amit seminar report(viii sem) final

8/7/2019 AMIT SEMINAR REPORT(VIII SEM) FINAL

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DIRECT TORQUE CONTROL OF INDUCTION MOTOR

USING ARTIFICIAL NEURAL NETWORK

A

SEMINAR REPORT

Submitted for the Partial fulfillment of the requirement of the

Degree

Of

BACHELOR OF TECHNOLOGY

in

ELECTRICAL ENGINEERING

Guided By: Submitted By:

Mr. Harish Khyani Mr.Amit Mathur

(Sr. Lecturer ) (IV B.Tech., Electrical Engg.)

Department of Electrical Engineering

Jodhpur Institute of Engineering & Technology, Jodhpur

Rajasthan Technical University, Kota (Raj.)

2011


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CE T CATE

Thisist certi y that the Semi ar Report entitled FACE RECOGNITION

USING ARTIFICIAL NEURAL NET ORK s mitted by Mr. Nik il

Mathur orthe partial fulfillment ofthe requirement ofthe Degree of Bachelor

of Technologyin Electrical Engineering of Jodhpur Institute of Engineering &

Technology, Jodhpur,is a record ofthe seminar work carried out by him.

Mr. Hari h Khyani Prof. Kusum Agarwal

(Sr. L turer)

(Head,Electrical Engg.)Date:

Place: Jodhpur


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ACKNOWLE GEMENT

The compilation of thisseminar would not have been possible without

the support and guidance of the following people and organization .With my

deep sense of gratitude ,I think my respected teachers forsupporting thistopic

of my seminar. This seminar report provides me with an opportunity to put

into knowledge of advanced technology. I t hereby take the privilege

opportunityto thank my guide and my friends whose help and guidance made

thisstudy a possibility.

As a student, I learnt manythings but unless I put all with the practical

knowledge asto how things really work and what are the problems generally

arise, I cannot expectto be an efficientstudent. So I thinksummer projectis an

indispensable part ofthe course.

His dedication & sincerity towards the project hel ped me a lot in

completion of project report and gave itthe present attractive look.

Last but notthe least, I would again like to express mysincere thanksto

all project guides fortheir constant friendly guidance during the entire stretch of

this report. Every new step I took was due to their persistent enthusiastic

backing and I acknowledge this with a deep sense of gratitude.

Date: Mr. Amit Mathur

Place: Jodhpur (IV B.Tech., ElectricalEngg.)


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ABSTRACT

This paper presents an improved direct torque of induction machine based on

artificial neural networks.Thisintelligenttechnique was used to replace, on the

one hand the conventional comparators and the selection table in orderto reduce

torque ripple, flux and stator current, on the other hand and the classic integral

proportional (PI) in orderto increase the response time period ofthe system,to

optimize the performances of the closed loop control, and to adjust the

parameters ofthe regulatorto changesin the reference level. Then we estimated

the rotorspeed using the Model Reference Adaptive Control MRAS method

based on measurements of electrical quantities of the machine.The validity of

the proposed methodsis confirmed bythe simulation results .


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CONTENTS

Sr. No. Topics Page No.

1. Introduction to A.N.N. 62. Resemblence with brain ... 7-83. Structure of neural network... 9-104. Architecture of neural networks ..... 11-125. Principle of DTC..... 13-156. Principle of artificial neural network... 16-177. Learning algorithm in neural networks...... 18-198. ANN structure for direct torque control..... 209. Simulation and interpretation of results...... 21-2310. Conclusion and future work...... 2411. Refrences ..... 25


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CHAPTER 1.

INTRODUCTION

A neural networkis a powerful data modeling toolthatis able to capture and represent

complex input/output relationships . In the broadersense, a neural networkis a collection of

mathematical models that emulate some of the observed properties of biological nervous

systems and draw on the analogies of adaptive biological learning. It is composed of a large

number of highly interconnected processing elements that are analogous to neurons and are

tied together with weighted connectionsthat are analogousto synapses.

To be more clear,let usstudythe model of a neural network with the help of figure.1.

