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ABSTRACT

The widely used cage induction motor is one of the most robust motor. There are many

techniques to control the speed of the induction motor such as stator voltage control and

frequency control etc. For achieving variable speed operation, the frequency control method

of the cage motor is the best method among all the methods of the speed control. Vector

control of the cage motor is considered fast response and high performance method to

achieve variable speeds using variable frequency source.

In the vector control method the induction motor can be operated like a separately excited DC

motor for high performance applications. In the last decade many closed loop speed control

techniques have been developed to provide good performance. However, the desired drive

specification still cannot be perfectly satisfied and/ or their algorithms are too complex.

Recently the fuzzy logic approach has been objected of an increasing interest and has found

application in many domains of control problem. The main advantage of fuzzy logic control

method as compared to conventional control techniques resides in fact that no mathematical

modeling is required for controller design and also it does not suffer from the stability

problem. In motion control, fuzzy logic can be considered as an alternative approach to

conventional feedback control. It has been recently demonstrated that dynamic performance

of electric drives as well as robustness with regards to parameter variations can be improved

by adapting the non-linear speed control techniques. Fuzzy logic is a non-linear control and it

allows the design of optimized non-linear controllers to improve the dynamic performance of

the conventional regulators.

In the project, the configuration and design of the fuzzy logic controller for indirect vector

based control of induction motor has been investigated. The fuzzy logic controller (FLC) has

been successfully simulated on a simulink model with the help of fuzzy logic toolbox. Itpresents a hybrid system controller, incorporating fuzzy controller with vector-control

method for induction motors. The vector-control method has been optimized by using fuzzy

controller instead of a simple P-I controller.

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The presented hybrid controller combines the benefits of fuzzy logic controller and vector-

control in a single system controller. High quality of the regulation process is achieved

through utilization of the fuzzy logic controller, while stability of the system during transient

processes and a wide range of operation are assured through application of the vector-control.

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CHAPTER 1

INTRODUCTION

This chapter gives an idea of General introductory idea of induction motor, conventional

controller with their problems, technology review, and problem identification with the aim of

this project.

1.1GENERAL

Ac motor drives are used in a multitude of industrial and process applications requiring high

performances. In high-performance drive systems, the motor speed should closely follow a

specified reference trajectory regardless of any load disturbances, parameter variations, and

model uncertainties. In order to achieve high performance, field-oriented control of induction

motor (IM) drive is employed. With the field orientation control (FOC) method, induction

machine drives are becoming a major candidate in high-performance motion control

applications, where servo quality operation is required. Fast transient response is made

possible by decoupled torque and flux control. However, the controller design of such a

system plays a crucial role in system performance. Conventional proportional integral

derivative (PID) control has difficulty in dealing with dynamic speed tracking, parameter

variations, and load disturbances. As a result, the motion control system must tolerate a certain

level of performance degradation. Generally, variable speed drives for Induction motor requireboth wide operating range of speed and fast torque response, regardless of load variations.

This leads to more advanced control methods to meet the real demand. Usually classical

control is used in motors drive. Design and implementation of conventional controls have the

following difficulties:

a) It depends on the accuracy of the mathematical model of the system that usually not known.

b) Drives are nonlinear systems and classical control performance with this system decrease.

c) Variation of machine parameters (especially in vector control) by load disturbance, motor

saturation or thermal variations do not cause expectation performance.

d) Classical linear control shows high performance only at one operating point.

e) With choose improperly coefficient, classical control cannot receive acceptable result and

suitable choose for constant coefficient in especial application condition with set point

varying, necessarily is not optimum.

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To implement conventional control, the model of the controlled system must be known. The

usual method of computation of mathematical model of a system is difficult. When there are

system parameter variations or environmental disturbance, the behavior of the system is not

satisfactory. Classical controller designed for high performance increases the complexity of

the design and hence the cost.

Advanced control based on artificial intelligence technique is called intelligent control [1].

Every system with artificial intelligence is called self-organizing system. On the 80th decade

the production of electronic circuits and microprocessors with high computation ability and

operating speed has grown very fast. The high power, high speed and low cost modern

processes like DSP, FPGA and ASIC ICs along with power technique switches like IGBT

made the intelligent control to be used widely in electrical drives. Intelligent control, act well

than conventional adaptive controls. Artificial intelligent techniques divide two groups: hard

computation and soft computation. Expert system belongs to hard computation, which has

been the first artificial intelligent technique. In recent two decades, soft computation is used

widely in electrical drives. They are,

1. Artificial Neural Network (ANN)

2. Fuzzy Logic Set (FLS)

3. Fuzzy-Neural Network (FNN)

4. Genetic Algorithm Based system (GAB)

5. Genetic Algorithm Assisted system (GAA)

Neural networks and fuzzy logic technique are quite different, and yet with unique

capabilities useful in information processing by specifying mathematical relationships among

numerous variables in a complex system, performing mappings with degree of imprecision,

control of nonlinear system to a degree not possible with conventional linear systems.

Fuzzy logic is a technique to embody human-like thinking into a control system. A fuzzy

controller can be designed to emulate human deductive thinking, that is, the process people

use to infer conclusions from what they know. Fuzzy control has been primarily applied to the

control of processes through fuzzy linguistic descriptions.

Recently the fuzzy logic approach has been objected of an increasing interest and has found

application in many domains of control problem [24]. The main advantage of fuzzy logic

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control method as compared to conventional control techniques resides in fact that no

mathematical modeling is required for controller design and also it does not suffer from the

stability problem. In motion control, fuzzy logic can be considered as an alternative approach

to conventional feedback control. It has been recently demonstrated that dynamic

performance of electric drives as well as robustness with regards to parameter variations can

be improved by adapting the non-linear speed control techniques. Fuzzy logic is a non-linear

control and it allows the design of optimized non-linear controllers to improve the dynamic

performance of the conventional regulators. In this thesis, the application of fuzzy logic

control to VCIMD is investigated. Fuzzy logic speed control is considered for the design of

the speed controller with the help of Simulink. The control performance of this controller is

evaluated by simulation.

1.2LITERATURE REVIEW

G The history of electrical motors goes back as far as 1820, when Hans Christian Orested

discovered the magnetic effect of an Electric current. One year later, Michael Faraday

discovered the electromagnetic rotation and built the first primitive D.C. motor. Faraday went

on to discover electromagnetic induction in 1831, but it was not until 1883 that Tesla

invented the A.C asynchronous motor.

Currently, the main types of electric motors are still the same, DC, AC asynchronous and

synchronous, all based on Orested, Faraday and Teslas theories developed and discovered

more than a hundred years ago.

Since its invention, the AC asynchronous motor, also named induction motor, has become the

most widespread electrical motor in use today. Induction motors have a simple and rugged

structure; moreover, they are economical and immune to heavy overloads. At present 80% of

all the electrical energy generated in India is converted to mechanical energy for utilization.

These facts are due to the induction motor advantage over the rest of motors. The main

advantage is that induction motor does not require the electrical connection between

stationary and rotating parts of the motor. Therefore, they do not need any mechanical

commutator(brushes), lading to the fact that they are maintenance free motors. Induction

motors also have low weight and inertia, high efficiency and a high overload capability.

Therefore, they are cheaper and more robust and less proves to any failure at high speed.

Furthermore, the motor can work in explosive environments because no sparks are produced.

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The only effective way of producing an infinitely variable induction motor speed drive is to

supply the induction motor with three phase voltages of variable frequency and variable

amplitude. A variable frequency is required because the rotor sped depends on the speed of

the rotating magnetic field by the stator. A variable voltage is required because the motor

impedance reduces at low frequency and consequently the current has to be limited by means

of reducing the supply voltages.

Before the days of power electronics, a limited speed control of induction motor was

achieved by switching the three-stator windings from delta connection to star connection,

allowing the voltage at the motor windings to be reduced. Induction motors are also available

with more than three stator winding to allow a change of the number of pole pairs. However,

a motor with several winding is more expensive because more then three connections to the

motor are needed and only certain discrete speed is available. Another alternative of method

of speed controlled can be realized by means of wound rotor induction motor, where the rotor

winding ends are brought out to slip rings. However, this method obviously removes most of

the advantage of the induction motor and it also introduces additional losses. Connecting

resistors or reactance in series with the stator winding of the induction motor achieves poor

performance.

