Optimization of controlling of performance characteristics of induction mo

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME
14
OPTIMIZATION OF CONTROLLING OF PERFORMANCE
CHARACTERISTICS OF INDUCTION MOTOR USING FUZZYLOGIC
1
Dr.K.Ravichandrudu, 2
P.Suman Pramod Kumar, 3
B.Hemanth Kumar, 4
T.Kiran Kumar
1
Krishnaveni Engg College/Dept of EEE, Guntur, A.P, India
2 & 3
C.R.Engg College/Dept of EEE, Tirupathi, A.P, India
4
PG student, C.R. Engg college, Tirupathi,A.P, India
ABSTRACT
Energy optimizing controllers interface with the ASD to minimize line power consumption
and that controller is on-line efficiency optimization controller using fuzzy logic. The on-line
efficiency optimization control on the basis of search, where the flux is decremented in steps until
the measured input power settles down to the lowest value. The control does not require the
knowledge of machine parameters, is completely insensitive to parameter changes, and the algorithm
is applicable universally to any arbitrary machine. In the present paper, a fuzzy logic based on-line
efficiency optimization control is proposed for an indirect vector controlled drive system. Fast
convergence is achieved by using adaptive step size of the excitation current. The low-frequency
pulsating torque generated by the efficiency controller has been suppressed by a feed forward
compensation algorithm.
Index Terms: Optimisation, Speed control, Frequency, Fuzzylogic, Torque and Induction motor
I. INTRODUCTION
Higher efficiency is important not only from the viewpoint of energy saving and cooling
system operation but also from the broad perspective of environmental pollution. The efficiency of a
drive system is a complex function of the type of the machine used, converter topology, type of
power semiconductor switches and the selected PWM algorithm. In addition, the control system has
a profound effect on the drive efficiency. A drive system normally operating at rated flux gives best
transient response. However, at light loads, rated flux operation causes excessive core loss, thus
impairing the efficiency of the drive. Since drives operate at light load most of the time, optimum
efficiency can be obtained by programming the flux. The online efficiency optimization control on
the basis of search, where the flux is decremented in steps until the measured input power settled
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down to the lowest value. The control does not require the knowledge of machine parameters, is
completely insensitive to parameter changes, and is applicable universally to any arbitrary machine.
In the present paper, a fuzzy logic based on-line efficiency optimization control is proposed
for an indirect vector controlled drive system.
A. Need for Efficiency Optimization:
The Indian power sector has come long way in power generation from 1300MW capacity
during independence to 102907MW at present. However in spite of government’s plans, the present
power availability is not good enough to cater to the needs of the country, as there is a peak shortage
of the power of around 10,000MW (13%) and 40,000 million units deficit (7.5%). Unless the system
efficiency improves in terms of technical improvements, the crisis will still continue. Energy savings
possible due to some major energy equipments such as transformers, motors etc.
Fig: 1.1 Principle of efficiency optimization control
The principle of efficiency optimization control with rotor flux programming at a steady-state
torque and speed condition is explained in Fig.1.1. The rotor flux is decreased by reducing the
magnetizing current, which ultimately results in a corresponding increase in the torque current
(normally by action of the speed controller); such that the developed torque remains constant. As the
flux is decreased, the iron loss decreases with the attendant increase of copper loss. However, the
total system (converter and machine) loss decreases, resulting in a decrease of dc link power. The
search is continued until the system settles down at the minimum input power point A, as indicated
in fig 1.2. Any excursion beyond the point A will force the controller to return to the minimum
power point.
B. Overview of Induction Motor
The induction motors have more advantages over the rest of motors. The main advantage is
that induction motors do not require an electrical connection between the stationary and the rotating
parts of the motor. Therefore, they do not need any mechanical commutator (brushes), leading to the
fact that they are maintenance free motors.
Besides, 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 speeds.
Furthermore, the motor can work in explosive environments because no sparks are produced.
Taking into account all of the advantages outlined above, the induction motors must be considered as
the perfect electrical to mechanical energy converter. However, mechanical energy is more than
often required at variable speeds, where the speed control system is not an insignificant matter.
Different controllers are developed to achieve it.

