EFFICIENCY OPTIMIZATION OF A VECTOR CONTROL …eprints.uthm.edu.my/8752/1/Sim_Sy_Yi.pdf · research...

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EFFICIENCY OPTIMIZATION OF A DIRECT TORQUE CONTROL (DTC)

INDUCTION MOTOR DRIVE

SIM SY YI

UNIVERSITI TUN HUSSEIN ONN MALAYSIA

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UNIVERSITI TUN HUSSEIN ONN MALAYSIA

STATUS CONFIRMATION FOR DOCTORAL THESIS



ACADEMIC SESSION : 2015/2016

I, SIM SY YI, agree to allow this Doctoral Thesis to be kept at the Library under the following terms: 1. This Doctoral Thesis is the property of the Universiti Tun Hussein Onn Malaysia. 2. The library has the right to make copies for educational purposes only. 3. The library is allowed to make copies of this report for educational exchange between higher

educational institutions. 4. ** Please Mark (√)

CONFIDENTIAL (Contains information of high security or of great importance to Malaysia as STIPULATED under the OFFICIAL SECRET ACT 1972) RESTRICTED (Contains restricted information as determined by the Organization/institution where research was conducted) FREE ACCESS

Approved by, (WRITER’S SIGNATURE) (SUPERVISOR’S SIGNATURE) Permanent Address: NO 4, LORONG J, JALAN MASJID, 83000, BATU PAHAT, JOHOR. Date : ___________________________ Date: _____________________________ NOTE: ** If this Doctoral Thesis classified as CONFIDENTIAL or RESTRICTED,

please attach the letter from the relevant authority/organization stating reasons and duration for such classifications.

√

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This thesis has been examined on date 14 December 2015 and is sufficient in fulfilling

the scope and quality for the purpose of awarding the Degree of Doctor of Philosophy.

Chairperson:

Prof. Madya Dr. Mohamad Nor bin Mohamad Than Faculty of Electrical and Electronic Engineering University Tun Hussein Onn Malaysia

Examiners:

Prof. Dr. Nasrudin bin Abd Rahim Deputy Vice Chancellor (Research & Innovation) University of Malaya

Dr. Shamsul Aizam bin Zulkifli Faculty of Electrical and Electronic Engineering University Tun Hussein Onn Malaysia

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SIM SY YI

A thesis submitted in

fulfilment of the requirement for the award of the

Doctor of Philosophy

Faculty of Electrical and Electronic Engineering

Universiti Tun Hussein Onn Malaysia

JANUARY 2016

ii

I hereby declare that the work in this project report is my own except for quotations

and summaries which have been duly acknowledged

Student : …………………………………….……….

SIM SY YI

Date : …………………………………….……….

Supervisor : …………………………………….……….

DR WAHYU MULYO UTOMO

Co Supervisor : …………………………………….……….

ASSOC. PROF. DR .ZAINAL ALAM BIN

HARON

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Specially dedicated to my beloved family

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ACKNOWLEDGEMENT

First of all, I would like to take this opportunity to express my deepest gratitude to my

research supervisors; Dr. Wahyu Mulyo Utomo and Associate Professor. Dr .Zainal

Alam Bin Haron, who has persistently and determinedly assisted me during the whole

course of this research. It would have been very difficult to complete this research

without the enthusiastic support, insight and advice given by my supervisors.

My utmost thank also goes to my family. Words cannot be expressed for how

grateful I am to my parents for all the support that gave me throughout my academic

years. Without them, I might not be the person I am today.

Last but not least, my special gratitude is extended to all members of the F1

research group that has been my guidance and giving the support for my research. It is

my greatest thanks and joy that I have met these research group. Thank you.

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ABSTRACT

Typical Direct Torque Control (DTC) Induction Motor (IM) drive has good efficiencies

while operating at its rated condition. Nevertheless, a motor drive operates far from its

rated operating condition, which impairs the efficiency. Therefore, a conventional DTC

IM drive system should be associated with a loss minimization strategy to maximize the

drive efficiency for a wider range of operation. For a given operating condition, the

losses in an IM can be minimized by adjusting to the appropriate flux level instead of

conventionally keeping constant at its rated flux value. Consequently, this research

proposed an online learning Artificial Neural Network Efficiency Optimization

(ANNEO) controller with the aim to generate an adaptive flux level to optimize the

efficiency of any different operating points, especially at low speed and low torque

condition. ANNEO is implemented with an online learning second order Levernberg-

Marquardt algorithm. The proposed controller uses the input power of the drive system

as the objective function and minimizes it. In order to further improve the system

dynamic response, an Artificial Neural Network speed controller (ANNSC) of DTC is

included. In ANNSC, the back propagation algorithm is employed. The goal of this

research is to achieve the efficiency optimization of DTC induction motor drives system

while preserve the superior speed performance of IM application over a wide speed

range. The entire DTC drive system along with the proposed adaptive flux ANN based

controller has been modeled in Simulink/Matlab and validated experimentally by the

ControlDesk 5.1 with dSPACE DS1103 Real-time Digital Signal Controller. An

efficiency improvement as high as 14.6% and 12.75% have achieved for simulation and

experimental testing respectively at low speed and low load condition. The simulation

and experimental results show that the proposed technique managed to generate adaptive

optimum flux level, hence improves the efficiency of the DTC induction motor drive

system while at the same time, preserving excellent speed performance of the systems.

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ABSTRAK

System pemacu motor aruhan (IM) Direct Torque Control (DTC) mempunyai

kecekapan yang baik ketika beroperasi pada keadaan kadar. Walau bagaimanapun,

kebanyakan masa motor beroperasi jauh daripada titik pengendalian yang kadar. Oleh

itu, kecekapan motor berkurang. Lantarannya, sistem pemacu DTC yang konvensional

seharusnya bersekutu dengan strategi peminimuman kerugian untuk memaksimumkan

kecekapan pemacu bagi pelbagai jenis operasi. Ia adalah satu fakta yang diketahui

bahawa pada satu titik pengendalian yang diberikan, kerugian di dalam satu motor

aruhan boleh dikurangkan dengan melaraskan fluks kepada tahap fluks yang sesuai.

Oleh yang demikian, penyelidikan ini mengamalkan Artificial Neural Network

Efficiency Optimization (ANNEO) yang pembelajaran online dengan matlamat untuk

menjana satu nilai fluks yang adaptif untuk mengoptimumkan kecekapan pada setiap

titik pengendalian yang berbeza, terutamanya apabila pada kelajuan motor yang

perlahan dan keadaan kilas yang rendah. ANNEO telah dilaksanakan dengan

pembelajaran online yang peringkat dua, iaitu Levernberg-Marquardt algoritma.