At the time the above described methods were the only once available to control the speed of

induction motors, whereas infinitely variable speed drives with good performance for DC

motors already exited. These drives not only permitted the operation in four-quadrants but

also covered a wide power range. Moreover, they had a good efficiency, and with a suitable

control even a good dynamic response. However, its main drawback was the compulsory

requirement of brushes.

With the enormous advances made in semiconductor technology during the last 20 years, the

required conditions for developing a proper induction motor drive are present. Theseconditions can be divided in two groups:

The decreasing cost and improved performance in power electronic switching devices.

The possibility of implementing complex algorithms in the new microprocessors.

However, one precondition had to make, which was the development of suitable methods to

control the speed of induction motors, because in contrast to its mechanical simplicity their

complexity regarding their mathematical structure (multivariable and non-linear) is not a

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trivial matter. It is in this field, that considerable research effort is devoted. The aim being to

find even simpler methods of speed control for induction machines so that high performance,

better transient response can be obtained.

High performance control and estimation technology for ac drives has gone through rapid

evolution in the recent years. They are now finding increasing acceptance in industrial drives

for applications, such as steel mills, paper mills, servos, machine tools, robotics, elevators,

and transportation systems. Traditionally, ac machines were looked upon as ideal for

constant speed applications. The introduction of solid-state variable frequency inverters in the

1960s ushered the modern age of ac drives. Open loop volts/Hz speed control was

introduced in the beginning. Gradually, other scalar control techniques were introduced to

improve the performance. Unfortunately, ac machines are nonlinear, parameter varying,

multi-variable with coupling effect, and have complex dynamics of higher order.

Incorporating machine in a feedback loop creates complex stability problem, and processing

of feedback signals becomes difficult. The invention of vector or field oriented controls in

Germany and the demonstration that induction motor can be controlled like a separately

excited dc motor brought renaissance in the high performance control of ac drives.

Unfortunately, for a number of years, the power electronics community did not take much

notice of it because the control and feedback signal processing were too complex to

implement, and engineers were generally unfamiliar with the dynamic machine model. The

advent of microprocessors made the vector control increasingly acceptable from the 1980's.

In fact, with vector control, ac drives not only became "brushless dc drives", but outperform

the dc drives because of higher transient current, increased speed range and lower rotor

inertia. It is interesting to note that high performance adaptive and optimal control techniques

which were previously studied (mainly analysis and simulation) with dc drives could now be

easily extended to vector controlled ac drives because of dc machine-like transient model.

The advent of modern digital signal processors, ASIC chips, personal computers, user-

friendly simulation tools, artificial intelligence (AI) techniques, and advancement of control

and estimation theories has continuously extended the frontier of control and estimation

technology. Fuzzy control (FC) provides a systematic way to incorporate human experience

in the controller. Recent literature has paid much attention to the potential of fuzzy control in

machine drive application. Many authors present a unique real time adaptive fuzzy controller

combined with the principles of fuzzy logic. Even two fuzzy logic controls are using for

coarse and fine control. Hence a properly designed fuzzy controller can outperform

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traditional PID controllers, both when machine is properly field oriented and when it

becomes detuned. Fuzzy logic provides a means for synthesizing a controller from

engineering experience that can be more robust, have better performance, and reduce cycle

times.

1.3 TECHNOLOGY REVIEW

Electrical machine is the workhorse in a drive system. Its evolution over the past century has

been slow and much less dramatic than that of power semiconductor devices and converter

circuits. Electrically, mechanically and thermally, a machine is a very complex system. The

advent of modern digital computers, improved modeling, simulation and programs, and

availability of new materials have contributed to higher power density, higher efficiency, and

improved reliability, reduced cost, and improved mechanical and thermal design in the recent

years. Both induction and synchronous machines have been widely used in variable speed

drives. Although the cage type induction motor is most commonly used in wide power range,

wound-rotor machines with slip power recovery control have been generally used in limited

speed range multi-megawatt drive applications. Control and estimation of high performance

ac drive have been a very fascinating and challenging area of research. The advent of

powerful microcomputers, digital signal processors, CAD and simulation tools, AI techniques

and advancement of control and estimation theories has continuously extended the frontier of

control and estimation techniques. Here I am reviewing control and estimation related to

induction motor drive.

The control and feedback signal processing of ac drives is considerably more complex than

the traditional dc drives [2], and this complexity is compounded if higher performance is

demanded. One reason for the complexity of the control and stability problem is that the

machine dynamics (d-q model) can be described by a higher-order nonlinear multivariable

state space equation. At a particular operating point, the system can be linearized on the basisof small signal perturbation, and then, the conventional linear feedback analytical methods,

such as the Nyquist and Bode techniques, can be applied. If the operating point changes, the

poles, zeros, and gain of the linearized system will also change, mandating a new set of

control parameters for the system. Of course, a fixed control structure with a fixed set of

control parameters can be defined so that the worst-case system performance is acceptable.

With the user-friendly simulation programs (such as SIMNON, ACSL, etc.) available today,

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the system can be conveniently studied with computer simulation avoiding the laborious

analytical techniques.

A simple, economical, but low-performance control method of the induction motor that is

extremely popular in industry is the open-loop V/Hz control. A small drift in speed and air-

gap flux due to a fluctuation in load torque and supply voltage, respectively, as well as

sluggish transient response, are of no consequence in the majority of applications. Scalar

speed and position feedback systems with inner flux, torque, and current control loops have

been used with increased control complexity where improved performance is necessary.

The vector or field-oriented control technique brought on a renaissance in modem high-

performance control of ac drives. This control method has found wide acceptance in

applications such as paper mills, textile mills, steel rolling mills, machine tools, servos, androbotics. With vector or decoupling control, the dynamics of ac drives is similar to that of dc

drives, and with current control, the conventional stability limit of ac machine does not arise.

This is indeed a remarkable accomplishment. The direct or feedback method, which was

developed by Blaschke, depends on unit vector generation from the machine terminal

voltages. As usual, harmonic noise becomes a problem in feedback signal processing, and the

method is difficult to use near zero speed because of the dominance of stator drop. Of course,

for servo-type applications, the unit vectors can be computed from stator currents and speed

signals. In the indirect or feed-forward method, which was developed by Hasse, the above

problems do not exist, but the controller is highly dependent on machine parameters. This

method has gained popularity in industrial applications. At present, significant R&D efforts

have been focused on parameter identification techniques. The so-called slip gain tuning in

order to have decoupling between the rotor flux and torque component of current has been

attempted by reactive power balancing, injecting a pseudo-random binary sequence, Kalman

filter estimation, and MRAC balancing of reactive power, torque, and voltages. While on-line

controller tuning with initial parameters is not difficult, tracking of controller parameters with

machine parameters during system operation is always a challenge. Recently, hybrid or

universal vector control has been suggested, where the indirect vector control operates in the

lower speed range but is switched to parameter-independent direct vector control in the

higher speed range. It should be mentioned here that the vector control can be applied to both

induction and synchronous machines and, in fact can be applied to the general ac system for

independent active and reactive power control. For self-control of the synchronous machine

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from zero speed, absolute shaft position sensor is mandatory, whereas for induction motor

control, the incremental encoder is satisfactory.

It is now evident that between the scalar and vector control methods, only two control types

are finding general acceptance. These are the open-loop V/Hz control for low performance

cost-effective applications and the indirect vector control for high-performance applications.

Again, the voltage fed PWM inverter is finding universal acceptance, as mentioned

previously.

A machine operating with rated flux gives optimum transient response, but at light-load

operation, the efficiency is non-optimum because of excessive core loss. The flux can be

weakened at light load using a function generator or on the basis of real time loss calculation,

but efficiency optimization control on the basis of search and real-time input powermeasurement is gaining momentum. The control can search the flux for optimum efficiency

at a steady-state light-load condition, but it switches to rated flux at the transient condition,

thus combining both the efficiency optimization and transient optimization features in a drive

system. Sensorless drive control is one recent trend because sensors add cost and reliability

problems to the drives. The most primary sensors of a drive are the stator current sensors.

