simulation of mras based speed sensorless...
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SIMULATION OF MRAS BASED SPEED SENSORLESS ESTIMATION
TECHNIQUES FOR INDUCTION MACHINE DRIVES USING
MATLAB/SIMULINK
AHMAD RAZANI BIN HARON
A project report submitted in partial fulfilment of the
requirements for the award of the degree of
Master of Engineering ( Electrical-Power )
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
MAY, 2006
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For you,
My dearest mother and father,
My brothers and sisters,
My lovely wife, son and daughter…
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ACKNOWLEDGEMENT
Alhamdullilah, praise be to Allah S.W.T., the Most Merciful and the Most
Compassionate. Peace be upon him, Muhammad, the messenger of God.
“To engage into this research was an experience to gain, knowledge to dig,
friendships to build and passion to achieve”
Firstly, I would like to express deepest gratitude, appreciation and thanks to
my supervisor, Assoc. Prof. Dr. Nik Rumzi Nik Idris, for his guidance, critics and
friendship. His longing for knowledge really aspire me.
Appreciation and thanks also should go to my friends for their encouragement
and motivation.
My highest appreciation also dedicated to my mother, father and siblings for
they are part of my life, always supporting me all the time.
Finally I would like to express my special thanks to my wife, Norah for her
love and never ending support, and our kids, Ahmad Aeman Danial and Nur
Batrisyia for theirs big hugs and smiles!
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ABSTRACT
This thesis is about the study of the speed sensorless estimation techniques of
the induction machine drives. Large variations of techniques are available depending
on the estimation requirement. MRAS based speed sensorless estimation is one of the
most versatile techniques available due to its good performance and straightforward
stability approach. This technique uses two different models (the reference model
and the adjustable model) which has made the speed estimation a reliable scheme
especially when the motor parameters are poorly known or having large variations.
Rotor flux based MRAS (RF-MRAS) and back e.m.f based MRAS (BEMF-MRAS)
are two variants of MRAS based speed estimation techniques which differ in terms
of quantity used but share almost the same structure realization. These facts give a
good platform for comparison. The tracking capability and sensitivity to parameters
variation are two key criteria of comparison in assessing the performance of the
estimators. Implemented in the direct torque control (DTC) structure and simulated
in the MATLAB/Simulink, the results obtained justify the dynamic performance of
the RF-MRAS and BEMF-MRAS estimators.
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ABSTRAK
Tesis ini berkenaan dengan kajian teknik-teknik anggaran laju tanpa penderia
di dalam pemacu mesin aruhan. Pelbagai variasi teknik-teknik boleh didapati
bergantung kepada kehendak anggaran. Anggaran laju tanpa penderia berasaskan
MRAS adalah salah satu daripada teknik-teknik yang sangat berkebolehan yang
boleh didapati kerana prestasinya yang baik dan menggunakan pendekatan kestabilan
secara terus. Teknik ini menggunakan dua model berbeza (model rujukan dan model
boleh laras) yang menjadikan anggaran laju satu skim yang bolehharap terutamanya
bila parameter-parameter motor kurang diketahui atau mempunyai variasi yang
besar. MRAS berasaskan fluks pemutar (RF-MRAS) dan MRAS berasaskan d.g.e
balik (BEMF-MRAS) adalah dua varian teknik-teknik anggaran laju berasaskan
MRAS yang berbeza dari segi kuantiti yang digunakan tetapi berkongsi struktur
yang hampir sama. Fakta-fakta ini memberikan platform yang baik untuk
perbandingan. Kemampuan untuk menjejak dan kepekaan kepada variasi parameter-
parameter adalah dua kriterium utama perbandingan dalam menilai prestasi kedua-
dua penganggar. Kedua-dua penganggar menggunakan struktur kawalan terus daya
kilas (DTC) untuk tujuan simulasi. Keputusan-keputusan yang diperolehi dari
MATLAB/Simulink mengesahkan prestasi kedua-dua penganggar RF-MRAS dan
BEMF-MRAS.
