performance and analysis of induction motor using

PERFORMANCE AND ANALYSIS OF INDUCTION MOTOR USING CONVENTIONAL

SVM CONTROLLER AND FUZZY LOGIC CONTROLLER

SRINIVASA RAO JALLURI1 & B.V.SANKER RAM

2

1Assistant Professor, Department of Electrical and Electronics Engineering, VNR VJIET, Hyderabad, Andhra Pradesh,

India

2Professor, Department of Electrical and Electronics Engineering, JNT University Hyderabad, Andhra Pradesh, India

ABSTRACT

In this Paper the speed control scheme of indirect vector controlled induction motor (IM) drive involves

decoupling of the stator current into torque and flux producing components. In this Paper fuzzy logic control (FLC) scheme

is implemented and it is applied to a two d-q current components model of an induction motor to achieve maximum torque

with minimum loss. An intelligent control based on Fuzzy logic controller is developed with the help of knowledge rule

base and membership functions are chosen according to the parameters of the motor model for efficient control. The

performance of Fuzzy logic controller is compared with that of the conventional Space vector Modulation (SVM)

controller in terms of dynamic response to sudden load changes. The analysis of electrical transients that occur during the

failure of the open circuit breaker in a three-phase inverter power supply of a DTC induction motor broke down with the

fuzzy logic control scheme. The performance of the induction motor (IM) drive has been analyzed under steady state and

transient conditions. The simulation model is tested using various tool boxes in MATLAB. Simulation results of both the

controllers are presented for comparison.

KEYWORDS: Induction Motor, SVM Control and Fuzzy Control

INTRODUCTION

In many of the industrial applications, an electric machine is the most important component. A complete

production unit consists primarily of three basic components, an electric machine, and energy – transmitting device and the

working (or driven) machine. An electric machine or motor is the source of motive power. An energy transmitting devices

delivers powers from electric motor to the driven machine (lathes, centrifugal pumps, drilling machines, lifts, conveyer

belts, food – mixers etc) An electric motor together with its control equipment and energy – transmitting devices forms an

electric drive. Drive means “System’s employed for the motion control”. Or A motor with suitable speed control

equipment is called drive. Because of the disadvantages of dc drive, Most of the industries are prefer ac drive than the dc

drive. Induction motors are being applied today to a wider range of applications requiring variable speed. Generally,

variable speed drives for Induction Motor (IM) require both 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. 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 [1]. The motor control issues are traditionally handled by switching table of svm controller.

However, the switces are very sensitive to parameter variations, load disturbances, etc.thus, the controller parameters have

to be continually adapted [2]-[5]. To develop an accurate system mathematical model due to unknown load variation,

unknown and unavoidable parameter variations due to saturation, temperature variations, and system disturbances [6]. In

International Journal of Electrical and Electronics

Engineering Research (IJEEER)

ISSN 2250-155X

Vol. 3, Issue 2, Jun 2013, 131-146

© TJPRC Pvt. Ltd.

132 Srinivasa Rao Jalluri & B.V. Sanker Ram

order to overcome the above problems, the fuzzy logic controller (FLC) is used for motor control. The implementation of a

fuzzy logic control scheme applied to a two d-q current components model of an induction motor [4].

In this Paper indirect vector control method is used for the control of induction motor. In the vector control, the

induction motor can be controlled like a separately excited dc motor. Because of the inherent coupling problem, an

induction motor cannot give such a fast response, this problem can be eliminated by, machine control is considered in the

synchronously rotating reference frame (de- q

e), where the sinusoidal variables appear as dc quantities in steady state, thus

giving fast transient response. 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. Usually classical control is used in electrical

motor drives. The classical controller designed for high performance increases the complexity of the design and the cost.

INDUCTION MOTOR MODELING

In modeling of three phase induction motor, it is necessary to derive the equations which are required to design

induction machine model. After the mathematical modeling we implement in the simulink. To design the indirect vector

control of I.M. drive, we have to know about vector control principle is based on the dynamic d-q model of the machine.