With the added stator voltage sensors, practically any other type of signal, such as flux,

torque, speed, power, power factor, and displacement factor can be estimated with a

microprocessor. Attempts are being made to enhance the drive performance by intelligent,

self-learning, or self-organizing control using expert systems, fuzzy logic, and neural network

techniques. The expert system is based on hard or precise computation, whereas fuzzy logic,

neural network and genetic algorithm are based on soft or approximate computation. With a

control based on AI, a system is often said to be intelligent, autonomous, adaptive,

self-organizing or learning [1]. A machine model is often unknown or ill defined or the

system may be nonlinear, complex, and multivariable with parameter variation problem. An

intelligent control can identify the model, if necessary, and give predicted performance even

with wide range of parameter variation.

1.4 PROBLEM IDENTIFICATION

The control and feedback signal processing of induction motors is considerably more

complex than the traditional dc drives, and this complexity is compounded if higher

performance is demanded. Though induction motors have advantageous characteristics, they

also possess nonlinear and time varying dynamic interactions. One reason for the complexity

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of the control and stability is that the machine dynamics (d-q model) can be described by a

higher order nonlinear multivariable equation. Using conventional PI controller, it is very

difficult and complex to design a high performance induction motor drive system, besides;

these controllers show either steady state error or sluggish response to the perturbation in

reference setting or during load perturbation. We want such control techniques, which can

improve the system response, and performance or self-organizing control system, which can

identify the system/model, if necessary, and give, predicted performance even with parameter

variation.

1.5 OBJECTIVE

From the above discussion we can conclude that conventional controllers for induction motor

drive suffer from the problem as mentioned above. The objective of this project is to analysison such control, which can give better transient response, high performance without so much

detail about the system. This can be possible by using intelligent control such as fuzzy

controller instead of conventional controller. Recently the fuzzy logic approach has been

objected of an increasing interest and has found application in many domains of control

problem. The main advantage of fuzzy logic control method as compared to conventional

control techniques resides in fact that no mathematical modeling is required for controller

design and also it does not suffer from the stability problem. In motion control, fuzzy logic

can be considered as an alternative approach to the conventional feedback control. It has been

recently demonstrated that dynamic performance of electric drives as well as robustness with

regards to parameter variations can be improved by adapting the non-linear speed control

techniques. Fuzzy logic is a non-linear control and it allows the design of optimized non

linear controllers to improve the dynamic performance of the conventional regulators.

In the project, the application of fuzzy logic control to vector controller induction motor drive

is investigated. Fuzzy logic speed control is considered for the design of the speed controller.The control performance of this controller is evaluated by simulation and implementation at

different operating conditions.

1.6 LAYOUT OF PROJECT REPORT

The present report is organized in the following way.

Chapter 1 describes introductory part with problem identification and objective.

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Chapter 2 describes the mathematical modeling of induction motor, transformation between

reference frames. It also describes various control strategies such as scalar, vector etc. Here

the main orientation is on Vector control methods for its various advantages. Indirect vector

control implementation is used in this project. It also deals with the need of intelligent control

instead of conventional control methods. The objective of the design of an intelligent control

system is similar to that for the adaptive control system. However, there is a difference, for an

intelligent control system, the range of uncertainty may be substantially greater than can be

tolerated by algorithms for adaptive systems. The object with intelligent control is to design a

system with acceptable performance characteristics over a very wide range of uncertainty

Chapter 3 describes the steady state performance of the induction motor drive in detail. It

also includes Parks transformation, its field oriented control in constant torque range below

base speeds. The detailed model of fuzzy logic based speed controller used for vector

controlled induction motor drive. The model of the vector controlled induction motor drive is

developed in MATLAB. Simulated results are present to demonstrate the dynamic and steady

state performance of the vector controlled induction motor drive.

Chapter 4 covers the theoretical development and practical implementation of induction

motor with fuzzy logic controller and describes MATLAB which is a computer simulation

program developed by Math Works Inc. Embedded within MATLAB version 7 is

SIMULINK. This is a program that allows the user to create mathematical blocks with inputs

and outputs; highly suited to designing a control system of this nature. Here indirect vector

control is implemented by using simulink. Fuzzy block is implemented in FIS editor. It also

deals with simulation of transient performance of the fuzzy logic based speed controller for

induction motor drive in vector controlled mode using the developed model in MATLAB.

Here simulated results for the starting, load application and load removal, and change in

reference speed are present to demonstrate the dynamic and steady state performance of the

vector controlled induction motor drive. Finally,

Chapter 5 includes the overall conclusion of this project work and highlights the direction of

further research.

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CHAPTER 2

MATHEMATICAL MODELLING OF INDUCTION MOTOR

This chapter deals with operating principle, dynamic model, synchronous rotating frame,

various control strategies with their merits and demerits, Indirect Vector control, and about

intelligent control

2.1 THE FUNDAMENTAL OPERATING PRINCIPLE FOR AN INDUCTION

MOTOR [1][14]:

The AC induction motor is a rotating electric machine designed to operate from a 3 phase

source of alternating voltage. When a set of three phase currents displaced in time from each

other by angular intervals of 120 is injected into a stator having a set of three phase windings

displaced in space by 120 electrical, a rotating magnetic field is produced. This rotating

magnetic field has a uniform strength and travels at an angular speed equal to its stator

frequency. It is assumed that rotor is standstill. The rotating magnetic field in the stator

induces electromagnetic forces in the rotor windings. As the rotor windings are short

circuited, current start circulating in them, producing a reaction. As know from Lenzs law,

the reaction is to counter the source of the rotor currents, i.e., the induced emf in the rotor

and, in turn, the rotating magnetic field itself. The induced emf will be countered if the

difference in speed of the rotating magnetic field and the rotor becomes zero. The only way

to achieve this it is for the rotor to run in the same direction as that of the stator magnetic

field and catch up with it eventually. When the differential speed between the rotor and

magnetic filed in the stator becomes zero, there is no emf, and hence zero rotor currents

resulting in zero torque production in the motor. Depending upon the shaft load, the rotor will

settle down to a speed, r , always less then the speed of the rotating magnetic field, called

the Synchronous speed of the machine, s. The speed differential is known as the slip speed,

sl. The elementary relationship between slip speed, rotor speed and stator frequency are

given below.

Synchronous speed is given as

s = 2fs , rad/sec

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Where fs is the supply frequency.

If m is the mechanical rotor speed, slip speed is

sl = s - r = s P/2 m , rad/sec (2)

where P is the number of poles.

The differential speed between the stator magnetic filed and rotor windings is slip speed, and

that is responsible for the frequency of the induced emf in the rotor and hence their currents.

Therefore, the rotor currents are at slip frequency, which can be obtained from the angular

slip speed by dividing it by 2. The slip is defined as

s

sl

s =

Combing equations (2) and (3), the rotor electrical speed is given as

s)(1 sr =

From this, the rotor speed in rpm, denoted by nr , is expressed as

s)(1nn sr =

Where ns is the synchronous speed or the speed of the stator magnetic filed in rpm, given by

P

f120n ss=

Figure 2.1 shows an idealized three-phase, two pole induction motor where each phase

winding in the stator and rotor is represented by a concentrated coil. The three-phase

windings, either in wye or delta form, are distributed sinusoidally or embedded in slots. In a

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wound rotor machine, the rotor winding is similar to that of stator, but in a cage machine, the

rotor has a squirrel cage like structure with shorted end rings.

