulation. It remains

to be a widely used method of variable voltage-constant frequency (VVCF) to a mass

of industry applications. For example, such driven system can be utilized to control

the light loaded motors, to be used as a soft-starter and to be a voltage regulator.

Benbouzid et al. (1996) investigated single phase capacitor motor efficiency

improvement by means of voltage control. The purpose of their paper is to present a

comprehensive study on the optimization of the energy consumption of induction

motors through a power factor control system. Indeed, minimum input power and


10

maximum efficiency occur at a characteristic optimum power factor value which can

be calculated for any induction motor. Efficiency is shown to be independent of

output power when variable voltage controller reduces the voltage approximately as

the square root of the load torque to maintain the required power factor during partial

loading condition. Experimental results, in the case of a single-phase capacitor

motor, have demonstrated that the use of such type of system has significant energy

conservation potential. In this study, the basic concept of voltage control is

experimentally explored using the motor power factor as the primary independent

control variable. This has the advantage of greatly simplifying the concept of voltage

control and provides a useful viewpoint for comparing various possible strategies

using secondary variables (such as current or slip) to control the motor voltage.

Simply stated, efficiency improvement by voltage control is achieved by

reducing the applied voltage whenever the torque requirement of the load can be met

with less than full motor flux (see in Figure 2.8. and Figure 2.9.). The reduced motor

flux results in reduced core loss and also in reduced stator copper loss, since the

magnetizing component of stator current is reduced.

Figure 2.8. Triac in Series for Induction Motor Voltage Control (Benbouzid et al.,

1996)


11

Figure 2.9. Voltage and Current Waveforms of Power Circuit (Benbouzid et al.,

1996)

The control algorithm determines the phase delay angle φ by marking the

instant of a voltage zero crossing and measuring the interval of time until the next

current zero crossing. Assume initially that α has been adjusted such that the current

waveform is sinusoidal. As the load on the motor decreases, the motor appears as a

more inductive load and harmonics are introduced into the current waveform. The

controller responds by decreasing α, causing a decrease in the applied voltage

fundamental component. The overall effect is that the motor appears as a less

reactive load than when the load first decreased. The controller continues to decrease

the firing delay angle α. A similar but reverse motor effect and control response

occurs for increases of the load up to a load corresponding to α equal zero. A firing

delay angle equal to zero signifies that full supply voltage has been applied to the

motor. The controller now simply sends gate pulse to the triac so as to allow full

conduction.

The presented energy-optimal control strategy for a single-phase capacitor

motor brings on substantial energy savings, particularly at low loads. The approach

suggested in this paper is sufficiently general that the same technique can be


12

extended to three-phase motor and applied to many motor drives, particularly in

industrial and commercial applications which include substantial periods at light

loads.

Hui et al. (2005) are focused on the effect of the input voltage on the motor's

energy loss and power factor in 3-phase ac voltage regulation circuit, which is

regulated by the control angle of the SCR. Simulation and experiment results are

given. It is proved that using this simple voltage regulation circuit can adjust the

motor's terminal voltage, satisfy the requirement of soft-starting, and simultaneously

save energy by using a proper algorithm (constant power factor algorithm to control

the firing angle of SCR).

According to the simulation results, they can deduce that when firing angle

increases, the stator phase voltage will decrease, and the power factor cosф of the

system will increase. Comparing the simulation results with the experiment results,

the conclusion can be drawn that the waves are similar. When the voltage varies, the

motor's energy loss and the power factor cosф vary in accordance with the theoretical

analysis, proving the correctness and reliability of the theoretical analysis and

simulation.

In their experimental work, a 20 kW motor is controlled on condition of

constant power factor, the measured data and calculation results are shown in Table

2.1. It shows that, when controlled by a 3-phase ac voltage regulation circuit, on

condition of constant output power, the 3-phase ac motor can get higher power factor

and efficiency. For example, when output power is 3.5 kW, after using 3-phase ac

voltage regulation circuit, the input power of the motor falls from 6.6 kW to 4.1 kW,

saving 37.9% of the input energy. The effect is obvious.


13

Table 2.1. Measure Data (Hui et al., 2005)

Output

Power

(kW)

Without 3-phase AC voltage

regulation circuit

With 3-phase AC voltage

regulation circuit

U (V) I (A) PF

Input

power

(kW)

U (V) I (A) PF

Input

power

(kW)

8 380 22 0.78 12 375 21 0.8 11

7.2 380 21 0.76 10.9 370 20 0.8 9.8

6.1 380 19 0.73 9.4 350 19 0.8 9.2

5.5 380 19 0.7 9.1 340 16 0.8 7.5

4.9 380 19 0.64 8.3 330 14 0.8 6.4

4.1 380 18 0.64 7.8 320 12 0.8 5.3

4 380 16 0.63 6.8 305 11 0.8 4.6

3.5 380 16 0.61 6.6 295 10 0.8 4.1

Paice (1968) investigated induction motor speed control by stator voltage

control. His paper establishes the fundamental laws relating to the speed control of

induction motors by simple voltage control and emphasizes the problems that may be

caused by excessive input currents which cause stator overheating. Eight different

thyristor voltage control circuits have been tested to determine a best control circuit

for three-phase motors and test results are given. A practical speed control for a 4-hp

motor is described and the way in which a special rotor design can minimize the

problem of excessive stator losses is convincingly demonstrated. Test results are

given in Figure 2.10.


14

Figure 2.10. Thyristor Power Test Circuits (Paice, 1968)

The tests indicated that Test Circuit 7 represented the best control approach

as far as the motor performance was concerned and the rotor power requirements

were not greatly different from the sine wave tests. Since the input current is not

significantly increased over the sine wave control, this thyristor circuit does not

unduly increase the stator loss.

Rowan et al. (1983) investigated induction motor performance improvement

by SCR voltage control. Minimum input power and maximum efficiency operation

occur at characteristic slip values which can be realized for any induction motor


15

operating at partial load by properly adjusting the amplitude of the applied stator

terminal voltages. These two criteria are shown to yield perceptibly different results

when the motor is driven from a silicon-controlled rectifier (SCR) voltage controller.

In addition, it is demonstrated that a constant power factor controller results in an

operating regime which is substantially poorer than operation at either minimum

input power or maximum efficiency. It is further shown that minimum stator current

and minimum power factor angle criteria yield results which are closer to the ideal

than the constant power factor controller.

The goal of their paper is to quantify the efficiency improvements that can be

realized with practical voltage controllers and to compare these results to the ideal

sine wave case. In particular, the effects of practical system behavior were

investigated which specifically include the effects of the magnetizing inductance, the

core loss, the stray no-load loss, the harmonics caused by the phase back, and the

silicon-controlled rectifier (SCR) conduction loss nonlinearities. Optimal values of

the thyristor delay angle were calculated and compared to the results obtained using a

conventional constant power factor control scheme. The potential of possible

alternative control schemes were discussed. Duty cycle curves were presented for a

specific machine. These duty cycle curves are restricted to the motor studied.

However, they are representative of the type of curves which would enable the

application engineer to attain a reasonable understanding of the potential energy

savings that can be realized for a specific application. The approach suggested in this

paper is sufficiently general that the same technique can be applied to many motor

drive applications.

Partial-load efficiency improvement of induction motors by controlling stator

voltage has been examined quantitatively. The analysis has included many practical

considerations such as motor and SCR nonlinearities. The results of this paper

indicate that energy savings will be difficult with such devices unless the motor is

operated essentially unloaded for significant periods of time. Although easy to

implement, the constant power factor controller does not result in an optimally

controlled motor, and algorithms which minimize power factor angle or stator power

appear to have substantial benefits over the constant power factor controller.


16

Gastli et al. (2005) have analyzed and simulated ANN-based soft starting of

voltage-controlled-fed induction motor drive system. Soft starters are used as

induction motor controllers in compressors, blowers, fans, pumps, mixers, crushers

and grinders, and many other applications. Soft starters use ac voltage controllers to

start the induction motor and to adjust its speed. This paper presents a novel artifical

neural network (ANN)-based ac voltage controller which generates the appropriate

thyristors’ firing angle for any given operating torque and speed of the motor and the

load. An ANN model was designed for that purpose. The results obtained are very

satisfactory and promising. The advantage of such a controller are its simplicity,

stability, and high accuracy compared to conventional mathematical calculation of

the firing angle which is a very complex and time consuming task especially in

online control applications.

Their paper proposes an artifical neural network (ANN)-based selection of

the thyristors firing angles of a voltage-controlled-fed IM drive system. The

controller operates in open loop and does not require any speed or voltage sensing.

The only sensor that is needed is a current sensor, which in most of applications is

used to protect the converter and the motor from over currents. The soft starter is

designed to meet the industrial requirements of compressors, blowers, fans, pumps,

mixers, crushers and grinders, etc.

A soft-starter-fed three-phase induction motor was modeled and simulated

using Matlab/Simulink Power system blocksets as shown in Figure 2.11. The

asynchronous motor and all power electronics switches were modeled according to

their operating characteristics.

The ANN model, used for the calculation of the appropriate thyristors’ firing

angle (α) as a function of the motor speed (ωm) and torque (Тe), has two input

variables (ωm and Тe) and one output variable (α) (see in Figure 2.12.).


17

Figure 2.11. Matlab/Simulink Simulation Model Used to Generate Data for ANN

Training (Gastli et al., 2005)

Figure 2.12. Torque Versus Speed Characteristics for Different Values of The

Thyristors Firing Angle (Gastli et al., 2005)

In this paper, a novel method for controlling soft-starter-fed induction-motor-

drive systems using ANN is introduced. The method consists of training a two-layer

ANN model on a set of data generated by simulation or experiments. The generated

data are the speed and torque patterns as inputs and their corresponding firing angle

patterns as output of the ANN model. The ANN model was trained successfully and


18

the results of comparison between the actual data and the ANN calculated data were

very satisfactory. To validate the effectiveness of the proposed soft starter control

scheme, an induction motor fan drive system, fed by the proposed soft starter, was

implemented by software program and hardware experimental setup. Several

simulations and experiments were carried out for different operating conditions and

the results were very satisfactory. Thus, the ANN approach has resolved the problem

of the complexity of the online determination of the appropriate thyristor firing angle

for any operating condition. It is also important to note that the controller operates in

open loop which has the advantage of being stable and does not require any speed, or

voltage sensing. The proposed soft starter is designed to meet the industrial

requirements of compressors, blowers, fans, pumps, mixers, crushers and grinders,

etc.

Ayyub (2006) have analyzed and simulated ANFIS based soft-starting and

speed control of ac voltage controller fed induction motor. An intelligent ac voltage

controller is proposed for the control of induction motor. It controls the motor speed

by adjusting the firing angles of the thyristors. Adaptive neuro fuzzy inference

system (ANFIS) based controller was designed for open-loop sensor less control. In

addition to simplicity, stability, and high accuracy such controller gives soft starting.

It is suitable to control induction motor as a soft starter and speed regulator in

compressors, blowers, fans, pumps, and many other applications. This paper aims at

applying ANFIS for selecting the thyristors firing angles of a voltage-controller

feeding the induction motor. The proposed controller operates in open loop without

any requirement of speed or voltage sensing. High accuracy was achieved with the

proposed controller and is simple.

The adaptive neuro fuzzy inference system is a fuzzy system and used in

classification, modeling and control problems. It is based on Takagi and Sugeno’s

fuzzy if-then rules, which is different from commonly used fuzzy logic controllers.

