COMPARATIVE ANALYSIS OF PI AND NEURAL BASED SPEED ... · COMPARATIVE ANALYSIS OF PI AND NEURAL BASED SPEED CONTROLLER FOR THREE PHASE INDUCTION MOTOR DRIVE 1R.Senthil Kumar , 2I.Gerald

COMPARATIVE ANALYSIS OF PI AND NEURAL

BASED SPEED CONTROLLER FOR THREE PHASE

INDUCTION MOTOR DRIVE

1R.Senthil Kumar, 2I.Gerald Christopher Raj, 3M.Lincy Luciana 1,3Department of EEE, M.Kumarasamy College of Engineering, Tamilnadu, India

2 Department of EEE, PSNA College of Engineering & Technology, Tamilnadu, India 1Corresponding Author Email id: [email protected],

Abstract—This paper presents the mathematical modeling of three phase induction motor drives fed by

three phase voltage source inverter using direct torque control strategy which is designed by new switching

function topology. The developed simulation model is applied with the artificial neural based speed

controller based on Least Mean Square (LMS) and Adaptive Linear Neuron (ADALINE) to enhance the

performance of three phase induction motor drive system. Under dynamic operating conditions the neural

speed controller is trained by the data obtained from closed loop speed controller of inductionmotor drive

system. The developed conventional and proposed simulation models are simulated using

MATLAB/Simulation software. The proposed neural based speed controller is important role to produce

accurate flux and torque tracking capability compare to conventional PI controllers. Intelligent based

controllers are needed to produce the reference torque for DTC. ADALINE network is developed for neural

controller for weight updation process. . The simulation results show that the neuro based speed controller is

more effective as compared to classical PI based speed controller during most of the operating conditions

considered.

Keywords— Artificial neural network, Direct torque control, Induction motor drive,PI Controller

I. INTRODUCTION

In industries induction motors are used in large numbers because of their individual characteristics such

as intrinsic ruggedness and low cost. Application like speed adjustable drives are used widely. Major

industries uses induction motors now a day. In early days induction motors are used in applications like

speed constant because the other methods of speed control are either highly cost or inefficient. Hence, this

expensive methods are used to avoid failure suddenly and to prevent damage.[1]

The arbitrary frame in symmetrical induction machine gives the equation which obtained from the

different modes of operation in computer simulation [2]. The block diagram of induction motor drive is

shown in Fig 1. The control schemes in induction motor drive uses high calculations and an algorithm which

leads to high cost, large computing and have costly controllers. To have the good performance of motor

which runs at accurate voltage and frequency for correct efficiency at many points we can introduce novel

theory.

In induction motors, field-orientation ASDs are current controlled so that each individual phases of stator

gives current value as a reference. Current control techniques are implemented in inverters which supply

motor. These are completely based on feedback of current sensors[13]. This current control scheme operates

that result in proper correct sequence of inverter states. Hence the actual and reference waveform follows

same waveforms.

International Journal of Pure and Applied MathematicsVolume 118 No. 20 2018, 2449-2461ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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Fig. 1. Typical Induction Motor Drive

The commonly used control method is direct torque control method where the stator flux is the integral of time of stator EMF. Hence its magnitude highly depends on stator voltage. Now the torque developed is propotional to flux vectors produced by the sine angle between stator and rotor. By selecting inverter states consecutively i.e the space vectors of stator voltage properly, we can control directly the magnitude of stator flux and torque developed[4].

To have better performance of motor, intelligent controllers are used. And also to control the torque reference of induction motor drive, artificial neural network is introduced. The two layer neural network named as ADALINE has separate input and output layer. This ADALINE neural network can be implemented by using delay, addition and subraction blocks. Alogorithm like Least Mean Square(LMS) is introduced in ADALINE network[5].

To have better torque characteristics, speed controller is implemented which is based on neural network. Dynamic model of three phase induction motor is developed in this paper.

II. DYNAMIC MODEL OF INDUCTION MOTOR

In α-β has reference frame variable and stationary frame variable in dynamic model of squirrel cage type

induction motor [6].

