International Review on Modelling and Simulations

(IREMOS)

Contents:

Algorithms, Schemes and Techniques of Space-Vector Modulation for Dual-Inverter Systems with Symmetrical Multilevel Phase Voltage by Valentin Oleschuk, Gabriele Grandi

1877

Modified Bacterial Foraging Algorithm Based OPF to Optimize Fuel Cost Function and Losses Using Shunt FACTS Device by K. Ravi, M. Rajaram

1887

Comparing Space Vector Pulse Width Modulation Switching Strategies in Three-Level NPC Inverter from Power Quality Point of View by M. R. Alizadeh Pahlavani, Y. Khadivi Vaneqi

1894

Closed Loop Control of Soft Switched Interleaved Buck Converter by R. Shenbagalaksmi, T. Sree Renga Raja

1910

FPGA Based Implementation of Selective Harmonic Elimination PWM for Cascaded Multilevel Inverter by Baharuddin Ismail, Muhd Hafiz Arshad, S. Thangaprakash

1919

Space Vector Modulated Z-Source H-Bridge Asymmetric Multilevel Inverter with Reduced Switches by M. Balachandran, P. Senthilkumar, N. P. Subramaniam

1927

Harmonic Reduction of Three-Phase Multilevel Inverter for Grid Connected Photovoltaic SystemUsing Closed Loop Switching Control by A. Ravi, P. S. Manoharan, M. Valan Rajkumar

1934

An Intelligent Controlled Bidirectional DC to DC Converter by G. Themozhi, S. Rama Reddy

1943

HVDC Light Systems: an Overview by M. Ajay Kumar, K. U. Archana, N. V. Srikanth

1951

Common Mode Voltage Reduction of Three-Level Back to Back Converter in DFIG with Balanced Neutral Point Voltage by Davood Mazhary

1960

A Solution Method to Optimize Power Converter Modeling for Real Time Digital Simulation Applications by Umashankar S., Vijayakumar D., Kothari D. P.

1969

(continued on inside back cover)

ISSN 1974-9821Vol. 5 N. 5

October 2012

PART

A

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International Review on Modelling and Simulations (IREMOS)

Editor-in-Chief: Santolo Meo Department of Electrical Engineering FEDERICO II University 21 Claudio - I80125 Naples, Italy santolo@unina.it

Editorial Board: Marios Angelides (U.K.) Brunel University M. El Hachemi Benbouzid (France) Univ. of Western Brittany- Electrical Engineering Department Debes Bhattacharyya (New Zealand) Univ. of Auckland Department of Mechanical Engineering Stjepan Bogdan (Croatia) Univ. of Zagreb - Faculty of Electrical Engineering and Computing Cecati Carlo (Italy) Univ. of L'Aquila - Department of Electrical and Information Engineering Ibrahim Dincer (Canada) Univ. of Ontario Institute of Technology Giuseppe Gentile (Italy) FEDERICO II Univ., Naples - Dept. of Electrical Engineering Wilhelm Hasselbring (Germany) Univ. of Kiel Ivan Ivanov (Bulgaria) Technical Univ. of Sofia - Electrical Power Department Jiin-Yuh Jang (Taiwan) National Cheng-Kung Univ. - Department of Mechanical Engineering Heuy-Dong Kim (Korea) Andong National Univ. - School of Mechanical Engineering Marta Kurutz (Hungary) Technical Univ. of Budapest Baoding Liu (China) Tsinghua Univ. - Department of Mathematical Sciences Pascal Lorenz (France) Univ. de Haute Alsace IUT de Colmar Santolo Meo (Italy) FEDERICO II Univ., Naples - Dept. of Electrical Engineering Josua P. Meyer (South Africa) Univ. of Pretoria - Dept.of Mechanical & Aeronautical Engineering Bijan Mohammadi (France) Institut de Mathmatiques et de Modlisation de Montpellier Pradipta Kumar Panigrahi (India) Indian Institute of Technology, Kanpur - Mechanical Engineering Adrian Traian Pleca (Romania) "Gh. Asachi" Technical University of Iasi ubomr oo (Slovak Republic) Slovak Univ. of Technology - Faculty of Mechanical Engineering Lazarus Tenek (Greece) Aristotle Univ. of Thessaloniki Lixin Tian (China) Jiangsu Univ. - Department of Mathematics Yoshihiro Tomita (Japan) Kobe Univ. - Division of Mechanical Engineering George Tsatsaronis (Germany) Technische Univ. Berlin - Institute for Energy Engineering Ahmed F. Zobaa (U.K.) Brunel University - School of Engineering and Design

