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CHAPTER 5

TORQUE RIPPLE MINIMIZATION OF INDUCTION

MOTOR USING ADALINE ANN

5.1 INTRODUCTION

The present cost and operating constraints of voltage source power

electronic converters limit their applications in the control of variable speed

motors and generators in the megawatt power range. For high power

applications where only a small continuous operating zone around the

machine rated value is required, Doubly Fed Induction Machines (DFIM)

provide an economic solution. Recent developments have revitalized research

activities in the area of DFIM. The expression 'doubly fed' applies to

machines where electrical power can be fed or extracted from two accessible

three phase windings. The wound-rotor induction machine is a good example.

Generally, the stator winding (through which most of the power flows) is

connected directly to the grid and the rotor winding is connected to a power

converter. The power rating of the rotor winding and hence the converter size

depend on the required speed range and the reactive-power requirements. This

fact can be of particular interest in systems with limited speed ranges, such as

variable-speed wind turbines. The attractiveness of the DFIM stems primarily

from its ability to handle large speed variations around the synchronous

speed. Another advantage is that the power electronic equipment to control

the machine has to handle only a fraction (maximum 20 30%) of the total

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power, thereby reducing the losses and the cost of the power electronic

converter.

Speed sensorless control strategies of induction motor are classified

as vector control or Field Oriented Control (FOC) and Direct Torque Control

(DTC). High performance electric drives require decoupled torque and flux

control. This control is commonly provided through field oriented control

(FOC), which is based on decoupling of the torque producing current

component and the flux-producing component. The performance of a vector

controlled induction motor drive is very much affected by parameter

variations of the motor, errors in flux measurements, errors in speed

measurement etc., and hence should be made robust and less sensitive to

disturbances and parameter changes. On the other hand, DTC offers quick and

robust performance. This type of control is essentially a sliding mode stator

flux-oriented control. The name direct torque control is derived from the fact

that it is possible to directly control the inverter states in order to reduce the

torque and flux errors within the prefixed band limits on the basis of the errors

between the reference and the estimated values of torque and flux. Also, it

provides a very quick and precise torque response without the complex field

orientation block and the inner current regulation loop. This scheme of control

is receiving wide attention in the recent literature. The main disadvantage of

DTC is that ripple is present in current, torque and flux. The current ripple

leads to additional harmonic loss where as torque ripple tries to induce speed

ripple in a low inertia system. These ripples can be reduced to some extend by

using ADALINE ANN for selecting the voltage vector.

5.2 DIRECT TORQUE CONTROL

The basic block diagram of DTC scheme as applied to a three phase

induction motor is shown in Figure 5.1. The stator currents of R and Y

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phases ia and ib, Vab is the stator terminal voltage, E and Ete are the flux error

and torque error respectively.

Figure 5.1 Schematic representation of basic DTC scheme

Basically, it uses torque and flux control loops where feedback

signals are estimated from the machine terminal voltages and currents. Stator

reference model of an induction motor is used for its implementation. Stator

flux is directly proportional to the induced emf and hence this scheme does

not depend on motor parameters other than the stator resistance. This scheme,

therefore, is a robust scheme in the flux weakening region. The basic

equations that are used for computing the stator flux linkage is given

Equation (5.1) where s is the stator flux linkage, Va is the stator voltage, ia is

the stator current and Rs is the stator resistance.

= (V i R ) dt (5.1)

ds and qs are the stator direct axis (d axis) and quadrature axis

(q axis) flux linkages and are calculated as given in Equations (5.2) and (5.3).

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= (V i R ) dt (5.2)

= V i R dt (5.3)

The d- axis and q- axis components of voltage and current are

calculated using the Equations (5.4) and (5.5) as given below where X is

either current or voltage.

X = (X X ) (5.4)

X = X (5.5)

The inverter switching states are represented by voltage vectors

comprising of six active vectors and two zero vectors represented as V0 to V7.

V0 and V7 are zero vectors represented at the centre o. The voltage vector

look up table is as given in Table 5.1. When a voltage vector is applied to the

inverter for a time t, the flux changes by a value as given in Equation (5.6).

With the rated flux, command torque is applied and the flux vector starts

rotating in the counter clockwise direction within the hysteris band depending

on the selected voltage vector.

Table 5.1 Voltage vector look up table

Voltage Vectors Switching states

V0 0 0 0

V1 1 0 0

V2 1 1 0

V3 0 1 0

V4 0 1 1

V5 0 0 1

V6 1 0 1

V7 1 1 1

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= V t (5.6)

The flux is altered in the radial direction due to flux loop error

where as torque is altered by tangential movement of the flux vector. Special

features of DTC control are as follows:

No feedback current control.

No traditional PWM algorithm is applied.

No need of vector transformation as in FOC.

Feedback signal processing is somewhat similar to stator flux-

oriented vector control.