The dynamic performance of an ac machine is somewhat complex because the three phase rotor windings move with

respect to the three-phase stator windings as shown in figure 3.4. Basically, it can be looked as a transformer with a

moving secondary. Where that coupling coefficients between the stator and rotor phase change continuously with the

change of rotor position θ, 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 3.4. Where ss qd

correspond to stator direct and quadrature axes, and rr qd

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

transformed referred the stator variables to a synchronously rotating reference frame fixed in the rotor. With such a

transformation (called park’s transformation). Later, in the 1930s H.C. Stanley showed that time – varying inductances in

the voltage equations of an induction machine to electric circuits in relative motion can be eliminated by the rotor variables

are transformed to a stationary reference frame fixed on the stator.

Axes Transformation

In this discuss about transform the three – phase stationary reference frame (as-bs-cs) variables in to two – phase

stationary reference frame (ss qd ) variables and then transform these to synchronously rotating reference frame

\(ee qd ), and vice versa.

Three Phase Stationary Reference Frame (as-bs-cs) Variables Into Two Phase Stationary Reference Frame (ds-q

s)

Assume that the ds-q

s axes are oriented at angle, The voltages

sdsV and

s

qsV can be resolved into as-bs-cs

components and corresponding inverse relation can be represented in the matrix from as.

Performance and Analysis of Induction Motor Using Conventional 133

SVM Controller and Fuzzy Logic Controller

s

s

s

os

ds

qs

V

V

V

=3

2

0.50.50.5

120θsin120θsinsinθ

120θcos120θcoscosθ

cs

bs

as

V

V

V

(1)

Where s0sV

is added as the zero sequence component, which may or may not be present. We have considered

voltage as the variable. The current and flux linkages can be transformed be similar equation. It is convenient to set θ =0,

so that the qs -axis is aligned with the as

-axis, Ignoring the zero sequences component, the transformation can be

simplified, and gives as

3

1

3

1-

3

1

3

1

0

3

2

V

V

ss

d

ss

q

bs

as

V

V (2)

This is the conversation matrix of the equation. These equations are in two phase stationary reference frame. [6]

Two Phase Reference Frame (ds - q

s) to Two Phase Synchronously Rotating Reference Frame (d

e-

q

e)

Figure 4 shows the synchronously rotating ee qd

axes, which rotate at synchronous speed ωe with respect to

the ss qd axes and the angle tωθ e The two phase

ss qd windings are transformed into the hypothetical

winding mounted on the ee qd axes.

The voltage on the ss qd axes can be converted or resolved into the

ee qd frame and corresponding

inverse relation can be represented in the matrix from as.

e

ds

e

qs

V

V =

e

e

e

e

θ cos

θsin

θsin

θ cos

s

ds

s

qs

V

V

(3)

Synchronously Rotating Reference Frame

For the two phase machine shown in Figure 3.4 we need to represent both ss qd and

rr qd variables in

synchronously rotating reference frameee qd . We write the following stator circuit equations

s

qsψ and sdsψ are

converted in to ee qd frame as:

dseqsqssqs ψωψdt

diRV (4)

The above equation belongs to Figure 1.

qsedsdssds ψωψdt

diRV (5)


Figure 1: Stationary Frame ds – as to Synchronously Rotating Frame de – qe Transformation

The last terms in Equations (4) & (5) can be defined as speed emf due to rotation of the axes, that is, when

0ωe , the equations revert to stationary frame, Note that the flux linkage in the ed and

eq axes induce emf in the eq

and ed axes, respectively, with π/2 lead angle. If the rotor is not moving, that is, 0ω r , the rotor equations for a

doubly – fed wound rotor machine will be similar to equations (4) & (5) .Where all the variables and parameters are

referred to the stator, since the rotor actually moves at speed rω , therr qd axes fixed on the rotor move at a speed

eω - rω relative to the synchronously rotating frame. Therefore, in ee qd

frame, the rotor equations should be

modified as

drreqrqrrqr ψωωψdt

diRV (6)

The above equation belongs to Figure 2.

qrredrdrrdr ψωωψdt

diRV (7)

A special advantage of the ee qd dynamic model of the machine is that all the sinusoidal variables in

stationary frame appear as dc quantities in synchronous frame. The flux linkage expressions in terms of the currents and

combined with the equations (4) – (7) then the electrical transient model in terms of voltage and currents can be given

matrix from as:

dr

qr

ds

qs

rrrremmre

rrerrmrem

mmessse

memsess

dr

qr

ds

qs

i

i

i

i

SLRLωωSLLωω

LωωSLRLωωSL

SLLωSLRLω

LωSLLωSLR

V

V

V

V

(8)



Where S is the Laplace operator, for a singly – fed machine, such as a cage motor, 0VV drqr .