The International Review on Modelling and Simulations (IREMOS) is a publication of the Praise Worthy Prize S.r.l.. The Review is published bimonthly, appearing on the last day of February, April, June, August, October, December. Published and Printed in Italy by Praise Worthy Prize S.r.l., Naples, October 31, 2012. Copyright 2012 Praise Worthy Prize S.r.l. - All rights reserved. This journal and the individual contributions contained in it are protected under copyright by Praise Worthy Prize S.r.l. and the following terms and conditions apply to their use: Single photocopies of single articles may be made for personal use as allowed by national copyright laws. Permission of the Publisher and payment of a fee is required for all other photocopying, including multiple or systematic copying, copying for advertising or promotional purposes, resale and all forms of document delivery. Permission may be sought directly from Praise Worthy Prize S.r.l. at the e-mail address: administration@praiseworthyprize.com Permission of the Publisher is required to store or use electronically any material contained in this journal, including any article or part of an article. Except as outlined above, no part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior written permission of the Publisher. E-mail address permission request: administration@praiseworthyprize.com Responsibility for the contents rests upon the authors and not upon the Praise Worthy Prize S.r.l.. Statement and opinions expressed in the articles and communications are those of the individual contributors and not the statements and opinions of Praise Worthy Prize S.r.l.. Praise Worthy Prize S.r.l. assumes no responsibility or liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained herein. Praise Worthy Prize S.r.l. expressly disclaims any implied warranties of merchantability or fitness for a particular purpose. If expert assistance is required, the service of a competent professional person should be sought.

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Manuscript received and revised September 2012, accepted October 2012 Copyright 2012 Praise Worthy Prize S.r.l. - All rights reserved

2007

Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Simulation of Induction Motor Drives

P. M. Menghal1, A. Jaya Laxmi2 Abstract AC motor drives are used in multitude of industrial and process applications requiring high performances. In high performance drive systems the motor speed should closely follow a specified reference trajectory regardless of any load disturbances and any model uncertainties. The controllability of torque in an induction motor with good transient and steady state responses form the main criteria in the designing of a controller. Though, PI controller is able to achieve these but with certain drawbacks. The gains cannot be increased beyond certain limit so as to have an improved response. Also it deteriorates the controller performance. With the advent of artificial intelligent techniques, these drawbacks can be mitigated. Base on the inability of conventional control methods like PI, PID controllers to work under wide range of operation. Artificial intelligent based controllers like ANN, Fuzzy controller, ANFIS, expert system, genetic algorithm are widely used in the industry. But, the main problem with the conventional fuzzy controllers is that the parameters associated with the membership functions and the rules depend broadly on the intuition of the experts. In this paper Fuzzy and ANFIS based control of induction motor is done. ANFIS performs better than the conventional and fuzzy and proves be more reliable. Copyright 2012 Praise Worthy Prize S.r.l. - All rights reserved. Keywords: Adaptive Neuro-Fuzzy Inference System (ANFIS), Sugeno Fuzzy Controller, Hebbian