Fig 2.1: Idealized three-phase, two-pole induction motor

One of the most fundamental principles of induction machines is the creation of a rotating

and sinusoidal distributed magnetic field in the air gap. Neglecting the effect of slots andspace harmonics due to nonideal winding distribution, it can be shown that a sinusoidal three

phase balanced power supply in the three phase winding creates a synchronously rotating

magnetic field. Rotor voltage is induced at slip frequency, which corresponding produces slip

frequency current in the rotor. The sinusoidal air gap flux density wave moving at speed we

induce voltage in the rotor conductors. The resulting rotor current wave lags the voltage wave

by the rotor power factor angle r. The stepped rotor mmf wave can be constructed from the

current wave, which can be approximated by dashed curve. Since the rotor is moving at speed

wr and its current wave is moving at speed wsl relative to the rotor, the rotor mmf can wave

moves at the same speed as that of the air gap flux wave. The torque expression can be

written as:

sinFlrB2

PT ppe

=

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Where P=number of poles, l= axial length of the machine, r= machine radius, Bp=peak value

of air gap flux density, Fp=peak value of rotor mmf, and =/2 + r is defined as the torque

angle.

2.2 DYNAMIC D-Q MODEL

As the per phase equivalent circuit of the machine, which is only valid in steady-state

condition. In an adjustable-speed drive, the machine normally constitutes an element within a

feedback loop, and therefore its transient behavior has to be taken into consideration. Besides,

high-performance drive control, such as vector- or field-oriented control, is based on the

dynamic d-q model of the machine.

The dynamic performance of ac machine is somewhat complex because the three-phase rotor

windings move with respect to the three-phase stator windings as shown in Fig 2.2 (a)

Basically, it can be looked on as a transformer with a moving secondary, where the coupling

coefficients between the stator and rotor phases change continuously with the change of rotor

position r. The machine model can be described by differential equations with time-varying

mutual inductances, but such a model tends to be very complex. A three-phase machine can

be represented by an equivalent two phase machine as shown in Figure 2.2 (b), where ds-qs

correspond to stator direct and quadrature axes, and dr-qr correspond to rotor direct and

quadrature axes. Although it is somewhat simple, the problem of time-varying parameters

still remains. R. H. Park, in the 1920s, proposed a new theory of electric machine analysis to

solve this problem. He formulated a change of variables, which, in effect, replaced the vari-

ables (voltages, currents, and flux linkages) associated with the stator windings of a

synchronous machine with variables associated with fictitious windings rotating with the

rotor at synchronous speed. Essentially, he transformed, or referred, the stator variables to a

synchronously rotating reference frame fixed in the rotor. With such a transformation (called

Park's transformation), he showed that all the time-varying inductances that occur due to an

electric circuit in relative motion and electric circuits with varying magnetic reluctances can

be eliminated. Later, in the 1930s, H. C. Stanley showed that time-varying inductances in the

voltage equations of an induction machine due to electric circuits in relative motion can be

eliminated by transforming the rotor variables to variables associated with fictitious

stationary windings. In this case, the rotor variables are transformed to a stationary reference

frame fixed on the stator. Later, G. Kron proposed a transformation of both stator and rotor

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variables to a synchronously rotating reference frame that moves with the rotating magnetic

field.

Fig 2.2 (a) Coupling effect in three-phase stator and rotor winding of motor,

(b) Equivalent two-phase machine

2.2.1Transformation between References FramesAs for analysis point of view transformation between axes is important for a three-phase

induction motor, whose as-bs-cs axes at 2/3 angle apart. Here transform is done first three

phase stationary reference frame (as-bs-cs) variables into two phase stationary frame (ds qs)

variables and then transform these to synchronously rotating frame (de-qe), and vice-versa.

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Fig-2.3 Stationary Frame a-b-c to ds-qs axes transformation

Assume that (ds qs) axes are oriented at angle. The voltage Vdss and Vqss can be

resolved into as-bs-cs components and can be represented in the matrix form as:

++

=

s

os

s

ds

s

qs

cs

bs

as

v

v

v

1)120sin()120cos(

1)120sin()120cos(

1sincos

v

v

v

The corresponding inverse relation is

+

+

=

cs

bs

as

s

os

s

ds

s

qs

v

v

v

0.55.00.5

)120sin()120sin(sin

)120cos()120cos(cos

v

v

v

Where Vsos is added as the zero sequence component, which may or may not present. Here

instead of voltage, current and flux linkage can be transformed by the above equations.

For =0, the qs axis is aligned with the as-axis. Ignoring the zero sequence component, the

transformation relation can be simplified as

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s

ds

s

qscs

s

ds

s

qsbs

s

qsas

v2

3v

2

1v

v2

3v

2

1v

vv

+=

=

=

and inversely

csbs

s

ds

ascsbsas

s

qs

v3

1v

3

1v

vv3

1v

3

1v

3

2v

+=

==

General equations are (15) & (16).

Figure 2.4 shows the synchronously rotating de qe axes, which rotate at synchronous speed

we with respect to the ds qs axes and the angle e=we t. The two-phase ds qs windings

are transformed into the hypothetical winding mounted on the de qe axes.

Fig-2.4: Stationary frame ds qs to synchronously rotating frame de qe transformation

The voltage on the ds qs axes can be converted into the de qe frame as follows:

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e

s

dse

s

qsds

e

s

dse

s

qsqs

cosvsinvv

sinvcosvv

+=

=

Similarly, resolving the rotating frame parameters into a stationary frame, the relations are

e

dse

qs

s

ds

edseqs

sqs

cosvsinvv

sinvcosvv

+=+=

Assume that the three phase stator voltages are sinusoidal and balanced, and are given by

)3

2te

cos(m

vcs

v

)3

2te

cos(m

vbsv

)te

cos(m

vas

v

++=

+=

+=

Substituting equations (19) - (20) in (13) (14) yields

)tsin(Vv

)tcos(Vv

em

s

ds

em

s

qs

+=

+=

Again, substituting equations (15)-(16) in (22)-(23), we get

)sin(Vv

)cos(Vv

mds

mqs

=

=

Equations (22)-(23) shows that Vqss and Vds

s are balanced, two-phase voltages of equal paek

values and the latter is at /2 angle phase lead with respect to the other component. Equations

(24) & (25) verify that sinusoidal variables in a stationary frame appear as dc quantities in a

synchronously rotating frame. This fundamental is used in the Vector control.

The variables in a reference frame can be combined and represented by a complex space

vector (or phasor):

s

ds

s

qs

s

qds jvvvV ==

The qe- de components can also be combined into a vector form:

ee jjs

ds

s

qs

e

s

dse

s

qse

s

dse

s

qsdsqs

e

qds

eV)ejv(v

)cosvsinj(v)sinvcos(vjvvv

==

+==

or inversely

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ej

dsqs

s

ds

s

qs )ejv(vvvV+==

The vector ej may be interpreted as a vector rotational operator (defined as a vector rotator-

VR-or unit vector) that converts rotating frame variables into stationary frame variables.

Cose and Sine are the Cartesian components of the unit vector. In eq(27) e-j is defined as

the inverse vector rotator (VR-1 ) that converts ds qs variables into de qe variables.

2.2.2 Synchronously Rotating Reference Frame Dynamic Model (Kron Equation) [1] [2] [14]:

For the two-phase machine as in fig 2.2 (b), we need to represent both d s qs and dr qr

circuits and their variables in a synchronously rotating de qe frame. The stator circuit

equation can be represented as:

s

ds

s

dss

s

ds

sqs

sqss

sqs

dt

diRv

dt

diRv

+=

+=

Where qss & ds

s are the q-axis and d-axis stator flux linkages, respectively. When these

equations are converted to de qe frame, the following equations can be written as:

qsedsdssds

dseqsqssqs

dt

diRv

dt

diRv

+=

++=

Where all the variables are in rotating form. The last term in eq (31) & (32) can be defined as

speed emf due to rotation of the axes, that is, when e=0, the equations revert to stationary

form. Since rotor actually moves at speed r, the d-q axes fixed on the rotor move at a speed

(e - r ) relative to the synchronously rotating frame. Therefore, in de qe frame, the rotor

equations should be modified as

qrredrdrrdr

drreqrqrrqr

)-(dt

diRv

)-(

dt

diRv

+=

++=

Figure 2.5 shows the de qe dynamic model equivalent circuits that satisfy equations (31) to

(34). This is the special advantage of the d e qe dynamic model of the machine is the all the

sinusoidal variables in stationary frame appear as dc quantities in synchronous frame.