Fig.2. Equivalent Circuit of SCIM (a) α axis (b) β axi

From Fig.2, stator, rotor voltage and rotor flux linkage of induction motor with stationary reference frame can be represented from equation (1) to (6)

International Journal of Pure and Applied Mathematics Special Issue

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sα s sα s sα m rα

d dV =R i +L i +L i

dt dt (1)

sβ s sβ s sβ m rβ

d dV =R i +L i +L i

dt dt (2)

r rα r rα m sα r rβ

d d0=R i +L i +L i +ω Ψ

dt dt (3)

r rβ r rβ m sβ r rα

d d0=R i +L i +L i -ω Ψ

dt dt (4)

rα m sα r rα rα0Ψ =L i +L i +Ψ (5)

rβ m sβ r rβ rβ0

Ψ =L i +L i +Ψ (6)

Where rα0

Ψ and rβ0

Ψ are the flux linkages of rotor in α-β axis.

In stationary reference frame, the rotating voltage components are with rotor speed of r

ω

r rα r m sα r r sα r rα0ω Ψ =ω L i +ω L i +ω Ψ (7)

r rβ r m sβ r r sβ r rβ0ω Ψ =ω L i +ω L i +ω Ψ (8)

The capacitor voltages expressions are,

cα cα cα0

1V = i dt+V

c (9)

cβ cβ cβ0

1V = i dt+V

c (10)

At no load condition in stationary reference frame, the squirrel cage induction motor expressions are expressed using equations from (1)-(10).

s s m

s s m

m r m r r r r

r m m r r r r

R +pL 0 pL 00

0 R +pL 0 pL0=

pL ω L R +pL ω L0

-ω L pL -ω L R +pL0

sα cα

sβ cβ

rα r rβ0

rβ r rα0

i V

i V+

i ω Ψ

i -ω Ψ

(11)

Using equation (11) the state equations are expressed as follows:

- p G G GA I +BI +V =0 (12)


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ss m

ss m

r m r r rm r

r m r r sm r

R 0 0 0L 0 L 0

0 R 0 00 L 0 LA= ,B=

0 ω L R ω LL 0 L 0

-ω L 0 -ω L R0 L 0 L

sα cα

sβ cβ

G

rα r rβ0

rβ r rα0

G

i V

i V= ,V =

i ω Ψ

i -ω Ψ

I

III. INDUCTION MOTOR- DIRECT TORQUE CONTROL

DTC has easy structure and there is no need in blocks like voltage modulation, current control loops.

DTC has no coordinate transformation and has good torque and flux dynamic performance. Fig 3 shows

proposed method of induction motor using software MATLAB/Simulink. In the proposed work,

conventional PI controller is replaced by ANN based network [7].

A. Torque and Flux Estimation

The direct, quadrature axis flux linkage and torque are calculated as follows [8]

d d s dF = (V -R I )dt (13)

q q s qF = (V -R I )dt (14)

2 2

d qF= (F +F ) , Phase angle

q-1

d

Ftan

F

(15)

e ds qs qs ds

3T = (F i -F i )

2 (16)

A classical DTC scheme has two hysteresis comparators, one for the stator flux linkage and the

second for the torque. A typical scheme is shown in Fig. 3 [9].

Fig. 3. Block Diagram of ANN based PWM inveter induction motor with DTC


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B. Space Vectors

The voltage vector is drifted by having an angle which is not higher than 90 degree with respect to

stator flux. This result is to increase in flux.

dc

a a b c

VV = (2S -S S )

3 (17)

dc

b a b c

VV = (-S +2S -S )

3 (18)

dc

c a b c

VV = (-S -S +2S )

3 (19)

C. Hysteresis controller for torque and flux

The reference torque and reference flux values are compared with their estimated values. The

reference torque of hysteresis output is denoted by hTe and the reference flux linkage is denoted by θ. These

hysteresis controller output has two number of values i.e 0 and 1. A hysteresis flux controller has the digital

output as 0 or 1. When the output is zero, the actual value is higher than reference value and also it is out of

hysteresis limit. When the output is one, the actual value is lower than reference value and also it is out of

hysteresis limit.