Learning Genetic Algorithm, Expert System

Nomenclature Rs The stator resistance Rr The rotor resistance Lm The magnetizing inductance of the motor Lls The stator leakage inductance Llr The rotor leakage inductance r The slip frequency which is the frequency of

the actual rotor current Llr The rotor leakage inductance referred to stator

side Rr The rotor resistance referred to stator side qs , ds q-axis and d-axis components of stator flux qr , dr q-axis and d-axis components of rotor flux iqs , ids q-axis and d-axis components of stator current iqr , iqr q-axis and d-axis components of rotor current vqs , vds q-axis and d-axis components of stator voltage vqr , vqr q -axis and d-axis components of rotor voltage p Number of poles The angular position of the rotor a Reference frame rotating speed J Moment of inertia (kg/m2) Te Electrical torque Tl Load torque e (k) Control error r (k) Reference signal y (k) Output signal e(k) Changed error u(Ri) The crisp u value corresponding to the

maximum membership degree

I. Introduction Induction motors play a vital role in the industrial

sector especially in the field of electric drives & control [1]-[43]. Without proper controlling of the speed, it is virtually impossible to achieve the desired task for a specific application. AC motors, particularly the Squirrel-Cage Induction Motors (SCIM), enjoy several inherent advantages like simplicity, reliability, low cost and virtually maintenance-free electrical drives. However, for high dynamic performance industrial applications, their control remains a challenging problem because they exhibit significant nonlinearities and many of the parameters, mainly the rotor resistance, vary with the operating conditions.

Field Orientation Control (FOC) or vector control of an induction machine achieves decoupled torque and flux dynamics leading to independent control of the torque and flux as far as separately excited DC motor is considered. FOC methods are attractive, but suffers from one major disadvantage.

They are sensitive to motor parametric variations such as the rotor time constant and an incorrect flux measurement or estimation at low speeds. Consequently, performance deteriorates and a conventional controller such as a PID is unable to maintain satisfactory performance under these conditions. Recently, there has been an increasing interest in combining artificial intelligent control tools with classical control techniques.

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Copyright 2012 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 5, N. 5

2008

The principal motivations for such a hybrid implementation is that with fuzzy logic, neural networks & rough sets issues, such as uncertainty or unknown variations in plant parameters and structure can be dealt with more effectively, hence improving the robustness of the control system. Conventional controls have on their side well established theoretical backgrounds on stability and allow different design objectives such as steady state and transient characteristics of the closed loop system to be specified. Several works were contributed to the design of such hybrid control schemes which was shown by various researchers.

Classical control systems like PI, PID control have been used, together with vector control methods, for the speed control of induction machines by various researchers. The main drawbacks of the linear control approaches were the sensitivity in performance to the system parameters variations and inadequate rejection of external perturbations and load changes. Intelligent, self-learning or self-organizing controls using expert systems, artificial intelligence, fuzzy logic, neural networks, hybrid networks, etc, have been recently recognized as the important tools to improve the performance of the power electronics based drive systems in the industrial sectors.

Combination of this intelligent control with the adaptiveness appears today as the most promising research area in the practical implementation & control of electrical drives. With the advent of artificial intelligent techniques, these drawbacks can be mitigated. One such technique is the use of Fuzzy Logic in the design of controller either independently or in hybrid with PI controller. Fuzzy Logic Controller yields superior and faster control, but main design problem lies in the determination of consistent and complete rule set and shape of the membership functions. A lot of trial and error has to be carried out to obtain the desired response which is time consuming. On the other hand, ANN alone is insufficient if the training data are not enough to take care of all the operating modes. The draw-backs of Fuzzy Logic Control and Artificial Neural Network can be overcome by the use of Adaptive Neuro-Fuzzy Inference System.

The resulted controller is composed of Sugeno fuzzy controller with two inputs and one output [10]-[14]. Assume that the fuzzy inference system has two inputs (x,y) and is e(k) and e(k) and one output. According to the error and error rate of change of the error the control system and the output data, ANFIS generates the appropriate fuzzy controller [15]-[16].

II. Modeling of Induction Motor Dynamic behavior of induction motor can be

expressed by voltage and torque which are time varying. The differential equations that are belonging to

dynamic analysis of induction motor are so sophisticated. Then with the change of variables the complexity of these equations decrease through movement from poly

phase winding to two phase winding (q-d). In other words, the stator and rotor variables like voltage, current and flux linkages of an induction machine are transferred to another reference model which remains stationary [1]-[6].