The most common neural network modelisthe multilayer perceptron (MLP). It is composed

of hierarchicallayers of neurons arranged so thatinformation flows from the inputlayerto the

outputlayer ofthe network. The goal ofthistype of networkisto create a modelthat correctlymapsthe inputto the output using historical data so thatthe model can then be used to produce

the output when the desired outputis unknown.

Figure 1.1. Graphical representation of MLP


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CHAPTER 2.

RESEMBLENCE WITH BRAIN

The brain is principally composed of about 10 billion neurons , each connected to

about 10,000 other neurons. Each neuron receives electrochemicalinputs from other neurons

at the dendrites. If the sum of these electrical inputs is sufficiently powerful to activate the

neuron, it transmits an electrochemical signal along the axon, and passes this signal to the

other neurons whose dendrites are attached at any of the axon terminals. These attached

neurons maythen fire.

So, our entire brain is composed of these interconnected electro-chemical

transmitting neurons. From a very large number of extremely simple processing units (each

performing a weighted sum of its inputs, and then firing a binary signal if the total input

exceeds a certain level) the brain manages to perform extremely complex tasks. This is the

model on which artificial neural networks are based.

Neural networkis a sequence of neuron layers. A neuron is a building block of a neural

net. Itis veryloosely based on the brain's nerve cell. Neurons will receive inputs via weighted

links from other neurons. This inputs will be processed according to the neurons activation

function. Signals are then passed on to other neurons.

In a more practical way, neural networks are made up of interconnected processing

elements called units which are equivalentto the brains counterpart,the neurons.Neural network can be considered as an artificial system that could perform

"intelligent" taskssimilar to those performed by the human brain. Neural networks resemble

the human brain in the following ways:

1. A neural network acquires knowledge through learning.2. A neural network's knowledge isstored within inter-neuron connection strengths

known assynaptic weights.

3.Neural networks modify own topology just as neuronsin the brain can die and newsynaptic connections grow.

Graphicallylet us compare a artificial neuron and a neuron of a brain with the help of figures

2.1 and 2.2 given below


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]

Figure 2.1. Neuron of an artificial neural network

Figure2.2.Neuron of a brain


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CHAPTER 3.

STRUCTURE OF NEURAL NETWORK

According to Frank Rosenblattstheoryin 1958,the basic element of a neural network

is the perceptron, which in turn has5 basic elements: an n-vector input, weights, summing

function, threshold device, and an output. Outputs are in the form of -1 and/or +1. The

threshold has a setting which governsthe output based on the summation ofinput vectors. If

the summation falls below the threshold setting, a -1 isthe output. Ifthe summation exceeds

the threshold setting, +1 isthe output. Figure 3.1 depictsthe structure of a basic perceptron

which is also called artificial neuron.

Figure 3.1. Artificial Neuron ( Perceptron)

The perceptron can also be dealt as a mathematical model of a biological neuron.

While in actual neurons the dendrite receives electrical signals from the axons of other

neurons,in the perceptron these electricalsignals are represented as numerical values.

A more technicalinvestigation of a single neuron perceptron showsthatit can have an

input vector X of N dimensions (asillustrated in figure.5). These inputs go through a vector W

of Weights of N dimension. Processed bythe Summation Node, "a" is generated where "a" is

the "dot product" of vectors X and W plus a Bias. "A" isthen processed through an activation

function which compares the value of "a" to a predefined Threshold. If "a" is below the

Threshold,the perceptron will not fire. Ifitis above the Threshold,the perceptron will fire one


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pulse whose amplitude is predefined.

Figure 3.2. Mathematical model of a perceptron


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CHAPTER 4.

ARCHITECTURE OF NEURAL NETWORK

4.1.Feed-forward networks:-Feed-forward ANNs allow signalsto travel one way only; from inputto output. There

is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed-

forward ANNs tend to be straight forward networks that associate inputs with outputs. They

are extensively used in pattern recognition. This ty pe of organisation is also referred to as

bottom-up ortop-down.

4.2.Feed-backnetworks:-

Feed-back networks can have signalstravelling in both directions byintroducing loops

in the network. Feedback networks are very powerful and can get extremely complicated.