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Fig-2.5: Dynamic de qe equivalent circuits of machine

(a) qe axis circuit,

(b) de axisCircuit

The flux linkage expression in terms of the currents can be written from fig-2.5 as follows:

)i(iL

)i(iLiL

)i(iLiL

)i(iL

)i(iLiL

)i(iLiL

drdsmdm

drdsmdrlrdr

drdsmdslsds

qrqsmqm

qrqsmqrlrqr

qrqsmqslsqs

+=++=++=

+=++=

++=

Combining the above expression with equations (31) to (34), the electrical transient model in

terms of voltage and currents can be given in matrix form as [14]:

++

++

=

dr

qr

ds

qs

rrmremmre

rrerrmrem

mmessse

memsess

dr

qr

ds

qs

i

i

i

i

SLR)L(SL)L(

)L(SLR)L(SL

SLLSLRL

LSLLSLR

v

v

v

v

Where S is the Laplace operator. For a singly fed machine, such as a cage ,motor, V qr=Vdr=0

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Fig-2.6 shows the block diagram of the machine model along with input voltage and output current

transformations.

Fig-2.6: Synchronously rotating frame machine model with input voltage and output current transformation

2.3 CONTROL PRINCIPLE OF INDUCTION MOTOR

The control of induction motor drives constitutes a wide area, and the technology has further

advanced in recent years. Induction motor drives with cage-type machines have been the

workhorses in industry for variable-sped applications in a wide power range that covers from

fractional horsepower to multi-megawatts. These applications include pumps and fans, paper

and textile mills, subway and locomotive propulsions, electric and hybrid vehicles, machine

tools and robotics, home appliance, heat pumps and air conditioners, rolling mills, wind

generation system, etc. In addition to process control, the energy saving aspect of variable

frequency drives is also important.

The control and estimation of Ac drives are considerably more complex than those of dc

drives and this complexity increase substantially if high performance is demanded. The main

reason for this complexity are the need of variable-frequency, harmonically optimum

converter power supplies, the complex dynamics of the ac machine, machine parameter

variations, and the difficulties of processing feedback signals in the presence of harmonics.

Complexity of the control and stability problem is that the machine dynamics (d-q model) can

be described by a higher-order nonlinear multivariable state-space equation. At a particular

operating point, the system can be linearized on the basis of small signal perturbation, and

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then, the conventional linear feedback analytical methods, such as the Nyquist and Bode

techniques, can be applied. If the operating point changes, the poles, zeros, and gain of the

linearized system will also change, mandating a new set of control parameters for the system.

Of course, a fixed control structure with a fixed set of control parameters can be defined so

that the worst-case system performance is acceptable. Such understanding is crucial to

developing appropriate control techniques and their implementations.

As there are different control techniques of induction motor drives including:-

Scalar Control,

Vector or Field-oriented Control,

Direct Torque and flux Control,

Intelligent Control

Scalar Control:-

As the name indicates, is due to magnitude variation of the control a variable only, and

disregards the coupling effect in the machine [11]. For example, the voltage of a machine can

be controlled by control the flux, and frequency or slip can be controlled to control the

torque. However, the flux and torque are also functions of frequency and voltage,

respectively. Scalar control is in contrast to vector or field-oriented control, where both the

magnitude and phase alignment of vector variables are controlled. Scalar-controlled drives

give somewhat inferior performance, but they are easy to implement. Despite the fact that

Voltage-Frequency (V/f) is the simplest controller, it is the most widespread, being in the

majority of the industrial applications. It is known as a scalar control and acts by imposing a

constant relation between voltage and frequency. The structure is very simple and it is

normally used without speed feedback. However, this controller doesnt achieve a goodaccuracy in both speed and torque response, mainly due to the fact that the stator flux and the

torque are not directly controlled. However, the open loop control is normally used in those

applications where steady state and transient response of ac drives is not an important issue

only the satisfactory performance is sufficient. The parameters of induction motors are

coupled to each other and the Scalar control lack in producing fast dynamic response. The

following features can be state for scalar control:

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Controlling variables are Voltage and Frequency

Simulation of variable AC sine wave using modulator

Flux provided with constant V/f ratio

Open-loop drive

Load dictates torque level

Scalar control technique has the following advantage:

Low cost

No feedback device required simple

Because there is no feedback device, the controlling principle offers a low cost and simple

solution to controlling economical AC induction motors. This type of drive is suitable for

applications, which do not require high levels of accuracy or precision, such as pumps and

fans.

However, Scalar control technique suffers from the following disadvantage:

Field orientation not used

Motor status ignored

Torque is not controlled

Delaying modulator used

With this technique, field orientation of the motor is not used. Instead, frequency and voltage

are the main control variables and are applied to the stator windings. The status of the rotor is

ignored, meaning that no speed or position signal is fed back. Therefore, torque cannot becontrolled with any degree of accuracy. Furthermore, the technique uses a modulator, which

basically slows down communication between the incoming voltage and frequency signals

and the need for the motor to respond to this changing signal.

Vector or Field-Oriented Control [5]:-

As scalar control is somewhat simple to implement, but the inherent coupling effect (i.e., both

torque and flux are functions of voltage or current and frequency) gives sluggish response

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and the system is easily prone to instability because of a high-order (fifth-order) system

effect. For example, the torque is increased by incrementing the slip (i.e., the frequency), the

flux tends to decrease. Here the flux variation is always sluggish. The flux decrease is then

compensated by the sluggish flux control loop feeding in additional voltage. This temporary

dipping of flux reduces the torque sensitivity with slip and lengthens the response time.

The foregoing problems can be solved by vector or field-oriented control. An induction motor

exhibits nonlinear multivariable and highly coupled characteristics. The vector control

technique, which is also known as Field-oriented control (FOC), allows a squirrel-cage

induction motor to be driven with high dynamic performance that is comparable to the

characteristic of a dc motor. The FOC technique decouples the two components of stator

current: one providing the air gap flux and the other producing the torque. It provides

independent control of flux and torque, and the control characteristic is linearized. The stator

current are converted to a fictitious synchronously rotating reference frame aligned with the

flux vector and are transformed back to the stator frame before feeding back to the machine.

A vector control technique has the following features:

Field-oriented control - simulates DC drive

Motor electrical characteristics are simulated- Motor Model

Closed-loop drive

Torque controlled indirectly

To emulate the magnetic operating conditions of a DC motor, i.e. to perform the field

orientation process, the flux-vector drive needs to know the spatial angular position of the

rotor flux inside the AC induction motor. With flux vector PWM drives, field orientation is

achieved by electronic means rather than the mechanical commentator brush assembly of theDC motor. Firstly, information about the rotor status is obtained by feeding back rotor speed

and angular position relative to the stator field by means of a pulse encoder. A drive that uses

speed encoders is referred to as a closed-loop drive. Also the motors electrical

characteristics are mathematically modeled with microprocessors used to process the data.

The electronic controller of a flux-vector drive creates electrical quantities such as voltage,

current and frequency, which are the controlling variables, and feeds these through a

modulator to the AC induction motor. Torque, therefore, is controlled INDIRECTLY.

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Vector control technique has the following unique advantages:

Good torque response

Accurate speed control

Full torque at zero speed

Performance approaching DC drive

Flux vector control achieves full torque at zero speed, giving it a performance very close to

that of a DC drive. However, this technique suffers from some disadvantage, but these can be

neglected in front of the advantages of these techniques:

Costly

Huge computational capability

Good identification of the motor parameters

To achieve a high level of torque response and speed accuracy, a feedback device is required.

This can be costly and also adds complexity to the traditional simple AC induction motor.

Direct Torque Control

Direct Torque Control - or DTC as it is called - is the very latest AC drive technology

developed by ABB and is set to replace traditional PWM drives of the open- and closed-loop

type in the near future. Direct Torque Control describes the way in which the control of

torque and speed are directly based on the electromagnetic state of the motor, similar to a DC

motor, but contrary to the way in which traditional PWM drives use input frequency and

voltage. DTC is the first technology to control the real motor control variables of torque

and flux. With the revolutionary DTC technology field orientation is achieved without

feedback using advanced motor theory to calculate the motor torque directly and without

using modulation. The controlling variables are motor magnetizing flux and motor torque.