Fig. 1. d q Model of Induction Motor

In Fig. 1 the sum of the stator leakage inductance and magnetizing inductance is called the stator inductance (Lls = Ls + Lm), and the sum of the rotor leakage inductance and magnetizing inductance is called the rotor inductance (Llr = Lr + Lm).

From the d-q equivalent circuit of the induction motor, we can derive the model equations. The flux linkages can be achieved as:

1

(1)

1

(2)

1

(3)

1

(4)

By substituting the current parameters in the above

equations, the following current equations are obtained as:

(5)

(6)

(7)

(8)

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Copyright 2012 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 5, N. 5

2009

where mq and md are the fluxes linkages over Lm in the q and d axes, The flux equation are written as follows:

(9)

(10)

1

1

1

1

(11)

The speed r in the above equations is related to the

torque by the following mechanical dynamic equation as:

2

(12)

Then is achievable from above equation, where:

p: number of poles J: moment of inertia (kg/m2)

III. Simulation of Induction Motor In the previous section, dynamic model of an

induction motor is expressed. The model constructed according to the equations has been simulated as shown in Fig. 2 and Fig. 3 in conventional mode of operation, of induction motor.

3 phase source is applied to conventional simulated model of an induction motor and the equations are given by:

2 sin (13)

2 sin 23

(14)

2 sin 23

(15)

By using Parks Transformation voltages are

transformed to two phase in the d-q axes, are applied to induction motor. In order to obtain the stator and rotor currents of induction motor in 3 phase Inverse park transformation is applied in the last stage [6].

IV. Fuzzy Logic Controller (FLC) Fuzzy Logic Controllers (FLC), based on fuzzy set

theory are used to represent the experience and knowledge of a human operator in terms of linguistic variables that are called fuzzy rules.

An experienced human operator adjusts the system inputs to get a desired output by just looking at the system output without any knowledge on the systems dynamics and interior parameter variations.

Fig. 2. Induction motor in d-q model

Fig. 3. Simulated Induction Motor Model in Conventional Mode

Therefore, a Fuzzy Logic Controller (FLC) becomes nonlinear and adaptive in nature having a robust performance under parameter variations with the ability to get desired control actions for complex, uncertain, and nonlinear systems without the requirement of their mathematical models and parameter estimation. FL based controllers provide a mathematical foundation for approximate reasoning, which has been proven to be very successful in a variety of applications. In modern control techniques, uncertainty and vagueness have a great amount of importance to be dealt with. The use of membership functions quantified from ambiguous terms in fuzzy logic control rules has given a pulse to speed up the control of the systems with uncertainty and vagueness. The introduction of fuzzy set theory and its application to control systems has become an important and useful tool in especially controlling nonlinear systems. The operation principle of a FLC is similar to a human operator. It performs the same actions as a human operator does by adjusting the input signal looking at only the system output. A FL based controller consists of three sections namely fuzzifier, rule base, and defuzzifier as shown in Fig. 4.

Fig. 4. The basic structure of fuzzy logic based controller

6Wr

5Te

4idr

3iqr

2ids

1iqs

TL TeW

r

rotor speed

iqs

Fqs

Fds

ids

Te

electrical torque

Fqr

Fqs

Fmq

iqr

iqs

Subsystem4

Fds

Fdr

ids

idr

Fmd

Subsystem2

Fmq

vqs

vds

Wr

Fmd

Fqr

Fqs

Fds

Fdr

Flux linkage calculation

3TL

2Vds

1Vqs

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2010

A fuzzy controller is responsible to adjust the speed of induction motor. Converting crisp value to fuzzy can be done by several methods. Triangular type membership functions are used here for partitioning the crisp universes into fuzzy subsets [13]. Therefore the following function is used to represent the fuzzy triangular membership functions:

, , ,

0 0

(16)

In the following the membership function of e and e

and three scalar values of each triangle that are applied into this controller as shown in Fig. 5.