If the speed

ωr is considered constant (infinite inertia load), the electrical dynamic of the machine are given by a fourth - order liner

system. Then, knowing the inputs qsV , dsV and eω the currents qsi , dsi and eω are independent. Then, the

dependent variables qsV , dsV , qri and qri can be solved from equation(2.8) .

The speed ωr in equation (8) cannot normally be treated as a constant. It can be related to the torques as

dt

dωJ

P

2T

dt

dωJTT m

Lm

Le (9)

Where LT =load torque, J=rotor inertia, and m = mechanical speed

The development of torque will be expressed in more general form, relating the d-q components of variables. The

torque can be generally expressed in the vector form as

rme Iψ2

P

2

3T

(10)

Then the torque Equation after simplified is

dsqsdsdse

qrdsdrqsme

iψiψ2

P

2

3T

iiiiL2

P

2

3T

(11)

Equations (8), (9), (11). Give the complete model of the electro-mechanical dynamics of an induction machine

synchronous frame. [6]

Direct or Feedback Vector Control

In the direct vector control method, the principal vector control parameters, *dsI and

*

qsI which are dc values

with the help of a unit vector (Cos eθ and Sin eθ ) generated from flux vector signals sdrψ and

s

qrψ . The resulting

stationary frame signals are then converted to phase current commands for the inverter. The flux signals sdrψ

and s

qrψ are

generated from the machine terminal voltages and currents with the help of the voltage model estimator.

The generation of a unit vector signal from feedback flux vector gives the name direct vector control. In the direct

vector control, the measurement of voltages and currents are required. In this the rψ is estimated by observer and flux

vectors are estimated from the voltage model method and current model method. The three voltages and three current

sensors are required. From rψ we get the speed eω and then get the angle eθ .


Indirect or Feed Forward Vector Control

In this modeling the indirect vector control method is used. In the indirect vector control the unit vector signals

(Cos eθ and Sin eθ ) are generated in feed forward manner, indirect vector control is very popular in industrial application.

Figure 3.2 explains the fundamental principle of indirect vector control with the help of phasor diagram. The

ss qd axes are fixed on the stator, and rr qd axes are fixed on the rotor moves at speed rω as shown.

Synchronously rotating axes ee qd is rotating ahead of the

rr qd axes by the positive slip angle slθ

corresponding to slip frequency slω . Since the rotor pole is directed on the ed axes and slre ωωω we can write

slrslree θθdtωωdtωθ (12)

Note that the rotor pole position is not absolute, but is slipping with respect to the rotor at frequency slω . The

phasor suggest that for decoupling control, the stator flux component of current dsI should be aligned on the ed axis, and

the torque component of current qsI should be on the eq

axis as shown.

Figure 2: Phasor Diagram Explaining Indirect Vector Control

For decoupling control, we can now make a derivation of control equations of indirect vector control with the help

of ee qd equivalent circuits. The circuit equations can be written as. From equations (12) to (15)

0)ψω(ωIR/dtdψ qrredrrdr (13)

0)ψω(ωIR/dtdψ qrreqrrqr (14)

From the rotor flux equations the currents qrdr I,I equations as

dsrmdrrdr I/LLψ1/LI (15)



qsrmqrrqr I/LLψ1/LI (16)

From the above equations we get,

qrsldsrrmdrrrdr ψωIR/LLψ/LR/dtdψ =0 (17)

0ψωIR/LLψ/LR/dtdψ drslqsrrmqrrrqr (18)

Where resl ωωω has been substituted.