Pengawal yang dicadangkan untuk penyelidikan ini menggunakan kuasa input sistem

pemacu sebagai fungsi objektif dan mengurangkannya. Untuk memajukan lagi sistem

gerak balas dinamik, Artificial Neural Network Speed Controller (ANNSC) DTC telah

ditambahkan pada sistem. Dalam ANNSC, algoritma penyebaran belakang telah

dilaksanakan. Matlamat kajian ini adalah untuk mencapaikan kecekapan optimum pada

sistem pemacu motor aruhan DTC dan pada masa yang sama mengekalkan prestasi

kelajuan motor yang unggul pada julat kelajuan yang luas. Seluruh sistem pemacu DTC

bersama-sama dengan pengawal fluks adaptif ANN telah dimodelkan dalam Simulink

/ Matlab dan disahkan secara eksperimen oleh ControlDesk 5.1 dengan DSpace

DS1103 Real-time Digital Signal Controller. Peningkatan kecekapan sebanyak 14.6%

dan 12.75% telah dicapaikan oleh ujian simulasi dan eksperimen masing masing

dikeadaan kelajuan motor yang perlahan dan keadaan kilas yang rendah Keputusan

ujian simulasi dan eksperimen menunjukkan bahawa teknik yang dicadangkan berjaya

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menjana tahap fluks optimum yang adaptif serta meningkatkan kecekapan sistem

pemacu motor aruhan DTC dan pada masa yang sama kekalkan prestasi cemerlang

kelajuan sistem.

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TABLE OF CONTENTS

TITLE i

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS viii

LIST OF TABLES xiii

LIST OF FIGURES xiv

LIST OF SYMBOLS AND ABBREVIATIONS xxi

LIST OF APPENDICES xxiii

CHAPTER 1 INTRODUCTION

1.1 Introduction 1

1.2 Research Background 1

1.3 Problem Statements 3

ix

1.4 Research Objectives 4

1.5 Research Scope 4

1.6 Thesis Outline 5

CHAPTER 2 LITERATURE REVIEW

2.1 Introduction 7

2.2 Variable Speed Drive (VSD)-Induction Motor

Drive System

7

2.2.1 Scalar Control 10

2.2.2 Vector Control 11

2.2.2.1 Field Oriented Control

(FOC)

12

2.2.2.2 Direct Torque Control

(DTC)

14

2.3 Efficiency Optimization of Induction Motor 17

2.4 Efficiency Optimization Technique 21

2.4.1 Search Control Technique (Physics-

based)

23

2.4.2 Loss Model Technique (Model based

technique)

23

2.4.3 Hybrid Control Technique 24

2.5 Review of Existing Previous Work 25

2.5.1 Vector Control of Induction Motor 25

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2.5.2 Efficiency Optimization of Vector

Control Induction Motor Drives

System

29

2.5.3 Result Comparison 39

2.6 Gap of Study 42

CHAPTER 3 RESEARCH METHODOLOGY

3.1 Introduction 44

3.2 Control Strategy of the Proposed Efficiency

Optimization of DTC IM Drive System

44

3.2.1 Flux and Torque Estimator Block 45

3.2.2 Flux and Torque Controller 47

3. 3 Proposed ANN Efficiency Optimization

Control Strategy

48

3.4 Learning Algorithm of Proposed ANNEO

Controller

52

3.5 Proposed ANN Speed Controller 58

CHAPTER 4 RESEARCH DESIGN AND RESULTS

4.1 Introduction 62

4.2 Overview of Systems Development 63

4.3 Simulation Model with the Implementation of

MATLAB/ SIMULINK

64

4.3.1 Direct Torque Controller Block 66

4.3.2 Space Vector Pulse Width

Modulation Inverter Block

67

4.3.3 Input Power and Efficiency Evaluation

Block

67

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4.3.4 Proposed ANN Efficiency Optimization

Controller Block

68

4.3.5 Proposed ANN Speed Controller Block 69

4.4 Hardware Development and Implementation 69

4.4.1 The Real-time Interface Integration

with dSPACE

70

4.4.1.1 Feedback of the Motor Speed

Signal

72

4.4.1.2 Feedback of the Motor

Current Signal

72

4.4.2 The Experiment Set-up 73

4.5 Results and Discussion 74

4.5.1 Simulation Tests for Efficiency

Improvement of the Proposed

ANNEO Controller.

75

4.5.2 Experimental Verification of the

Efficiency Improvement of the

Proposed ANNEO Controller

84

4.5.3 Results Discussion on Efficiency

Improvement of the Proposed

ANNEO Controller tests

93

4.4.4 Simulation Tests for Step Speed

Variation

104

4.5.5 Experimental Verification of Speed

Variation

106

4.5.6 Results Discussion on Speed Variations

Tests 108

4.5.7 Simulation Tests for Constant Speed

with Load Disturbance

109

4.5.8 Experimental Verification of Constant

Speed with Load Disturbance

112

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4.5.9 Results Discussion on Constant Speed

with Load Disturbance Tests 114

CHAPTER 5 CONCLUSION AND FUTURE WORK

5.1 Conclusion 116

5.2 Future Work 117

LIST OF PUBLICATIONS 119

REFERENCES 123

APPENDIX 135

VITAE 175

xiii

LIST OF TABLES

2.1 Gap of study for the efficiency optimization IM

drives from several recent works where the input

power is used as the objective function of the EO

controller

43

4.1 Resulting flux level determined by the adaptive

ANNEO for different operating points ( Simulation )

93

4.2 Resulting flux level determined by the adaptive

ANNEO for difference operating points

( Experiment )

93

4.3 Efficiency of the DTC with Constant Flux and

Optimum Flux reference corresponding to different

speeds and torques ( Simulation )

94

4.4 Efficiency of the DTC with Constant Flux and

Optimum Flux reference corresponding to different

speeds and torques ( Experiment )

95

4.5 Settling time comparison for the speed variation on

simulation and experimental tests. 109

4.6 Speed deviation comparison for the sudden load

disturbance on simulation and experimental tests 115

xiv

LIST OF FIGURES

2.1 Block diagram of a variable speed induction motor drive

system

9

2.2 General classification of IM control methods 9

2.3 Indirect vector control method 13

2.4 Direct vector control method 13

2.5 Hysteresis controlled of DTC induction motor drive 15

2.6 Block Diagram of a SVPWM-DTC IM drive system 17

2.7 Torque production with three different flux level in the light

load condition. (a)-nominal flux, (b) medium flux (optimum

flux) and (c) low flux

19

2.8 Types of losses of converter drive system with the flux

variation

20

2.9 Efficiency improvement by the flux program at variable

torque with constant speed

21

2.10 Categories of efficiency optimization control 22

2.11 Induction motor drive operated under real-time Efficiency

Optimization Technique

22

2.12 ωr and Te as the input of the optimization control block 30

2.13 ωr ,Te, Pin as well as the ∆ω as the inputs of the optimization

control block

33

2.14 Utilize the Te as the input to the optimization control block 35

2.15 Input to the optimization control block accordingly to the

input power, Pin and output power, Pout

38

3.1 Block Diagram of an efficiency optimization control system

of DTC IM drives

45

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3.2 Block Diagram of an efficiency optimization control system