16
II. SPEED CONTROL TECHNIQUES
General controllers that have been developed
A. Scalar controllers
Despite the fact that “Voltage-Frequency” (V/f) is 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 simple and it is
normally used without speed feedback. However, this controller does not achieve a good accuracy in
both speed and torque responses, mainly regarding to the fact that the stator flux and torque are not
directly controlled. Even though, as long as the parameters are identified, the accuracy in the speed
can be 2% (except in a very low speed), and the dynamic response can be approximately around
50ms.
B. Vector Controllers
In these types of controller, there are control loops for controlling both the torque and the
flux. The most widespread controllers of this type are the ones that use vector transform such as
either Park or Ku. Its accuracy can reach values such as 0.5% regarding the speed and 2% regarding
the torque, even when at stand still. The main disadvantages are the huge computational capability
required and the compulsory good identification of the motor parameters.
C. Field Acceleration Method
This method is based on the maintaining the amplitude and the phase of the stator current
constant, whilst avoiding electromagnetic transients. Therefore, the equations can be simplified
saving the vector transformation, which occurs in the vector controllers. This technique has achieved
some computation reduction, thus overcoming the main problem with vector controllers and
allowing this method to become an important alternative to vector controllers.
III. VECTOR CONTROL
A. Variable-Frequency Induction Motor Drive
This presents a variable-frequency AC motor drive in which a pulse width modulated (PWM)
inverter is used as a variable-voltage variable-frequency source to drive an induction motor in
variable-speed operation. The drive, including the motor, the power converter, and the speed control
system, by using Power System Block set and Simulink blocks. The electrical part of the AC motor
drive, including the PWM inverter, is built using the Universal Bridge block. The induction motor is
represented by the Asynchronous Machine block, which models both electric and mechanical
dynamics. The control system, including current and speed regulators, is built using Simulink blocks.
B. Description of the Induction Motor Drive
The induction motor requires a variable-frequency three-phase source for Variable-speed
operation. The figure shows a block diagram of the power circuit of a typical variable-frequency
induction motor drive.
Figure: 3.1 Variable-Frequency Induction Motor Drive

17
Figure: 3.2 Three phase IGBT
The power grid AC voltage is converted into a fixed DC voltage by the rectifier.The
harmonics are filtered out by an LC filter to provide a smooth DC voltage, which is then applied to
the inverter input.
The inverter consists essentially of six power switches that can be metal-oxide semiconductor
field-effect transistors (MOSFET), gate turn-off thyristors (GTO), or insulated gate bipolar
transistors (IGBT), depending on the drive power capacity and the inverter switching frequency
(Hz). The inverter converts the DC link voltage into an adjustable three-phase AC voltage. Different
control schemes can be used to control the inverter output voltage and frequency. One of the most
utilized schemes is pulse width modulation (PWM) in which you obtain three-phase variable
sinusoidal voltage waveforms by modulating the on and off times of the power switches. In
industrial drive applications, the PWM inverter operates as a three-phase variable-frequency,
variable-voltage source with fundamental frequency varying from zero to three times the motor
nominal frequency. In some control schemes where a three-phase, variable-frequency current source
is required, current control loops are added to force the motor currents to follow an input reference
(usually sinusoidal).
The inverter-fed induction motor drive can be controlled with various schemes depending on the
application, desired performance, and controller design complexity
C. Field-Oriented Variable-Speed Induction Motor Drive
This illustrates a variable-speed induction motor drive using field-oriented control. 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. The figure 3.3
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 ω is compared to the reference ω* and the error is processed by the speed controller to
produce a torque command Te*
Figure: 3.3 Field oriented variable frequency Induction Motor