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CONTENTS
SUBJECT PAGE
TITLE i
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
CONTENTS vii
LIST OF TABLES xi
LIST OF FIGURES xii
LIST OF SYMBOLS xiv
LIST OF ABBREVIATIONS xvi
CHAPTER TITLE PAGE
1 INTRODUCTION 1
1.1 Overview 1
1.2 Significance of study 3
1.3 Objectives 4
1.4 Scope of study 4
1.5 Work methodology 5
1.6 Literature review 6
1.7 Thesis organization 8
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2 INDUCTION MACHINE DYNAMIC 10
2.1 Introduction 10
2.2 Dynamic equations of induction machine 12
2.3 Induction machine control strategies 15
2.3.1 Scalar control 15
2.3.2 Field oriented vector control 17
2.3.3 Direct torque control 18
2.4 Summary 20
3 THE ART OF SPEED SENSORLESS ESTIMATION
SCHEMES 21
3.1 Introduction 21
3.2 Problems with estimations 22
3.2.1 Parameter sensitivity 23
3.2.2 Pure integration 23
3.2.3 Overlapping-loop problem 24
3.3 Speed sensorless estimation strategies 24
3.3.1 Rotor slot harmonics 25
3.3.2 Open loop estimators 26
3.3.3 Observers 28
3.3.3.1 Luenberger observer 29
3.3.3.2 Kalman filter observer 30
3.3.4 Model reference adaptive system estimators 32
3.4 Advantages and disadvantages of speed sensorless
estimation schemes 34
3.5 Summary 36
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4 RF-MRAS VS. BEMF-MRAS BASED SPEED
ESTIMATORS 37
4.1 Introduction 37
4.2 RF-MRAS estimator vs. BEMF-MRAS estimator 37
4.2.1 RF-MRAS estimator 38
4.2.1.1 RF-MRAS stability 40
4.2.2 BEMF-MRAS estimator 43
4.2.2.1 BEMF-MRAS stability 45
4.3 Simulation set up 47
4.3.1 Tracking capability 48
4.3.2 Parameter sensitivity 49
4.4 Summary 49
5 SIMULATION RESULTS AND DISCUSSION 50
5.1 Introduction 50
5.2 Speed response dynamics 51
5.2.1 Tracking capability 53
5.2.1.1 Open loop estimator 53
5.2.1.2 RF-MRAS 54
5.2.1.3 BEMF-MRAS 55
5.2.2 Effect of parameters variation 56
5.2.2.1 Effect of incorrect Rr setting 57
5.2.2.2 Effect of incorrect Rr setting 59
Effect of incorrect J setting 62
5.4 Summary 66
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6 CONCLUSION AND FUTURE WORKS 67
6.1 Conclusion 67
6.2 Recommendation for future work 68
REFERENCES 70
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LIST OF TABLES
TABLE NO. TITLE PAGE
3.1 Trends and tradeoffs of speed estimation schemes 34
4.1 IM’s parameters 48
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LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 A cut-away view of a squirrel cage induction motor 11
2.2 Induction machine d-q equivalent circuit in arbitrary
reference frame 14
2.3 Scalar control scheme 16
2.4 A typical FOC structure 17
2.5 DTC structure 19
3.1 Type of speed sensorless estimation strategies 22
3.2 Open loop speed calculation scheme structure 28
3.3 Luenberger based speed estimation structure 30
3.4 Extendend Kalman filter scheme block diagram 32
3.5 General structure of MRAS based estimator scheme 33
4.1 Speed estimation using rotor-flux based MRAS 39
4.2 MRAS equivalent nonlinear feedback system 40
4.3 Simulink implementation of RF-MRAS estimator 42
4.4 Back e.m.f based MRAS structure 44
4.5 Simulink implementation of BEMF-MRAS estimator 46
4.6 Estimator and DTC implementation in Simulink 47
5.1 Comparison of rotor speed response 51
5.2 Factors leading to instability of BEMF-MRAS based44
speed estimator 52
5.3 Open loop estimator’s speed tracking capability with
different speed reference 54
5.4 RF-MRAS estimator’s speed tracking capability with
different reference speed 55
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5.5 BEMF-MRAS estimator’s speed tracking capability with
different reference speed 56
5.6 Effect of incorrect setting of Rr value to RF-MRAS
estimator’s speed response 58
5.7 Effect of incorrect setting of Rr value toBEMF-MRAS
estimator’s speed response 59
5.8 Effect of incorrect setting of Rs value to RF-MRAS
estimator’s speed response 60
5.9 Variation in RF-MRAS rotor flux linkages due to
changes in stator resistance setting 61
5.10 Effect of incorrect setting of Rs value to BEMF-MRAS
estimator’s speed response 62
5.11 Effect of incorrect setting of J value to RF-MRAS
estimator’s speed response 63
5.12 Effect of incorrect setting of J value to BEMF-MRAS
estimator’s speed response 64
5.13 Effect of incorrect setting of J value to RF-MRAS
estimator’s torque response 65
5.14 Effect of incorrect setting of J value to BEMF-MRAS
estimator’s torque response 65
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LIST OF SYMBOLS
B - Motor friction constant
dsi - d-axis stator current expressed in stationary reference frame
qsi - q-axis stator current expressed in stationary reference frame
dri - d-axis rotor current expressed in stationary reference frame
qri - q-axis rotor current expressed in stationary reference frame
Lm - Magnetizing self-inductance
J - Motor moment of inertia constant