of DTC IM drives

47

3.3 Architecture of proposed ANNEO controller 50

3.4 Architecture of proposed ANN speed controller 59

4.1 The general testing diagram of the proposed ANNEO DTC 64

4.2 Complete Simulink block of the proposed efficiency

optimization control for the direct torque control induction

motor drive system

65

4.3 Simulink circuit of the Direct Torque Control block 66

4.4 Simulink circuit of the SVPWM control technique for the

three phase’s inverter

67

4.5 Simulink block for the efficiency and input power evaluation. 68

4.6 Simulink circuit for the proposed ANN efficiency optimization controller

69

4.7 Simulink circuit for the proposed ANN speed controller 69

4.8 The connection between Matlab and dSPACE 70

4.9 Real-time interface model with dSPACE I/O Blocks 71

4.10 The experimental set-up of proposed efficiency optimization

control for the direct torque control induction motor drive

system

73

4.11 Simulation results of the system performance for the speed

at 500rpm and a 0.2Nm load torque applied where the

proposed ANNEO controller is activated at the time =0.5s:

(a) before the ANNEO controller is activated; (b) after the

ANNEO controller is activated

76

4.12 Simulation result of the d-q stator flux circle at 500rpm and

a 0.2Nm load torque applied, before and after the proposed

ANNEO controller activated

76






77

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77




(a) before the ANNEO controller is activated; (b)after the


78




78






79

4.18 Simulation result of the d-q stator flux circle at 800 rpm and



79






80

4.20 Simulation result of the d-q stator flux circle at 1100rpm

and a 0.2Nm load torque applied, before and after the

proposed ANNEO controller is activated

80






81

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81






82




82






83




83

4.27 Experimental results of the system performance for the

speed at 500rpm and a 0.2Nm load torque applied where the

proposed ANNEO controller is activated at the time =10s

(a): before the ANNEO controller is activated; (b): after the


85



proposed ANNEO controller is activated at the time =10s.



86




87

xviii






(a): before the ANNEO controller activated; (b): after the

ANNEO controller activated

88


speed at 1100rpm and a 0.2Nm load torque applied where

the proposed ANNEO controller is activated at the time

=10s. (a): before the ANNEO controller is activated; (b):

after the ANNEO controller is activated

89






90






91






92

4.35 The efficiency comparison for rated flux and optimal flux

value for the speed of (a) 1400rpm, (b) 1100rpm, (c) 800rpm

and (d)500rpm corresponding to different torques value

(simulation)

97

4.36 The efficiency comparison for rated flux and optimal flux

value for the speed of (a) 1400rpm, (b) 1100rpm, (c) 800rpm

99

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and (d)500rpm corresponding to different torques value

(experimental)

4.37 The efficiency comparisons for rated flux and optimal flux

value for different load torques, (a) 0.2Nm, (b) 0.4Nm, (c)

0.6Nm and (d) 0.8Nm corresponding to different speed value

(simulation)

101

4.38 The efficiency comparisons for rated flux and optimal flux

value for different load torques, (a) 0.2Nm, (b) 0.4Nm, (c)

0.6Nm and (d) 0.8Nm corresponding to different speed value

(experimental)

103

4.39 The speed, current and torque response for a step speed

variation under the 0.2Nm load torque applied: (a) online

learning ANNSC, (b) offline learning ANNSC

105

4.40 The speed, current and torque response for a step speed

variation under the 0.8Nm load torque applied: (a) online

learning ANNSC, (b) offline learning ANNSC

106

4.41 The speed and current response for a step speed variation

under the 0.2Nm load torque applied: (a) online learning

ANNSC, (b) offline learning ANNSC

107

4.42 The speed and current response for a step speed variation

under the 0.8Nm load torque applied: (a) online learning

ANNSC, (b) offline learning ANNSC

107

4.43 The speed, current and torque response for a constant speed

of 500rpm with load disturbance of 0.2Nm and 0.8Nm at the

time of 0.7s, and 1s: (a) online learning ANNSC, (b) offline

learning ANNSC

110


of 800rpm with load disturbance of 0.2Nm and 0.8Nm at the

time of 0.7s, and 1s: (a) online learning ANNSC, (b) offline

learning ANNSC

110


of 1100rpm with load disturbance of 0.2Nm and 0.8Nm at

111

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the time of 0.7s, and 1s: (a) online learning ANNSC, (b)

offline learning ANNSC


of 1400rpm with load disturbance of 0.2Nm and 0.8Nm at

the time of 0.7s, and 1s: (a) online learning ANNSC, (b)

offline learning ANNSC

111

4.47 The speed and current responses at constant speed of

500rpm with load disturbance of 0.2Nm and 0.8Nm at the

time of 6s, and 10s: (a) online learning ANNSC, (b) offline

learning ANNSC

112




learning ANNSC

113




learning ANNSC

113




learning ANNSC

114

xxi

LIST OF SYMBOLS AND ABBREVIATIONS

f - Frequency

Idq - d-q Axis Current

Ls - Stator Self Inductances

Lr - Rotor Self Inductances

Lm - Mutual Inductances

p - Pole

Pin - Input Power

Pout - Output Power

Rs - Stator Resistance

Rr - Rotor Resistance

Te - Torque

Vdq - d-q Axis Voltage

ѰrefANN - Optimum Flux reference

Ѱref0 - Rated Flux reference

η - Efficiency

xxii

ANNEO - Artificial Neural Network Efficiency Optimization

ANNSC - Artificial Neural Network speed controller

DTC - Direct Torque Control

FOC - Field Oriented Control

GA - Genetic Algorithms

IM - Induction Motor

LMC - Loss-Mode-Based Control

PWM - Pulse Width Modulation

SC - Search Control

SVPWM - Space Vector Pulse Width Modulation

VC - Vector Control

VSD - Variable Speed Drive

xxiii

LIST OF APPENDICES

A Modeling of induction Motor 135

B Space Vector Pulse Width Modulation technique ( SVPWM )

141

C Motor Parameters 153

D C++ Program for the Proposed Drive System 154

E DS1103 Controller Board 174

1

CHAPTER 1

INTRODUCTION

1.1 Introduction

In this chapter, the introduction of this research will be explained in detail which

consists of the research background towards the focusing of the research study,

problem statements, research objectives, research scopes and research outline.

1.2 Research Background

The increasing emphasis on energy saving highlights the importance of attaining

higher motor efficiency under all operating conditions especially for industrial

applications. According to International Energy Agency (IEA) 2011, it was estimated

that electric motor drive account for between 43% and 46% of all global electricity

consumption, giving rise to about 6040Mt of CO2 emissions. By 2030, without

comprehensive and effective energy‐efficiency policy measures, energy consumption

from electric motors is expected to rise to 13360 TWh per year and CO2 emissions to

8570Mt per year. In fact, 64% of it was consumed by the industrial sector, and about

15% of final energy use in industry worldwide (IEA 2007), which is reported around

2

4488TWh per year of electricity consumption. It is estimated that a full implementation

of efficiency improvement options could reduce worldwide electricity demand by

about 7% (IEA 2008). The end‐users now spend USD 565 billion per year on

electricity used in the electric motor drive, by 2030, that could rise to almost USD 900

billion [1], [2].