Fig. 5. Triangular Membership of e and e

The fuzzy rules represent the knowledge and abilities of a human operator who makes necessary adjustments to operate the system with minimum error and fast response. In order to model the actions that a human operator would decide whether the change, in the controller output is to be increased or decreased according to the error (k) and its change e(k), it is necessary to observe the behaviors of the error signal e(k) and its change e(k) on different operating regions.

In the following the fuzzy rules decision table implemented into the controller are given in Table I.

The final control action is the crisp output that is defuzzified from the resultant fuzzy values of the fuzzy rule base. The fuzzy output of the rule base is obtained by triggering the active rules for the kth sampling instant corresponding to the values e(k) and e(k). For any point (e(k), e(k)) on the trajectory plot of e(k) vs e(k), there are maximum two intercepting fuzzy sets on each one of the universes e(k) and e(k). Thus, for any sampling instant, the value of e(k) activates only one or two fuzzy sets in the universe of e. Similarly, the value of e(k) for the kth sampling instant also activates only one or two

fuzzy sets in the universe of e(k)[1],[8],[13],[16]. The method called the center of area is widely used in fuzzy logic control applications; here also this method is applied to generate the crisp value at the output. The center of area method yields the following:

,,,

,,, (17)

where, u(Ri) is the crisp u value corresponding to the maximum membership degree of the fuzzy set that is an output from the rule decision table for the rule Ri. The conventional simulated induction motor model as shown in Fig. 3 is modified by adding Fuzzy controller which is shown in Fig. 6.

The pictorial configuration of fuzzy logic based controlled induction motor drive is shown in Fig. 6. This model is achieved by improving the conventional simulation of induction motor by control system enhancement. The d-q model of Induction Motor, Parks transformation and inverse Parks transformation are same to the conventional model, but AC source is replaced with PWM inverter which is controlled by Fuzzy Controller.

The induction motor rotor speed is applied to the Fuzzy Controller. As the first step, the speed is normalized between zero and one and then it is compared to one. Then error and change in error will be calculated.

The produced crisp value will be applied to fuzzifier model and the preferred fuzzy value will be produced.

Then after defuzzification the achieved crisp value will change the frequency of PWM inverter. The Fuzzy controller model is shown in Fig. 7. Speed output terminal of induction motor is applied to fuzzy controller, and in the initial stages of induction motor the error is maximum.

According to fuzzy rules, FC produces a crisp value and this value will change frequency of sin wave in the speed controller. The sin wave is compared with triangular wave, and firing signals of IGBTs are generated in PWM. The frequency of these firing signals also gradually change as shown in Fig. 8 and the frequency of applied voltage to Induction Motor will increase [12].

TABLE I MODIFIED FUZZY RULE DECISION

e NB NS ZZ PS PB

e

PB ZZ NS NS NB NB PS PS ZZ NS NS NB ZZ PS PS ZZ NS NS NS PB PS PS ZZ NS NB PB PB PS PS ZZ

V. Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller

A novel design of an Adaptive Neuro Fuzzy Inference System (ANFIS) for controlling some of the parameters,

NB 1

NS Z PS PB (e)

-0.05 -0.1 0 0.05 0.1 0.1

e

NB NS Z PS PB

-0.05 0 0.05 0.1 0.1

NB 1

NS Z PS PB (e)

-0.5 -1 0 0.5 1 1.5

e

NB NS Z PS PB

0

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2011

such as speed, torque, flux, voltage, current, etc. of the induction motor is presented in this paper.

Fig. 6. Fuzzy Control Induction Motor Model

Fig. 7. Fuzzy Controller model

Induction motors are characterized by highly non-linear, complex and time-varying dynamics and inaccessibility of some of the states and outputs for measurements.

Hence it can be considered as a challenging engineering problem in the industrial sector. Various advanced control techniques have been devised by various researchers across the world. Some of them are based on the fuzzy techniques. Fuzzy logic based controllers are considered as potential candidates for such an application. AC motor drives are used in multitude of industrial and process applications requiring high performances.