The consequent part of the rule is a function of input variables. The system

considered in this paper has two inputs as the load torque (T) and the desired motor

speed (ωm), and the firing angle (α) is the only output (see in Figure 2.13.). The

inference mechanism of ANFIS is mathematically expressed by the set of rules.


19

These rules are generated through the experience of operating the system, which may

be feedback from the plant operator, design engineer, or the expert.

Figure 2.13. Structure of ANFIS AC Voltage Controller (Ayyub, 2006)

The input was the reference speed ( ref ω ) and simulation studies for motor

starting, braking and speed control were carried. The reference torque (T ref) was

generated from the reference speed and the load model (2). These two references (ref

ω and Tref) were the input to the ANFIS controller. The output from the controller is

the firing delay angle α. The triggering logic and gate pulse generator block (it is

indicated as Pulses block in the diagram) generated the required gating patterns for

the thyristors as in Figure 2.14. The ac voltage controller described above can be

used for soft starting, in addition for speed control. Soft starting can be accomplished

by manipulating the speed reference signal in such a way that the stress on the motor

is minimized.


20

Figure 2.14. Simulation Model with ANFIS Controller (Ayyub, 2006)

Sastry et al. (1997) are focused on the optimal soft starting of voltage-

controller-fed induction machine drive based on voltage across thyristor. AC voltage

controllers are used as induction motor starters in fan or pump drives and the crane

hoist drives. This paper presents a method of identifying the end of soft start of an ac

voltage-controller-fed induction motor (IM) drive based on the voltage across the

nonconducting thyristor through a dynamic simulation of the whole drive system. A

two point current minimization technique is adopted to operate the drive system at

the required optimal voltage under all operating conditions. This minimizes the

motor losses. Graphic modeling of the whole drive system is done in a modular

format using Design Star and dynamic simulation is done using SABER. The

dynamic simulation results of the whole drive system are supported with

experimental data.

During soft start, initially the thyristors are fired at an α equal to αmax. When

the motor is at standstill, the voltage across the nonconducting thyristor is measured

and stored as VREF. Then, α is decremental by 0.50 /cycle (ALPSTP1) until α

reaches αmax - 4. This ensures an initial rise in motor current. The fundamental

component of the line current drawn is rectified and sampled once in every 30 ms. If

the current is less than the current limit CURLIM for a given motor, then is

decremented by 0.10 /cycle (ALPSTP2) until the identification of the end of soft

start. If current exceeds the current limit CURLIM, then α will not be decremented

until the motor accelerates or the current limit is not seen. The end of optimal soft


21

start is identified with the fall of voltage across the nonconducting thyristor to a

value below 75% of the value when the motor is at standstill.

After identifying the end of an optimal soft start, a delay of 30 cycles is

provided to allow the flux to settle. Then the process of optimization is activated by

incrementing α by 0.10 /cycle. A two-point current minimization technique is used

for optimization. At any sampling instant tn, the new value of α, αnew is calculated

based on the difference between two successive current samples and I tn-1 and Itn

αnew = αold + Δα

where αnew is the value of α at any time instant tn, αold is the value of α at time instant

tn-1, and Δα is the step change in per cycle.

As long as the difference (Itn-1 - Itn) is positive, the real time optimization

process continues. Once the difference becomes negative, incrementing of α is

stopped and this value of α is known as αopt. This condition is identified as the end of

optimization routine.

Rajaji et al. (2008) have studied the subject of adaptive neuro fuzzy based

soft starting of voltage controlled induction motor drive. Their paper presents a novel

neuro fuzzy based ac voltage controller to generate the firing pulses for appropriate

thyristors for any given operating torque, speed of the motor and the load. An ANFIS

model has been designed to achieve the proposed algorithm. MATLAB/SIMULINK

package has been used to simulate the proposed method. Simulation results presented

in this paper explain the advantages of proposed soft starting method over

conventional method. The advantages of proposed method are its simplicity,

stability, and accuracy and fast response.

Soft starters allow the machine to start, vary its speed and stop with minimum

mechanical electric stresses on the equipment. This can be done by appropriate

adjustment of the induction motor terminal voltage. However, adjusting the voltage

for a given operating condition of speed and torque is not a simple task. To adjust the

voltage, the firing angle α of the thyristors shall be calculated for each operating

condition. Firing angle is a nonlinear function of motor speed and torque and it is

very difficult to find the exact value of α for any motor speed and torque. Some

methods of closed loop control technique to achieve this have been developed. In this


22

method, speed sensor is required to acquire the signal feedback. Some researchers

have proposed and developed a method of optimal soft starting without a speed

sensor in which sensing of thyristors voltages is very much required.

In this paper simulation procedures and results have been presented for the

proposed method and they have been compared with the conventional soft starter

results. The proposed soft starter strategy may be recommended for industrial

requirements such as compressors, air blowers, pumps, air conditioners etc.

This model was simulated using set of data obtained by simulations (see in

Figure 2.15.). The generated data are speed and torque patterns inputs and their

corresponding firing angle patterns as output of the ANFIS model. The results

obtained by this ANFIS model shown in this paper are more accurate and this

approach has solved the problem of the complexity of the online determination of the

appropriate thyristors firing angle for any operating condition. The proposed control

methodology can eliminate the speed or voltage sensors since it is an open loop

control.

Figure 2.15. Proposed ANFIS Based Soft Starter Strategy (Rajaji et al., 2008)


23

Rajaji et al. (2008) have also studied the same subject of fuzzy and anfis

based soft starter fed induction motor drive for high performance applications.

Dayong et al. (2011) have modeled and simulated a soft starting system of

asynchronous motors based on fuzzy adaptive control. Considering the large starting

current of the asynchronous motors, this paper presents a soft starting system of the

asynchronous motors based on fuzzy adaptive control whose control rule and control

factor can be adjusted by itself. According to the starting characteristic of

asynchronous motors, taking current deviation and deviation change rate as input and

the change of trigger angle of thyristors as output, the fuzzy adaptive control strategy

is put forward. The system model for the soft starting of asynchronous motors is

established and the system is simulated based on MATLAB/SIMULINK. Compared

with the traditional fuzzy control method, starting current and torque oscillations can

be eliminated effectively, an ideal constant current soft starting control effect for

asynchronous motors is achieved, so fuzzy adaptive control method is superior. The

simulation results show the validity and stronger robustness.

Figure 2.16. shows the schematic block diagram of the soft starting system

with fuzzy adaptive control. This control system combines adaptive control theory

with fuzzy control theory and uses a fuzzy control technology with adaptive learning

algorithm. According to the actual current deviation and deviation change rate,

parameters k1, k2 and k3 is adjusted by adaptive controller by using fuzzy adaptive

control algorithm, which realizes the self-adjusting of fuzzy control rules and

generates better dynamic control effect.

Figure 2.16. The Schematic Block Diagram of Soft Starting System with Fuzzy

Adaptive Control (Dayong et al., 2011)


24

According to the starting characteristic of asynchronous motors, the fuzzy

adaptive control strategy is put forward. The system model for the soft starting of

asynchronous motors is established and the system is simulated based on

MATLAB/SIMULINK. Compared with the traditional fuzzy control method, starting

current and torque oscillations can be eliminated effectively, so the fuzzy adaptive

control method mentioned in this paper will improve the system reliability. The

simulation results demonstrate that the fuzzy adaptive control system can surely

achieve an ideal constant current soft starting control of asynchronous motors and a

strong robust characteristic. It can be forecasted that the soft starting control drives

for asynchronous motors using the fuzzy adaptive control method mentioned in this

paper will gain wider acceptance in the future.

Teke et al. (2011) have investigated the subject of implementation of fuzzy

logic controller using FORTRAN language in PSCAD/EMTDC. In their paper, they

emphasized that fuzzy logic controllers have gained widespread use by engineers and

practitioners due to their design simplicity, closeness to human reasoning and

suitability for control applications. Fuzzy logic controlled studies are usually

performed in MATLAB, or sometimes in PSCAD/EMTDC (Power Systems

Computer Aided Design/Electromagnetic Transient including d.c.) with MATLAB

interfacing. The PSCAD/EMTDC software does not include a component for a fuzzy

logic controller. This paper is, to the best of our knowledge, the first work that

designs and implements a fuzzy logic controller in PSCAD/ EMTDC by using

FORTRAN codes. In this way, several drawbacks which arise from the interfacing

between MATLAB and PSCAD/EMTDC are eliminated. The simulation section of

this paper consists of a case study based on a fuzzy logic-controlled PWM inverter in

which the performance of the developed fuzzy logic controller is evaluated.


25

2.2. Conclusion of Literature Review

Several studies are carried out on about motor power efficiency control in the

literature. Some of them used experimental techniques and the others used simulation

analyses. They mainly focused on the control of the motor speed to obtain energy

efficiency. Also different control methods to obtain the best energy optimization

were searched.

When the studies investigated, it is obviously seen that the energy efficiency

methods are limited. These efficiency methods are variety starting methods, motor

drive methods, use of high efficient motors etc.. Motor starting methods are mostly

used for minimizing the inrush current instead of providing energy efficiency.

Nowadays, high efficient motors have the one of the largest gains in motor efficiency

that achieved through greater use of copper and electrical steel. However, many of

the studies in the literature focus on developing motor drive systems to get energy

efficiency. In general, these studies focus on motor speed control. According to the

required load value, it is aimed to achieve energy efficiency by setting the optimum

speed in these studies. But, as seen in the literature review there is not enough

number of studies about “stator flux optimization in induction motors”. The existing

study’s efficiency values are approximately same and low.


26

3. ENERGY EFFICIENCY METHODS ON MOTORS Murat Mustafa SAVRUN

27

3. ENERGY EFFICIENCY METHODS ON MOTORS

The basic elements of the industry are electric motors. An electric motor is an

electromechanical device that converts electrical energy to mechanical energy. This

mechanical energy is used for, rotating a pump impeller, fan or blower, driving a

compressor, lifting materials water pumping, ventilation, cooling etc. Electric motors

are used at home (mixer, drill, and fan) and in industry. It is estimated that motors

use about 70% of the total electrical load in industry (Töpfer) Modern electrical

motors are available in many different forms, such as single phase motors, three-

phase motors, brake motors, synchronous motors, asynchronous motors, special

customized motors, two speed motors, three speed motors, and so on, all with their

own performance and characteristics (Kjellberg et al., 2003).

Induction motors are the most widely used electric motor type in industry

because the features of having a simple and robust structure, requiring less

maintenance, their low cost and their reliability etc.. Single-phase induction motors,

as well as the characteristics of having a simple and robust structure, requiring less

maintenance etc. because of the availability of single-phase power supply in almost

every household is also widely used in homes as a refrigerator, washing machine,

fan, air conditioning, food mixer, microwave oven, stove etc. (Hrabovcova et al.,

2010, Xiuhe et al., 2010).