This method still requires further research in order to improve the motors performance, as

well as achieve a better behavior regarding environmental compatibility, that is desired

nowadays for all industrial applications. Direct Torque Control techniques have the following

features:

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Direct control of flux and torque

Indirect control of stator currents and voltages

Approximately sinusoidal stator fluxes and stator currents

High dynamic performance even at stand still

The following advantages are associated with DTC:

Absence of co-ordinate transform,

Absence of voltage modulator block, as well as other controllers such as PID motor flux

and torque

Minimal torque response time, even better than the vector controllers.

However, DTC techniques are associated with following disadvantage:

Possible problems during staring,

Requirement of torque and flux estimators, implying the consequent parameters

identification,

Inherent torque and stator flux ripple.

2.4 VECTOR CONTROL [1][5]

As scalar control provide satisfactory steady-state performance, but their dynamic response is

poor. An induction motor exhibits nonlinear multivariable and highly coupled characteristics.

The vector control technique, which is also known as Field-oriented control (FOC), allows a

squirrel cage induction motor to be driven with high dynamic performance that is comparable

to the characteristic of a DC motor. The FOC techniques decouple the two components of

stator current: one providing the air gap flux and other producing the torque. It provides

independent control of flux and torque, and the control characteristic is linearized. The stator

currents are converted to a fictitious synchronously rotating reference frame aligned with the

flux vector and are transformed back to the stator frame before feeding back to the machine.

The two components are d-axis ids analogous to armature current, and q-axis iqs analogous to

the filed current of a separately excited dc motor.

Principle of Vector Control

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Ac induction motor drives require a coordinates control of stator current magnitudes,

frequencies, and their phases, making it a complex control. As with the dc-drives,

independent control of the flux and torque is possible in ac drives. The stator current phasor

can be resolved, say, along the rotor flux linkages, and the component along the rotor flux

linkages is the field-producing current, but this requires the position of the rotor flux linkages

at every instant; that this is dynamic, unlike in the dc machine. If this is available, then the

control of ac machines is very similar to that of separately excited dc machines. The

requirement of phase, frequency, and magnitude control of the currents and hence of the

flux phasor is made possible by inverter control. The control is achieved in field coordinates

(hence the name of this control strategy, field-oriented control); sometimes it is known as

vector control, because it relates to the phasor control of the rotor flux linkages. The

implementation of the vector control is in fig-2.7, where machine model is represented in a

synchronously rotating reference frame. The inverter generate currents ia , ib , and ic in

response to the corresponding command currents ia* , ib* , and ic* from the controller. The

machine terminal currents ia , ib , and ic are converted to isds and isqs components by three

phase to two phase transformation. These are then converted to synchronously rotating frame

(into ids and iqs components) by the unit vector components cose and sine before

applying to the machine. The machine is represented by internal conversions into the (de qe

) model.

The controller makes two stages of inverse transformation so that the line control ids* and

iqs* currents correspond to the machine currents ids and iqs respectively. In addition, the

unit vector (cose and sine) ensures correct alignment of ids current with the flux vector r

and ids current is perpendicular to it.

There are essentially two methods of vector control. One, called direct or feedback method

and the other known as indirect or feed forward method. The methods are different

essentially by how the unit vector (cose sine ) is generated for the control.

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Fig:-2.7 Vector control implementation principle with machine de qe model

2.4.1 Direct or Feedback Vector Control [15]

The basic block diagram of the direct vector control method for a PWM voltage-fed inverter

drive is in figure-2.8. The principal vector control parameters, i ds* and iqs

*, which are dc values

in synchronously rotating frame, are converted to stationary frame (defined as vector rotation

(VR)) with the help of a unit vector (cose and sine) generated from flux vector signals drs

and drs . The resulting stationary frame signals are then converted to phase current

commands for the inverter. The flux signals drs and dr

s are generated from the machine

terminal voltages and currents with the help of the voltage model estimator, (fig-2.10). A flux

control loop has been added for precision control of flux. The torque component of current

iqs* is generated from the speed control loop through a bipolar limiter. The torque,

proportional to iqs (with constant flux), can be bipolar. It is negative with negative iqs, and

correspondingly, the phase position of iqs becomes negative. An additional torque control

loop can be added within the speed loop, if desired. Figure 2.8 can be extended to field-

weakening mode by programming the flux command as a function of speed so that the

inverter remains in PWM mode. Vector control by current regulation is lost if the inverterattains the square-wave mode of operation. In vector control, the correct alignment of current

ids in the direction of flux r

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Fig-2.8 Direct vector control block diagram with rotor flux orientation

The correct alignment of current ids in the direction of flux r and the current iqs

perpendicular to it are important parameter in vector control.

Let de qe frame is rotating at synchronous speed e with respect to stationary frame ds qs,

and at any instant, the angular position of the de-axis is e, where e = et . From the fig-2.9,

we can say:

Fig-2.9 ds qs and de qe phasors showing correct rotor flux

31

22s

qr

s

drr

r

s

qr

e

r

sdr

e

er

s

qr

er

s

dr

sin

cos

words,otherIn

sin

cos

+=

=

=

=

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Where vector r is represented by magnitude r .

These unit vector signals ( cose and sine), when used for vector rotation, give a ride of

current ids on the de-axis (direction of r ) and current iqs on the qe-axis. At this condition,

qr=0 and dr = r . When the iqs polarity is reversed by the speed loop, the iqs position in

Figure 2.9 also reverses, giving negative torque [15]. The generation of a unit vector signal

from feedback flux vectors gives the name

Direct Vector Control

In the direct vector control method, it is necessary to estimate the rotor flux components drs

and qrs so that unit vector and rotor flux can be calculated by equations (47) (49). Two

commonly used methods of flux estimation are Voltage Model and Current Model. In

Voltage model method, the machine terminal voltages and currents are sensed and the fluxes

are computed from the stationary frame ( ds qs ) equivalent circuit. The block diagram is as

follows:

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Fig-2.10 Voltage model feedback signal estimation block diagram

Problem with Direct Vector Control

Any error in the unit vector or distortion associated with the feedback signals willaffect the performance of the drive.

At low frequency, voltage signals Vdss and Vqs

s are very low. In addition, ideal integration

becomes difficult because dc offset tends to build up at the integrator output.

The parameter variation effect of resistance Rs and inductance Lls , Llr , and Lm tend to

reduce accuracy of the estimated signals. Particularly, temperature variation of Rs becomes

more dominant.

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In the low-speed region, the rotor flux components can be synthesized more easily

with the help of speed and current signals.

2.4.2 Indirect or Feed forward Vector Control

The problems such as inherent coupling effect, parameter variations give sluggish response

and the system is easily prone to instability. This can be solved by vector or field oriented

control. By implementing FOC, dc machine like performance can be obtained by an

induction motor as the machine control is considered in a synchronously rotating reference

frame where the sinusoidal variables appear as dc quantities in steady state. The indirect

vector control method is essentially the same as direct vector control, except the unit vector

signals (cose and sine) are generated in feed forward manner. Indirect vector control is very

popular in industrial applications [22]. Let ds qs axes are fixed on the stator, but the dr qr

axes, which are fixed on the rotor, are moving at speed r . Synchronously rotating axes de

qe are rotating ahead of the dr qr axes by the positive slip angle sl corresponding to slip

frequency sl . Since the rotor pole is directed on the de axis and e = r+ sl, we can write:

Fig: 2.11 Phasor diagram explaining Indirect Vector Control

So, we can write

slrslree )dt(dt +=+==

Here rotor position is not absolute, but is slipping with respect to the rotor at frequency sl.