In high performance drive systems the motor speed should closely follow a specified reference trajectory regardless of any load disturbances and any model uncertainties. The controllability of torque in an induction motor with good transient and steady state responses form the main criteria in the designing of a

controller. Though, PI controller is able to achieve these but with

certain drawbacks. The gains cannot be increased beyond certain limit so as to have an improved response. Moreover, it introduces non linearity into the system making it more complex for analysis. Also it deteriorates the controller performance.

With the advent of artificial intelligent techniques, these drawbacks can be mitigated. One such technique is the use of Fuzzy Logic in the design of controller either independently or in hybrid with PI controller. Fuzzy Logic Controller yields superior and faster control, but main design problem lies in the determination of consistent and complete rule set and shape of the membership functions.

A lot of trial and error has to be carried out to obtain the desired response which is time consuming. On the other hand, ANN alone is insufficient if the training data are not enough to take care of all the operating modes.

The draw-backs of Fuzzy Logic Control and Artificial Neural Network can be overcome by the use of Adaptive Neuro-Fuzzy Inference System.

The main concept of a neuro-fuzzy network is derived from the human learning process, where an initial knowledge of a function is first setup by fuzzy rules and then the degree of function approximation is iteratively improved by the learning capabilities of the neural network.

Hence ANFIS combines the learning power of neural network with knowledge representation of fuzzy logic. Neuro fuzzy techniques have emerged from the fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) and form a popular framework for solving the real world problems.

A neuro fuzzy system is based on a fuzzy system which is trained by a learning algorithm derived from neural network theory. While the learning capability is an advantage from the viewpoint of FIS, the formation of linguistic rule base will be advantage from the viewpoint of ANN. There are several approaches to integrate ANN and FIS and very often the choice depends on the applications. Assume that the fuzzy inference system has two Inputs x and y and one output, that in this paper the inputs will be e(k) and e(k)[12],[15],[17],[35] A first-order Sugeno fuzzy model has rules which are as follows: Rule1:

If x is A1 and y is B1, then f1 = p1x + q1y + r1 Rule2:

If x is A2 and y is B2, then f2 = p2x + q2y + r2 In the Sugeno model if-then rules are used, and output

of each rule is linear combination of inputs plus a constant value. The learning algorithm applied to this paper is Hebbian. Hebb rule is the oldest and the most famous of all learning rules. This method is feed forward and unsupervised and the weights will be adjusted by the following formula:

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P. M. Menghal, A. Jaya Laxmi

Copyright 2012 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 5, N. 5

2012

Fig. 8. ANFIS model

The ANFIS model is shown in Fig. 8. It states that if the cross product of output and input is positive, then it results in increase of weight, otherwise decrease of weight. Fig. 9 shows ANFIS layout.

M1

M2

M5

M1

M2

M5

e

e

Pro

Pro

Pro

Pro

Pro

Pro

Norm

Norm

Norm

Norm

Norm

Norm

sugeno

sugeno

sugeno

sugeno

sugeno

sugeno

L1 L2 L3 L4 L5 L6

Fig. 9. ANFIS layout

In layer 2 of ANFIS layout, the triangular membership function is same as that of the fuzzy controller model.

The function is given by:

, , ,

Layer 3 indicates the pro (product) layer and its output is product of inputs, which is given by:

Layer 4 represent Norm and it calculates the ratio of ith

firing strength to sum of all firing strengths. The obtained output is normalized firing strength, which is given by:

Layer 5 is an adaptive node with functionality as

follows:

That pi, qi, ri are consequent parameters, which are initially are set to 0.48, 0.25 and 1 respectively. Then they are adaptively adjusted with Hebbian learning algorithm. Layer 6 calculates the output which is given by :

Fig. 10 shows the overall structure of Adaptive Neuro-

Fuzzy model.

Fig. 10. Overall Neuro-Fuzzy simulation model

VI. Results and Discussion Modeling and simulation of Induction motor in

conventional, fuzzy and ANFIS method is done using MATLAB/SIMULINK.

The parameters chosen for simulation are given in Appendix. The results of simulation for induction motor are shown by Figs. 11, 12, 13 that show the speed responses.