Energy saving can be achieved by various methods in induction motors. The

first is in the motor design itself, through the use of better materials, design, and

construction. Motor losses can be divided into five main categories. Two of these

categories are iron losses in the core, and windage and friction losses that are

classified as no-load losses because they remain constant regardless of the load. Load

losses, which vary with the load, are stator copper losses, rotor losses, and stray load

losses. All motor losses can be influenced by design and construction considerations,

by the quality of the design and manufacturing processes (ABB). Another method

used for induction motors in energy saving is variable frequency drive system. This

method provides mechanism to reduce current inrush when starting a motor and can

reduce the speed of the motor. Reduction of motor speed can dramatically reduce the


28

amount of energy used by a motor (Power E). Another method is start with the

capacitor. Capacitors are connected to increase power factor. When used in this

manner they are called power factor correction capacitors (Larabee et al., 2005)

Other energy saving methods which are commonly known are direct starting, wye-

delta starting and soft starting.

3.1. Motor Starting Methods

The three-phase induction motors represent the most significant load in the

industrial plants, over the half of the delivered electrical energy. The starting process

of the induction motor, specially the medium and large size ones, may produce

voltage dips on power system. At rest the induction motor circuit behaves as a

transformer with the secondary short circuited which is highly inductive. The power

factor is low (around 10 to 20%) and a high current is drawing, commonly 6-10

times the rated value causing this undesirable effect (Da Silveira et al., 2009).

A 3-phase induction motor is theoretically self-starting. The stator of an

induction motor consists of 3-phase windings, which when connected to a 3-phase

supply creates a rotating magnetic field. This will link and cut the rotor conductors

which in turn will induce a current in the rotor conductors and create a rotor

magnetic field. The magnetic field created by the rotor will interact with the rotating

magnetic field in the stator and produce rotation. Therefore, 3-phase induction

motors employ a starting method not to provide a starting torque at the rotor, but

because of the following reasons (Inaam I).

§ Reduce heavy starting currents and prevent motor from overheating.

§ Provide overload and no-voltage protection.

§ Provide energy efficiency (Inaam I).

There are several solutions to provide these reasons, the most common are:

§ Direct-On-Line Starting Method

§ Shunt Capacitance Method

§ Delta-Wye Starting Method


29

§ Autotransformer Method

§ Series Resistance and Reactance Method

§ Soft Starter Method

§ Variable Frequency Drive (VFD)

3.1.1. Direct-On-Line Starting Method

Direct on-line (DOL) is the traditional and simplest method of motor starting,

and most other methods are based against it. It is also called across the line start. This

method is the direct connection of the terminal voltage to the motor stator with no

additional components, and also for this reason is most economical in terms of

installation cost and ease of use (see Figure 3.1.). It is also one of most reliable and

robust methods. Of all the starting methods it produces the highest inrush current,

usually six to eight times the rated current, and the highest starting torque; and due to

the high starting torque it has the shortest acceleration time (apart from the shunt

capacitor start). The DOL method is most commonly used for small motors relative

to the size of the generation and system, due to the fact that the startup of a small

motor will only have a low impact on the system, and in particular the voltage drop.

Other drawbacks include the mechanical stress put on the motor’s load and the low

startup efficiency due to the high reactive power consumed at startup. This approach

is typically not suitable for large motors (Wigington, 2010).

Figure 3.1. Direct-On-Line Starter Circuit Diagram


30

3.1.2. Shunt Capacitance Method

Induction motors typically have very low power factor during starting and as

a result have very large reactive power consumption. This effect on the system can

be reduced by adding capacitors to the motor during starting (Larabee et al., 2005).

Through it plays a role in compensation, the capacity of paralleled compensation

capacitor is small in order to avoid the over-voltage and over-current from self-

excited oscillation that caused by induction motor and paralleled capacitor, and thus

leads to less compensation system. When the motor is starting, this method can track

and compensate the reactive power consumed by the motor, so it is able to reduce the

starting current and shorten the starting time on the premise of ensuring a high

starting torque; when the motor runs normally, it also can meet the motor's normal

demand of reactive power, and it will reduce line losses, improve the grid’s power

factor, which is benefit to the economical operation of the grid (Li et al., 2009).

In general, capacitor starting is used for relatively large motors that need fast

starting or improved efficiency during startup. In the system tested, it was found that

capacitor starting could reduce up to half the maximum voltage dip as compared to

the DOL start and still maintain other adequate starting characteristics. Capacitor

starting is a reliable and robust method for motor starting on weak electrical system

(Wigington, 2010).

3.1.3. Delta-Wye Starting Method

This is a starting method that reduces the starting current and starting torque

(Kjellberg et al., 2003). This is particularly true for motor voltages of less than 1000

V. With this type of starting method, a normally delta-connected stator is connected

in wye during the initial startup phase. It is most common for the motor to reach full

speed before the transition to the delta connection is made. (McElveen et al., 2001).

It applies a starting voltage in a wye configuration which effectively reduces

the applied voltage to each phase winding by 58% (1/√3) of the rated voltage. As a

result, the starting current and torque are both reduced to 33.3% of the full voltage


31

values as torque is proportional to the square of the terminal voltage. Once the motor

speed reaches about 70% of the rated speed, the supply connection to the motor will

change to a delta connection thus giving it a full voltage supply. Although the

voltage is being reduced thus giving a lower inrush current, this method however

fails to overcome some of the other problems faced by the DOL starter. A common

problem faced by the Delta-Wye starter is the spike that occurs during transition

from star to delta configuration (Pillay et al., 2009).

The Delta-Wye starter is the most common and cheapest of the reduced

voltage starters. Its rugged and simple construction ensures good durability with

minimal maintenance. Its startup cost is slightly higher than that of a Direct-On-Line

starter as there are more components required, however, its reduced voltage

capabilities gives it an advantage over the Direct-On-Line starter in terms of long

term cost and motor maintenance (Pillay et al., 2009).

3.1.4. Autotransformer Method

Autotransformer starters are one of the most effective means of obtaining a

reduced voltage during starting with standard taps ranging from 50-80 percent of

normal rated voltage. A motor starting study can be used to select the proper voltage

tap (and the lower line current inrush) which results in acceptable voltages in the

electrical power system during the motor start (Williams et al., 1978).

The motor is started with this reduced voltage, and then after a pre-set

condition is reached the connection is switched to line voltage. This condition could

be a preset time, current level, bus volts, or motor speed (Larabee et al., 2005).

There are two configurations for the Autotransformer. The first being the

open transition starter and the second the closed transition starter. During the starting

period of an open transition starter, the Autotransformer applies a reduced voltage to

the induction motor until the motor accelerates to the speed determined by the

transformer tap. Once this is achieved, the transformer tap contactor will disconnect

and another contactor will close a connection between the motor and the full voltage

supply to achieve full speed. Due to this, when the motor is reconnected to the


32

supply, a spike in current will occur which will cause a high current to travel through

the motor windings, similar to a Delta-Wye open transition starter. This can cause

damage over time. To overcome this problem, the Korndorffer starter was developed

which is a closed transition starter. With this method, there are no instances when the

motor is totally disconnected from the supply thus eliminating the current spike seen

in the open transition starter (Pillay et al., 2009). With the closed circuit method there

is a continuous voltage applied to the motor. The benefit with the autotransformer

starting is in possible lower vibration and noise levels during starting (Larabee et al.,

2005).

Another benefit of the Autotransformer lies in the users’ ability to select

different tap values. With this, the user may change the tap to suit a particular

application. This is extremely useful especially when a particular application requires

higher torque which can be achieved by increasing the starting voltage. The cost of

installing an Autotransformer is more expensive compared to the other conventional

motor starters but it offers many benefits as stated earlier (Pillay et al., 2009).

3.1.5. Series Resistance or Reactance Method

A switchable primary series resistor or reactor bank can be added at the motor

terminals to limit the current or limit change in the current, respectively. The resistor

bank will cause a drop in voltage across it reducing the current. The heat dissipated

from the resistor also needs to be taken into consideration. Series resistor starting is

usually only performed for small motors. When using a series reactor bank, it will

oppose the inrush current initially and reduce the terminal voltage proportionally.

The most advantageous characteristic of the series reactor starting is that the voltage

increases over time as a function of the rate of change of the current without

additional control. The added reactance will also further increase the starting reactive

power and thus lower the starting efficiency. Switching transients will also occur if it

is connected in an open-circuit (Wigington, 2010).


33

3.1.6. Soft Starter Method

Direct-On-Line starting of large ac motors may present difficulties for the

motor itself and the loads supplied from the common coupling point because of the

voltage dips in the supply during starting, especially if the supply to which the motor

belongs is weak. An uncontrolled starting may cause a trip in either overload or

under voltage relay, resulting in starting failure. This is troublesome for field

engineers since the motor cannot be reenergized until it cools down to an allowable

temperature in a long time period. Furthermore, the number of starts per day is

limited to only a few attempts. Therefore, the current and torque profiles of the motor

during starting are to be carefully tailored. Conventional methods involves

electromechanical reduced starting comprises of auto transformer starting, star-delta

starting, resistance or reactor starting. All these methods have drawbacks such as

need for frequent inspection and maintenance, non-simultaneous switching of motor

phases to the supply, failures in the moving parts due to the large number of

switching etc. (Rafeek et al., 2013).

VFD is another method where speed is varied by varying the frequency fed

to the motor. Here V/f is kept constant by changing both voltage and frequency, but

this method is not in common use due to the requirement of converter and inverter

stages which will results in increase of cost. Besides the developments and progress

in commercial soft-starter technology, numerous attempts have been made on the

performance analyses and control techniques of three-phase induction motors (IMs)

fed from a thyristorized voltage controller (Rafeek et al., 2013).

Soft starters allow the machine to start, vary its speed and stop with

minimum mechanical electric stresses on the equipment. This can be done by

appropriate adjustment of the induction motor terminal voltage. Each phase has two

thyristors and which are connected in anti-parallel connection (see Figure 3.2.).

Thyristors are fired according the sequence of firing pulses. At least two thyristors

must conduct simultaneously to allow current to flow through the load and that the

firing angle α is measured from the zero crossing of phase A voltage. However,

adjusting the voltage for a given operating condition of speed and torque is not a


34

simple task. To adjust the voltage, the firing angle α of the thyristors shall be

calculated for each operating condition. Firing angle is a nonlinear function of motor

speed and torque and it is very difficult to find the exact value of α for any motor

speed and torque (Rajaji et al., 2008).

Figure 3.2. Soft Starter Circuit Diagram

Utilizing a dynamic function for the triggering angle of thyristors in the

voltage controller proves to be a simple and effective way to improve transient

performance. By employing a proper triggering function, the rate at which main flux

builds up is decreased and transient torque is smoothed. But when the firing angle is

larger, time taken to attain steady state becomes larger (Rafeek et al., 2013).

3.1.7. Variable Frequency Drive

A variable-frequency drive (VFD) is a system for controlling the rotational

speed or torque of an alternating current (AC) electric motor by controlling the

frequency of the electric power supplied to the motor. By extension, a VFD also

controls horsepower. A VFD is the other name of the adjustable-speed drive (ASD)

(Randall et al., 2009). Variable frequency drive (VFD) usage has increased

dramatically in HVAC applications. The VFDs are now commonly applied to air

handlers, pumps, chillers and tower fans (Mistry et al., 2012).


35

Induction motor’s speed is directly proportional to the supply frequency. So

by changing the frequency, the synchronous speed and the motor speed can be

controlled below and above the normal full load speed. The voltage induced in the

stator is proportional to the product of slip frequency and air-gap flux. The motor

terminal voltage can be considered proportional to the product of the frequency and

flux, if the stator voltage is neglected. The variable frequency control below the rated

frequency is generally carried out by reducing the machine phase voltage, along with

the frequency in such a manner that the flux is maintained constant. Hence, it uses

power electronics device to vary the frequency of input power to the motor, thereby

controlling motor speed (Mistry et al., 2012).