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Figure: 3.4 Field oriented control principle
Figure 3.4 shows the rotor flux and torque can be separately controlled by the stator direct-
axis current Ids and quadrature-axis current Iqs, respectively.
The stator quadrature-axis current reference iqs* is calculated from torque reference Te*
---------------- (3.1)
as where Lr is the rotor inductance, Lm is the mutual inductance, and |φr|est is the estimated rotor
flux linkage given by
----------------- (3.2)
where λr = Lr / Rr is the rotor time constant.
The stator direct-axis current reference ids* is obtained from rotor flux reference input |φr|*
--------------------- (3.3)
The rotor flux position θe required for coordinate's transformation is generated from the rotor speed
ωm and slip frequency ωsl
------------ (3.4)
The slip frequency is calculated from the stator reference current iqs* and the motor parameters.
-------------- (3.5)
The iqs* and ids* current references are converted into phase current references ia*, ib*, ic*
for the current regulators. The regulators process the measured and reference currents to produce the
inverter gating signals. The role of the speed controller is to keep the motor speed equal to the speed
reference input in steady state and to provide a good dynamic during transients. It can be of
proportional-integral type.
D. Modeling the Induction Motor Drive
The induction motor is modeled by an Asynchronous Machine block. The motor used in this
case study is a 5 HP, 460 V, four-pole, 50 Hz motor having the following parameters: Rs = 0.406 ,
Lls = 2.13 mH, Lm = 49.4 mH, Rr = 0.478 , Llr = 2.13 mH.
The current regulator, which consists of three hysteresis controllers, is built with Simulink
blocks. The motor currents are provided by the measurement output of the Asynchronous Machine
block.

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July
Fig: 3.5
The conversions between abc and
dq_abc blocks of shown below the rotor flux is calculated by the Flux Calculation block
Fig: 3.7
The rotor flux position (θe) is calculated by the Teta Calculation block
The stator quadrature-axis current reference (iqs*) is calculated by the iqs*_Calculation block.
Fig: 3.9
The speed controller is of proportional
E. Asynchronous machine
The electrical part of the machine is represented by a fourth
mechanical part by a second-order system. All electrical variables and parameters are referred to the
stator. This is indicated by the prime signs in the machine equations given
quantities are in the arbitrary two-axis reference frame (dq frame). The subscripts used are defined as
follows
d - d axis quantity,q - q axis quantity,r
m - Magnetizing inductance
F. Electrical System
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME
19
Fig: 3.5 Fig: 3.6
The conversions between abc and dq reference frames are executed by the abc_dq and
Fig: 3.8
e) is calculated by the Teta Calculation block
axis current reference (iqs*) is calculated by the iqs*_Calculation block.
Fig: 3.10
The speed controller is of proportional-integral type and is implemented using Simulink blocks.
part of the machine is represented by a fourth-order state-space model and the
order system. All electrical variables and parameters are referred to the
stator. This is indicated by the prime signs in the machine equations given below. All stator and rotor
axis reference frame (dq frame). The subscripts used are defined as
q axis quantity,r - Rotor quantity,s - Stator quantity,l - Leakage inductance,
Fig: 3.11
August (2013), © IAEME
dq reference frames are executed by the abc_dq and
axis current reference (iqs*) is calculated by the iqs*_Calculation block.
integral type and is implemented using Simulink blocks.
space model and the
order system. All electrical variables and parameters are referred to the
below. All stator and rotor
axis reference frame (dq frame). The subscripts used are defined as
Leakage inductance,

20
------------------- (3.6)
------------------ (3.7)
G. Mechanical System
The Asynchronous Machine block parameters are defined as follows
Parameter Definition
Rs, Lls Stator resistance and leakage inductance,
R’r, L’lr Rotor resistance and leakage inductance
Lm Magnetizing inductance,
Ls, L’r Total stator and rotor inductances
Vqs, iqs q axis stator voltage and current,
V’qr, i’qr q axis rotor voltage and current
Vds, ids d axis stator voltage and current,
V’dr, i’dr d axis rotor voltage and current
φqs, φds Stator q and d axis fluxes,
φ'qr, φ'dr Rotor q and d axis fluxes
ωm Angular velocity of the rotor,
θm Rotor angular position
p Number of pole pairs,
ωr Electrical angular velocity (ωm x p)
θr Electrical rotor angular position (θm x p),
Te Electromagnetic torque
Tm Shaft mechanical torque
J Combined rotor and load inertia coefficient. Set to infinite to
Simulate locked rotor.
H Combined rotor and load inertia constant. Set to infinite to
simulate locked rotor.
F Combined rotor and load viscous friction Coefficient
Reference Frame
The following relationships describe the abc-to-dq reference frame transformations applied to
the Asynchronous Machine phase-to-phase voltages.