Eqs. (20) and (21) represent the hysteresis band limits for the flux tables and Eqs. (22)–(24)

represent the three levels hysteresis bands of the torque[10]:

hf = 0 for F < Fref + hf (20)

hf = 1 for F < Fref – hf (21)

hTe = 1 for Te < Tref − hTe (22)

Terr = 0 for Te = Tref (23)

hTe = −1 for Te < Tref + hTe (24)

D. Sector determination

As mentioned previously, the space vector plane has divided into six regions; each sector occupies

60 degree[11]. The sector determination is summarized in Table I.The Simulink block diagram represents

sector determination is shown in Fig.4.

TABLE I. SECTOR DETERMINATION

secto

r

1 2 3 4 5 6

θ

−30

≤ θ

<

30

30

≤ θ

<

90

90

≤ θ

<

150

150

≤ θ <

210

210

≤ θ <

270

270 ≤

θ <

330


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1

sector

<=

>

<=

>

<=

>

<=

>

<=

>

>

<=

AND

AND

OR

AND

AND

AND

6

5

4

3

2

1

Convert

Convert

Convert

Convert

Convert

Convert

-90

-150

-150

150

150

90

90

30

-30

-90

30

-30

1

angle

Fig. 4 Simulink block diagram of sector determination.

IV. IMPLEMENTATION OF ADALINE NETWORK IN MATLAB / SIMULINK

In ADALAINE, the inputs are cross product with updated weights and then added to form the final

output results. The artificial neuron model is shown in Fig .6. The estimated output and target reference

value is compared and the error signal is produced which is trained by the LMS algorithm and the updated

weights are produced to train the network. The initial weight is set to zero or according to the system

performance. The learning rate is set to a small value. LMS based ADALINE control algorithm is shown in

Fig. 6.The initial weight and the current weight are updated using LMS algorithm until the error becomes

small. [12]

Fig. 6. Artificial Neuron model

The amplitude of the desired source current is estimated by ANN is given in Eqn. 25

j

n j

n=1

Y= i w (25)

The updated weighted equation is given in Eqn. 26

w j+1 =w j +αe(n)i(n) (26)

where Y is the amplitude of the desired source current, α is learning rate, e(n) is error between

output equation and target value, i(n) is input values and w(j) is weights of the ADALINE network. An

ADALINE extraction process is shown in Fig.7 In proposed work, the neural network is used for controlling

purpose. The load voltage can be sensed and compared with reference voltage. The error signal is given to

the neural network to produce the control signal. The proposed work has minimum error to improve the

performance. This control signal is compared with the signal generated from the pulse generator. The speed

can be sensed and compared with reference speed after producing error it fed into neural network model. It

produces reference torque and it fed into direct toque control model.


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Fig. 7. ADALINE extraction process

V. SIMULATION DIAGRAM AND RESULTS

Fig. 8. Simulation diagram of neuro based speed controller for DTC of IM Drive

Fig. 8 Shows simulation diagram of DTC induction motor drives where neuro based speed controller

was developed. Fig. 9 brings out the stator flux response at reference value 0.4 Wb. From the result, PI

controller has not reached their actual value at 0.2 seconds. Fig. 10 shows the stator flux components (Fds,

Fqs). Here the numbers of cycles are more in neural based speed controllers. Fig. 11 shows the flux

trajectory at the same state and Fig. 12 shows the stator current DQ axis for both PI controller and neural

based speed controller. In Fig. 13 three phase stator current waveform for abc frame are shown.

(a)PI Controller

(b)Neural Controller

Fig.9. Actual flux at reference value = 0.4 Wb.


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(a)PI Controller


Fig. 10. Stator Flux components(Fds and Fqs)

(a)PI Controller


Fig. 11. Flux trajectory (Fds, Fqs) at reference value = 0.4 Wb


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(a)PI controller


Fig. 12. Stator current components in stator reference frame.

(a)PI controller

The speed response of the DTC Drive system is studied. In Fig. 14.Resuly shows accurate neural based

speed controller has accurate speed tracking capability. Fig. 15 shows the actual torque when the reference

torque of 15 to −15 N m, In neural based controller torque ripples also

(b)Neural controller

Fig. 13. Stator current for three phases

(a)PI controller


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(b)Neural controller

Fig. 14.Speed response for 500 rpm.