Lr - Rotor self-inductance
Ls - Stator self-inductance
P - Pair of poles
Rr - Rotor resistance
np - Number of pole pairs
Rs - Stator resistance
Te - Instantaneous value of electromagnetic torque
TL - Load torque
Tr - Rotor time constant
dsv - d-axis stator voltage expressed in stationary reference frame
qsv - q-axis stator voltage expressed in stationary reference frame
drv - d-axis rotor voltage expressed in stationary reference frame
qrv - q-axis rotor voltage expressed in stationary reference frame
ω - Angular speed
Vs - Stator voltage
rω - Rotor speed
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rω̂ - Estimated rotor speed
eθ - Stator voltage angle
eω - Synchronous speed
dsψ - d-axis stator flux linkage expressed in stationary reference frame
qsψ - q-axis stator flux linkage expressed in stationary reference frame
drψ - d-axis rotor flux linkage expressed in stationary reference frame
qrψ - q-axis rotor flux linkage expressed in stationary reference frame
σ - Total leakage reactance
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LIST OF ABBREVIATIONS
IM Induction machine/motor
IGBT Insulated Gate Bipolar Transistor
DC Direct Curent
AC Asynchronous Current
V/F Volts per Hertz
FOC Field Oriented Control
DTC Direct Torque Control
PWM Pulse Width Modulated
DSP Digital Signal Processor
MRAS Model Reference Adaptive System
E.M.F Electromotive Force
RF-MRAS Rotor Flux Based Model Reference Adaptive System
BEMF-MRAS Back E.M.F Based Model Reference Adaptive System
ANN Artificial Neural Network
OLS Ordinary Least-Square
BPN Backpropagation Network
CHAPTER 1
INTRODUCTION
1.1 Overview
Induction motor (IM) can be considered as the ‘workhorse’ of the industry
because of its special features such as low cost, high reliability, low inertia,
simplicity and ruggedness. Even today IMs especially the squirrel cage type, are
widely used for single speed applications rather than variable speed applications due
to the complexity of controlling algorithm and higher production cost of IM variable
speed drives. However, there is a great interest on variable speed operation of IM
within the research community mainly because IMs can be considered as a major
industrial load of a power system. On the other hand the IMs consume a considerable
amount of electricity generated. The majority of IMs are operated at constant speed,
determined by the pole pair number and the stator supply frequency.
It is well known fact that electric energy consumption of the appliances can
be reduced by controlling the speed of the motor. The three phase variable speed IM
drives are therefore encouraged to be used in the industry today as an attractive
solution forever increasing electricity generation cost.
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During the last decade, with the advancement of power electronics
technology, a high speed switching devices such as IGBTs were introduced and a
more precise motor control strategies, such as vector control techniques, were
developed. As a result, today IMs can be used in any kind of variable speed
applications, even as a servomechanism, where high-speed response and extreme
accuracy is required.
Vector control technique is used for high performance variable drive systems.
In the vector control scheme, a complex current is synthesized from two quadrature
components. One of which is responsible for the flux level in the motor and another,
which controls the torque production in the motor. In actual fact the control problem
is reformulated to resemble control of a DC motor. Vector control offers attractive
benefits including wide range of speed control, precise speed regulation, fast
dynamic response, operation above based speed and etc. The principals of vector
control are now well established at high performance IM drives.
In order to implement the vector control technique, the motor speed
information is required. Tachogenerators, resolvers or incremental encoders are used
to detect the rotor speed. However, these sensors impair the ruggedness, reliability
and simplicity of the IM. Moreover, they require careful mounting and alignment and
special attention is required with electrical noises. Speed sensor needs additional
space for mounting and maintenance and hence increases the cost and the size of the
drive system. However, in one aspect, the speed sensor elimination reduces the total
cost of the drive system. On the other hand the sensorless drive system is more
versatile due to the absence of the numerous problems associated with the speed
sensor as discussed previously. Therefore it is encouraged to use the sensorless
system where the speed is estimated by means of a control algorithm instead of
measuring. However eliminating the speed sensor without degrading the
performance is still a challenge for engineers.