Feedback networks are dynamic; their 'state' is changing continuously until they reach an

equilibrium point. They remain at the equilibrium point until the input changes and a new

equilibrium needs to be found. Feedback architectures are also referred to as interactive or

recurrent, although the latterterm is often used to denote feedback connectionsin single-layer

organisations.

4.3.Networklayers:-

The commonest ty pe of artificial neural network consists of three groups, or

layers, of units: a layer of input unitsis connected to a layer of hidden units, which

is connected to a layer of output units.

1.The activity of the input units represents the raw information that is fed into the

network.

2. The activity of each hidden unitis determined bythe activities ofthe input units and

the weights on the connections between the input and the hidden units.

3. The behavior ofthe output units depends on the activity ofthe hidden units and the

weights between the hidden and output units.Thissimple type of networkisinteresting because the hidden units are free to construct

their own representations of the input. The weights between the input and hidden units

determine when each hidden unitis active, and so by modifying these weights, a hidden unit

can choose whatit represents.

We also distinguish single-layer and multi-layer architectures. The single-layer


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organisation,in which all units are connected to one another, constitutesthe most general case

and is of more potential computational power than hierarchically structured multi-layer

organisations. In multi-layer networks, units are often numbered bylayer,instead of following

a global numbering.


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CHAPTER-5

PRINCIPLE OF THE DTC

The diagram of CDTC for an induction motor drive isshown in Figure 1. Te* and s* are

torque and flux reference values;Te and s are the estimated torque and stator flux values;

* isthe command speed value; isthe realspeed value and s isthe stator flux angle.

Figure 5.1. Diagram of the CDTC method

A PI or PID controller is used to determine the reference torque, based on the difference

between the reference and the instantaneousspeed ofthe motor.The basic idea of the DTC

conceptisto choose the best vector ofthe voltage, which makesthe flux rotate and produce

the desired torque. During this rotation, the amplitude of the flux remainsin a pre-defined

band. In orderto control the induction motor, the supply voltage and stator current are

sampled. Only two phase currents are needed to measure iA and iB, the third phase can be

calculed as follow: iC=-iA-iB. The stator flux on the stationary reference axes is estimatedas follows:


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Equation-1

Where isthe stator flux and Rsisthe stator resistance. The module of the stator flux is

given by equation (2),the developed electromagnetic torque Te ofthe motor can be evaluated

by equation (3) and the angle between the referential and sis presented by equation (4).

Equation-2

Equation-3

Equation-4

The estimated values ofthe torque and stator flux are compared to the command values,Te*

and s* respectively. It can be seen from figure 1 thatthe error between the estimated torque

Te and the reference torque Te* isthe input of a three level hysteresis comparator, where the

error between the estimated stator flux magnitude s and the reference stator flux magnitude

s* isthe input of a two level hysteresis comparator.Finally,the outputs ofthe comparators

with stator flux sector,where the stator flux s pace vector is located, select an appropriate

inverter voltage vector from the switching Table 1.The selected voltage vector will be applied

to the induction motor atthe end ofthe sample time .


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Table 1. The switching table for basic DTC

Vectors V1,,V6 representthe six active vectorsthat can be generated by a voltage source

inverter (VSI) where V0 and V7 are the two zero voltage vectors. Figure 2 givesthe partition

ofthe complex plan in six angularsectors Si=16.

Figure 5.2. Partition of the complex plan in six angular sectors

When flux is in zone i, vector Vi+1 or Vi-1 isselected to increase the level ofthe flux, and

Vi+2 or Vi-2 isselected to decrease it. Atthe same time, vector Vi+1 or Vi-2 isselected toincrease the level oftorque, and Vi-1 or Vi-2 isselected to decrease it.

If V0 or V7 isselected,the rotation of flux isstopped and the torque decreases whereasthe

amplitude of flux remains unchanged. This shows that the choice of the vector tension

depends on the sign of the error of flux and torque independently from its amplitude. This

explains why the output ofthe hysteresis comparator of flux and torque must be a Boolean

variable. We can add a band of hysteresis around zero to avoid useless commutations when

the error of flux is verysmall.With thistype of hysteresis comparator, we can easily control

and maintain the end ofthe vector flux within a circular ring.


