Innovative improved Direct Torque Control of Doubly Fed ... improved Direct Torque Control of Doubly...

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 16 (2016) pp 9099-9105

© Research India Publications. http://www.ripublication.com

9099

Innovative improved Direct Torque Control of Doubly Fed Induction

Machine (DFIM) using Artificial Neural Network (ANN-DTC)

Abderrahim ZEMMIT 1, Sabir MESSALTI 1 , Abdelghani HARRAG1,2

1Electrical Engineering Department, Faculty of Technology, Mohamed Boudiaf University, 28000 Msila , Algeria. 2CCNS Laboratory, Electronics Department, Faculty of Technology, Ferhat Abbas University, 19000 Setif, Algeria.

Abstract

This paper presents a new advanced Direct Torque Control of

doubly fed induction motor using Artificial Neural Network

controllers, which conventional IP and switching table have

been replaced by new Artificial Neural Network controllers to

overcome the most drawbacks and limitations of classical

DTC control especially high torque and flux ripples, accuracy

and the convergence speed under sudden changing torque

reference and/or speed reference. The Artificial Neural

Network controllers parameters that optimizes the

performances of conventional Direct Torque Control of

doubly fed induction motor have been optimized in offline

mode where several training sets have been tested based on

conventional IP and switching table rules, then the best

Artificial Neural Network controllers are embedded in DTC-

DFIM system in online mode. The proposed ANN controllers

have shown better performance compared to conventional

DTC in both the transient and steady states, in which

numerous benefits related to flux and torque ripples

overshoot and response time have been confirmed.

Keywords: Artificial Neural Network, Double Feed Induction

Machine (DFIM), Direct Torque Control, DTC, ANN

Switching table, low torque and flux ripples.

INTRODUCTION

Today, due to huge progress in power electronics, doubly fed

induction machines (DFIMs) became one of the best

promising solutions for many applications especially for wind

energy conversion, variable speed application, railway

traction, marine propulsion, and hydroelectric power stations,

etc[1-3]. The widespread use of doubly fed induction

machines is justified by following advantages:

DFIMs can generate reactive current and produce

constant-frequency electric power at variable speed

operation.

DFIM can be fed and controlled stator or rotor by various

possible combinations;

The power flow can be modulated

DFIM can be operate in wide range of speed variation

around the synchronous speed (until ± 30%), so, it The

active and reactive power can be control separately;

the stator and rotor currents are measurable;

Many DFIMs parameters can be controlled independently

(torque, flux, and power factor).

Although, the previous advantages of DFIM, its brushes and

slip rings structure are the most drawbacks, which it require

permanent maintenance.

The growing interest of the doubly fed induction machine

(DFIM) has motivates many researchers to develop and

improve it continuously, which they study the including

different parts such as converter parts, conception machine,

materials and the control of DFIM. In last point, a large

number of techniques have been proposed since it can exhibits

many merits, the field oriented control (FOC) techniques are

developed firstly, However, it highly dependent on the

parameters of the induction machine and constant gains PI

controllers may become unable to provide the required control

performance. Hence PI controller limitations and its

complexity of implementation are the most drawbacks [4-9].

Moreover, Direct torque control (DTC) was proposed in

1980s and then it was well developed in power electronics and

drives application for its excellent steady state and transient

performance [10], and Direct Self Control (DSC) by

Depenbrock [11] have provided better steadystate and

transient torque control conditions rather than FOC

techniques. In addition, Direct Control techniques do not

require current regulators, nor coordinate transformations

or specific modulations like PWM or SVM for pulse

generation. However Direct Control techniques present some

many drawbacks compared to the FOC such as difficulties of

torque control at very low speeds, variable switching

frequency, and the lack of direct current control

In recent years , many improvements have introduced to

Direct Control techniques to reduce torque ripple [12-14] and

achieved constant switching frequency such as Direct

Mean Torque Control (DMTC) [7], Direct Torque

Control with Space Vector Modulation (DTC-SVM) [15]. In

addition, many intelligent control strategies have been

proposed such as artificial neural network[16-20], the fuzzy

logic control (FLC) [17], sliding mode control (SMC) [21-23],

backstepping control , Fuzzy-SVM , adaptive fuzzy vector

controller (AFVC) [17-28].

In this paper, new combined DTC-ANN method applied to

doubly fed induction motor has been proposed and studied.

Modelling of DFIM and details of proposed control strategy

have been presented. The performances in terms of torque

tracking, torque and flux ripples, and accuracy have been

demonstrated and compared.

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9100

DOUBLY FED INDUCTION MACHINE MODEL

The state-all-flux DFIM dynamic model expressed in ( , )

axes rotational reference frame is given by the following

equations:

(1)

s s s s

s s s s

r r r r r

r r r r r

dV R IdtdV R IdtdV R IdtdV R Idt

(2)

s s s r

s s s r

r r r s

r r r s

l I MIl I MIl I MIl I MI

The electromagnetic torque is given by :

3

) 3( 2

em s r s rs

pMC I IL

DIRECT TORQUE CONTROL OF DFIM

DTC is an advanced control method involving direct control

of the electromagnetic torque and the flux developed in the

double feed Induction machine. At the heart of the control

system in the DTC are the flux and torque hysteresis

controllers and an optimal switching state logic block. An

appropriate DFIM model is essential for the correct estimation

of the electromagnetic torque and stator flux. The estimation

of these quantities is carried out by measurements of the

DFIM currents, flux linkages and the DC voltage. The

estimated values of the motor torque and flux are input to the

two hysteresis controllers wherein, a comparison between the

estimated and the actual values of the quantitates is performed

[25].