(a)PI Controller

(b) Neural Controller

Fig. 15 Actual torque and reference torque from 15 to −15 N m.

minimized by adjusting learning rate of ADALINE model. Comparison results of PI controller and neural

network controller for stator current and speed was shown in Table II.

Table result shows overshoot has less in neural based controller.so it has reached steady state quickly

compare conventional PI controller.

TABLE II. COMPARISON RESULTS OF PI CONTROLLER AND NEURAL NETWORK

CONTROLLER FOR STATOR CURRENT

SPECIFIC

ATION

STATOR CURRENT Ia

STATOR

CURRENT Ib

STATOR CURRENT

Ic

PI NEURAL PI NEUR

AL

PI NEURAL

RiseTime 1.9450e+004 1.9592e+003 1.5496

e+003

3.0503

e+003

5.4200

e+003

1.4260e+004

Overshoot 20.5153 751.7938 82.288

0

76.968

1

257.50

16

129.0985

Peak 52.1699 52.1699 25.669

9

25.669

9

35.986

6

35.9866


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VI. CONCLUSION

This paper attempts to summarize recent developments in induction motor drives control using

Neural based DTC control. Various techniques, models and neural based speed controller have been

analyzed in this paper.Here new proposed neuro based speed controller has developed for better flux and

torque tracking capability using MATLAB/SIMULINK Software. Then DTC based IM drives model is

developed and different waveforms are analyzed. Most of the waveforms transient has occurred initially

after some time all waveform has reached its steady state..In future work the faults detection and diagnosis

using more intelligent techniques and various control methods of induction motor also will be analyzed.

REFERENCES

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[2] P. C Krause and C. H Thomas, ”Simulation of Symmetrical Induction Machinery” IEEE Trans., Power Apparatus and Systems Vol. PAS-84, No. 11,1965

[3] A. M. Bazzi, A. D. Dominguez-Garcia, and P. T. Krein “A method for impact assessment of faults on the performance of field oriented control drives: A first step to reliabilty modeling” in Proc. IEEE Appl. power Electron. Conf. Expo., 2010, pp. 256–263

[4] Andrzej M. Trzynadlowski,”Control of Induction motor”, Academic press publication

[5] Sundararju, K., and R. Senthil Kumar. "Modelling and analysis of relative power system with cascaded multilevel inverter STATCOM using fuzzy controller." Journal of advances in chemistry 12.10 (2016).

[6] G . R .Slemon, “ Modeling of Induction Machines for Electric Drives” IEEE Trans. Ind. Appl., Vol. 25, No.6, pp. 1126-1131, Nov/Dec 1989.

[7] Shorouk ossama Ibrahim,Khaled Nagdy Faris,Esam abo Elzahab,”Implemetation of fuzzy modeling system for fault detection and diagnosis in three phase induction motor drive system”,Journal of Electrical systems and Information technology 2,PP 27-46,2015.

[8] Ansari, A., Deshpande, D., 2010. Mathematical model of asynchronous machine in MATLAB Simulink

[9] Chow, M.Y., et al., 1998. Intelligent motor fault detection. In: Jain, L.C. (Ed.), Intelligent Techniques in Industry. CRC Press.

[10] Abdul Wahab, H.F., Sanusi, H., 2008. Simulink model of direct torque control of induction machine. Am. J. Appl. Sci. 5 (8), 1083–1090.

[11] R.Senthil Kumar, S.Kirthika and K.Sundararaju,” Analysis Of Single-Stage High-Frequency Resonant Ac/Ac Converter Using Artifical Neural Networks”International journal of pure and applied mathematics,Vol.117.,No.8.,pp-161-165.,Nov 2017

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[13] Uma,J,Jeevanandham,A,2015, ‘Performance of Four phase Switched Reluctance Motor Drive using Single Pulse Width Modulation Technique under constant turn off angle and random turn off angle’, ARPN Journal of Engineering and Applied Sciences, VOL. 10, NO. 5, pp.2203-2208


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COMPARATIVE ANALYSIS OF PI AND NEURAL BASED SPEED ... · COMPARATIVE ANALYSIS OF PI AND NEURAL BASED SPEED CONTROLLER FOR THREE PHASE INDUCTION MOTOR DRIVE 1R.Senthil Kumar , 2I.Gerald