For decoupling control, it is desirable that

qrψ =0 (19)

That is,

/dtdψqr= 0 (20)

So that the total rotor flux rψ is directed on the ed axis.

Substituting the above conditions in equations (16) & (17), and simplified we get

dsmrr

r

r iLψ̂dt

ψ̂d

R

L (21)

r

qs

r

msl

ψ̂

i

τ

Lω (22)

Where

r

rr

R

Lτ = rotor time constant, drr ψψ has been substituted.

If rotor flux rψ̂ = constant, which is usually the case, i.e. /dtψ̂d r

=0 and

dsI = rψ̂ / mL (23)

In other words, the rotor flux is directly proportional to current dsI in steady state.

The qsI is estimated as follows.

From equation (16) of d-q model derivation

The Torque is given by

)IψI(ψ(3/2)(P/2)T drqrqrdre (24)

From the Equation (8) and substitute in the above equation we get


)I(ψ(3/2)(P/2)T qrdre (25)

after substitution the value of qrI then the equation qsI as

)ψ̂)(T/L(L(2/3)(2/P)I remrqs (26)

From these equation we can write

The field component of the stator current

mr

*

ds /Lψ̂I (27)

Similarly,

The torque component of the stator current *

qsI

qsI = (2/3) (2/p) (Lr/Lm) (Te

*/

estrψ̂ ) (29)

Therefore the slip speed

ωsl* = Lm/ τr. (Iqs

*/

estrψ̂ ) (30)

To implement the indirect vector control strategy, it is necessary to take equations (12), (22), (23), (29), & (30)

into consideration and these equations are implemented in simulink. [1]

FUZZY CONTROL SYSTEM

A Fuzzy control system essentially embeds the experience and intuition of a human plant operator. If accurate

mathematical model is available with known parameters it can be analyzed, but it is time consuming and tedious

Figure 3: Fuzzy Speed Controller in Vector Drive System

The Figure 3 shows the Fuzzy control of indirect vector control. The controller observes the pattern of speed loop

error signal and correspondingly updates the output DU. So that the actual speed rω matches the commanded speed*rω .

There are two inputs signals to the Fuzzy controller, one is error E = *rω - rω and another one is change in error CE

which is integrating of error. These two inputs are converted to per unit signals e and ce by dividing respective scale



factors i.e. e=E/GE & ce=CE/GC. Similarly output of plant control signal U is derived from DU by multiplying the scale

factor GU i.e. DU=du*GC. After that it is derivate to generate the output signal U.

In vector controlled drive this controlled output U is *qsΔI current. The advantage of fuzzy control in terms of

per unit variables is that the same control algorithm can be applied to all the plants of same family. Their scale factors can

be constants or programmable. The Figure 4 shows that the inputs are normalized and then applied to Fuzzy controller. The

defuzzified output is de - normalized and it will be derivated to generate the output signal.

Figure 4: Structure Fuzzy of Control in Feedback System.

Figure 5 shows GE and GDE are the normalized inputs to Fuzzy logic controller. Where GE=1/reference input

and GDE=1/ (reference input*10). After normalization these inputs are becomes to E and IE i.e. Error and Integrated

Errors. Output GU is the denormalized to generate the output signal U. which is saturated in range of +400 to -400. All the

MFS are symmetrical for positive and negative values of variables.

Figure 5: Fuzzy Controller

Rule Matrix

Figure 6 shows corresponding rule table for the speed controller. The top row and left column of the matrix

indicate the fuzzy sets of variables E, IE, respectively, and the Mfs of output variable GU are shown in the body of matrix.

There may be 7*7 = 49 possible rules in the matrix.


Figure 6: Rule Table

RESULTS AND DISCUSSIONS

First the indirect vector control of induction motor drive with SVM controller is designed, by Proper adjustments

of the gains to get simulated results. After this indirect vector control drive with Fuzzy controller is designed by proper

adjustments of membership functions to get simulated results.