Understanding the basic principles behind VFD operation requires

understanding the three basic sections of the VFD: the rectifier, dc bus, and inverter

(see Figure 3.3.) (Carrier, 2005).

Figure 3.3. VFD Circuit Diagram (Mistry et al., 2012)

The rectifier in a VFD is used to convert incoming ac power into direct

current (dc) power. One rectifier will allow power to pass through only when the

voltage is positive. A second rectifier will allow power to pass through only when the

voltage is negative. Two rectifiers are required for each phase of power. Since most

large power supplies are three phase, there will be a minimum of 6 rectifiers used.

Appropriately, the term “6 pulse” is used to describe a drive with 6 rectifiers. A VFD

may have multiple rectifier sections, with 6 rectifiers per section, enabling a VFD to

be “12 pulse,” “18 pulse,” or “24 pulse” (Carrier, 2005).


36

After the power flows through the rectifiers it is stored on a dc bus. The dc

bus contains capacitors to accept power from the rectifier, store it, and later deliver

that power through the inverter section. The dc bus may also contain inductors, dc

links, chokes, or similar items that add inductance, thereby smoothing the incoming

power supply to the dc bus (Carrier, 2005).

The final section of the VFD is referred to as an “inverter.” The inverter

contains transistors that deliver power to the motor. The “Insulated Gate Bipolar

Transistor” (IGBT) is a common choice in modern VFDs. The IGBT can switch on

and off several thousand times per second and precisely control the power delivered

to the motor. The IGBT uses a method named “pulse width modulation” (PWM) to

simulate a current sine wave at the desired frequency to the motor (Carrier, 2005).

There are numerous advantages of using variable speed drives in motor

applications. The reduction in energy consumption is perhaps the variable speed

drive’s greatest advantage and one of the reasons they have become so popular in

modern systems. AC motors are generally designed to operate at fixed speeds and be

able of handling peak loads. This can be quite energy inefficient as the motor will

operate at the fixed speed even when the loads are dramatically reduced. Since

variable speed drives are able to control the power supplied to the motor they can

reduce the speed and thus power, which in turn makes them much more energy

efficient. In the case of variable torque loads, a much lower torque is needed at lower

speeds as opposed to higher speeds. In addition to energy savings, VFDs provide

benefits as low motor starting current, reduction of thermal-mechanical stress on

motors, high power factor and lower KVA (Sibson, 2012).

Whilst there are many advantages and benefits of using variable speed drives

in motor driven systems there are also some disadvantages. Compared to other speed

control methods for motors, variable speed drives have a higher initial cost. When

operating the motors for constant torque applications at low speeds, motor heating

can become an issue (Sibson, 2012).


37

3.2. High Efficiency Induction Motors

High- and premium-efficiency AC motors achieve greater efficiency by

reducing the loss of heat and magnetic field as electricity goes through the motor. In

modern premium-efficient models, losses account for only 3-6% of the energy that

flows through the motor. Losses can be divided into five major types: stator power

losses, rotor power losses, magnetic core losses, friction and windage losses, and

stray load losses. Of these, stator power losses represent the largest category (37% of

total energy loss), yet there are few opportunities to reduce these losses without also

decreasing the power available to create the magnetic field. Similarly, stray load

losses (16% of total energy loss) can theoretically be addressed by redesigning

several features of the stator winding, but each design change may in fact increase

losses in other areas. Rotor power losses, magnetic core losses and friction and

windage losses can be reduced by using higher quality materials and optimizing the

design for larger magnetic fields and greater electricity flow. To date, the largest

gains in motor efficiency have been achieved through greater use of copper and

electrical steel; the higher the use of these materials, the higher the efficiency, the

main loss reductions possible via these measures have already been tapped in the

highest-efficiency motors now commercially available, and further loss reductions

are much more difficult and costly to achieve (Tokuoka, 2010).


38

4. MOTOR DRIVER MODELING Murat Mustafa SAVRUN

39

4. MOTOR DRIVER MODELING

Due to the increasing number of induction motors in industry applications

and residential appliances especially air conditioners, many utilities and industry

firms are affected by the high inrush current that may cause important failures. The

problem is more severe in areas where the motors represent a high portion of the

power demand. If soft starters are substituted in such applications, reduction of

inrush current can be achieved and on the other hand, energy saving can also be

possible (Rajaji et al., 2008). The soft starters have following advantages over

conventional starters:

§ Smooth acceleration, which reduces mechanical stress on the drive system.

§ Reduced stress on electrical supply due to reduced starting current, hence

elimination of voltage dip and brown out conditions.

§ Energy saving at light load conditions. Copper and iron losses will also be

reduced (Ayyub, 2006.)

Energy savings by voltage control through soft starter is achieved by

reducing the applied voltage if the load torque requirement can be met with less than

rated flux. This way, core loss and stator copper losses can be reduced. In soft starter

fed induction motor system, smooth acceleration with reduced stress on the

mechanical drive system is achieved. This is due to high starting torque hence

increase the life and reliability of belts, gear boxes, chain drives, motor bearings and

shafts. Smooth acceleration reduces also stress on the electrical supply due to high

starting currents meeting utility requirements for reduced voltage starting and

eliminating voltage dip and brown out conditions (Rajaji et al., 2008).

Soft starters allow the machine to start, vary its speed and stop with minimum

mechanical electric stresses on the equipment. This can be done by appropriate

adjustment of the induction motor terminal voltage. However, adjusting the voltage

for a given operating condition of speed and torque is not a simple task. To adjust the

voltage, the firing angle α of the thyristors shall be calculated for each operating


40

condition. Firing angle is a nonlinear function of motor speed and torque and it is

very difficult to find the exact value of α for any motor speed and torque (Rajaji et

al., 2008).

This thesis proposes a fuzzy logic control (FLC) based selection of the

thyristors firing angles of a voltage-controlled- fed IM drive system in running mode.

Energy saving and minimum electrical stress in running mode is achieved by

adjusting the stator voltage. The firing angle α of the thyristor which control the

voltage of stator is a nonlinear function of torque and torque change. The controller

operates in closed loop and requires a speed sensor. The other sensor that is needed is

a current sensor, which in most of applications is used to protect the converter and

the motor from over currents. The driver is designed to meet the industrial

requirements of compressors, blowers, fans, pumps, mixers, crushers and grinders,

etc. which are constant speed-variable torque applications.

4.1. Mathematical Modeling

This study is an efficiency improvement in constant speed-variable torque

motor applications. This type of motor is simple, economical and increasingly

employed in low to medium power applications, especially where the load torque

varies as the square of the motor speed. Such applications are typically compressors,

pumps and fan loads (Kai et al., 2005).

Many industrial induction-motor drives are not operating at full capacity all

the time, and in many cases, electric motors may be left on in an idling mode

between work cycles. Motor losses during these light load periods are primarily iron-

core losses. If these could be reduced during light-load periods by reducing the

voltage, the total energy employed might be reduced (Hui et al., 2005).

The input power to an induction motor Pin is in the form of three phase

electric voltages and currents. The first losses encountered in the machine are I2R

losses in the stator winding (the stator copper loss PSCL). Then some amount of

power is lost as hysteresis and eddy currents in the stator (Pcore). The power

remaining at this point is transferred to the rotor of the machine across the air gap


41

between the stator and rotor. This power is called air gap power PAG of the machine.

After the power is transferred to the rotor, some of it is lost as I2R losses (the rotor

copper losses PRCL), and the rest is converted from electrical to mechanical form

(Pconv). Finally friction and windage losses PF&W and stray losses Pmisc are subtracted.

The remaining power is the output of the motor Pout (Chapman, 1999). The

equivalent circuit and power flow diagram of an induction motor is given in Figure

4.1. and 4.2.

Figure 4.1. Equivalent Circuit of an Induction Motor

Figure 4.2. Power Flow Diagram of an Induction Motor

The input power is:

φφφ cos3 IVPin = . (4.1)

Where, φI is phase current while φV is phase voltage.

The stator copper loss is:


42

1213 RIPSCL = . (4.2)

The stator iron loss is:

Ccore R

EP2

13= . (4.3)

The rotor copper loss is:

2223 RIPRCL = . (4.4)

Based on the above formula, the relation between the loss and the motor

voltage & current can be deduced; the influence of the regulated voltage on the

motor loss and power factor φcos can also be drawn.

§ Mechanical loss: is directly proportional to the rotational speed. Variety

of voltage has little influence on speed, so P, can be considered constant.

§ Stray loss: is directly proportional to the square of current but in inverse

proportion to the square of the stator voltage.

§ The stator iron loss: When the stator voltage is less than the rated value,

the iron loss is directly proportional to the square of voltage

approximately, so when the stator voltage decreases, the stator loss will

drastically decrease correspondingly.

§ The rotor copper loss: depends on the rotor current, while the rotor

current depends on voltage and load. When the stator voltage decreases,

main flux decreases too, so the rotor current increases, and the rotor

copper loss is in inverse proportion to the square of voltage. So when the

stator voltage decreases, the rotor current and copper loss will increase.


43

§ The stator copper loss: depends on the stator current, while the stator

current is the vector sum of the rotor current and the excitation current,

so when the stator voltage varies, the stator copper loss varies little.

§ Power factor: increases while the stator voltage decreases (Hui et al.,

2005).

In order to achieve efficiency by reducing motor losses, the stator voltage

should be controlled with six inverse parallel connected thyristors are placed in series

with the motor supply. This connection provides a bidirectional full-wave

symmetrical control (Rashid, 2001). In the steady-state, an induction motor operates

as an inductive load. Hence, a phase current typically lags its respective line-to

neutral voltage by a phase angle φ (Lipo, 1971). The current carried by the thyristor

may not fall to zero = when the input voltage goes negative and may continue

until β=wt (see in Figure 4.3.). The conduction angle;

αβθ −= . (4.5)

of the thyristor depends on the firing delay angle α and the load impedance angle.

The holdoff angle;

φαθπγ −=−= . (4.6)


44

Figure 4.3. Typical Waveforms of Single-Phase Stator Voltage Controller with an R-

L Load.

Thus the voltage RMS of the load satisfies the equation;

)()sin2(12

0 wtdwtVV i∫+

=θα

απ. (4.7)

This equation indicates the final voltage fed on the motor is a nonlinear

function of α and θ, where α can be controlled and θ can't be controlled directly (Kai

et al., 2005).

4.2. Power Circuit Model

The motor driver fed three-phase induction motor was modeled and simulated

using PSCAD blocksets. The asynchronous motor and all power electronics switches

were modeled according to their operating characteristics. The power circuit of the

proposed motor drive is shown in Figure 4.4.


45

Figure 4.4. Power Diagram of Proposed Motor Drive

4.2.1. Motor Parameters

In the study, firstly the squirrel cage induction motor power value was

determined. All simulation studies are performed with this rating. The motor which

is selected from manufacturer catalog is modeled in accordance with the catalog

information. The selected motor parameters are as given below;

Rated Power: 7.5 kW Efficiency: % 82.5

Power Factor: 0.8 Frequency: 50 Hz

Pole: 4 pole Slip: 0.0926 (Full_Load)

Voltage: 400 V 3ɸ Rated Current: 16.3 A

The squirrel cage induction motor model of PSCAD is shown in Figure 4.5.