21
--------------(3.8)
In the preceding equations, θ is the angular position of the reference frame, while β= θ- θr is
the difference between the position of the reference frame and the position (electrical) of the rotor.
Because the machine windings are connected in a three-wire Y configuration, there is no homopolar
(0) component.
----- (3.9)
The following table shows the values taken by θ and β in each reference frame (θe is the position of
the synchronously rotating reference frame).
Reference Frame
θ β
Rotor θr 0
Stationary 0 βr
synchronous θe (θe – θr)
Table 3.1 Reference frame
The choice of reference frame affects the waveforms of all dq variables. It also affects the
simulation speed and in certain cases the accuracy of the results. The following guidelines are
suggested in
Nominal:
The nominal apparent power Pn (VA), rms line-to-line voltage Vn (V), and frequency fn (Hz).
Stator:
The stator resistance Rs (Ώ or p.u.) and leakage inductance Lls (H or p.u.).
Rotor:
The rotor resistance Rr (Ώ or p.u.) and leakage inductance Llr’ (H or p.u.), both referred to the stator.
Magnetizing inductance:
The magnetizing inductance Lm (H or p.u.).
Mechanical:
For the SI units dialog box: the combined machine and load inertia coefficient J (kg.m2), combined
viscous friction coefficient F (N.m.s), and pole pair’s p.
For the p.u. units dialog box: the inertia constant H (s), combined viscous friction coefficient F (p.u.),
and pole pair’s p.
Initial conditions:
Specifies the initial slip s, electrical angle θe (deg), stator current magnitude (A or p.u.), and phase
angles (deg): [slip, th, ias, ibs, ics, phaseas, phasebs, phasecs]

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Inputs and Outputs:
The stator terminals of the Asynchronous Machine block are identified by the A, B, and C letters.
The rotor terminals are identified by the, b, and c letters. The neutral connections of the stator and
rotor windings are not available; three-wire Y connections are assumed. The Simulink input of the
block is the mechanical torque at the machines shaft. When the input is positive, the asynchronous
machine behaves as a motor. When the input is negative, the asynchronous machine behaves as a
generator. The Simulink output of the block is a vector containing 21 variables.
abc to dq0A
Park transformation is performed from the three-phase (abc) reference frame to the dq0 reference
frame. The abc_to_dq0 Transformation block computes the direct axis, quadratic
axis, and zero sequence quantities in a two-axis rotating reference frame for a three-phase sinusoidal
signal. The following transformation is used:
-------- (3.11)
Where ω= rotation speed of rotating frame
The transformation is the same for the case of a three-phase current; you simply replace the
Va, Vb, Vc, Vd, Vq, and V0 variables with the Ia, Ib, Ic, Id, Iq, and I0 variables. This transformation is
commonly used in three-phase electric machine models, where it is known as a Park transformation.
It allows to eliminate time-varying inductances by referring the stator and rotor quantities to a fixed
or rotating reference frame. In the case of a synchronous machine, the stator quantities are referred to
the rotor. Id and Iq represent the two DC currents flowing in the two equivalent rotor windings (d
winding directly on the same axis as the field winding, and q winding on the quadratic axis),
producing the same flux as the stator Ia, Ib, and Ic currents. The block can be used in a control system
to measure the positive-sequence component V1 of a set of three-phase voltages or currents. The Vd
and Vq (or Id and Iq) then represent the rectangular coordinates of the positive-sequence component.
The Math Function block and the trigonometric Function block to
--------------- (3.12)
This measurement system does not introduce any delay, but, unlike the Fourier analysis done
in the Sequence Analyzer block, it is sensitive to harmonics and unbalances.
Inputs and Outputs
abc
The first input the vectorized sinusoidal phase signal to be converted [phase A phase B phase C].
sin_cos
The second input a vectorized signal containing the [sin (ωt) cos (ωt)] values, where ω is the rotation
speed of the reference frame.
dq0
The output is a vectorized signal containing the three sequence components [d q o].
dq0 to abc Transformatio
A Park transformation from the dq0 reference frame to the abc reference frame. The
dq0_to_abc Transformation block performs the reverse of the so-called Park transformation, which is
commonly used in three-phase electric machine models. It transforms three quantities (direct axis,
quadratic axis, and zero-sequence components) expressed in a two-axis reference frame back to
phase quantities. The following transformation is used:

23
-------------------- (3.13)
---- (3.14)
Where ω= rotation speed of rotating frame
The transformation is the same for the case of a three-phase current; you simply replace the
Va , Vb , Vc , Vd , Vq , and V0 variables with the Ia, Ib, Ic, Id, Iq, and Io variables. The dq0_to_abc
Transformation block is used in the model of the Synchronous Machine block where the stator
quantities are referred to the rotor. The Park transformation then eliminates time-varying inductances
by referring the stator and rotor quantities to a fixed or rotating reference frame.
Inputs and Outputs
dq0
The first input a vectorized signal containing the sequence components [d q 0] to be converted.
sin_cos
The second input a vectorized signal containing the [sin (ωt) cos (ωt)] values, where ω is the rotation
speed of the reference frame.
abc
The output is a vectorized signal containing the three-phase sinusoidal quantities [phase A phase B
phase C].
IV. FUZZY LOGIC
A. Fuzzy Logic
Fuzzy logic is all about the relative importance of precision. The Fuzzy Logic Toolbox for
use with MATLAB is a tool for solving problems with fuzzy logic. Fuzzy logic sometimes appears
exotic or intimidating to those unfamiliar with it, but once you become acquainted with it, it seems
almost surprising that no one attempted it sooner. In this sense fuzzy logic is both old and new
because, although the modern and methodical science of fuzzy logic is still young, the concepts of
fuzzy logic reach right down to our bones.
Fig: 4.1
A mapping input to the appropriate outputs is shown. Between the input and the output a
black box does the work. In almost every case you can build the same product without fuzzy logic,
but fuzzy is faster and cheaper.
B. Membership Functions
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 fancy name for a simple concept.
C. Membership Functions in the Fuzzy Logic Toolbox
The only condition a membership function must really satisfy is that it must vary between 0
and 1. The function itself can be an arbitrary curve whose shape we can define as a function that suits
us from the point of view of simplicity, convenience, speed, and efficiency.

24
A classical set might be expressed as
A = {x | x > 6} -------------- (4.1)
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, µA(x) | x €X} ---------- (4.2)
µA(x) is called the membership function (or MF) of x in A.
Fig: 4.2
Fig:4.3
Fig: 4.4
Two membership functions are built on the Gaussian distribution curve: a simple
Gaussian curve and a two-sided composite of two different Gaussian curves. The two functions are
gaussmf and gauss2mf.
The generalized bell membership function is specified by three parameters and has the
function name gbellmf. The bell membership function has one more parameter than the Gaussian
membership function, so it can approach a non-fuzzy set if the free parameter is tuned. Because of
their smoothness and concise notation, Gaussian and bell membership functions are popular methods
for specifying fuzzy sets. Both of these curves have the advantage of being smooth and nonzero at all
points.
Fig: 4.5 Fig: 4.6

25
V. WORKING WITH SIMULINK
The Fuzzy Logic Toolbox is designed to work seamlessly with Simulink. Once a fuzzy
system is created using the GUI tools or some other method, it is ready to embed directly into a
simulation. To build Simulink systems that use fuzzy logic, find the Fuzzy Logic Controller block in
the Fuzzy Logic Toolbox library, which you can open either by selecting Fuzzy logic toolbox in the
Simulink Library Browser, or by typing Fuzblock at the MATLAB prompt.
A. Sugeno-Type Fuzzy Inference
Sugeno-Type Fuzzy Inference was introduced by Sugeno, in 1985; it is similar to the
Mamdani method in many respects. The first two parts of the fuzzy inference process, fuzzifying the
inputs and applying the fuzzy operator, are exactly the same. The main difference between Mamdani
and Sugeno is that the Sugeno output membership functions are either linear or constant. A typical
rule in a Sugeno fuzzy model has the form
If Input 1 = x and Input 2 = y, then Output is z = ax + by + c
For a zero-order Sugeno model, the output level z is a constant (a=b =0).
The output level zi of each rule is weighted by the firing strength wi of the rule. For example, for an
AND rule with Input 1 = x and Input 2 = y, the firing strength is
wi = And Method (F1(x), F2(y)) --------- (4.6)
Where F1, 2 (.) are the membership functions for Inputs 1 and 2. The final output of the system is the
weighted average of all rule outputs, computed as
------------ (4.7)
Proposed Approach
A. Indirect vector controlled induction motor incorporating the efficiency optimization
controller
Fig: 5.1 Indirect vector controlled induction motor with efficiency optimization controller block
diagram
The principle of efficiency optimization control with rotor flux programming at a steady-state
torque and speed condition is explained in Fig.:5.1. The rotor flux is decreased by reducing the
magnetizing current, which ultimately results in a corresponding increase in the torque current
(normally by action of the speed controller); such that the developed torque remains constant. As the
flux is decreased, the iron loss decreases with the attendant increase of copper loss.