At Load Conditions

In this the machine is stepped up to speed using the speed reference after that which is subjected to a step change

at 0.6sec, and also load disturbance at 0.3sec.Drive with SVM controller speed response has small peak at 0.02sec, but in

case of fuzzy controller speed response quickly and smoothly responds to the programmable speed reference, as shown in

Figure 7.1. After a sudden load disturbance at 0.3 sec the speed response of the drive with SVM controller has small

decrement in speed from 122 R.P.M to 118 R.P.M as shown in Figure 7.2 (a). But in case of fuzzy controller the speed

decrement is small compared to the conventional SVM controller as shown in Figure 7.2 (b).Drive with svm, Fuzzy

controller current responses are sinusoidal throughout the simulation period, as shown in Figure 7.3. Slightly small

disturbance of currents occurred at initial start up of motor from standstill, at step change also the current response changes

slightly but with in short time it reaches to a previous position, and at the sudden load disturbance at 0.3sec there is a

slight change in current as shown in Figure 7.3.d-axis current of the drive with SVM and Fuzzy controller is constant

throughout the simulation period as shown in Figure 7.4. At the initial start up of the motor from standstill, Drive with

SVM controller torque response has a larger peak compared to drive with fuzzy controller as shown in Figure 7.6.Q-axis

current is constant except at step change, as shown in Figure 7.5.Drive with SVM controller rotor flux has peak over shoot,

but in case of fuzzy controller it can be eliminated, as shown in Figure 7.7.

LOAD RESULTS

Figure 7. 1 (a): Commanded & Achieved Speeds of Induction Motor Drive with SVM Controller at Variable Load



Figure 7.1 (b): Commanded & Achieved Speeds of Induction Motor Drive with Fuzzy Controller at Variable Load

Figure 7.2 (a): Commanded & Achieved Speeds of Induction Motor Drive with SVM Controller at Variable Load

Figure 7.2 (b): Commanded & Achieved Speeds of Induction Motor Drive with Fuzzy Controller at Variable Load

after Zoom


Figure 7.3 (a): Achieved three Phase Currents of Induction Motor Drive with SVM Controller at Variable Load

Figure 7.3 (b): Achieved Three Phase Currents of Induction Motor Drive with Fuzzy Controller at Variable Load

Figure 7.4 (a): D-Axis Current (Synchronous Frame) of the Induction Motor Drive with SVM Controller at

Variable load



Figure 7.4 (b): D-Axis Current (Synchronous Frame) of the Induction Motor Drive with Fuzzy Controller at

Variable Load

Figure 7.5 (a): Q-Axis Current(Synchronous Frame) of the Induction Motor Drive with SVM Controller at

Variable Load

Figure 7.5(b): Q-Axis Current(Synchronous Frame) of the Induction Motor Drive with Fuzzy Controller at

Variable Load


Figure 7.6 (a): Load Torque & Electromagnetic Torque Developed by the Induction Motor Drive with SVM

Controller at Variable Load

Figure 7.6 (b): Load Torque & Electromagnetic Torque Developed by the Induction Motor Drive with Fuzzy


Figure 7.7 (a): Commanded & Achieved (Estimated) Rotor Flux of the Induction Motor Drive with SVM Controller

at Variable Load



Figure 7.7 (b): Commanded & Achieved (Estimated) Rotor Flux of the Induction Motor Drive with Fuzzy


SCOPE FOR THE FUTURE WORK

The Indirect vector control of induction motor with Fuzzy logic controller has advantages over SVM controller.

For improve the dynamic performance of Indirect vector control of induction motor will implement by using the soft

computing techniques like Fuzzy-Neural network method, GA based control algorithms.

REFERENCES

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4. C. C. Lee, “Fuzzy Logic in control systems; Fuzzy Logic Controller – Part – I”, IEEE Trans. on systems, man and

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5. Ljubomir Francuski, Filip Kulic,” Fuzzy PI Controller for vector control of induction machine”, 9th

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Neural Network Applications in Electrical Engineering, NEURAL-2008.

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7. P.Vas, sensor less vector and direct torque control, oxford university press, Inc., New York, 1998.

8. Bimal k. Bose, “Modern power electronics and AC drives”, prentice hall PTR publication

performance and analysis of induction motor using

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