Squirrel cage induction motor parameter setting block is shown in Figure 4.6.

Waveforms of full load input power and input reactive power are shown in Figure

4.7. Motor current and voltage waveforms at full load are shown in Figure 4.8., and

no load and full load speed waveforms are shown in Figure 4.9.

Figure 4.5. Squirrel Cage Induction Motor Model


46

Figure 4.6. Squirrel Cage Induction Motor Parameter Setting

The squirrel cage induction machine can be operated in either 'speed control'

or 'torque control' modes. In speed control mode, the machine rotates at the speed

specified at the input W. In torque control mode, the speed is calculated based on the

machine inertia, damping, the input torque and the output torque. Normally, the

machine is started in speed control mode with the W input set to rated per-unit speed

(say 0.98) and then switched over to torque control after the initial transients of the

machine die out.

A, B and C: These terminals are the 3-phase electrical connection points

(phases A, B and C) of the stator. A star connected stator is modeled.

W: Speed input in per-unit. The machine runs at this speed while in speed

control mode.

S: A switch to select speed control mode (1) or torque control mode (0).

T: Torque input in per-unit. If the machine is in torque control mode then

the machine computes the speed based on the inertia and damping coefficient, this

input torque and the output torque.


47

(a)

(b)

Figure 4.7. Modeled Motor a) Active Power, b) Reactive Power


48

(a)

(b)

Figure 4.8. Modeled Motor Phase a) Voltage Waveform b) Current Waveform


49

(a)

(b)

Figure 4.9. Modeled Motor a) No-Load Speed b) Full-Load Speed


50

4.2.2. Thyristor Block Design

The proposed driver is an ac voltage controller in which the voltage is

adjusted through the setting of the thyristors firing angle. Figure 4.10. shows a

typical configuration of a symmetrical voltage controller (Gastli et al., 2005). Each

phase has two thyristors and which are connected in anti-parallel connection.

Thyristors in this configuration are fired according to the sequence of firing pulses.

Figure 4.11. shows the firing sequence of six thyristors in the proposed driver circuit.

From this figure, it should be noted that at least two thyristors must conduct

simultaneously to allow current to flow through the load (Rajaji et al., 2008).

Figure 4.10. Symmetrical AC Voltage Controller of Driver


51

Figure 4.11. Firing Sequence of Thyristors

4.3. Proposed Fuzzy Logic Based Control System

The whole control system has two inputs which are motor speed and

electrical torque, and the accurate firing angle is the only one output. Torque

estimator block determines the torque value via motor speed feedback. Firing angle

generation block produces appropriate firing angles according to the motor load. The

firing angle optimization block determines the suitability of the electrical torque

values and motor torque and problematic situations and optimizes the firing angles.

The whole control system is shown in Figure 4.12.

Figure 4.12. The Whole of Proposed Fuzzy Logic Based Control System


52

4.3.1. Torque Estimator Block Design

The torque estimator block is an estimator for determining the torque value of

motor in running. This block was implemented by using FORTRAN codes in the

PSCAD/EMTDC program and as seen in Figure 4.13. and Table 4.1.

Figure 4.13. Firing Angle Generation Block Table 4.1. Codes of Torque Estimator Block #local real spd “spd: instant motor speed” #local real slip “slip: instant motor slip value” #local real sliper “sliper: motor slip change” #local real xtime “xtime: simulation time” slip=9.26 sliper = 0 spd = 0 xtime=$i222 if (xtime.LT.0.7) then $o111=1 end if if (xtime.GT.0.7.AND.xtime.LT.0.7001) then aaa=$i111 sliper=(((slip-(((1500-(spd*1500))*100)/1500))*100)/slip)*100 $o111=((2*0.1**17)*sliper**4)-((4*0.1**13)*sliper**3)-((1*0.1**9)*sliper**2)-((7*0.1**5)*sliper)+0.9999 end if

The full load slip information of the motor is known by the estimator. The

input of the block is the running speed information of motor. The estimator

determines the value of the momentary slip from motor speed in under 0.7 seconds.

Then, the block determines the value of the torque that the motor is running at that

instant by comparing the full load slip and momentary slip values. The output is


53

produced by the torque estimator in 0.7001 seconds as the instant torque value of

motor.

4.3.2. Firing Angle Generation Block Design

The firing angle generation block is a controller for controlling the torque

angle and generating the firing angles according to the operator experiences. An

output which is the function of an input is aimed to obtain. The firing angle

generation block has only one input which is load torque and the firing angle is the

only output. The block is given in Figure 4.14.

Figure 4.14. Firing Angle Generation Block

The PSCAD model was used for estimating the performance of the driver and

has given satisfactory results. The program is run several times with a fixed firing

angle and varying load torque to determine the correct firing angles for the torque

values. The steady state values of the torque and firing angle are calculated after each

run. The same procedure is repeated for different values of the firing angle. Figure

4.15. shows the torque-versus-firing angle characteristics obtained for different

values of thyristors’ firing angle.


54

Figure 4.15. Torque versus Firing Angle Characteristics.

An equation (4.8) is obtained that determines the relationship between the

load torque and firing angles from graph. The component of firing angle generation

block has been created by using FORTRAN code in the PSCAD/EMTDC. The block

is created according to the formula given below.

11867,29613757,479175007,4166 2345 +−+−+−= xxxxxy . (4.8)

FORTRAN codes to define the firing angle generation block are given in Table 4.2.;

Table 4.2. Codes of Firing Angle Generation Block #local real trq “trq: instant torque value” trq = 0 trq =$input1 if(trq.LE.0.5) then $output1=(-4166.7*(trq**5))+(7500*(trq**4))-(4791.7*(trq**3))+(1375*(trq**2))-(296.67*trq)+118 end if if(trq.GT.0.5) then $output1 = 6 end if

0

20

40

60

80

100

120

140

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Firin

g An

gle

Torque


55

4.3.3. FL Based Firing Angle Optimization Block Design

4.3.3.1. Fuzzy Logic System

Normally in logic we have a series of statements which are either true or

false, yes or no, 0 or 1. In this context, the statement ‘the temperature is 25 degrees

Celsius’ is an objective one and is either true or false. However, for many situations

the answer is more like ‘not sure’ – ‘maybe’ – ‘that depends’ and so on. There is no

certainty to the situation – it depends upon the context. Fuzzy logic deals with

uncertainty in engineering by attaching degrees of certainty to the answer to a logical

question. So to analyze the imprecise requirements a fuzzy logic based approach is

used (Kaur et al., 2012, Vernon).

In general a Fuzzy Logic System (FLS) is a nonlinear mapping of an input

data vector into a scalar output where the vector output case decomposes into a

collection of independent multi input/single output systems. The richness of fuzzy

logic is that there are enormous numbers of possibilities that lead to lots of different

mappings. This richness does require a careful understanding of fuzzy logic and the

elements that comprise a FLS (Mendel, 1995).

To implement fuzzy logic technique to a real application requires the

following three steps:

§ Fuzzification – convert classical data or crisp data into fuzzy data or

Membership Functions (MFs)

§ Fuzzy Inference Process – combine membership functions with the control

rules to derive the fuzzy output

§ Defuzzification – use different methods to calculate each associated output

and put them into a table: the lookup table. Pick up the output from the

lookup table based on the current input during an application (Bai et al.,

2006).


56

Figure 4.16. Fuzzy Logic System

4.3.3.1.1. Rules

Fuzzy control rule can be considered as the knowledge of an expert in any

related field of application. The fuzzy rule is represented by a sequence of the form

IFTHEN, leading to algorithms describing what action or output should be taken in

terms of the currently observed information, which includes both input and feedback

if a closed-loop control system is applied. The law to design or build a set of fuzzy

rules is based on a human being’s knowledge or experience, which is dependent on

each different actual application.

A fuzzy IF-THEN rule associates a condition described using linguistic

variables and fuzzy sets to an output or a conclusion. The IF part is mainly used to

capture knowledge by using the elastic conditions, and the THEN part can be utilized

to give the conclusion or output in linguistic variable form. This IF-THEN rule is

widely used by the fuzzy inference system to compute the degree to which the input

data matches the condition of a rule.

In the case of two input one output fuzzy system (MISO), fuzzy control rules

have the form;

R1 : IF x is A1 AND y is B1 THEN z is C1 ,

R2 : IF x is A2 AND y is B2 THEN z is C2 ,

Rn : IF x is An AND y is Bn THEN z is Cn.


57

Where x, y and z are linguistic variables representing two process state

variables and one control variable; Ai , Bi and Ci are linguistic values of linguistic

variables x, y and z in the universe of discourse U, V and W, respectively, with

i=1,2,…,n; and implicit sentence connective also links the rules into a rule set or,

equivalently, a rule base.

A fuzzy control rule, such as “IF x is Ai AND y is Bi THEN z is Ci” is

implemented by a fuzzy implication function (fuzzy relation) i R and is defined as

follows:

)()]()([ νµνµνµµ τττ CBA and →= .

(4.9)

Where → denotes a fuzzy implication function (Lee, 1990, Bai et al., 2006).

4.3.3.1.2. Fuzzy Inference Engine

Fuzzy inference is the process of formulating the mapping from a given input

to an output using fuzzy logic. The mapping then provides a basis from which

decisions can be made, or patterns discerned. There are two types of fuzzy inference

systems that can be implemented that Mamdani-type and Sugeno-type. These two

types of inference systems vary somewhat in the way outputs are determined (Fuzzy

L.T., 1995).

Max-Min inference is widely used inference technique. In max-min

inference, the implication operator used is min, i.e,

),min()( iiiiij babatruthm =→= . (4.10)

Where → is a fuzzy implication which effectively means if then . Given two fuzzy sets A and B, it is illustrated in Figure 4.17. (Cuma, 2006).


58

Figure 4.17. Max-min Inference (Cuma, 2006)

The max-min inference mechanism is used in this thesis; however, there are

more inference operators other than max-min inference.

4.3.3.1.3. Fuzzification

Fuzzification is another step to apply a fuzzy inference system. Most

variables existing in the real world are crisp or classical variables. One needs to

convert those crisp variables (both input and output) to fuzzy variables, and then

apply fuzzy inference to process those data to obtain the desired output. Finally, in

most cases, those fuzzy outputs need to be converted back to crisp variables to

complete the desired control objectives (Bai et al., 2006).

4.3.3.1.4. Defuzzification

The conclusion or control output derived from the combination of input,

output membership functions and fuzzy rules is still a vague or fuzzy element and

this process in called fuzzy inference. To make that conclusion or fuzzy Output

available to real applications, a defuzzification process is needed. The defuzzification

process is meant to convert the fuzzy output back to the crisp or classical output to

the control objective. Remember, the fuzzy conclusion or output is still a linguistic

variable, and this linguistic variable needs to be converted to the crisp variable via

the defuzzification process. Three defuzzification techniques are commonly used,


59

which are: Mean of Maximum method, Center of Gravity method and the Height

method (Bai et al., 2006).

- Mean of Maximum (MOM) Method

The Mean of Maximum (MOM) defuzzification method computes the

average of those fuzzy conclusions or outputs that have the highest degrees. More

specifically, in the case of discrete universe, the control action may be expressed as;

∑ ==

n

ii

nw

y10 . (4.11)

Where is the support value at which the membership function reaches the

maximum value ( ), and is the number of such support values.