Hysteresis-band control generates flux and torque ripple and

switching frequency is not constant (like hysteresis-band

current control).

The main advantages of DTC scheme are as follows:

DTC are robust and provide fast response.

No requirements for co-ordinate transformation and there is

no requirements for PWM pulse generation and current

regulators.

Absence of separate voltage modulation block.

The main disadvantages of DTC are as follows:

DTC produces ripple in torque and flux

The pulsations in torque and flux affect the accuracy of speed

estimation.

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It also results in higher acoustic noises and harmonic losses.

High torque ripple and slow transient response to the step

changes in torque during start-up.

5.3 ADALINE ANN FOR TORQUE RIPPLE MINIMIZATION

The ANN used here for torque ripple minimization is the

ADALINE (Adaptive Linear Model) ANN. It comes under the category of

single layer feed forward neural networks. ADALINE uses bipolar (1 or -1)

activations for its input signals and its target outputs. The weights on the

connections from the input units to the ADALINE are adjustable. The

ADALINE also has a bias, which acts like an adjustable weight on a

connection whose activation is always 1. An ADALINE has only one output

unit. Delta rule, also known as Least Mean Square rule or Widrow-Hoff rule

is used for training the ADALINE. The architecture of ADALINE is given in

Figure 5.2.

w1 b

.

.

. y

.

.

wn

Figure 5.2 Architecture of an ADALINE

X1

Xn

1

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5.4 ADALINE ANN BASED DTC SCHEME APPLIED TO DFIM

Doubly Fed Induction Machine (DFIM) is constructed with wound

rotor and slip rings. General block diagram of a DFIM drive is given in

Figure 5.6. Three phase AC supply is fed directly to the stator in order to

reduce the cost instead of feeding through converter and inverter. The rotor

output, which is at low voltage, is fed to the rectifier. The rectified output is

inverted and given to the secondary of a step down transformer and fed back

to supply from the primary of the transformer. The step down transformer

improves the system power factor for a restricted speed range closer to

synchronous speed.

Figure 5.3 General block diagram of a DFIM drive

The mathematical model of a DFIM derived from first principles is

given in Equations (5.6) to (5.9).

V = R + (5.6)

V = R + j (5.7)

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= L + L (5.8)

= L + L (5.9)

The electromagnetic torque, T , is calculated as given in

Equation (5.10)

T = p . sin (5.10)

where = 1-Lm2/LsLr, V and V are the voltage vectors per phase of stator and

rotor respectively, i and i are the current vectors per phase of stator and

rotor respectively, R and R are the resistance per phase in ohms of stator

and rotor respectively, L and L are the inductance per phase in henry of

stator and rotor respectively, and are the flux linkages per phase in

webers of stator and rotor respectively, is the synchronous speed in rad/s.,

p is the number pairs of poles and is the torque angle. Differentiating the

stator and rotor flux linkage and the electromagnetic torque with respect to

time, we get Equations (5.11) to (5.13).

s

dt =

1

s

RsLm

LsLr

coss r

-Rs

Lss2+ cos

s r (5.11)

d

dt =

2

Lm

LsLr

+ sins r

- coss r

+ sin Vs r+

s.Vr (5.12)

r

dt =

1

r

RrLm

LsLr

coss r

-Rr

Lrr2+ cos

s r (5.13)

The space vector representation of voltage and flux linkage vectors

is given in Equations (5.14) to (5.17).

s= | s|

) (5.14)

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r= | | (5.15)

Vs = |Vs|)

(5.16)

Vr = |Vbus|)

(5.17)

In the conventional DTC scheme (Figure 5.4), the estimated flux

magnitude and torque are compared with their reference values and errors

thus obtained are given as inputs to the flux and torque estimator. The flux

and torque estimators then generate signals proportional to the respective

errors which along with the rotor position are given to the hysterisis

comparator.

Figure 5.4 Basic block diagram of conventional DTC scheme

Hysteresis is a property by which the change in the magnetization

lags behind change in the magnetic field. The hysteresis comparator can be

described as a comparator which compares a processed value with a standard

value which follows hysteresis property. The difference is given to the

switching state selector.

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The ripple present in the torque, stator current and rotor current can

be reduced by properly selecting the switching sequence such that the pulses

are given to the inverter which drives the DFIM with fewer harmonics.

ADALINE ANN, which has the property of extracting useful

information from noisy signal, is used here for controlling the rotor switching

voltage vectors in an efficient manner such that torque and the stator current

ripples are very less as compared to the existing methods. The parameters of

ADALINE ANN used in the present work are given in Table 5.2. The

proposed DTC scheme where ADALINE ANN is used for selecting inverter

the switching pattern is shown in Figure 5.5.