Electric motors drive both core industrial processes, they are utilized throughout

all industrial branches though main applications vary. With only some exceptions,

electric motors are the main source for the provision of mechanical energy in industry.

In recent years, many studies have identified large energy efficiency potentials in

electric motors and motor systems with many saving options showing very short

payback period and high cost effectiveness [3].

Induction Motor (IM), is diffusely used in electrical devices and particularly

consume a large fraction of all electric power. Thus, they are responsible for more

energy consumed by the electric motor and as a prime target for efficiency

improvement. According to IEA’s estimates, about 68% of electricity consumed by

electric motors are used by the medium size motor, which rated from 0.75kW to

375kW, the vast majority of which are induction motors. Therefore, efficiency

improvement in IM has been getting much attention in recent years. Increasing

efficiency on IM gives significant to energy saving. The efficiency optimization is

important not only from the viewpoint of energy saving but also from broad

perspective of environmental pollution control [1].

In order to realize the efficiency improvement, there has been enhancements in

using high-quality design, construction techniques, and materials. However, expert

control algorithm contributes the most in improving drive performance especially

when a motor operates at light load [4]. It is commonly known that the IM has good

efficiencies while operating at full load. However, at lighter load, which is a condition

that many machines experience for a significant portion of their service life, the

efficiency decreases to a large extent. Therefore, it is important to maximize the

efficiency of the motor drive system while operating in adjustable speed applications.

Generally, the efficiency of an IM drives can be improved by reducing flux level

when it operates under light load conditions. Existing loss minimization techniques

have different approaches and are selected accordingly to an application or drive type.

At present, the optimal efficiency control techniques for IM drives are commonly

classified into two categories namely 1) Search Control, (SC) and 2) Loss-Mode-Based

3

Control, (LMC) [5]–[13]. Primary idea of both techniques is to make the flux amplitude

change with the change of IM operating conditions, even though these two types have

dissimilar achievement and implementation way [14]–[16].

1.3 Problem Statements

Basically, when an IM is operated at a rated condition, i.e. rated load torque and rated

speed, the efficiency of motor is high and gives the best transient response. However,

in many applications, a motor operates far from its rated operating point, particularly

at light load, which is a condition that many machines experience for a significant

portion of their service life. At light load, the reference flux magnitude is held to its

initial value, and this cause the efficiency decreased to a large extent. At this light load

condition, rated flux operation causes excessive core loss, thus impairing the drive

efficiency due to the imbalance between iron and copper losses.

It is well known that for a given operating point, the efficiency of IM can be

improved by minimizing its losses. This can be achieved by reducing appropriate level

of magnetic flux or by programming the flux to obtain the balance between the copper

and iron losses.

Primary challenge of the efficiency optimization control of the IM drive system at

variable load operation is to obtain the optimum flux value. This optimum flux value

ensures a minimum input power that leading to maximum efficiency of the drives

systems, at any operating point over the centire torque and speed range. Meanwhile, it

is also important to remain the good primeval characteristics of speed response. In

addition, the IM drive is normally claimed as unreliable and sensitive to the

environmental changes due to its non-linear characteristics. Thus, designing a robust

efficiency optimization control needs to take into account of temperature variation,

magnetic saturation and load disturbances.

Therefore, the challenge of this research is not limited to predict the extent to

which the flux can be reduced, at any operating points over the entire torque and speed

range, which maximizes the efficiency, while at the same time, preserving and

enhancing the system performance to deal with the uncertainties of the system, such as

the dynamic response load disturbances.

4

1.4 Research objectives

An investigation to determine the system efficiency as well as the drive performance

over a wide speed range has been done. The proposed control strategy should be able

to improve the overall efficiency while at the same time, preserving the superior

transient performance of the drive. The aim of this research is to develop an efficiency

optimization of a DTC induction motor drive system.

The objectives of this research are:

i. to design and simulate the proposed DTC induction motor drive using

Matlab/Simulink simulation package.

ii. to develop a controlled program of optimum DTC induction motor drive along

with the proposed ANNEO and ANNSC controller by using dSPACE DS1103

Real-time Digital Signal Controller.

iii. to evaluate the effectiveness and robustness of the proposed controllers

towards the system’s efficiency improvements and the system performances

by simulation and experimentally.

1.5 Research Scope

The scope of this research is to focus on the efficiency optimization of the DTC

induction motor drives while at the same time preserves the excellent speed

performances. The scope of this research will be conducted according to the following

stage:

i Simulation model development

- The Matlab Simulink is used in order to model and simulate the DTC IM

drive to identify the system performances.

5

ii Controller program development

- A controller for the efficiency optimization DTC motor drive system to

improve its efficiency is designed.

- The speed controller to deal with the uncertainties of the system such as the

speed variation and dynamic response load disturbance is designed.

iii Hardware development

- The prototype of the proposed efficiency optimization of a DTC induction

motor drive system is built.

- The interface of dSPACE DS1103 Real-time Digital Signal Controller board

for the proposed DTC motor drive is constructed.

- The performances of the developed optimization DTC motor drive is

evaluated.

1.6 Thesis Outline

The outlines of the structure for this thesis are given as follow:

Chapter 2 presents the overview of control method for the induction motor, the DTC

control technique are further discussed. The basic principle of the efficiency

optimization control of the IM drives as well as the concept of various efficiency

improvement algorithm is provided. In addition, the reviews of previous and currently

research conducted in efficiency optimization vector control of induction motor drives

systems are presented.

Chapter 3 devotes the developments of the complete drive system with the proposed

algorithm. The prospective of the proposed neural network control implemented on

the efficiency optimization control and the speed control is discussed. Levernberg-

Marquardt learning scheme is implemented for ANNEO controller. The EO controller

is designed based on the Artificial Neural Network search algorithm as a predictor of

the optimum flux in order to produce an adaptive flux level. On the other hand, instead

6

of the conventional PID based speed controller (SC), back propagation ANN based

speed controller, ANNSC is proposed in order to allow better performance. Generally,

the weight updating can be done in two primary ways, namely the online and offline

training. Both online and offline training will be applied to the proposed ANNSC in

this research to verify its validity. The control algorithm of the neural network is given

in details.

Chapter 4 provides the minutiae explanation for both simulation and hardware

implementation in this research. The proposed controllers are simulated by using

Matlab-Simulink. The experimental hardware setup is controlled through the dSPACE

DS1103 Real-time Digital Signal Controller and interfaced by the ControlDesk 5.1. In

addition, the promising performance of the proposed controllers is obtained through

simulation and then verified by the relevant experiment results. Further analysis and

discussion on the obtained result are provided.

Chapter 5 presents the summaries of the contribution of this research and the

recommendation for future research direction.