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CHAPTER-6

PRINCIPLE OF ARTIFICIAL NEURAL NETWORK

One of the most important features of Artificial Neural Networks (ANN) is their

ability to learn and improve their operation using a training data . The basic element of an

ANN is the neuron which has a summer and an activation function asshown in Figure 6.1.

The mathematical model of a neuron is given by:

where (x1, x2 xN) are the input signals of the neuron, (w1, w2, wN) are their

corresponding weights and b a bias parameter. is a tangentsigmoid function and y is the

outputsignal ofthe neuron.

Figure 6.1. Representation of the artificial neuron

The ANN shown in Figure 6.2 can be trained by a learning algorithm which performs the

adaptation of weights ofthe networkiteratively untilthe error between target vectors and the

output of the ANN is less than a predefined threshold. Nevertheless, it is possible that the

learning algorithm did not produce any acceptable solution for allinputoutput association

problems. Anyway, results depend on several factors :

Network architecture (number oflayers, number of

neuronsin each layer, etc.).

Initial parameter values w (0).

The details ofthe inputoutput mapping.

Selected training data set (pairs ofinputs and their corresponding desired outputs).

The learning-rate constant.


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Figure 6.2. Structure of neural network


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CHAPTER-7

Learning Algorithms in Neural Networks

The most popularsupervised learning algorithm is backpropagation , which consists

of a forward and backward action. In the forward step,the free parameters ofthe network are

fixed, and the inputsignals are propagated throughoutthe network from the firstlayerto the

lastlayer. In the forward phase, we compute a mean square error.

where di is the desired response,yi is the actual output produced by the networkin

response to the input xi, kisthe iteration number and N isthe number ofinput-outputtraining

data.The second step ofthe backward phase,the errorsignalE(k)is propagated throughout

the network of Figure 6.2 in the backward direction in orderto perform adjustments upon the

free parameters ofthe network in order to decrease the errorE(k) in a statisticalsense. The

weights associated with the output layer of the network are therefore updated using the

following formula:

where wji isthe weight connecting thejth neuron ofthe outputlayerto the ith neuron

of the previous layer, is the constant learning rate. Large values of may accelerate the

ANN learning and consequently fasters convergence but may cause oscillations in the

network output, whereas low values will cause slow convergence. Therefore, the value of

hasto be chosen carefullyto avoid instability.


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Figure 7.1. Flowchart for training backpropagation networks

To ensure fast convergence, we change the formula of equation (12) as shown in

equation (13) where is a positive constant called momentum constant.

The concrete back propagation training process isshown in the flowchart of Figure

12. Once the ANN is trained properly, it should be adequately tested using data which is

different from the training setin orderto testthe validity ofthe model.


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CHAPTER-8

ANN structure for Direct Torque Control

The basic structure of Direct Torque Neural Network Control (DTNNC) method for

induction machine is presented in Figure 13. The artificial neural network replaces the

switching table selector block and the two hysteresis controllers. After several tests, we

choose an architecture 3-10-10-3, i.e. with two hidden layer, with a number of epochs of

3000 and an error of 10-3. The ANN inputs are the error between the estimated flux value

and its reference value,the difference between the estimated electromagnetic torque and the

torque reference and the position of flux stator vector represented by the number of

corresponding sector. The ANN output layer is composed of three neurons. Each neuron

representsthe state of one ofthe three pairs ofthe vectorthat will be applied to the induction

motor. The rest ofthe whole system isthe same like the classicalstructure of DTC presented

in Figure 8.1.

Figure 8.1. Structure of DTC using ANN strategy


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CHAPTER-9

SIMULATION AND INTERPRETATION OF RESULTS

To test the performances of the fuzzy logic and neural networks control with direct

torque control,the simulation ofthe system was conducted using the MATLAB tool. Motors

parameters forsimulation are given in Table 3. Figures9.1-9.4show a comparison between

the CDTC, DTFC and DTNNC.