Direct torque control is based on the flux orientation, using

the instantaneous values of voltage vector. An inverter

provides eight voltage vectors, among which two are zeros.

These vectors are errors as well as the stator flux vector

chosen from a switching table according to the flux and torque

position. In this technique, we don’t need the rotor position in

order to choose the voltage vector. This particularity defines

the DTC as an adapted control technique of AC machines and

is inherently a motion sensorless control method [24-26]. The Fig.1 shows the schematic of Direct Torque Control of

doubly fed induction motor.

Figure 1: The diagram of the DFIM-DTC system.

As shown in Fig. 2, the position vectors and trajectory of the

stator flux is divided into six sectors. There are also 8 voltage

vectors which correspond to possible inverter states.

Figure 2: Vectors and Trajectory of the stator flux.

The flux estimator can be obtained by the following equation:

t

ssss dtIRVt0

) ( )( (4)

The stator flux Øsref and the torque Cemref are compared with

respective estimated values, and errors are processed through

hysteresis-band controllers. The digital outputs from the

hysteresis comparators along with the sector number are

shown in Table 1. The correct voltage vector is then selected.

The corresponding switch position for the inverter, to achieve

the selected voltage vector is shown in Table 2.



9101

Table 1: Switching table for voltage vectors.

Table 2: Switch positions and their voltage vectors.

The choice of the zero vectors (V0, V7) produces a smaller

torque and flux variations compared with the active vectors.

Then, the zero vectors are not really needed to keep the torque

and flux controlled; however, it is used to reduce the torque

and flux ripples at steady state operation. For almost every

application of DTC, it is advantageous if the torque and flux

ripples are minimized as much as possible [27].

PROPOSED DTC-ANN

The proposed DTC-ANN consist on two ANN controllers, the

first one replace the switching table which it providing the

voltage vector, the input of ANN controller are Cflux the stator

flux error , Ccpl the torque error and N sector number , this

ANN is based on feed -forward back propagation with four

hidden layers having 4,14,16 and 3 neurons in each layer

respectively and a logsig as activation functions, the output

layer has three neurons providing voltage vector, the proposed

ANN Switching Table is shown in Fig. 3.

Figure 3: The neural networks Switching Table with

Matlab /Simulink

The second ANN controller replace the conventional IP, it

based on feed forward back propagation with three hidden

layers having 10,14 and 1 neurons in each layer respectively

and a logsig as activation functions for first and second layer ,

however and the third layer has purelin activation function,

the input of ANN controller is the difference between

measured speed Wm and reference speed Wref , however the

output layer is Ceref . The proposed ANN Speed controller is

shown in Fig. 4. The weights are determined by training

algorithm.

Figure 4: The Neural Networks Speed Controller.

The proposed DTC-ANN scheme including both ANN

controllers is illustrated in the Fig.5.

Figure 5: DTC-ANN scheme .

SIMULATION RESULTS AND COMPARISON To demonstrate the efficiency of the proposed DTC-ANN

strategy, a comparative study between the conventional DTC

and the proposed DTC-ANN has been carried out using

0.8kW DFIM [4], which many performance parameters have

been studied.



9102

a) Zoom A

b) Zoom B

Figure 6: DTC-ANN under speed variation.

The simulation results obtained with no load as starting up

condition and connecting the nominal load as normal

operating condition are presented in following figures.

Figure 7: DTC-ANN under variable load.

The Fig.6 and Fig.7 illustrate the speed, electromagnetic

torque respectively, while Fig.8 shows the flux,

0.5 1 1.5 2 2.50

20

40

60

80

100

120

140

160

180

t(s)

Speed(rad/s

)

DTC-IP

DTC-ANN

Wref

Zoom A Zoom B

0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65

140

150

160

170

180

190

t(s)

Speed(rad/s

)

DTC-IP

DTC-ANN

Wref

IP

ANN

Response time :IP : 0.35sANN : 0.01 sReduction ratio : 1/35

Response time :IP : 0.24sANN : 0.62 sReduction ratio : 1/2.58

Zoom A

0.8 0.85 0.9 0.95 1 1.05

150

151

152

153

154

155

156

157

158

159

160

t(s)

Speed(r

ad/s

)

DTC-IP

DTC-ANN

Wref

Zoom B

ANNIP

Overshoot :IP : 7 rad/sANN : 0.8rad/s Reduction ratio : 1/8.75

0 0.5 1 1.5 2 2.5-4

-2

0

2

4

6

8

t(s)

Torq

ue(N

m)