7

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This chapter presents an overview of efficiency optimization for variable speed

induction motor drive, followed by its theoretical background. The fundamentals

knowledge of induction motor modeling with the involved transformation is first

described. Then, the fundamental efficiency optimization control approach is

described. Background studies of the available control method that have been

implemented in IM drive system are discussed further. Review of previous work for

various types of efficiency optimization control method for a variable speed drive as

well as the advantages and disadvantages are also presented.

2.2 Variable Speed Drive (VSD) - Induction motor drive system

The development in the field of electric drives has never stopped since the first

inception of the first principle of the electrical motor by Michael Faraday in 1821. The

world dramatically changed after the first induction machine was patented (US Patent

381968) by Nikola Tesla in 1888.

8

Before semiconductor and fast microprocessor or digital signal processor

become available, DC motors have been used extensively for the past decade. Even

though DC motor is more expensive, it provided simpler control structure due to its

inherent decoupled control of torque and flux. Meanwhile, the induction motors were

commonly applied as fixed speed machine due to their fixed connection to fixed

frequency and voltage supply. However, main drawbacks of DC motor were the

presence of brushes and commutators and thus signify the limitation of its workplace

[17]–[19].

Compared with DC Motor, the induction motor has distinct advantages, such as

having no commutator and brushes required, thus, leading to the facts as a

maintenance-free motor, ruggedness, lower rotor inertia, simpler protection,

economical, compactness [20]–[23]. The main drawback that makes AC motors retreat

from the industry was the inherent coupling between torque and flux.

Developing of new control principle, algorithm, and modern control have led to

a new generation of variable speed induction motor drives. The advent of power

electronic converters with forced commutation in 1960s and later with the power

semiconductors (BJT, GTO, and IGBT) made possible the use of the induction motor

as a variable speed drive in the last two decades [24], [25]. Variable speed drives have

facilitated the revolution of industrial automation leading to higher productivity and

better quality in various industries and home appliances.

Variable speed drives are established when an electric motor is combined with a

power electronic converter. By introducing variable speed to a driven load, it is

possible to optimize the efficiency of the entire systems and makes the greatest

efficiency gain possible. The variable speed induction motor drive system consist of

few component, such as the driver controller, a controllable power converter, an

electric motor which drives a mechanical load at any adjustable speed, as shown in

Figure 2.1 [25]–[27].

9

Figure 2.1: Block diagram of a variable speed induction motor drive system [25]–

[27].

The development of variable speed AC drives was tardy until the 1980s when

the rapid improvement in power electronics as well as the microprocessors revolution

has made the complex control algorithm possible. These advanced control methods

have made the induction motor possible for high performance and comparable with

that of the DC machine. This made the AC machines the dominating machine in the

drives market and seeing wider use in variable-speed applications [28], [29], [30].

Plenty of control methods for induction motor in the field of electric motor drives

have been drawing the attention of the researcher. Various control methods for IMs

have been proposed. Generally, IM control methods can be divided mainly into open

and closed loop control technique, which are also known as scalar and vector control

[28], [31]–[33]. The general classification of IM control methods is presented in Figure

2.2.

Figure 2.2: General classification of IM control methods [28], [31]–[33].

IM Control methods

Scalar based controller

Vector based controller

Field oriented control - FOC

Direct torque control - DTC

Controller

Power Converter IM Mechanical

Load

Commands

Power Supply

10

IM control methods are divided depending on what quantities they are control.

If magnitude of parameters is inspected and controlled, the control technique is

assigned as a scalar control. This technique is mainly implemented through direct

measurements of the machine parameter. It is usually employed in low-performance

variable speed drives. For high-performance variable speed drive systems, vector

control is implemented, in which the control variables consider the inspection and

controlled by the instantaneous values of positions of the controlled parameter, thus

permitting high dynamic performance of the drive. These techniques are realised in

both direct measurements and estimation of the machine parameter [32], [34], [35].

2.2.1 Scalar Control

At the beginning, a study on efficiency controller has been done for scalar control,

sometimes known as constant Volts/Hertz method. Scalar Control is the simplest

control method for the controlling the IM. Optimization of this method is based on the

relationship valid for steady states, with assumption that rated flux is proportional to

the voltage over constant frequency. The philosophy is to control the amplitude and

frequency of the stator voltage in order to keep the stator flux constant through motor

speed range [33].

Main advantages of this control method are that it is not complicated, low cost

and it is commonly used without position control requirements or the need for speed

feedback. Scalar control offers good steady-state response. However, this scheme

cannot perform decoupling between input and output, thus result in the problem of

independent control of outputs, for example, torque and flux. In addition, this

controller does not attain fine precision in either speed or torque response especially

in low speed since stator flux, and torque is not diametrically controlled [36]–[38].

Due to the uncontrolled transient and unknown position angle of control variables

resulted by lack of feedback, the system is unable to permit the best dynamic response

performance, which limits the application of this control method [39].

11

2.2.2 Vector Control

To obtain better dynamic and high precision, scalar control has been phased out by

more effective control methods. At present, the highlight is on vector control in order

to achieve decoupling in high-performance IM drives which offer far better dynamic

performance than those with scalar control. Vector control (VC) are aimed for

independent control of the machine torque and flux producing stator currents [40].

The vector control is based on the relationship valid for dynamic states, not only

the magnitude and frequency (angular speed), but also its instantaneous positions of

voltage and current are controlled. Thus, the vector control system acts on the position

of the space vectors and provides their correct orientation for both steady state and

transient conditions. This ensure dynamically decouples fast flux and torque control

and belongs to high-performance control implemented in a closed-loop fashion [28],

[31], [39], [41], [42]. Vector control presents several benefits, such as precise speed

regulation, a wide range of speed control and fast dynamic response.

Essentially, the control problem is reformulated to resemble the control of a DC

motor. Thus, the advantage of the DC motor control of being able to decouple the flux

and torque is thereby opened up. The vector control algorithm is based on two

fundamental ideas. The first is the flux and torque producing currents. An induction

motor can be modeled and controlled most simply using two quadrature currents rather

than the familiar three phase currents applied to the motor. These two currents are

called direct (Id), and quadrature (Iq), and they are responsible for producing flux and

torque respectively in the motor. By definition, the Iq current is in phase with the stator

flux, and Id is at right angles. Of course, the actual voltages applied to the motor and

the resulting currents are in the familiar three-phase system. The move between a

stationary reference frame and a reference frame, which is rotating synchronously with

the stator flux, becomes a problem then. This leads to the second fundamental idea

behind vector control. The second fundamental idea is that of reference frames. The

idea is on reference frame is to transform a quantity that is sinusoidal in one reference

frame, to a constant value in a reference frame, which is rotating at the same frequency.

Once a sinusoidal quantity is transformed to a constant value by careful choice of

reference frame, it becomes possible to control that quantity with appropriate

controller [32],[43].