26
B. Efficiency Optimization Control
Fig: 5.2 Efficiency optimization control block diagram
Figure 5.2 explains the fuzzy efficiency controller operation. The input dc power is sampled
and compared with the previous value to determine the increment Pd. In addition, the last excitation
current decrement (L ids) is reviewed. On these bases, the decrement step of ids* is generated
from fuzzy rules through fuzzy inference and defuzzification.
The adjustable gains Pb and Ib, generated by scaling factors computation block, convert the
input variable and control variable, respectively, to per unit values so that a single fuzzy rule base
can be used for any torque and speed condition. The input gain Pb as a function of machine speed wr
can be given as
Pb=a wr+b -------------------- (5.1)
Where the coefficients a and b were derived from simulation studies. The output gain Ib is
computed from the machine speed and an approximate estimate of machine torque Te
Ib=c1 wr-c2 Te+c3 -------------------- (5.2)
The membership functions for the fuzzy efficiency controller are shown in figure 5.3. Due to
the use of input and output gains, the universe of discourse for all variables are normalized in the [-1,
1] interval. It was verified that, while the control variable ids*, required seven fuzzy sets to provide
good control sensitivity, the past control action L ids* (i.e. ids* (k - 1)) needed only two fuzzy
sets, since the main information conveyed by them is the sign. The small overlap of the positive (P)
and negative (N) membership functions is required to ensure proper operation of the height
defuzzification method, i.e., to prevent indeterminate result in case L ids* approaches zero.
Table 5.3 Rules base for Fuzzy Efficiency Controller
figure:5.3 Membership functions for efficiency controller (a) change of DC link power ( Pd(pu)) (b)
Last change in excitation current (L ids*(pu)) (c) Excitation current control increment ( ids*(pu))

27
C. Feed forward Pulsating Torque Compensation
Fig: 5.4 Feed forward pulsating torque compensator block diagram
As the excitation current is decremented with adaptive step size by the fuzzy controller, the
rotor flux Ψdr will decrease exponentially which is given as
------------------- (5.3)
where λr = Lr/Rr is the rotor time constant and Lm the magnetizing inductance. The decrease of flux
causes loss of torque, which normally is compensated slowly by the speed control loop. Such
pulsating torque at low frequency is very undesirable because it causes speed ripple and may create
mechanical resonance. To prevent these problems,a feed forward pulsating torque compensator has
been proposed.Under correct field orientation control, the developed torque is given by
------------------- (5.4)
For an invariant torque, the torque current Iqs, should be controlled to vary inversely with the
rotor flux. This can be accomplished by adding a compensating signal Iqs* to the original Iqs*’ to
counteract the decrease in flux Ψdr (t) where t € [0, T] and T is the sampling period for efficiency
optimization control. Let iqs (0) and Ψdr (0) be the initial values for iqs and Ψdr, respectively, for the k-
th step change of ids*. For a perfect compensation, the developed torque must remain constant, and
the following equality holds
----------------------(5.5)
Solving for Iqs(t) yields
------------------- (5.6)
where Ψdr (t) is governed by above equation with, substituted for ids*. To implement such
compensation, above equations are adapted to produce Iqs(t), using flux estimate Ψdr and command
in Iqs* place of actual signals. A good approximate solution for Iqs(t) can be obtained by replacing
the denominator of the above equation by its steady-state value estimate Ψdr (t).In this case the
compensation can be implemented in two steps as shown in Fig. 4. First, the value for the
compensating torque current step is Computed by discrete
------------------- (5.7)