Table 5.2 Parameters of ADALINE ANN

Parameter Value

Architecture ADALINE

Activation Function Sigmoid

No. of layers 3

Learning algorithm Delta learning rule

No. of neurons in input layer 4

No. of neurons in hidden layer 4

No. of neurons in output layer 4

Initial weight Random values (from 0 to 1)

No. of iterations 1000

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Figure 5.5 Block diagram of ANN tuned DTC scheme

The torque and flux estimators estimate the actual values of torque

and flux from the measures values of rotor terminal voltages. The difference

between the actual speed and reference speed is given to PID controller as

input. The outputs of PID controller are torque, flux and position of the flux

axis. The values of torque and flux obtained from the PID controller are

compared with their corresponding estimated values obtained from the torque

and flux estimator and torque error and flux error are calculated. These errors

thus obtained along with the position of flux vector obtained from the PID

controller are given as inputs to the ADALINE ANN. The output of neural

network is used select the converter switching sequence such that the resultant

torque and stator current contain fewer ripples.

5.5 RESULTS AND DISCUSSION

The proposed DTC scheme with ADLINE ANN is simulated in

Matlab –Simulink. A step torque of -50Nm to +50Nm is applied to the

conventional DTC scheme. The torque output obtained is as given in

Figure 5.6. The corresponding torque spectrum on an enlarged scale is shown

in Figure 5.7. The stator current spectrum and the rotor current spectrum

from conventional DTC are shown in Figures 5.8 and 5.9.

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Figure 5.6 Torque output obtained from conventional DTC scheme

Figure 5.7 Torque spectrum obtained from conventional DTC scheme

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Figure 5.8 Stator current spectrum obtained from conventional DTC

scheme

Figure 5.9 Rotor current spectrum obtained from conventional DTC

scheme

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The simulation is repeated for the ADALINE ANN tuned DTC

scheme for the same torque input. The torque output along with the torque

spectrum is depicted in Figures 5.10 and 5.11. The stator and rotor current

spectrums from ADALINE ANN tuned DTC are shown in Figures 5.12

and 5.13.

Figure 5.10 Torque output obtained from ADALINE ANN tuned DTC

scheme

Figure 5.11 Torque spectrum obtained from ADALINE ANN

tuned DTC scheme

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Figure 5.12 Stator current spectrum obtained from ADALINE ANN

tuned DTC scheme

Figure 5.13 Rotor current spectrum obtained from ADALINE ANN

tuned DTC scheme

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A comparative study is carried out here with the results obtained

from conventional DTC and ADALINE ANN tuned DTC schemes. It is very

clear from the Figures 5.7 and 5.11 that the torque output from ADALINE

ANN tuned DTC scheme contain fewer ripple as compared to the torque

output from the conventional DTC scheme. Similar to the torque, the stator

current waveform from ADALINE ANN based DTC contain fewer ripple as

compared to that from conventional DTC (Figures 5.8 and 5.12). On

analyzing the Figures 5.9 and 5.13, it is very clear that the rotor current from

ADALINE ANN tuned DTC contain fewer ripples as compared to the rotor

current from conventional DTC. Thus the results obtained from ADALINE

ANN tuned DTC scheme applied to DFIM shows the superiority of the

proposed scheme in improving the performance of the DFIM. The

specifications of the motor used for the study is given in Table 5.3.

Table5.3 Specifications of the machine used for DTC

Parameter Value

Power 7.5kW

No.of phases 3 phase

Frequency 50Hz

No. of poles 4

Stator Voltage per phase 220V

Rotor voltage per phase 220V

Stator resistance 1.3534 ohms

Rotor resistance 1.3534 ohms

Stator inductance 1.513ohms

Rotor inductance 150.67mH

Mutual inductance 150.67mH

Slip 0.5

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5.6 VALIDATION OF RESULTS

The superiority of the proposed design method is validated by

comparing the results obtained from the proposed design with that available

in a published work. Results obtained from Gonzalo Abad et al (2008) were

considered for the comparison. The torque response available in literature is

given Figure 5.14. The torque response from the proposed scheme as given

Figure 5.11 is compared with that from the literature.

Figure 5.14 Torque response available in literature

It is clear from the Figures 5.11 and 5.14 that torque ripple in the

proposed scheme is less in comparison that available in literature.

5.7 CONCLUSIONS

Doubly fed induction motors are gaining popularity in wind farms.

However, the torque and current ripple pose a major problem when used for

applications like wind farms and steel rolling mills. This is mainly due to the

switching of the inverter and the converter. This torque ripple is reduced by

using ADALINE ANN for selecting the switching pattern of the inverter.

With the reduction in pulsations present in torque and current, power loss due

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to harmonics is reduced there by increasing the energy efficiency. Simulation

results prove the superiority of the proposed scheme.

In the next chapter, summary of the work carried out, the concrete

conclusions and the scope for the future work are dealt.