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In this thesis, the speed sensorless estimation concept via implementation of
Model Reference Adaptive System (MRAS) schemes was studied. It is a well known
fact that the performance of MRAS based speed estimators is beyond par from other
speed estimators with regards to its stability approach and design complexity.
Although this thesis is all about MRAS based speed estimators, but it is also the aim
of this project to investigate several speed sensorless estimation strategies for IMs.
Explanations on the type of control strategies also were briefly discussed. As far as
simulation works is concerned, the MRAS based speed sensorless estimation
schemes chosen in this thesis has been implemented in the direct torque control
structure (DTC) to evaluate the estimators’ performance.
1.2 Significance of study
With the maturing technology of the vector-controlled drives, the need for
speed information is crucial for control purposes and traditionally, this information
can be extracted using mechanical sensor mounted on the motor shaft. However, the
presence of such sensor has reduced the system reliability and increases the drives
system’s size and the overall cost. These problems have attracted the interest of
many researchers to develop techniques that can eliminate the use of shaft sensor.
This effort has lead to growth of various speed sensorless estimation schemes based
on the simplified motor models.
Therefore, it is the intention of this work to share the motivation of the
previous researchers to study the speed sensorless estimation strategies. Though it
has gone through a maturing period of over 20 years, improvement and enhancement
of such system is still required. This effort might become a first step to the author to
involved into detail researches of the speed sensorless control in future.
4
The reason behind adopting the MRAS based speed sensorless estimation
strategies in this research is so obvious because it has been proclaimed as one of the
best methods available, especially when the motor parameters are poorly known or
have large variations. Though the performance of MRAS based estimators is
considerably good at high speed but operation at low and zero speed is still a
problem to overcome.
1.3 Objectives
The objectives of this research are outlined as follows:
1) To study the various speed estimation schemes available with main focus
will be on the MRAS based schemes.
2) To model and simulate rotor flux based MRAS (RF-MRAS) and back
e.m.f based MRAS (BEMF-MRAS) speed estimators for IM drives using
toolboxes available in MATLAB/Simulink.
3) To evaluate and compare the performance of the selected MRAS based of
speed estimators in terms of tracking capability and parameters
sensitivity.
1.4 Scope of study
The works undertaken in this project are limited to the following aspects to
ensure the scopes of study are within the anticipated boundary.
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1. Sensorless estimation of rotor speed using open loop, RF-MRAS and
BEMF-MRAS estimators only.
2. IM parameters are known or readily available.
3. Simulation of MRAS based speed estimators will consider the effect
of parameters variation.
4. Speed estimators are implemented in the direct torque control (DTC)
structure.
5. Simulation in MATLAB/Simulink.
1.5 Work methodology
The research methodology is undertaken according to these stages:
1. Study of the IM dynamic equations related to RF-MRAS and BEMF-
MRAS speed estimator’s structure.
2. Construct the RF-MRAS and BEMF-MRAS using Simulink blocks.
3. Implementation in direct torque control scheme.
4. Examine the estimated and actual rotor speed response, with and
without effect of parameters variation in MATLAB/Simulink.
5. Evaluate performance of RF-MRAS and BEMF-MRAS based on
simulation results.
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1.6 Literature review
Speed sensorless estimation has greatly evolved from an open loop, low
performance strategy to closed loop, high performance strategy over the past
decades. The need of developing such technique is essential to adapt to the
advancement in the control strategy, especially the vector control techniques.
Looking back into the past, Abbondanti [1] has become the first to propose
calculating of rotor speed based on the motor model. His innovation has been further
improved by Nabae [2], Jotten and Maeder [3], and Baader [4] and they had used it
in some practical AC drive systems. The fact is that, the real time calculating of the
speed has difficulties for the realization because it is largely dependent on the
motor’s parameters.
Tamai [5] and Schauder [6] had opened a new horizon to speed sensorless
field for which they had introduced the MRAS to identify the rotor speed. Their
contribution is widely used and referred because identification of speed is more
robust than calculation of speed. Shauder [6] in his paper has proposed a RF-MRAS
technique to estimate the rotor speed based on comparison between the outputs of
two estimators known as the reference model and the adjustable model. The
performance is acceptably good but effect of parameter variation and drift problem is
a drawback to be carefully study.