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VI. RESULTS AND CASE STUDY
A. Specifications of the Induction Motor
H.P = 5 Power Rating of 3- Ø induction motor
V = 440V Voltage Rating of 3- Ø induction motor
Frequency F = 50Hz
N = 1500 rpm Rated speed
P = 4 No. of poles
Rs = 0.406 Stator Resistance
Rr = 0.478 Rotor Resistance
Lls = 2.13mH Stator Leakage Inductance
Llr = 2.13mH Rotor Leakage Inductance
Lm = 49.4mH Mutual Inductance
Program : Fuzzy logic controller
[System]
Name='efficiency'
Type='mamdani',
Version=2.0,
NumInputs=2,
NumOutputs=1,
NumRules=14,
AndMethod='min',
OrMethod='max',
ImpMethod='min',
AggMethod='max',
DefuzzMethod='centroid'
[Input1]
Name='input1'
Range= [-1 1]
NumMFs=7
MF1='mf1':'trimf', [-1.17 -1 -0.5]
MF2='mf2':'trimf', [-1 -0.5 -0.3]
MF3='mf3':'trimf', [-0.5 -0.3 0]
MF4='mf4':'trimf', [-0.3 0 0.3]
MF5='mf5':'trimf', [0 0.3 0.5]
MF6='mf6':'trimf', [0.3 0.5 1]
MF7='mf7':'trimf', [0.5 1 1.33]
[Input2]
Name='input2'
Range= [-0.1 0.1]
NumMFs=2
MF1='mf1':'trimf', [-0.3 -0.1 0.001]
MF2='mf2':'trimf', [0.001 0.1 0.3]
[Output1]
Name='output1'
Range= [-1 1]
NumMFs=7

29
MF1='mf1':'trimf', [-1.333 -1 -0.7]
MF2='mf2':'trimf', [-1 -0.7 -0.4]
MF3='mf3':'trimf', [-0.7 -0.4 0]
MF4='mf4':'trimf', [-0.4 0 0.4]
MF5='mf5':'trimf', [0 0.4 0.7]
MF6='mf6':'trimf', [0.4 0.7 1]
MF7='mf7':'trimf', [0.7 1 1.29]
[Rules]
7 1, 6 (1) : 1
7 2, 2 (1) : 1
6 1, 5 (1) : 1
6 2, 3 (1) : 1
5 1, 5 (1) : 1
5 2, 3 (1) : 1
4 1, 4 (1) : 1
4 2, 4 (1) : 1
3 1, 3 (1) : 1
3 2, 5 (1) : 1
2 1, 2 (1) : 1
2 2, 6 (1) : 1
1 1, 1 (1) : 1
1 2, 7 (1) : 1
VII. SCOPE OF THE PAPER
Recommendations for continuing efforts related to the efficiency optimization controllers include
Final hardware implementation of fuzzy controller using Microprocessor, Micro controller,
Digital signal processing and testing the configuration.
Demonstration of hard ware implemented controllers in an industrial setting.
VIII. CONCLUSIONS
The objective of this paper was to study fuzzy controls which optimize the adjustable speed
drives on the basis of energy efficiency.
It was observed that efficiency has been optimized by minimizing the input power and the
output power is maintained constant with the help of Fuzzy controller. The fuzzy controller controls
the pulses of the inverter which supplies the induction motor. The net result is found to be a
reduction in the voltage applied to the motor, a reduction in the developed torque, an increase in
speed, a constant power output and a reduced input due to a reduction in the overall losses. (Increase
in copper losses more than compensated for reduction in core losses).
This was observed mainly through the graphs obtained as outputs from the MATLAB
simulink. The graphs were obtained for the system with and with out fuzzy controller.
It was further concluded that that for all load torques the output power is maintained almost
constant.
Input power minimization has been done at all load torques and the input power decreases
i.e. the efficiency is increased at all loads.