12

These two main control methods can be further divided into a few number of

different control strategies depending on their functionality [39]. The vector control

can be implemented in many different ways but only several basic schemes that are

offered on the market. The most popular strategies among them are Field Oriented

Control (FOC) and Direct Torque Control (DTC) and Direct Torque Control - Space

Vector Pulse Width Modulation (DTC-SVM) [29], [31], [33], [34], [39].

In early 1970s, the appearance of the Field oriented control (FOC) allowed a

considerable increase of dynamic performance of the induction motors [44].

Theoretically, FOC that based on Fleming's law [45] makes the control performance

of induction motor as good as the DC motor’s where torque and flux are decoupled

and hence could be controlled independently. However, during the practical practice

of engineering application, the actual performance of vector control will be worse than

predicted due to the effect of factors such as inaccurate control model and variable

motor parameters [46]. Several methods are investigated to inquire into this problem

and some improved techniques such as flux observer, rotor resistance identification

are adopted in order to reduce the effect of this variation so that the control

performance of FOC can be satisfied in most of applications [44], [45]. The Direct

Torque Control was first introduced by Takahashi around the mid-1980s has found

great success with the notion to reduce the dependence on parameters of induction

motor and increase the precision and the dynamic of flux and torque response [47].

2.2.2.1 Field Oriented Control (FOC)

FOC have made possible the application of induction motors for high-performance

applications where traditionally only DC drives were applied. The field oriented

scheme enables the control of the induction motor in the same way as separately

excitation DC motors. One of the methods used in variable frequency drives or variable

speed drives to control the torque and thus the speed of three-phase AC electric motors

by controlling the current. As in the DC motor, with FOC torque control of induction

motor is achieved by controlling the torque current component and flux current

component independently.

13

The basic schemes of indirect and direct methods of FOC are shown in Figures

2.3 and 2.4. The direct FOC method depends on the generation of unit vector signals

from the stator or air-gap flux signals. The air-gap signals can be measured directly or

estimated from the stator voltage and current signals. The stator flux components can

be directly computed from stator quantities. In these systems, the rotor speed is not

required for obtaining rotor field angle information. In the indirect FOC method, the

rotor field angle and thus the unit vectors are indirectly obtained by summation of the

rotor speed and slip frequency [28].

Figure 2.3: Indirect vector control method [28].

Figure 2.4: Direct vector control method [28].

The FOC consists of controlling the stator currents represented by a vector. This

control is based on projections which transform a three phase time and speed

dependent system into a two coordinates, d and q coordinates time invariant system.

These projections lead to a structure similar to that of a DC machine control.

Field orientated controlled machines need two constants as input references

which are the torque component that aligned with the q coordinate and the flux

V I

Inverter

IM FOC

Slip

1/s

Ψ* Te*

θ

+ +

FOC Inverter IM

Ψ Vector Measurement /Estimation

θ

V I Ψ* Te*

ω

ω ω_cal

14

component that aligned with d coordinate. As FOC is simply based on projections, the

control structure handles instantaneous electrical quantities. This makes the control

accurate in every working operation, and independent of the limited bandwidth

mathematical model.

Fundamental requirements for the FOC are the knowledge of two currents and

the rotor flux position. Knowledge of the rotor flux position is the core of the FOC. In

fact, if there is an error in this variable the rotor flux is not aligned with d-axis and the

current components are incorrectly estimated. In the induction machine, the rotor

speed is not equal to the rotor flux. The basic method is the use of the current model.

Thanks to FOC it becomes possible to control, directly and separately, the torque and

flux of the induction motors. Field oriented controlled induction machines obtain every

DC machine advantage is instantaneous control of the separate quantities allowing

accurate transient and steady state management.

2.2.2.2 Direct Torque Control (DTC)

Direct Torque Control, DTC is a suitable technology for the high performance electric

drive system and is characterized by simple algorithms, fast dynamic response, and

strong robustness. It was introduced in the middle of 1980’s and was considered as an

alternative technique to the FOC. It was first introduced for IM by Takahashi and

Noguchi [47] in 1984 and the Direct Self Control method by Depenbrock [46], [48],

[49] in 1985.

DTC scheme is very simple in its basic, which consists of DTC controller, torque

and flux calculator, and Voltage Source Inverter (VSI). In recent years, the use of DTC

strategies has become more universal and popular for induction motor drives and

seems to have very rapid growth and development. The methods were characterized

by their simplicity, good performance, and robustness. The control philosophy of DTC

is to control directly the inverter state to maintain the stator flux and torque within

hysteresis band limits which requires no current regulator loops while similar

performance of the FOC can be achieved at the same time or much better.

Unlike the FOC method, DTC works without any external measurement of rotors

mechanical position. It has many advantages compared to FOC, such as less machine

15

parameter dependence, simpler implementation, and quicker dynamic torque response.

It only needs to know the stator resistance and terminal quantities (v and i) in order to

perform stator flux and torque estimations. The configuration of DTC is simpler than

FOC system due to the absence of frame transformer, current controlled inverter and

position encoder, which introduces delays and requires mechanical transducer.

The configuration of the conventional DTC drive as proposed by Takashi is

shown in Figure 2.5. It consists of two loops, the magnitude of stator flux and the

torque respectively. Hence, DTC performs the separate control of the stator flux and

torque, which is known as decouple control.

Figure 2.5: Hysteresis controlled of DTC induction motor drive [47].

The three level hysteresis comparator take the output error between the estimated

torque T and the reference torque Tref while the error from the estimated stator flux Ψ

and reference flux magnitude Ψref is fed into the two-level hysteresis comparator [50].

The estimated values are calculated by means of the adaptive motor model [51]. The

pair of hysteresis comparators is used to minimize torque and flux errors to zero.

Besides, it also determines the appropriate voltage vector selection and the period of

voltage vector selected.

The position of the stator flux is sync with the resulting torque error and flux

error of the hysteresis block are used as the input for the selection table. The position

of the stator flux is divided into six different sections. The decent voltage vector is

selected based on the selection table. The selection table blocks are responsible for

proper inverter switching state selection at each sampling time in order to confirm the

torque and flux errors lays within hysteresis band [52].

As a matter of fact, the fundamental concept of DTC is to produce the appetent

torque by diametrically manipulate the stator flux vector. The instantaneous value of

Hysteresis Comparator

Hysteresis Comparator

Voltage Source Inverter (VSI)

Stator Flux and

Torque Estimator

T

Terr

θ

Tref*

Ѱref

Ѱ

Ѱerr

Sa

0 IM Voltage vector

Selector

V

Sb Sc

I

16

the stator flux and torque are calculated from the stator variable by utilizing a close

loop torque and flux calculator. By selecting appropriate inverter state, independent

and direct control of stator flux and torque can be achieved. Meanwhile, the stator

voltage and currents are indirectly controlled and therefore, currents feedback loops

are unnecessary [53], [54].