From the above figure, we can conclude that for decoupling control, the stator flux

component of current ids should be aligned on the de axis, and the torque component of

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current iqs should be on the qe axis. For decupling control, we can write the control equations

of indirect vector control with the help of d e qe equivalent circuits Fig (2.5). The rotor

equations can be written as

0)(iRdt

d

0)(iRdt

d

drreqrr

qr

qrredrrdr

=++

=+

The rotor flux linkage expression can be given as

qsmqrrqr

dsmdrrdr

iLiL

iLiL

+=

+=

From the above equations, we can write

qs

r

mqr

r

qr

ds

r

mdr

r

dr

iL

L

L

1i

iL

L

L

1i

=

=

The rotor currents in eq (51) and (52), which are inaccessible, can be eliminated with the

help of equations (55) & (56) as

resl

drslqsr

r

m

qr

r

rqr

qrsldsr

r

m

dr

r

rdr

where

0iRL

L

L

R

dt

d

0iRL

L

L

R

dt

d

=

=++

=+

Where sl = e rhas been substituted.

For decoupling control, it is desirable that qr= 0 that is 0dt

dqr = , so that the total rotor

flux r is directed on the de axis.

Substituting the above conditions in eq (57) & (58), we get

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qs

rr

rmsl

dsmrr

r

r

iL

RL

iLdt

d

R

L

=

=+

Where r = drhas been substituted.

If rotor flux r = constant, which is usually the case, then from eq (59),

dsmr iL =

The motor developed torque is directly related to i*qs as follows:

*

r

e

r

m*qs

*

qs

*

te

*

qs

*

drr

m

e

T

L

L

3P

4i

iKTtheniL

L

dt

d

4

3

T

=

=

=

2.4.3 Field oriented control block for Induction motor Drive

This figure 2.12 illustrates a variable-speed induction motor drive using field-oriented control

[2].

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Fig 2.12 Field Oriented Control Scheme for Induction Motor Drive

In this control scheme, a dq coordinates reference frame locked to the rotor flux space vector

is used to achieve decoupling between the motor flux and torque. They can thus be controlled

separately by stator direct-axis current and quadrature-axis current respectively, as in a DC

motor. This figure shows a block diagram of a field-oriented induction motor drive.

The induction motor is fed by a current-controlled PWM inverter, which operates as a three-

phase sinusoidal current source. The motor speed is compared to the reference speed *

and the error is processed by the speed controller to produce a torque command Te*. As

shown in figure 2.11 the rotor flux and torque can be separately controlled by the stator

direct-axis current ids and quadrature axis current iqs, respectively.

2.5 CONTROLLER

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The most important aspect of the indirect vector control of induction motor is the

transformation of currents into a torque producing component and a flux-producing

component. For this, accurate estimation of the parameter (unit vector) is required because

this depends on the effectiveness in producing the appropriate torque command.

Conventional vector control uses the Proportional-Integral controller to generate the torque

command. A conventional PI controller requires accurate sensor input and appropriate values

of the PI constants to produce high performance drive. In contrast, Fuzzy logic controllers use

heuristic input-output relations to deal with vague and complex situations. Hence fuzzy logic

controller offers the benefits of low cost and higher reliability.

2.6 NEED OF INTELLIGENT CONTROL

One of the primary purposes of classical feedback control is to increase robustness for a

control system, i.e., increase the degree to which the system performs when there is

uncertainty. Classical linear control provides robustness over a relatively small range of

uncertainty [31]. Adaptive control techniques have been developed for systems that must

perform over large ranges of uncertainties due to large variations in parameter values,

environmental conditions, and signal inputs. These adaptive techniques generally incorporate

a second feedback loop, which is outside the first feedback loop. This second feedback loop

may have the capability to track system parameters, environmental conditions, and input

characteristics; feedback control then may vary parameters in compensation elements of the

inner loop to maintain acceptable performance characteristics.

The objective of the design of an intelligent control system is similar to that for the adaptive

control system. However, there is a difference. For an intelligent control system, the range of

uncertainty may be substantially greater than can be tolerated by algorithms for adaptive

systems. The object with intelligent control is to design a system with acceptable

performance characteristics over a very wide range of uncertainty [18]. For example, the

range of uncertainty for available data may be different than expected because it may be

necessary to deal with unquantified data, highly complex data structures, or extremely large

amounts of data. Traditional control systems, which operate with large uncertainty, typically

depend on human intervention to function properly. However, human intervention is

unacceptable in many real-time applications and automatic techniques for handling

uncertainty must be developed. In a typical drive control application, a number of problems

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must be faced that are also generic to design of controllers for large dynamic systems. Some

examples of these problems are presented here [18] [31]:

Mathematical model of the system;

Sensor data overload, which may arise from data redundancy or from specialized

data rarely needed by the system;

Sensor data fusion and mapping into the proper control feedback law; Systems not

robust enough to handle large parameter excursions;

Machine parameter variation problem

Systems that cannot be used for high-speed real-time control because they requiretime-consuming artificial intelligence calculations;

Systems where sensor choice and placement are still unsolved.

These examples provide motivation for the concept of intelligent control and illustrate the

need for real-time intelligent components. Three approaches that have the potential for

intelligent control are:

1. EXPERT SYSTEMS

2. FUZZY LOGIC

3. NEURAL NETWORKS

Artificial Intelligence is machine emulation of the human thinking processes. The term began

to be systematically used since the Dartmouth College conference in 1956 when artificial

intelligence was defined as computer processes that attempt to emulate the human thought

processes that are associated with activities that require the use of intelligence. Human brain

is the most complex machine on earth. For a long time, the neuro-biologists have been taking

the bottom-up approach to understand the brain structure and its functioning, and the

behavioral scientists, such as psychologists and psychiatrists, the top down approach to

understand the human thinking process. However, our knowledge about the brain is so

inadequate at present that it is expected to take another 50 to 100 years to understand the

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human brain and its thinking process. Since human brain is the ultimate intelligent machine,

the question is: Is it possible to generate such intelligence or at least a part of it, artificially

with the help of a computer so that it can solve our complex problems which are difficult to

solve in traditional way? In early age, it was perceived that human brain takes decision on the

basis of yes-no or true-false reasoning. In 1854, George Boole first published his article

Investigations on the laws of thought, and Boolean algebra and set theory were born as a

result. Gradually, the advent of electronic logic and solid state ICs ushered the modem era of

Von Neumann type digital computation. Digital computers were defined as intelligent

machines because of their capability to process human thought-like yes ( 1 ) or no ( 0 ) logic.

Of course, using the same binary logic, computers can solve complex scientific, engineering,

and other data processing problems. Since the 1960s and in the early 1970s, it was felt that

computers have severe limitations being able to handle only algorithmic-type problems. An

entirely new way of structuring software that closely matches the human thinking process,

called Expert System was born.

The new branch of software engineering is called Knowledge Engineering. This new breed

of Knowledge Engineers was responsible for the acquisition of knowledge from the human

experts in a particular domain and translating it into software. In the 1980s, expert system

applications proliferated in industrial process control, medicine, geology, agriculture,

information management, military science, and space technology, just to name a few.

Since the mid 1960s, a new theory called Fuzzy Logic or fuzzy set theory was propounded

which gradually helped to supplement the expert system as an AI tool. L. A. Zadeh, the

originator of this theory, argued that most of human thinking is fuzzy or imprecise in nature,

and therefore, Boolean logic (which is represented by crisp 0 and 1) cannot adequately

emulate the thinking process. However, the general methodology of reasoning remaining the

same, it was defined as fuzzy expert system. In recent years, fuzzy logic has emerged as an

important AI tool to characterize and control a system whose model is not known, or ill-

defined. It has been widely applied in process control, estimation, identification, diagnostics,

stock market prediction, agriculture, military science, etc. While the traditional digital

computer is very efficient in solving expert system problems and somewhat less efficient in

solving fuzzy logic problems, its inability to solve pattern recognition and image processing

type problems was seriously felt since the beginning of the 1990s. In fact, expert system

techniques which held so much promise in the 1980s could not fulfill the expected

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computational needs. Therefore, peoples attention was recently focused on a new branch of

AI, called artificial neural network (ANN) or neural network.

Fundamentally, the human brain is constituted of billions of nerve cells, called neurons, and

these neurons are interconnected to constitute the biological neural network. Our thinking

process is generated by the action of this neural network. The ANN tends to simulate the

neural network by electronic computational circuits. The ANN technology is the most generic

for emulation of human thinking. It has been applied to process control, diagnostics,

identification, character recognition, robot vision, flight scheduling, financial prediction, etc.