The torque and flux references used in the simulation results of the Fuzzy direct

torque controlstrategy are 10 N.m and 0.91 wb respectively. The machine is running at 100

rad/sec. The sampling period ofthe system is50 s. All four figures are the responsesto step

change torque command from zero to 10 N.M, which is applied at 0 sec.

The simulation resultsin Figure 9.1 (a, b and c) show the response of electromagnetic

torque of the CDTC, fuzzy DTC and neural network respectively. It can be seen that the

torque's ripples with fuzzy direct torque control in steady state is significantly reduced

compared to conventional and neural networks DTC. It is obvious from Figure 9.1.d that in

fuzzy direct torque control, the torque trajectory is established quickly than in the

conventional or the neural network DTC. The torque trajectories with conventional and

neural networks DTC in start- up are almostsimilar.

(a).CDTC. (b). DTFC. (c). DTNNC. (d) Conventional, Fuzzy

and neural DTC plots

Figure 9.1. Electromagnetic torque response

Figure 9.2 (a, b and c) illustratesthe response ofstator flux magnitude ofthe CDTC,

fuzzy DTC and neural network respectively. Compared with the CDTC, ripple ofstator flux


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with fuzzy and neural network DTC is reduced significantly. The stator flux of the fuzzy

DTC hasthe fast response time in transientstate asshown in Figure 9.2.d.

(a).CDTC. (b). DTFC. (c). DTNNC. (d) Conventional, Fuzzy

and neural DTC

Figure 9.2. The stator flux magnitude

The simulation results in Figure9.3 (a, b and c) show that the current's stator ripples with

direct torque neural networks control in steady state is significantly reduced compared to

CDTC.Compared to the neural DTC, ripple of stator current with fuzzy DTC is almost

similar.

(a). Fuzzy DTC. (b).Neural network DTC. (c). CDTC.

Figure 9.3. The stator current magnitude

Figure 9.4 (a, b and c) describes the stator flux vectortrajectory which is almost

circular. In this figure it can be noticed that fuzzy controller offersthe fasttransient responses

and has better performance than the CDTC method. Compared to the CDTC, ripple ofstator

flux trajectory of neural networkissignificantly reduced.


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(a). Fuzzy DTC. (b).Neural network DTC. (c). CDTC.

Figure 9.4. The stator flux vector trajectory

In allthe simulations presented here, we can easealy observethat our methods reaches

better performances than the CDTC method with respect to reducing the torque, flux and

current ripple and maintaining a good torque response.

Table 2. Induction Motor parameters


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CHAPTER-10

CONCLUSION AND FUTURE WORK

In this paper, an improvement for direct torque control algorithm of induction

machine is proposed using two intelligent approaches which consists of replacing the

switching table selector block and the two hysteresis controllers. Simulations have shown that

the two proposed strategies have better performancesthan the CDTC. In fact,they alloaw a

significant reduced torque and stator flux ripples and a good starting behavior. Using the

intelligent techniques, the selection of the voltage vector becomes much convenient and the

switching state can be obtained when the error of the torque and stator flux is attained. The

validity ofthe proposed control is confirmed by the simulative results. None of the known

advantages ofthe CDTC are impacted by the proposed methods. It has been found that the

directtorque fuzzy controlstrategy allows a higher dynamic behaviorthan the conventional

and neural network DTC. In the future research, the simulative results will be brought into

the experimental system to prove the proposed neural network and fuzzy logic control. A

digitalimplementation ofthese intelligent controls may be performed using different devices

such as custom design, programmable logic, etc. In a Field Programmable Gate Array

(FPGA), which is a family of programmable devices, multiple operations can be executed in

parallelso that algorithms can run faster, which is required for controlsystems.


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REFRENCES

1. International Journal of Computer Applications, Volume 10, November 2010.2. ELECTRONICS FOR YOU- Part 1 April 2001 & Part 2 May 20013. ELECTRONIC WORLD - DECEMBER 20024. MODERN TELEVISION ENGINEERING- Gulati R.R5. IEEE IN TELLIGENT SYS TEMS - MAY/JUNE 20036. WWW.FACEREG.COM7. WWW. IMAGESTECHNOLOGY.COM8. WWW.IEEE.COM

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