- Center of Gravity (COG) Method

The Center of Gravity method (COG) is the most popular defuzzification

technique and is widely utilized in actual applications. This method is similar to the

formula for calculating the center of gravity in physics. The weighted average of the

membership function or the center of the gravity of the area bounded by the

membership function curve is computed to be the crispest value of the fuzzy

quantity. In the case of discrete universe, this method yields,

∑∑

=

== n

i i

n

i ii

w

wwy

1

10

)(

)(

µ

µ. (4.12)

Where n is the number of quantization levels of the output.

- The Height Method (HM)

This defuzzification method is valid only for the case where the output

membership function is an aggregated union result of symmetrical functions. This


60

method can be divided into two steps. First, the consequent membership function F i

can be converted into a crisp consequent x = f i where fi is the center of gravity of Fi.

Then the COG method is applied to the rules with crisp consequents, which can be

expressed as

∑∑

=

== m

i i

m

i ii

w

fwx

1

1 . (4.13)

Where is the degree to which the th rule matches the input data. The advantage of

this method is its simplicity. Therefore many neuro-fuzzy models use this

defuzzification method to reduce the complexity of calculations (Bai et al., 2006).

4.3.3.2. Fuzzy Logic Controller Modeling

In this study, the fuzzy logic controller is used to control the torque and

produced electrical torque to get the accurate operating performance of the proposed

driver. Takagi and Sugeno’s fuzzy if-then rules representation, which is different

from commonly used fuzzy logic controllers, is used. An output, which is the

function of two variable of the motor, is aimed to be determined. The system

considered in thesis has two inputs which are the numerical difference of load torque

and produced electrical torque (error) and the error change (error_rate), and the firing

angle optimization value (Δα) is the only output. The inference mechanism of fuzzy

logic controller is mathematically expressed by the set of rules. These rules are

generated through the experience of operating the system, which may be feedback

from the plant operator, design engineer, or the expert.

The block diagram of the proposed fuzzy logic control system is shown in

Figure 4.18.


61

Figure 4.18. Proposed FL Control System Block Diagram

Since MATLAB has a fuzzy logic toolbox, FL applications are generally

performed in MATLAB. PSCAD do not include any FL components in its library.

So, fuzzy logic components which are used to design FL controller of driver have

been created by using FORTRAN code in PSCAD. The block diagram of the

proposed fuzzy logic control system of PSCAD is shown in Figure 4.19.

Figure 4.19. Proposed FL Control System Block Diagram of PSCAD

4.3.3.2.1. Fuzzification

The design procedure of FL controller continues with the selection of

membership functions for the inputs and the output of the controller. The non-fuzzy

(numeric) input variables are transformed into the fuzzy set (linguistic) variables by

fuzzification part, which is clearly defined boundary. The required input range of

error is described as linguistic variables such as LD, MD, SD, N, SI, MI and LI. The

required input range of error_rate is described as linguistic variables such as LD,

MD, SD, N, SI, MI, and LI. The membership functions of the inputs are shown in

Figure 4.20. and Figure 4.21.


62

Figure 4.20. Membership Function of Input 1 - error

Figure 4.21. Membership Function of Input 2 - error_rate

The linguistic terms for membership functions that will be used in the rule

base are labeled on each of the corresponding membership function. These are given

in Table 4.3. (Cuma, 2006).

Table 4.3. Linguistic terms for the error, erate and Δα error error_rate Δα

LD Large Decrease LD Large Decrease BN Big Negative

MD Medium Decrease MD Medium Decrease MN Medium Negative

SD Small Decrease SD Small Decrease SN Small Negative

N Neutral N Neutral Z Zero

SI Small Increase SI Small Increase SP Small Positive

MI Medium Increase MI Medium Increase MP Medium Positive

LI Large Increase LI Large Increase BP Big Positive

The membership functions are curves that define how each point in the input

space is mapped to a membership value between 0 and 1. The inputs map the

appropriate membership functions. The most common membership functions are

triangular, trapezoidal, bell shaped and gaussian functions.

LIMISINSDMDLD

10.50.20.0250-0.025-0.2-0.5

1.0

-1

LIMISINSDMDLD

0.080.060.040.020-0.02-0.04-0.06-0.08

1.0


63

In the literature, the maximum error occurs in the bell membership function,

Cauchy and gaussian membership functions is seen that the error in intermediate

levels. Triangular and trapezoidal membership functions is seen to be very close to

each other outputs and error values are minimum. With these triangular and

trapezoidal membership scheme, the input state of variable no longer jumps abruptly

from one state to the next. The membership functions change gradually from one

state to the next. A certain amount of overlap is desirable otherwise the controller

may run into poorly defined states, where it does not return a well-defined output.

Because of this investigation, triangular and trapezoidal membership functions have

been chosen for fuzzy logic controller.

The determination of membership functions is fulfilled by FORTRAN codes.

FORTRAN codes to define the input range of error and error_rate are given in Table

4.4.


64

Table 4.4. Input Ranges of error and error_rate xerror(1) =-1 xerror(2) =-0.5 xerror(3) =-0.2 xerror(4) =-0.025 xerror(5) =0 xerror(6) =0.025 xerror(7) =0.2 xerror(8) =0.5 xerror(9) =1 xerate(1) =-0.08 xerate(2) =-0.06 xerate(3) =-0.04 xerate(4) =-0.02 xerate(5) =0 xerate(6) =0.02 xerate(7) =0.04 xerate(8) =0.06 xerate(9) =0.08

Membership function of error’s FORTRAN codes are listed in Table 4.5.;

Table 4.5. Membership Function Codes of error if(input_error.GE.xerror(1).AND.input_error.LT.xerror(2)) errorout(7) =1

if(input_error.GE.xerror(2).AND.input_error.LT.xerror(3)) errorout(7) =negative_slopeof_error*(input_error-xerror(3))

if(input_error.GE.xerror(2).AND.input_error.LT.xerror(3)) errorout(6) =positive_slopeof_error*(input_error-xerror(2))



if(input_error.GE.xerror(4).AND.input_error.LT.xerror(5))


65

errorout(5) =negative_slopeof_error*(input_error-xerror(5))








if(input_error.GE.xerror(8).AND.input_error.LT.xerror(9)) errorout(1) =1

Membership function of error_rate’s FORTRAN codes are listed in Table 4.6.;

Table 4.6. Membership Function Codes of error_rate if(input_erate.GE.xerate(1).AND.input_erate.LT.xerate(2)) erateout(7) =1

if(input_erate.GE.xerate(2).AND.input_erate.LT.xerate(3)) erateout(7) =negative_slopeof_erate*(input_erate-xerate(3))

if(input_erate.GE.xerate(2).AND.input_erate.LT.xerate(3)) erateout(6) =positive_slopeof_erate*(input_erate-xerate(2))



if(input_erate.GE.xerate(4).AND.input_erate.LT.xerate(5))


66

erateout(5) =negative_slopeof_erate*(input_erate-xerate(5))








if(input_erate.GE.xerate(8).AND.input_erate.LT.xerate(9)) erateout(1) =1

4.3.3.2.2. Decision Making

For the designed controller, there are two inputs error and error_rate, which

have 7 membership functions for each. Normally, there must be 49 rules in the rule

base. The rule bases for the fuzzy controller are listed in Table 4.7., Table 4.8.

summarizes the rule base and Table 4.9. shows the rule base fuzzy logic controller;

Table 4.7. Rule Base for the FL Controller 1- IF error is (LI) AND error_rate is (LI) THEN DS is (DS1)

2- IF error is (LI) AND error_rate is (MI) THEN DS is (DS2)

3- IF error is (LI) AND error_rate is (SI) THEN DS is (DS3)

4- IF error is (LI) AND error_rate is (N) THEN DS is (DS4)

5- IF error is (LI) AND error_rate is (SD) THEN DS is (DS5)


67

6- IF error is (LI) AND error_rate is (MD) THEN DS is (DS6)

7- IF error is (LI) AND error_rate is (LD) THEN DS is (DS7)

8- IF error is (MI) AND error_rate is (LI) THEN DS is (DS8)

9- IF error is (MI) AND error_rate is (MI) THEN DS is (DS9)

10- IF error is (MI) AND error_rate is (SI) THEN DS is (DS10)

11- IF error is (MI) AND error_rate is (N) THEN DS is (DS11)

12- IF error is (MI) AND error_rate is (SD) THEN DS is (DS12)

13- IF error is (MI) AND error_rate is (MD) THEN DS is (DS13)

14- IF error is (MI) AND error_rate is (LD) THEN DS is (DS14)

15- IF error is (SI) AND error_rate is (LI) THEN DS is (DS15)

16- IF error is (SI) AND error_rate is (MI) THEN DS is (DS16)

17- IF error is (SI) AND error_rate is (SI) THEN DS is (DS17)

18- IF error is (SI) AND error_rate is (N) THEN DS is (DS18)

19- IF error is (SI) AND error_rate is (SD) THEN DS is (DS19)

20- IF error is (SI) AND error_rate is (MD) THEN DS is (DS20)

21- IF error is (SI) AND error_rate is (LD) THEN DS is (DS21)

22- IF error is (N) AND error_rate is (LI) THEN DS is (DS22)

23- IF error is (N) AND error_rate is (MI) THEN DS is (DS23)

24- IF error is (N) AND error_rate is (SI) THEN DS is (DS24)

25- IF error is (N) AND error_rate is (N) THEN DS is (DS25)

26- IF error is (N) AND error_rate is (SD) THEN DS is (DS26)

27- IF error is (N) AND error_rate is (MD) THEN DS is (DS27)

28- IF error is (N) AND error_rate is (LD) THEN DS is (DS28)

29- IF error is (SD) AND error_rate is (LI) THEN DS is (DS29)

30- IF error is (SD) AND error_rate is (MI) THEN DS is (DS30)

31- IF error is (SD) AND error_rate is (SI) THEN DS is (DS31)

32- IF error is (SD) AND error_rate is (N) THEN DS is (DS32)


68

33- IF error is (SD) AND error_rate is (SD) THEN DS is (DS33)

34- IF error is (SD) AND error_rate is (MD) THEN DS is (DS34)

35- IF error is (SD) AND error_rate is (LD) THEN DS is (DS35)

36- IF error is (MD) AND error_rate is (LI) THEN DS is (DS36)

37- IF error is (MD) AND error_rate is (MI) THEN DS is (DS37)

38- IF error is (MD) AND error_rate is (SI) THEN DS is (DS38)

39- IF error is (MD) AND error_rate is (N) THEN DS is (DS39)

40- IF error is (MD) AND error_rate is (SD) THEN DS is (DS40)

41- IF error is (MD) AND error_rate is (MD) THEN DS is (DS41)

42- IF error is (MD) AND error_rate is (LD) THEN DS is (DS42)

43- IF error is (LD) AND error_rate is (LI) THEN DS is (DS43)

44- IF error is (LD) AND error_rate is (MI) THEN DS is (DS44)

45- IF error is (LD) AND error_rate is (SI) THEN DS is (DS45)

46- IF error is (LD) AND error_rate is (N) THEN DS is (DS46)

47- IF error is (LD) AND error_rate is (SD) THEN DS is (DS47)

48- IF error is (LD) AND error_rate is (MD) THEN DS is (DS48)

49- IF error is (LD) AND error_rate is (LD) THEN DS is (DS49)

Table 4.8. FL Controller Decision Rules

Error_rate

LD MD SD N SI MI LI

Erro

r

LD DS49 DS48 DS47 DS46 DS45 DS44 DS43

MD DS42 DS41 DS40 DS39 DS38 DS37 DS36

SD DS35 DS34 DS33 DS32 DS31 DS30 DS29

N DS28 DS27 DS26 DS25 DS24 DS23 DS22

SI DS21 DS20 DS19 DS18 DS17 DS16 DS15

MI DS14 DS13 DS12 DS11 DS10 DS9 DS8

LI DS7 DS6 DS5 DS4 DS3 DS2 DS1


69

Table 4.9. FL Rule Base Fuzzy Logic Controller

Error_rate

LD MD SD N SI MI LI Er

ror

LD SN SN SN MN MN BN BN

MD SN SN SN SN MN MN BN

SD Z Z Z Z Z Z Z

N Z Z Z Z Z Z Z

SI Z Z Z Z Z Z Z

MI BP MP MP SP SP SP SP

LI BP BP MP MP SP SP SP

Fuzzy inference process is realized by one of the Mamdani or Takagi-Sugeno

methods. Fuzzification of inputs and applying the fuzzy operator are completely the

same in both methods. The main difference is that the output membership functions

of Takagi-Sugeno method are linear or constant. Takagi and Sugeno method is used

in the proposed driver.