The history of ANN technology is not new. It was gradually evolving since the 1950s, but

the glamour of modem digital computer and expert system techniques practically

camouflaged the neural network evolution in the 1960s and 1970s. Since the beginning of

the 1990s, neural network as AI tool has captivated the attention of practically the whole

scientific community. This new form of machine intelligence has suddenly been elevated to

transcendental heights. Often, it is held as the greatest technological advance since the

invention of the transistor. It is predicted to touch almost every scientific and engineering

application by the early 21st century. Of course, we need to wait and see to what extent this is

true.

This Project is concerned with the fuzzy logic controller for induction motor drive. Withthese tools, a system is said to be intelligent, learning, or have self-organizing

capability. Traditionally, the design of a control system is dependent on the explicit

description of its mathematical model and parameters. Often, the model and the parameters

are unknown, or ill-defined. The system, again, may be complex with nonlinearity and

parameter variation problems. An intelligent or self-organizing control system can identify

the model, if necessary, and give predicted performance even with wide range of parameter

variation.

2.6.1 Necessity of Fuzzy Logic

The control algorithm of a process that is based on Fuzzy Logic or a fuzzy inference system

is defined as a fuzzy control. In general, a control system based on Artificial Intelligent is

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defined as intelligent control. A fuzzy control system essentially embeds the experience and

intuition of a human plant operator, and sometimes those of a designer and / or researcher of

a plant. The design of a conventional control system is normally based on the mathematical

model of a plant. If an accurate mathematical model is available with known parameters, it

can be analyzed, for example, by a Bode or Nyquist plot, and a controller can be designed for

the specified performance. Such a procedure is tedious and time-consuming, although CAD

programs are available for such design. Unfortunately, for complex processes, such as cement

plants, nuclear reactors, and the like, a reasonably good mathematical model is difficult to

find. On the other hand, the plant operator may have good experience for controlling the

process.

Power electronics system models are often ill-defined. Even if a plant model is well-known,

there may be parameter variation problems. Sometimes, the model is multivariable, complex,

and nonlinear, such as the dynamic d-q model of an ac machine. Vector or field-oriented

control of a drive can overcome this problem, but accurate vector control is nearly

impossible, and there may be a wide parameter variation problem in the system. To combat

such problems, various adaptive control techniques are their. Fuzzy control, on the other

hand, does not strictly need any mathematical model of the plant. It is based on plant operator

experience and heuristics, as mentioned previously, and it is very easy to apply. Fuzzy

control is basically an adaptive and nonline ar control, which gives robust performance for a

linear or nonlinear plant with parameter variation. In fact, fuzzy control is possibly the best

adaptive control among the techniques discussed so far.

2.6.2 Fuzzy Set Theory & Membership Functions:

Fuzzy logic starts with the concept of a fuzzy set. A fuzzy set is a set without a crisp, clearly

defined boundary. It can contain elements with only a partial degree of membership.

To understand what a fuzzy set is, first consider what is meant by what we might call a

classical set. A classical set is a container that wholly includes or wholly excludes any given

element. In fuzzy logic, the truth of any statement becomes a matter of degree. Any statement

can be fuzzy. The tool that fuzzy reasoning gives is the ability to reply to a yes-no question

with a not-quite-yes-or-no answer. This is the kind of thing that humans do all the time but it

is a rather new trick for computers.

Membership Functions

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A membership function (MF) is a curve that defines how each point in the input space is

mapped to a membership value (or degree of membership) between 0 and 1. The input space

is sometimes referred to as the universe of discourse, a fuzzy set is an extension of a classical

set. If X is the universe of discourse and its elements are denoted by x, then a fuzzy set A in

X is defined as a set of ordered pairs.

A = {x, A(x) | x X}

where A(x) is called the membership function (or MF) of x in A.

The membership function maps each element of X to a membership value between 0 and 1.

The simplest membership functions are formed using straight lines.

Of these, the simplest is the triangular membership function, as shown in figure 2.13(a) and ithas the function name trimf. It is nothing more than a collection of three points forming a

triangle.

Fig 2.13(a) Triangular MF Fig 2.13(b) Trapezoidal MF

The trapezoidal membership function, as shown in figure 2.13(b) has function name trapmf

and has a flat top and really is just a truncated triangle curve. These straight line membership

functions have the advantage of simplicity.

If-Then Rules

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Fuzzy sets and fuzzy operators are the subjects and verbs of fuzzy logic. These if-then rule

statements are used to formulate the conditional statements that comprise fuzzy logic. A

single fuzzy if-then rule assumes the form

If x is A then y is B

where A and B are linguistic values defined by fuzzy sets on the ranges (universes of

discourse) X and Y, respectively. The if-part of the rule "x is A" is called the antecedent or

premise, while the then-part of the rule "y is B" is called the consequent or conclusion. The

antecedent is an interpretation that returns a single number between 0 and 1. On the other

hand, the consequent is an assignment that assigns the entire fuzzy set B to the output

variable y. In the if-then rule, the word "is" gets used in two entirely different ways

depending on whether it appears in the antecedent or the consequent. Interpreting an if-then

rule involves distinct parts: first evaluating the antecedent (which involves fuzzifying the

input and applying any necessary fuzzy operators) and second applying that result to the

consequent (known as implication). In the case of two-valued or binary logic, if-then rules

don't present much difficulty. If the premise is true, then the conclusion is true. If the

restrictions of two-valued logic is relaxed and let the antecedent be a fuzzy statement, then if

the antecedent is true to some degree of membership, then the consequent is also true to that

same degree.

In other words in binary logic:

In fuzzy logic:

0.5 p 0.5 q (Partial antecedents provide partial implication.)

The antecedent of a rule can have multiple parts in which case all parts of the antecedent are

calculated simultaneously and resolved to a single number using the logical operators. The

consequent of a rule can also have multiple parts, in which case all consequents are affected

equally by the result of the antecedent.

The consequent specifies a fuzzy set be assigned to the output. The implication function then

modifies that fuzzy set to the degree specified by the antecedent. The most common ways to

modify the output fuzzy set are truncation using the min function (where the fuzzy set is

chopped off) or scaling using the prod functions (where the output fuzzy set is squashed).

Defuzzification

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Conversion of fuzzy output to a crisp output is defined as defuzzification, the number

obtained thus as the output of defuzzification should be such that it can be sent to the process

as a control signal.

Various methods of defuzzification are as listed below:

centroid: centroid of area method

bisector: bisector of area method

mom: mean of maximum method

som: smallest of maximum method

lom: largest of maximum method

Summary of Fuzzy System

A fuzzy system basically consists of formulation of the mapping from a given input set to an

output set using fuzzy logic. This mapping process provides the basis from which the

inference or conclusion can be made.

A fuzzy inference system process can be summarized in following steps:

Fuzzification of input variable

Application of fuzzy operator (AND, OR, NOT) in the IF part of the rule

Implication from the antecedent to the consequent (THEN part of the rule)

Aggregation of the consequents across the rule

Defuzzification

2.7 FUZZY LOGIC SPEED CONTROLLER PRINCIPLE AND DESIGN

The fuzzy logic speed controller block in a vector-controlled drive system used, shown in fig:

2.14. The controller observes the pattern of the speed loop error signal and correspondingly

updates the output DU so that the actual speed r matches the command speed r* [28].

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Fig 2.14 Fuzzy speed controller in vector-controlled drive system

There are two input signals to the fuzzy controller, the error E= r* - r and the change in

error, CE, which is related to the derivative dE/dtof error. In a discrete system,

dE / dt = E / t = CE / T s ,

where CE= E in the sampling time Ts. With constant Ts, CEis proportional to dEldt. The

controller output DU in a vector-controlled drive is iqs* current. This signal is summed or

integrated to generate the actual control signal Uor current iqs*. From the physical operation

principle of the system, we can write a simple control rule in FL as

IFEis near zero (ZE) AND CE is slightly positive (PS)

THEN the controller outputDUis small negative

whereEand CEare the input fuzzy variables, DUis the output fuzzy variable, and ZE, PS,

and NS are the corres