DTC-IP

DTC-ANN

Cr

Zoom A

Zoom B

0.1 0.2 0.3 0.4 0.5 0.6-3

-2

-1

0

1

2

3

4

5

6

7

t(s)

Torq

ue(N

m)

DTC-IP

DTC-ANN

Cr

Zoom A

ANN

IP

Response time :IP : 0.57sANN : 0.25 sReduction ratio : 1/2.28

0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6

-2

-1.5

-1

-0.5

0

0.5

t(s)

Torque(N

m)

DTC-IP

DTC-ANN

Cr

ANN IP Overshoot :IP : 1.78 NmANN : 0.11 NmReduction ratio : 1/16.18

Zoom A1

0.8 0.85 0.9 0.95 1 1.05

2.6

2.8

3

3.2

3.4

3.6

t(s)

Torq

ue(N

m)

DTC-IP

DTC-ANN

Cr

Zoom B

IPANNRipple :IP : 0.42 NmANN : 0.2 NmReduction ratio : 1/2.1



9103

From Fig. 6, we can see that at starting up with no load or in

case of nominal load, the DTC-ANN controller reaches its

speed reference rapidly without overshoot compared to the

conventional DTC. As consequence, the excellent dynamic

performance of torque and flux control is evident.

From Fig. 7, its clear that the electromagnetic torque obtained

by proposed DTC-ANN controller better performances

characterised by less response time and low overshoot.

The improvement of DTC-ANN controller regarding stator

flux ripple in steady state is undeniably clear in Fig. 8.

Figure 8: Stator flux reduction ripple.

Fig. 9. shows the flux signals issued by conventional DTC

and proposed DTC-ANN controllers, more than 47%

(0.055Wb for conventional DTC instead of 0.029Wb for

proposed DTC-ANN).

Figure 9: Nominal DFIM flux using DTC-ANN.

Table 3 summarizes the main improvements of the proposed

DTC-ANN compared to conventional DTC.

Table. 3. Comparative study between conventional DTC

and proposed DTC-ANN.

Performance DTC - IP DTC -

ANN

Improve-

ment (%)

W (rad/s) Response time (s) 0.62 0.24 61

Ouvershoot (rad/s) 7 0.8 88

Ce (N.m)

Response time (s) 0.57 0.25 56

Ouvershoot (N.m) 1.78 0.11 93.8

Ripple (N.m) 0.42 0.2 52.3

s (Wb)

Ripple (Wb) 0.055 0.029 47.2

IP ANN00

IP

V - VImprovement( ) = ×100

V

From Figures 6 to 9, and the Table 3, the proposed ANN-DTC

strategy constitutes a practical alternative to the conventional

control strategy and it has many features and advantages such

as:

a. It has good performances;

b. Reduced torque and flux ripples.

c. The ability to track the electromagnetic torque

driving the speed the its reference efficiently.

d. Fast flux and torque responses;

CONCLUSION In this paper, a novel improved direct torque control strategy

using ANN for DFIM (DTC-ANN) has been proposed and

investigated. In which, modelling of DFIM and proposed

DTC-ANN strategy have been discussed in detail. In addition,

the development of Artificial Neural Network controllers has

been explained (structure, training, layers, rules). A

comparative study between conventional DTC scheme and the

proposed DTC-ANN has been presented. Simulation results

demonstrate the superiority of the proposed DTC-ANN

compared to conventional DTC in term of faster converging

speed, better accuracy, and low ripple. Therefore the proposed

DTC-ANN is more suitable for DFIM control.

-1.5 -1 -0.5 0 0.5 1 1.5-1.5

-1

-0.5

0

0.5

1

1.5

Stator flux-a

Sta

tor

flux-b

DTC-IP

DTC-ANN

Zoom A

-1.06 -1.04 -1.02 -1 -0.98 -0.96 -0.94 -0.92 -0.9 -0.88 -0.86

-0.8

-0.75

-0.7

-0.65

-0.6

-0.55

Stator flux-a

Sta

tor

flux-b

DTC-IP

DTC-ANNZoom A

IPANN

0 0.5 1 1.5 2 2.50

0.2

0.4

0.6

0.8

1

1.2

1.4

t(s)

Sta

tor

Flu

x (

Wb)

DTC-IP

DTC-ANN

Zoom A

1.9 1.92 1.94 1.96 1.98 21.13

1.14

1.15

1.16

1.17

1.18

1.19

1.2

1.21

1.22

t(s)

Sta

tor

Flu

x (

Wb)

DTC-IP

DTC-ANN

Zoom A

ANN

IPRipple :IP : 0.055WbANN : 0.029 WbReduction ratio : 1/1.9



9104

APPENDIX:

Double Feed Induction Machine parameters:

Pn = 0.8 kW

Un = 220/380 V

F = 50 Hz

I = 3.8/2.2 A

Vr = 3×120 V; 4.1 A

Ω = 1420 tr/min

Rs = 11.98 Ω

Rr = 0.904 Ω

Ls = 0.414 H

Lr = 0.0556 H

M = 0.126 H

P = 2

J = 0.01 kg.m2

f = 0.001

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