30
REFERENCES
1. “Efficiency Optimization Control of A.C Induction Motors: Initial Laboratory Results”,
M.W.Turner, V.E.McCornick and J.G.Cleland, EPA Project EPA/600/SR-96/008
2. “Fuzzy logic based on-line efficiency Optimization of an indirect vector–controlled Induction
motor drives”, G.C.D. Sousa, B.K.Bose, J.G.Cleland, IEEE Transaction on Industrial
Electronics, vol. 42, no.2, pp.192-198, April 1995.
3. “ Fuzzy logic based improvements in efficiency optimization of induction motor drives”. Juan
M. Moreno-Eguilaz, M. Cipolla-Ficarra, P.J.da Costa Branco, Juan Peracaula,
4. “A Fuzzy-Logic-Based Energy Optimizer for AC Motors”, John G. Cleland ,Vance E.
McCormick and M. Wayne Turner Center for Digital Systems Engineering, Research Triangle
Institute, Research Triangle Park, NC 27709, Pp: 1777-1784
5. “An Efficient Controller for an Adjustable Speed Induction Motor Drive” G. 0. Garcia, J. C.
Mendes Luis, R. M. Stephan, and E. H. Watanabe IEEE TRANSACTIONS ON
INDUSTRIAL ELECTRONICS, VOL. 41, NO. 5, OCTOBER 1994 533 Pp: 533-539
6. “Loss Minimization Control of an Induction Motor Drive” Parviz Famouri and Jimmie J.
Cathey, Senior Member, IEEE IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS,
VOL. 27. NO. 1, JANUARY FEBRUARY 1991 Pp: 32-37
7. “An Efficiency-Optimization Controller for Induction Motor Drives” M.E.H. Benbouzid, N.S.
Nait Said Pp: 63-64
8. “Fuzzy Efficient-Optimization Controller for Induction Motor Drives” F. Zidani, M.E.H.
Benbouzid, D. Diallo Author Affiliation: University of Picardie “Jules Verne,” 7, Rue du
Moulin Neuf, 80000 Amiens, France. pp:43-44
9. “Loss Minimization of a Fuzzy Controlled Induction Motor Drive” F. Zidani*, M.E.H.
Benbouzid, Senior Member, IEEE and D. Diallo, Member, IEEE University of Batna, Algeria
Centre de Robotique, d’Electrotechnique et d’ Automatique University of Picardie “Jules
Verne”7, Rue du Moulin Neuf - 80000 Amiens, France Pp: 629-633
10. “Fast Efficiency Optimization Techniques for the Indirect Vector-Controlled Induction Motor
Drives” Chandan Chakraborty, Senior Member, IEEE, and Yoichi Hori, Senior Member,
IEEE Pp: 1070-1076
11. “Electrical Machinery”, Khanna Publishers , Dr.P.S.Bimbra
12. “Modern power Electronics and AC Drives ” Bimal k.Bose
13. “Math works release notes on Fuzzy logic” MATLAB 7.0
14. “Math works release notes on Sim power systems” MATLAB 7.0
15. Pradeep B Jyoti, J.Amarnath and D.Subbarayudu, “Application of Neuro-Fuzzy Controller in
Torque Ripple Minimization of Vector Controlled Vsi Induction Motor Drive”, International
Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 3, 2013,
pp. 121 - 127, ISSN Print : 0976-6545, ISSN Online: 0976-6553.
16. Abhijit D. Ghorapade and Snehal S. Mule, “Simulation of Igbt Based Speed Control
System for Induction Motor using Fuzzy Logic”, International Journal of Electronics
and Communication Engineering & Technology (IJECET), Volume 4, Issue 3, 2013,
pp. 282 - 291, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472.
17. Vaibhav B. Magdum, Ravindra M. Malkar and Darshan N. Karnawat, “Study & Simulation of
Direct Torque Control Method for Three Phase Induction Motor Drives”, International Journal
of Electrical Engineering & Technology (IJEET), Volume 2, Issue 1, 2011,
pp. 1 - 13, ISSN Print : 0976-6545, ISSN Online: 0976-6553.

31
BIOGRAPHY
P. SUMAN PRAMOD KUMAR, obtained his Bachleor’s degree from
NBKR IST, Nellore Dist & Master’s Degree in 2005 from Bharath Institute of
Higher Education & Research, Chennai. Presently Working as Associate Professor
of EEE in chadalawada Ramanamma Engg college, Tirupati A.P, India. His area of
interest are in Control Systems, Electrical Distribution System & AC Machines.
Dr.K.RAVICHANDRUDU obtained his B.E from Andhra University and M.Tech & Ph.D
from S.V.University, Tirupathi. His area of interest are in Powersystem engineering, electrical
machines.
B.HEMANTH KUMAR working as Assistant Professor in Chadalawada Ramanamma
Engineering College, Tirupathi. His area of interest are in Power Electronics, Electrical Drives and
Electrical Machines.
T.KIRAN KUMAR PG student of Chadalawada Ramanamma Engineering College, Tirupathi,
A.P. His area of interest are in Power Electronics and Drives and Electrical Machines.

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