The major problems in conventional hysteresis-based DTC was the performance

of the system directly dependent on estimation of stator flux and torque, thus,

improper estimation will result in incorrect voltage vector selection. In addition,

conventional hysteresis-based DTC also produces high torque ripples, stator current

distortion in terms of low-order harmonics and variable switching frequency due to its

hysteresis comparators [18]. Variety methods have been proposed to overcome these

issues, space vector modulation technique was one of the enticing candidates.

Although DTC is gaining its popularity, there are some drawbacks that can be

rectified to improve the performance. The space vector depends on the reference

torque, and flux is used to solve this problem [55]. Reference voltage vector is then

realized using a voltage vector modulator. DTC based on SVPWM not only preserve

its transient virtue but also yields the superior performance in the steady state over a

wide speed range.

The SVPWM technique is somewhat similar to the Sine 3rd harmonic PWM

technique, but the means of implementation is different. It is used to emerge desire

voltage or current for motor phase signal. This control method is progressively

welcomed for the AC drive motor applications since it establish smallest harmonic

currents and produce largest output voltage with same DC bus voltage. Commonly,

PWM control technique compares the three phase’s sinusoidal waveforms with a

triangle carrier to produce switching position patterns. The space vector theory denotes

some extra enhancements for both harmonic copper loss and output crest voltage. The

maximum output voltage produced is 32 =1.155 times larger based on space vector

theory as compared to traditional sinusoidal modulation. This makes possibilities for

higher voltage to feed the motor than the easier sub-oscillation modulation method.

Higher torque at high speed with high efficiency can be achieved by this modulator

[56], [57].

In conventional hysteresis based DTC systems, next switching condition of the

VSI is generated directly from torque and flux errors. Nonetheless, the SVPWM-DTC

17

import a constant switching frequency signal to VSI by the stator reference voltage

vectors which produce from the flux and torque errors [58]. Block scheme of the DTC-

SVPWM is presented in Figure 2.6.

Figure 2.6: Block Diagram of a SVPWM-DTC IM drive system [56]–[58].

As shown in Figure 2.6, the motor speed ω is compared with the desired speed

reference ωref , the resulting speed errors is fed into the proposed controller to generate

the torque reference value, Tref. The output errors, Terr between the estimated torque T

and the reference torque Tref, as well as the errors from estimated stator flux Ψ and

reference flux magnitude Ψref, is fed into the torque and flux controller respectively.

The estimated values are calculated by means of the adaptive motor model.

The 3 phase’s current, Iabc is required to transform to the 2 phase’s current, Idq by

the Clarke transformation. The two phase’s voltage, Vdq is generated through the torque

and flux controller from respective errors and convert back to the three phase’s voltage

to feed the SVPWM blocks.

2.3 Efficiency Optimization of Induction Motor

Induction motors, particularly those are widely used in electrical devices, consume

large fraction of electric power. Thus, they responsible for most energy consumption

and as prime target for improvement in efficiency [59]. In order to realize the efficiency

improvement, some researches enhanced it by using high-quality materials [60]–[62],

design and construction techniques. However, there is another promising algorithm that

can be applied directly to a drive controller, which is the expert control algorithm. It

0

Torque controller

Flux controller

SVPWM

IM

Flux and Torque Estimator

Speed controller

T

Terr ωref

ω

ωerr Tref*

Ѱref

Ѱ

Ѱerr

VDC

2ϕ 3ϕ

Vabc Vds Vqs

2ϕ

3ϕ

Iabc Ids Iqs

18

contributes the most to improve drive performance, especially when the motor operates

at non-rated condition, which is low speed and light load [5], [63]. The foundation of

such a control can be described as follow.

Basically, when an induction motor operates at rated condition, i.e. rated load

torque and speed, the efficiency of the motor is quite high and gives the best transient

response. However, in many applications, a motor operates far from the rated operating

point, particularly at light load where the reference flux magnitude is held on its initial

value, and this causes problems. At light load, rated flux operation causes excessive

core loss, thus impairing the efficiency of the drive due to imbalance between iron and

copper losses. For a given operating point, the induction motor efficiency can be

improved by minimizing losses and reducing the magnetic flux level appropriately or

by programming the flux to obtain balance between the copper and iron losses [6],

[25], [52], [64]–[66]. Due to the fact that electromagnetic losses in a machine is a direct

function of magnetic flux, thus, with proper adjustment of flux, the appropriate balance

between iron and copper losses can then be achieved [7], [63], [66]–[68]. In this

condition, the motor flux is more than necessary for the development of required

torque. Therefore, to improve the motor efficiency, its air gap flux must be reduced.

The strategy to reduce drive losses to minimum or in other words, to obtain

minimum power input by adjusting flux level according to motor load, is called

energy-optimal control [69], [70]. This control strategy is also known as efficiency

optimization control or loss minimization controls, some named it as part load

optimization as well.

The fundamental control strategy of efficiency optimization control is hereafter

explained. Electromagnetic torque of the induction motor can be approximated by

[43], [71].

rme IIkT = (2.1)

Where : Te= electromagnetic torque.

K=constant.

Im=magnetizing current.

Ir=rotor current.

19

For a given load torque, the motor’s electromagnetic torque can be obtained by

the combination of magnetizing current and torque producing rotor current. Therefore,

it is possible to achieve same torque with different combination of flux and current

value. The motor is normally designed to work with optimum near rated load.

However, for every speed and load condition, there exists an optimum flux level where

maximum efficiency can be achieved [67], [72].

Generally, low efficiency achievement during low load condition is due to

inappropriate flux level selection. The efficiency improvement can be achieved by

proper flux adjustment. Illustration of relationship between flux level and

electromagnetic torque is shown in Figure 2.7.

Figure 2.7: Torque production with three different flux level in the light load

condition. (a)-nominal flux, (b) medium flux (optimum flux) and (c) low

flux [67], [72].

As depicted in Figure 2.7, a vector diagram of low loaded motor at three different

rotor flux levels, which are nominal, medium and low are presented. The developed

electromagnetic torque, represented by the shaded areas, is proportional to ΨrIr, for all

the three cases. At nominal flux, as shown in Figure 2.7 (a), the rotor current, Ir is small

but the stator current, Is and magnetizing current, Im is large. Thus, rotor copper losses

are low but both stator copper and core losses are high.

When rotor flux is reduced to half of its nominal value, as shown in Figure 2.7

(b), the magnetizing current, Im and stator current, Is are reduced. However, the rotor

current, Ir is doubled to create same amount of electromagnetic torque. This reduces

core and stator copper losses considerably but increases rotor copper losses. As a

result, motor losses in Figure 2.7 (b) are smaller than the one in Figure 2.7 (a).

Ψr

Im Ir Is torque

Ir

Ψr

Im

Is torque

Ψr

Im

Is Ir

torque

(a) (b) (c)

20

However, if rotor flux is reduced even more, which is shown in Figure 2.7 (c), the core

losses will still decrease but as both rotor and stator copper losses increase again,

hence, it increases total losses again. A clearer depiction of the relationship between

flux level and losses are show in Figure 2.8 [71].