There are several ways to define the result of a rule, but one of the most

common and simplest is the "max-min" inference method (min. operator), in which

the output membership function is given the truth value generated by the premise.

An example of a max-min inference method is given in Figure 4.22., Figure

4.23. and Table 4.10. It is estimated that the error is -0.02 and the error_rate is -

0.037;

Figure 4.22. Input of error Signal

0.025-0.025 0

LIMISINSDMDLD

10.50.2-0.2-0.5

1.0

-1


70

Figure 4.23. Input of error_rate Signal Table 4.10. Fuzzy Decision Rule Table of Example

Error_rate

LD MD SD N SI MI LI

Erro

r

LD DS49 DS48 DS47 DS46 DS45 DS44 DS43

MD DS42 DS41 DS40 DS39 DS38 DS37 DS36

SD DS35 DS34 DS33 DS32 DS31 DS30 DS29

N DS28 DS27 DS26 DS25 DS24 DS23 DS22

SI DS21 DS20 DS19 DS18 DS17 DS16 DS15

MI DS14 DS13 DS12 DS11 DS10 DS9 DS8

LI DS7 DS6 DS5 DS4 DS3 DS2 DS1

According to the values of input signals, the fuzzy decision rule table is

arranged. The membership values (Mem.V.) of decision blocks at the intersection

points are determined from the membership functions of input signals.

The membership values of the input signals determined as DS34(0.8,0.8),

DS33(0.8,0.3), DS27(0.3,0.8) and DS26(0.3,0.3) for the example. Min operator

chooses the minimum ones of membership values for each decision blocks. The

membership values of blocks are determined as DS34(0.8), DS33(0.3), DS27(0.3)

and DS26(0.3). After the minimum implication method, the process continuous with

the defuzzification step.

4.3.3.2.3. Defuzzification

It is the process of converting the controller outputs in linguistic labels

represented by fuzzy set to real control (analog) signals. Sugeno’s wtaver (weighted

-0.02 0-0.06 -0.04

LIMISINSDMDLD

0.080.060.040.02-0.08

1.0


71

average) method is selected for defuzzification and it is a special case of Mamdani

model. For real valued inputs x1,..., xn, the output y of the fuzzy system is a

weighted average of the outputs of all the rules. The solution of defuzzification

process results from this equation (Teke, 2005).

∑∑

=

== M

il

M

ill

w

ywy

1

1 . (4.14)

An example which is explained in above for the interpretation of output with

minimum implication method and defuzzification process is given in the below

formula;

26..27..33..34..26..2627..2733..3334..34

VMemVMemVMemVMemVMemDSVMemDSVMemDSVMemDStFuzzyOutpu

+++×+×+×+×= (4.15)

The design procedure is finished, and the resulting controller is tested with

simulation studies. To obtain the results, the output signal of fuzzy logic controller is

sent to the firing pulse generation block (see in Figure 4.24.). This block arranges the

output signal of FL controller as a firing pulse for each thyristors. Firing pulses is

given in Figure 4.25.


72

Figure 4.24. Firing Pulse Generation Block

Figure 4.25. Firing Pulses for Each Thyristors


73

4.4. Over and Under Voltage Protection Unit

An over voltage protection which removes the motor from operation when a

high-voltage condition occurs. Over voltage damages insulations. So, over voltage

protection must be used to extend the lifetime of motor. The over voltage protection

unit is created by using PSCAD library equipment as shown in Figure 4.26. The unit

allows the high voltage for 0.5 seconds. But, the unit removes the motor from

operation when supply voltage exceeds the 15% of nominal voltage and the time

exceeds 0.5 seconds.

An under voltage protection which removes the motor from operation when a

low-voltage condition occurs. The benefit of that protection is that the motor will not

draw excessive current or which prevents a large induction from starting under low

voltage conditions. The under voltage protection unit is created by using PSCAD

library equipment as shown in Figure 4.27. The unit allows the low voltage for 0.5

seconds. But, the unit removes the motor from operation when supply voltage

decreases the 15% of nominal voltage and the time exceeds 0.5 seconds.

Figure 4.26. Over Voltage Protection Unit


74

Figure 4.27. Under Voltage Protection Unit

4.5. Over Current Protection Unit

Over current is a condition in motors when too much current is running

through the motor. Overloading of the circuit, overheating or even a short circuit or

fire can cause over current. An over current protection which removes the motor

from operation when a high-current condition occurs. The over current protection

unit is created by using PSCAD library equipment as shown in Figure 4.28. The unit

allows the high currents for 0.5 seconds. But, the unit removes the motor from

operation when supply current exceeds the 1.5 times of nominal current and the time

exceeds 0.5 seconds.

Figure 4.28. Over Current Protection Unit

5. SIMULATON RESULTS Murat Mustafa SAVRUN

75

5. SIMULATION RESULTS

5.1. Proposed PSCAD Model and System Parameters

In this chapter, the performance and effectiveness of the proposed system is

tested with different case studies. The circuit diagram of the modeled system for

simulation study of motor driver is shown in Figure 5.1.

In this simulation study, it is aimed to have the following properties by

adjusting the stator voltage of motor for different loads:

§ Energy saving at lightly loads

§ Power factor improvement

§ Reduction of inrush current

§ Reduction of electrical stress

System parameters of the simulation are listed in the Table 5.1.;

Table 5.1. System Parameters Supply Voltage

Line-to-Line Voltage 400 V

Frequency 50 Hz

Motor Parameters

Rated Power 10 hp / 7.5 kW

Pole 4 pole

Voltage (V) 230 V

Rated Current (I) 16.3 A

Efficiency (η) %82.5

Power Factor (pf) 0.8

Frequency 50 Hz

Thyristor Parameters

Thyristor on Resistance 0.01 ohm

Thyristor off Resistance 6101×


76

Figure 5.1. Circuit Diagram of Motor Driver


77

5.2. Case 1: No Load

The idling (no_load) situation of motor is examined in the Case 1. The

operation graphics of motor which is running with and without driver is compared. In

all simulation results, the system starts to work as driverless and the driver is

activated at 0.7001 th second. The simulation results are given in Figure 5.2. – Figure

5.12. for the Case 1.

Figure 5.2. Motor Load Voltage With_Driver

Figure 5.3. Motor Load Voltage Without_Driver


78

As it seen from Figure 5.2. and Figure 5.3., rms value of the motor load

voltage is 230 V in without_driver operation. But, rms value of the motor load

voltage is decreased to 140 V in with_driver operation by using thyristors.

Figure 5.4. Motor Load Current With_Driver

Figure 5.5. Motor Load Current With_Driver in Large Scale


79

Figure 5.6. Motor Load Current Without_Driver

Figure 5.4. and Figure 5.6. show that motor load current is decreased through

the voltage reduction in the running mode of motor. However, the rms value of

motor load current is 8 A in without driver operation, the rms load current value is

decreased to 2.7 A in with driver operation.

Figure 5.7. Motor Input Power With_Driver


80

Figure 5.8. Motor Input Power Without_Driver

The starting current of asynchronous motor is 5-8 times larger than the rated

current. It is clearly seen in Figure 5.8., motor consumes more power in starting

mode than running mode. The power consumption value of motor is approximately

403 W in running mode. As it is seen from Figure 5.7., when driver has been used,

the running currents of motor are decreased. So, the power consumption value of

motor is reduced to 140 W in running mode.

Figure 5.9. Motor Input Reactive Power With_Driver


81

Figure 5.10. Motor Input Reactive Power Without_Driver

Due to the air-gap between the stator and rotor, asynchronous motor attracts a

larger magnetizing current than transformers. Motor consumes less active power

from network to meet iron losses of stator and friction losses of rotor at no load

operation. The active power component of current is smaller than the reactive power

component (magnetizing current) of current which is drawn by the motor from

network. Therefore, motor no-load power factor is 0.1 to 0.2 or 0.3 which is such a

small value.

As it is seen from Figure 5.9. and Figure 5.10., reactive power component

(magnetizing current) of motor current is decreased by reducing the stator voltage.

So, the power factor value of motor is increased at no load operation.

As it is clearly seen from Figure 5.11. and 5.12., although current, voltage

and power values of motor are reduced, the desired motor speed values remained

constant in the Case 1.


82

Figure 5.11. Speed of Motor With_Driver

Figure 5.12. Speed of Motor Without_Driver

When the results of Case 1 are investigated in detail, figures obviously show

that energy efficiency is obtained from active power and power factor. When motor

is operated without driver, it consumes 403 W. If the driver is used, approximately

262 W active power is saved and 65% energy efficiency is achieved. All efficiency

values were calculated after the driver had started.


83

5.3. Case 2: 10% Loaded

The situation of 10% torque of motor is examined in the Case 2. The

operation graphics of motor which is running with and without driver is compared.

The graphics are presented in Figure 5.13. – Figure 5.23. for the Case 2.




84

It can be clearly seen from Figure 5.13. and Figure 5.14., rms value of the

motor load voltage is not changed that as 230 V in without_driver operation. But,

with the use of the driver, rms value of the motor load voltage is decreased to 160 V.




85


Motor load current waveforms are presented in Figure 5.15. and Figure 5.17.

Figures obviously show that the rms value of motor load current is smaller in driver

operation. However, the rms value of motor load current is 8.1 A in without driver

operation, the rms load current value is decreased to 4.3 A in with driver operation.



86


Figure 5.19. presents that when motor is operated without driver, the power

consumption value is approximately 1.169 kW in running mode. As it is seen from

Figure 5.18., when driver has been used, the running currents of motor are decreased.

So, the power consumption value of motor is reduced to 0.966 kW in running mode.



87


Figure 5.20. and Figure 5.21. show that reactive power component


So, the power factor value of motor has been improved for the operation of 10%

torque.



88

As it is clearly seen from Figure 5.22. – Figure 5.23., although current,

voltage and power values of motor are reduced, the desired motor speed values

remained constant again in the Case 2.


When the results of Case 2 are examined in detail, figures obviously show


is operated without driver, it consumes 1.169 kW. If the driver is used,

approximately 203 W active power is saved and 17.37% energy efficiency is

achieved. Moreover 66% power factor efficiency is also achieved.