From Figs. 11, 12, 13, it is observed that the rising time drastically decreases when fuzzy controller is added to simulation model.

Fig. 11. Speed Response of Conventional Controller

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2013

Fig. 12. Speed Response of Fuzzy Controller

Fig. 13. Speed Response of ANFIS

ANFIS shows better results against the FC, and all the three results are taken in same period of time. As apparent in Fig. 12 the induction motor speed after fuzzy logic controller application shows less rising time to arrive the final value.

For example at sample time '4' the speed of the induction motor using fuzzy controller reaches steady state value faster than conventional controller and ANFIS controller. Figs. 15, 16, 17 show the graphical representation of the torques. In Fig. 15, it is observed that the torque at no-load condition converges to zero at second sample time but in dynamic model the convergence is happening five times later.

Fig. 14. Torque Response of Conventional Controller

Fig. 15. Torque Response of Fuzy Controller

Fig. 16. Torque Response of ANFIS

As it is apparent from Figs. 14, 15, 16, the torque response for ANFIS and Fuzzy converging to zero in less duration of time when compared to conventional controller.

Fig. 17. Stator Current of Conventional Controller

Fig. 18. Stator Current of Fuzzy Controller

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2014

Fig. 19. Stator Current of ANFIS

Fig. 20. Rotor Current of Conventional Controller

Fig. 21. Rotor Current of Fuzzy Controller

Fig. 22. Rotor Current of ANFIS

From simulation results, it is observed that the rising time drastically decreases when FC is added and ANFIS shows better results against the FC, and all three results are taken in same period of time.

For understanding how is effect of ANFIS on the motor activity improvements, the comparative approach will clarify the difference between conventional simulation, Fuzzy controller based simulation and ANFIS simulation. After modeling, simulation of Induction motor in conventional, fuzzy and ANFIS based models are done on MATLAB/SIMULINK. Figs. 19, 20, 21 and 22 show the stator currents and rotor currents of 3 different controller under dynamic conditions. Table II shows numerical comparison between these three different methods in the speed domain of induction motor.

TABLE II

SPEED COMPARISON BETWEEN CONVENTIONAL, FUZZY CONTROLLER AND ANFIS CONTROLLER

Speed

Time line

Speed in conventional

Simulation (rpm)

Speed in FC based simulation (rpm)

Speed in ANFIS based

simulation (rpm)

0.5 65 400 600 1 150 800 1000 2 240 1680 1710 4 580 1710 1710 8 1460 1710 1710 10 1640 1710 1710

It is observed from the table that at different time

intervals, the peak speed reached for FC and ANFIS is very high and fast when compared to conventional controller.For example at time 4 sec ,speed in conventional controller is 580 rpm where as with fuzzy and ANFIS it is 1710 rpm.

VIII. Conclusion In this paper, simulation results of the induction motor

are presented in conventional, fuzzy control and ANFIS based models.

As it is apparent from the speed curve in two models, the fuzzy controller drastically decreases the rising time, in the manner which the frequency of sin waves are changing according to the percentage of error from favorite speed, so firing signals of IGBTs in PWM are continuously changing, then the frequency of applied voltage to IM naturally will increase.

According to the direct relation of induction motor speed and frequency of supplied voltage the speed will increase also. With results obtained from simulation, it is clear that for the same operation condition of induction motor using fuzzy controller had better performance than the conventional controller.

And also with comparing ANFIS model with FC model it is apparent adding learning algorithm to the control system will decrease the rising time more than expectations and it proves ANFIS to better than FC Conventional controller.

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Copyright 2012 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 5, N. 5

2015

Appendix Following are the parameters of the induction motor

chosen for the simulation studies: V = 220 f = 60 HP = 3 Rs = 0.435

Rr = 0.816 Xls = 0.754 Xlr = 0.754 Xm = 26.13 p = 4 J = 0.089 rpm = 1710

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[2] Tze Fun Chan and Keli Shi, Applied intelligent control of induction motor drives, (IEEE Willey Press, First edition, 2011).

[3] P.C. Krause, Analysis of Electrical Machinery and Drives System, (IEEE Willey Press, 2002).