Figure 2.8: Types of losses of converter drive system with the flux variation [71].

For certain steady state, in light load condition and at certain speed, typical losses

in a converter drive system and its variation with the adjustable flux is presented in

Figure 2.8. When flux, Ψr is reduced from rated value, core loss will decrease, but

copper and converter losses increase. However, the total loss decreases to a minimum

which leads to maximum efficiency. However, one can observe that total losses

increase again after optimum value. Thus, it is desirable to set rotor flux at Ψr

(optimum) so that overall drive system efficiency is optimum.

Figure 2.9 shows typical optimum flux program for variable torque and constant

speed and compares corresponding efficiency with that of rated flux. Note that at rated

torque condition, the flux should be set close to rated value, and there is no significant

efficiency improvement. The improvement of efficiency becomes larger as the torque

is decreased.

Time

Torque Speed Total Loss

Copper loss

Converter loss Core loss

Flux (Ψr) →Decreasing ↑ Ψr (rated)

↑ Ψr (optimum)

21

Figure 2.9: Efficiency improvement by the flux program at variable torque with

constant speed [71].

On the basic of the flux reduction in the three cases in Figures 2.7 and 2.8, it can

be concluded that, for a given load, minimum losses can be achieved by the proper

adjustment of flux to obtain optimum flux level.

Nevertheless, flux reduction has a lot of limitations. For a given operating point,

when the flux is reduced, stator frequency will increase, hence, the pull-out torque of

the motor will be reduced and make it more sensitive to sudden load disturbances.

Thus, the speed for vector controlled motor drive will drop significantly and only when

the motor field has been restored, the motor speed can be recovered. Therefore, when

designing efficiency optimization control, it is important to ensure that the drive can

withstand load disturbances with optimum flux obtained.

2.4 Efficiency Optimization Technique

A number of strategies have been published on efficiency optimization control for IM

drives system, especially at light load. In general, the optimal efficiency control

techniques or Loss Minimization Technique for IM drives in real time are commonly

classified by two categories namely, (1) Search Control (SC), and (2) Loss-Mode-

Based Control (LMC) as shown in Figure 2.10. The primary idea is to make flux

amplitude varies with the change of IM operating conditions even though these two

Ter Torque (Te) 0

Flux program for

ƞ optimization

Ψr (rated)

ƞ at programmed flux

ƞ at rated flux Ψr

Effic

ienc

y (ƞ

)

Flux

(Ψr)

22

types have dissimilar achievement and implementation way [5]–[9], [14], [15], [73]–

[78]. Afterward, the third technique, (3) Hybrid, which uses both techniques

mentioned above was invented. It improves each technique's disadvantages to achieve

a better performance [14], [15], [42]. On the other hand, certain replace the SC

approach with others such as Minimum Stator Current (MSC) [79], estimation of iron

losses [80] , and power-factor improvement method [4], [64]. However, this method

is suboptimal and cannot produce maximum efficiency. Figure 2.11 show a typical IM

motor drive operates under control of real-time efficiency optimization control

technique.

Figure 2.10: Categories of efficiency optimization control [5]–[9].

Figure 2.11: Induction motor drive operated under real time Efficiency Optimization

Technique [14].

Control Algorithm

Inverter

+ DC bus -

Sensors/ Estimators

Induction Machine

Commands

Efficiency Optimization Vector Control of IM

Search Control (SC)

Hybrid Control

Loss Model- Based Control (LMC)

Efficiency Optimization

Technique

23

2.4.1 Search Control Technique (Physics-based technique)

SC utilized measured IM input power, Pin or DC-link power in optimization process.

It drives the control input to minimize Pin from the current and voltage measurements,

regardless of the motor rating or parameter. For a given output power, Pin=Po+Ploss,

minimizing Pin is equivalent to minimizing Ploss for a given Po [81].

This approach based on varying the flux up to the point where the measuring

power input is minimum for one point of operation [42]. More detail, at steady state,

for a given load torque and speed, the flux level (or its equivalent variables) iteratively

search until minimum input power is achieved. This method has the merit of robustness

to parameter variation while the effects of parameter changes are expressed in loss

model and hybrid strategies. In addition, it does not require any model of the system

and thus search process is simple and is applicable universally to any motors [15], [79].

The drawback of this approach is relatively long response time, flux convergence

to its optimal value is too slow and its performance is dependent on the quality of

power input measurement. In addition, noise and disturbances are not measured

accurately in measured signal. Thus, the optimal flux cannot be assured, and maximum

efficiency cannot be obtained. Moreover, the torque and flux ripples will be produced

because in real-time drive system, the stable state will not be achieved when search

process is slow. In simple way, the search controller utilizes feedback to measure input

power and iteratively change flux level until minimum input power is detected [5],

[14], [15], [42], [79], [82], [83].

2.4.2 Loss Model Technique (Model based technique)

Loss model controller, LMC utilizes the model of all important losses occurring in IM

during operation to compute optimum flux for a given load and speed that minimize

losses, thus they depend on motor parameters. It does not include closed-loop power

measurement but might implement feedback [14]. This approach does not produce

torque ripples [5], with fast response time and high speed of convergence in searching

minimum loss point and stability in the drive when great change in torque or speed is

24

demanded [6], [8], [42], [82], [84]–[86]. Since the LMC do not rely on any hardware

modification on existing drives, makes it more popular and universal than minimum

power input (search) based efficiency controller [42].

However, the performance of LMC depends on the accuracy of modeling of

motor drive and losses. There is always a trade-off between accuracy and complexity

in the development of loss model [9]. In addition, this approach is not robust to

parameter variation due to temperature changes and magnetic circuit saturation, it

requires exact knowledge of parameter for controlled machine to achieve true optimal

operation, the power loss modeling and calculation of the optimal control can be very

complex [15]. Considering that, both fundamental losses and harmonic losses

produced by inverter should be taken into consideration. Incorporating with the afore

mentioned parameter variation phenomena, therefore, the model of the drive system is

complex, and simplified model will result in suboptimal output [79].

In a compendious way, loss-model-based controllers use a functional loss model

to compute losses and to select an optimum flux level that minimizes this loss. This

method is faster than search methods but sensitive to parameter variations [82], [87].

Among loss minimization algorithm for an induction motor, a loss model based

approach has the advantages of fast response and high accuracy [9].

2.4.3 Hybrid Control Technique

The hybrid Control technique combines the features from both search and loss model

control techniques, which proposes mixing good characteristics of two optimization

strategies. Hybrid techniques require a motor or system model to search for minimum

power loss, and then use electromechanical principles and mathematical

characteristics to achieve optimality. It combines both advantages of the mentioned

approach but also rectifies the disadvantages. The two main examples of hybrid

techniques among several possible combinations of search and loss model based control

characteristics are: (i) applying Perturb and Observe (P&O) on Ploss model, and (ii) using

a parameter-dependent estimator with a search control technique [81].

The hybrid technique is not a straight forward process, each control technique

performs control strategy individually. During transient process LMC is used to

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