20% torque situation of motor is examined in the Case 3. The operation

graphics of motor which is running with and without driver is compared. The

simulation results are given in Figure 5.24. – Figure 5.34. for the Case 3.


89





voltage is decreased to 180 V in with_driver operation by controlling the stator

voltages.


90




91


Figure 5.26. and Figure 5.28. display that motor load current is decreased

through the voltage reduction in the running mode of motor. However, the rms value

of motor load current is 8.2 A in without driver operation, the rms load current value

is decreased to 5.9 A in with driver operation.



92


The power consumption value of motor is approximately 1.953 kW in

running mode as shown in Figure 5.30. As it is seen from Figure 5.29., when driver

has been used, the running currents of motor are decreased. So, the power

consumption value of motor is reduced to 1.809 kW in running mode.



93





torque.



94





Figures obviously show that energy efficiency is obtained from active power

and power factor, when the results of Case 3 are examined in detail. When motor is

operated without driver, it consumes 1.953 kW. If the driver is used, approximately

144 W active power is saved and 7.37% energy efficiency is achieved. Moreover

31% power factor efficiency is also achieved.


Situation of 30% torque of motor is examined in the Case 4. The operation




95




motor load voltage is 230 V in without_driver operation. But, with the use of driver,

rms value of the motor load voltage is decreased to 200 V.


96




97



Figures obviously show that the rms value of motor load current is 8.6 A in without

driver operation, the rms load current value is decreased to 7.35 A in with driver

operation.



98








99





torque.



100








approximately 87 W active power is saved and 3.16% energy efficiency is achieved.

Moreover 14% power factor efficiency is also achieved.


40% torque situation of motor is examined in the Case 5. The operation




101





voltage is decreased to 220 V in with_driver operation by controlling the stator

voltages.


102




103


Figure 5.48. and Figure 5.50. display that motor load current is decreased

through the voltage reduction in the running mode of motor. However, the rms value

of motor load current is 9.3 A in without driver operation, the rms load current value

is decreased to 8.75 A in with driver operation.



104


The power consumption value of motor is approximately 3.583 kW in

running mode as shown in Figure 5.52. As it is seen from Figure 5.51., when driver

has been used, the running currents of motor are decreased. So, the power

consumption value of motor is reduced to 3.542 kW in running mode.



105





torque.



106





Figures obviously show that energy efficiency is obtained from active power

and power factor, when the results of Case 5 are examined in detail. When motor is

operated without driver, it consumes 3.583 kW. If the driver is used, approximately

41 W active power is saved and 1.14% energy efficiency is achieved. Moreover 7%

power factor efficiency is also achieved.


Situation of 50% torque of motor is examined in the Case 6. The operation




107




motor load voltage is 230 V in without_driver operation. But, with the use of driver,

rms value of the motor load voltage is decreased to 225 V.


108




109



Figures obviously show that the rms value of motor load current is 10.03 A in

without driver operation, the rms load current value is decreased to 9.9 A in with

driver operation.



110








111





torque.



112








approximately 16 W active power is saved and 0.36% energy efficiency is achieved.

Moreover 3% power factor efficiency is also achieved.

5.8. Case 7: Dynamic Performance

The dynamic performance situation of motor is examined in the Case 7. The

motor has been operated in different loads as respectively 0%, 10%, 30%, 20%, 40%,

0%, 10% and 0%. The dynamic performance waveforms are shown in Figure 5.68.

and 5.69.


113

Figure 5.68. Dynamic Performance With_Driver

Figure 5.69. Dynamic Performance Without_Driver

When the figures examined in detail, it is obviously seen that energy

efficiency is obtained from active power. Energy efficiency in lightly loads is easily

observed from the figures. Efficiency is emphasized more clearly in the Figure 5.70.


114

Figure 5.70. Comparison of Power Consumption in Dynamic Performance

5.9. Case 8: Short Circuit Performance

The system generally operates in steady state conditions but the characteristic

of the system in the short circuit or abnormal conditions must be known (Teke,

2005). The dynamic short circuit performance of 10 hp induction machine is shown,

respectively in Figure 5.71, 5.72 and 5.73 during a short-circuit fault at the terminals.

Initially the motor is running at steady state conditions with a constant load

torque. The short circuit fault at the terminals is simulated by setting the fault

condition at time t=2 sec. At that instant, the short circuit occurs and causes a high

increase in current and a decrease in voltage. If the peak input current increases, this

increase causes premature diode failure, which may occur within minutes. If rms

input current increases, this increase causes additional heating in the input wiring,

which may result in damage to the installation (Schneider). To prevent this adverse

effects, the under voltage and the over current protections directly start up. The

protections block the operation in 0.1 seconds (see Figure 5.83, 5.84 and 5.8.).


115




116


6. CONCLUSION Murat Mustafa SAVRUN

117

6. CONCLUSION

By the day, because of the exponential growth of world population, industrial

developments and the world economy, the energy consumption values of the world is

increasing exponentially. Exhaustible sources not cover the growing energy needs of

the world in the coming years. Hence, world prefers the use of renewable energy

instead of fossil fuels. Besides, the importance that is given to energy efficiency

applications is increasing to use limited energy as more efficient. To use the clean

energy as more efficient, new policies and regulations are determined in Turkey as

well as all over the world.

Industrial sectors are the most energy consuming sector in the world.

Industrial motors use a significant portion of the total industrial energy use. Induction

motors are widely used in industry because of their cost, reliability, robustness and

easy maintenance. Hence, minimizing losses in induction machines would be a major

step towards minimizing the global electric load and energy consumption.

Efficiency of induction machine system improvement activities are divided

into three main directions as machine, power electronics, and control. Such

improvement activities include driver performance, starting methods, induction

motor improved components, and motor loss reduction.

Despite designing the motors for their highest efficiency at the full load,

many industrial induction motors are not operating at full load all the time. In most

instances, motors run under light loads or no load conditions. Typical of such loads

are noncontinuous extruders, conveyors, and some types of fans and compressors

that are constant speed-variable torque applications. In this type of loads that are

been operating in light load operation, a reduction in losses and an improvement in

motor efficiency can be obtained by reducing the stator voltage of the motor.

In this thesis efficiency improvement of lightly load induction motor by

controlling the stator voltage has been examined. Efficiency improvement by stator

flux control is achieved by reducing the applied voltage whenever the torque

requirement of the load can be met with less than full motor flux. Because of


118

reduction in stator flux, the magnetizing component of stator current, core loss and

also copper loss are reduced.

The motor load voltage (stator voltage) is reduced by using thyristor firing

angles according to the torque requirement of the motor. As can be seen from the

Figure 6.1., stator voltage is smaller in with driver operation. The appropriate load

voltage is setting for each torque value. Stator voltage control is performing in 50%

and below loaded conditions, since energy efficiency can be obtained in lightly

loaded operations.

Figure 6.1. Comparison of Load Voltage with and without Driver

The motor load current is reduced by reducing the stator voltage according to

the torque requirement of the motor. As can be seen from the Figure 6.2., load

current is smaller in with driver operation. It means that the motor is consuming

smaller energy with the driver.

100

120

140

160

180

200

220

240

0.0 0.1 0.2 0.3 0.4 0.5

Volta

ge

Torque

Load Voltage

with FLBEC

without FLBEC


119

Figure 6.2. Comparison of Load Current with and without Driver

The total harmonic distortion (THD) of the input current of motor which

operated with the proposed driver as a function of the load is presented in Figure 6.3.

When THD value of the proposed system and THD value of studies in the literature

are compared, it is obviously seen that the proposed system has better THD values.

Figure 6.3. Comparison of Load Current with and without Driver

When the motor current and voltage decrease, the value of motor speed varies

little. It is obviously seen from the Figure 6.4., the motor speed value varies less than

1% that is an acceptable value.

0

2

4

6

8

10

12

0.0 0.1 0.2 0.3 0.4 0.5

Curr

ent

Torque

Load Current

with FLBEC

without FLBEC

0

10

20

30

40

50

60

70

0 0,1 0,2 0,3 0,4 0,5 0,6

THD

%

Motor Load

Total Harmonic Distortion


120

Figure 6.4. Comparison of Motor Speeds with and without Driver

Figure 6.5. Comparison of Power Consumptions with and without Driver

It can be clearly seen from Figure 6.5. and Table 6.1., that when controlled by

driver on lightly loaded conditions, motor can get higher efficiency values. In

conclusion, the presented energy efficiency strategy for 10 hp induction motor brings

a substantial energy savings, particularly at low loads. Proposed driver can be used

for improving the efficiency of electric motors which are constant speed and variably

loaded applications. The driver detects the motor load by using the feedback of

motor speed and delivers the precise amount of energy required by the motor. This

reduces unnecessary energy use. The driver has concluded to save up to 65% of the

energy normally used in appropriate applications.

1200

1250

1300

1350

1400

1450

1500

1550

1600

0.0 0.1 0.2 0.3 0.4 0.5

Spee

d

Torque

Motor Speeds

with FLBEC

without FLBEC

0500

100015002000250030003500400045005000

0 0,05 0,1 0,15 0,2 0,25 0,3 0,4 0,5

Inpu

t Po

wer

(W)

Torque (pu)

Power Efficiency

with FLBEC

without FLBEC


121

Table 6.1. FL Power and Power Factor Efficiency

Proposed driver not only results in a measureable reduction in energy, but

also an improvement in power factor values. In driverless case, due to the large phase

difference between current and voltage, the motor consumes much reactive power. In

driver case, the amount of consumed reactive power is decreased by reducing the

phase difference between current and voltage. So power factor efficiency can be

ensured (see in Figure 6.6. and Table 6.1.).

Figure 6.6. Comparison of Power Factors with and without Driver

The motor power efficiency control with thyristor is not a new topic but not

much work has been reported on it yet. There are only a few theoretical and

simulation studies related about this subject. This study gives some help to literature

Torque Power Efficiency (%) Saved Power (W) PF Efficiency (%)0 65,01 2620,05 29,21 229 92,860,1 17,37 203 66,670,15 11,10 173 46,430,2 7,37 144 31,430,25 4,93 116 21,950,3 3,16 87 14,890,4 1,14 41 7,140,5 0,36 16 3,13

Power Savings & Efficiencies as a Result of Voltage Reduction

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,05 0,1 0,15 0,2 0,25 0,3 0,4 0,5

Pow

er F

acto

r

Torque (pu)

Power Factor Efficiency

with FLBEC

without FLBEC


122

with new controller, and different case studies. The publications made as a result of

this study will also contribute to scientific literature. Further research could be

carried out in the following areas:

§ Performance evaluation of the motor under the different switching

techniques.

§ Investigation and comparison of the efficiency values under the different

topologies of driver.

§ Comparison of the performance of the simulation results and laboratory

application results.

123

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129

BIOGRAPHY

Murat Mustafa SAVRUN was born in Osmaniye, Turkey in 1987. He

received his B.S. degree in Electrical and Electronics Engineering Department from

Çukurova University in 2010. After completion his B.S. education, he started MSc

education in Electrical and Electronics Engineering Department in Çukurova

University. He has been working as a Research Assistant in Electrical and

Electronics Engineering Department of the Osmaniye Korkut Ata University since

2010. His research areas are Energy Efficiency, Motor Drives and Power Electronics.

Çukurova university institute of natural and …

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