Peng and Fukao [7] has proposed a new technique of MRAS based speed
estimation to overcome the problem in RF-MRAS proposed by Schauder [6]. The
scheme which is based on back e.m.f, shows a better performance and robustness due
to elimination of pure integrators in the reference and adjustable model. Another
scheme which an extension of BEMF-MRAS also has been proposed. This scheme
uses reactive power information as the tuning signal rather than the back e.m.f or
rotor flux quantity. This scheme is further investigate by M. Ta-Cao et al. [8] which
shows superior robustness compared to previous MRAS schemes.
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A more powerful and robust estimator based on artificial neural network
MRAS has been proposed by Ben-Brahim et al. [9] which exploit the classical
backpropagation network (BPN) algorithm for the online training of the neural
network to estimate the rotor speed. It is experimentally verified at the lowest speed
limit and even at zero-speed operation. Cirrincione and Pucci [10] proposed an
improvement of the MRAS artificial-neural-network (ANN)-based speed observer
presented by Ben-Brahim et al. [9]. In spite of using BPN algorithm, it uses the
ordinary least-square (OLS) algorithm to solve the problem associated with linearity.
From the study, it is observed that the OLS MRAS outperforms the BPN MRAS
proposed previously.
Although there are various techniques available for speed sensorless
estimation, but not enough effort has been put to review the schemes comparatively.
Illas et al. [11] have investigated and compared several speed sensorless estimation
schemes for field oriented control of IM drives. Speed estimations using speed
estimator, MRAS, speed observer, Kalman filter and rotor slot ripple have been
review and simulated to evaluate the performance based on some figures of merit.
Marwali and Kehyani [12] have performed a comparative study of the RF-MRAS
and BEMF-MRAS evaluated in indirect vector control system. The studies focus on
the level of the difficulty in tuning the adaptive gains and the speed tracking
performances. From the simulation and experimental studies, they have shown that
the BEMF-MRAS is better compared to RF-MRAS. Bodson and Chiasson [13] have
considered three representative approaches such as the adaptive method, least-square
method and nonlinear method for speed estimation. The methods are compared in
terms of their sensitivity to parameters variation, their ability to handle load and their
speed tracking capability.
Some studies related to parameter variation effects in sensorless vector
controlled drives are already available [14][15]. For example, impact of rotor
resistance variation on transient behavior of the drive was studied by Ilas et a1. [11]
and by Griva et a1. [16] through simulation. Viorel and Hidesiu [17] and Armstrong
et al. [18] have studied impact of rotor resistance, stator resistance and mutual
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inductance variation in low speed region experimentally. The only available
comprehensive investigations of steady-state speed estimation errors caused by
parameter variation effects appear to be works by Gimenez et a1. [19] and Jansen
and Lorenz [20]. However, in both cases structure of the drive dealt with is direct
rotor flux oriented control that combines a MRAS based speed estimator with a
closed loop flux observer and includes a mechanical system model. The validity of
results obtained by Gimenez and Jansen is thus restricted to that specific drive
structure.
1.7 Thesis organization
Speed sensorless estimation is a vast subject of research. MRAS speed
estimators constitute one part of it which significantly influence the maturing of this
field. To study such a vast subject at one time is almost possible; therefore, only
MRAS framework will be studied thoroughly in this work. For that reason, the
organization of the materials in this thesis is indeed intentionally to make available
all the information related to the subject of study. The organization of this thesis is
outlined as follows:
Chapter 2 presents the general theory of the IM dynamics. The IM dynamics
equations extensively used for estimation algorithm were explained. Brief
explanation on IM control strategy also was included.
Chapter 3 gives an overview of speed sensorless estimation strategies
available in literature. Speed estimation techniques are briefly reviewed to give an
idea of the concept and the need for a robust and stable speed estimator. Since the
estimators are uniquely best in its own class, therefore, their trends and trade off
were highlighted at the end of this chapter.
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Chapter 4 presents the rotor speed estimation using MRAS based strategy; the
RF-MRAS and the BEMF-MRAS. All the schemes were described thoroughly in
terms of mathematical equations, construction, implementation and performance.
The simulation setup for selected schemes i.e. the RF-MRAS and BEMF-MRAS
were presented.
Chapter 5 discusses the simulation results for the two estimators. Estimators’
response at different values of speed reference was studied. The performance of the
estimators with effect of parameters variation was also examined. Analysis and
discussion were made to critically evaluate the performance of the two estimators.
In Chapter 6, a thorough conclusion of the research was presented. Some
suggestions for future works also were highlighted.