[4] Ned Mohan, Advanced Electric Drives: Analysis, Control Modeling using Simulink,(MNPERE Publication ,2001).

[5] A A Ansari, D M Deshpande,Mathematical Model of Asynchronous Machine in MATLAB Simulink, International Journal of Engineering Science and Technology, Vol. 2(5), 2010, pp1260-1267.

[6] S. J. Chapman, Electric machinery fundamentals, Singapore: (McGraw-Hill, 1991).

[7] Dal Y. Ohm, Dynamic Model of induction motor for vector control, Drivetech, Inc., Blacksburg, Virginia.

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Copyright 2012 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 5, N. 5

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drives, (2000) IEEE International Symposium on Industrial Electronics (ISIE'2000) Universidad de las Amricas-Puebla, Mxico 4 - 8 December, 2000, 1, pp. 324-329.

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Authors information 1Faculty of Electronics, Military College of Electronics and Mechanical Engineering, Secunderabad-500015 and Research Scholar, EEE Dept., Jawaharlal Nehru Technological University, Anantapur-515002, Andhra Pradesh, India E-mail: prashant_menghal@yahoo.co.in 2Dept of EEE, Jawaharlal Nehru Technological University, College of Engineering, Kukatpally, Hyderabad-500085, Andhra Pradesh, India E-mail: ajl1994@yahoo.co.in

P. M. Menghal was born in Khapa (Village), Nagpur (District), Maharashtra India on 7th Feb 1975. He received B.E. degree in Electronics and Power Engineering from Nagpur University, Nagpur in 1998. He received his Master Degree in Control Systems, from Government College of Engineering, Pune, University of Pune, India in 2000 and pursuing

Ph.D. at JNT University, Anantapur. Presently he is working as a Faculty in Radar and Control Systems Department, Faculty of Electronics, Military College of Mechanical Engineering, Secunderabad, Andhra Pradesh , India. He is a Member of IEEE, Institute of Engineers (M.I.E.), Kolkata India, Indian Society of Technical Education(M.I.S.T.E.), IETE, Indian Science Congress and System Society of India (S.S.I.) . He has many research publications in various international and national journals and conferences. His current research interests are in the areas of Real Time Control system of Electrical Machines, Robotics and Mathematical Modeling and Simulation

Dr. A. Jaya Laxmi was born in Mahaboob Nagar District, Andhra Pradesh, on 07-11-1969. She completed her B.Tech. (EEE) from Osmania University College of Engineering, Hyderabad in 1991, M. Tech.(Power Systems) from REC Warangal, Andhra Pradesh in 1996 and completed Ph.D.(Power Quality) from Jawaharlal Nehru Technological University

College of Engineering, Hyderabad in 2007. She has five years of Industrial experience and 13 years of teaching experience. She has worked as Visiting Faculty at Osmania University College of Engineering, Hyderabad and is presently working as Associate Professor, JNTU College of Engineering, JNTUH, Kukatpally, Hyderabad. She has 30 International Journals to her credit. She has 70 International and National papers published in various conferences held at India and also abroad. Her research interests are Neural Networks, Power Systems & Power Quality. She was awarded Best Technical Paper Award for Electrical Engineering in Institution of Electrical Engineers in the year 2006. Dr. A. Jaya laxmi is a Member of IEEE and IAO, Life Member of System society of India, Fellow of Institution of Electrical Engineers Calcutta (M.I.E) and also Life Member of Indian Society of Technical Education (M.I.S.T.E), MIETE, Indian Science Congress.

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International Review on Modelling and Simulations (IREMOS)

(continued from outside front cover)

Impedance Source Converter Based Stabilization of a Stand Alone Wind Generator Using Inverted Sine PWM by K. Premalatha, S. Vasantharathna

1976

Elimination of Speed Kink in Brushless DC Motor Operating in Open Loop by Binu K. Baby, Saly George

1982

A New Approach on Fault Discrimination for Diagnosis of Two-Winding Transformers Using Transfer Function Method by Danial Gorzin, Ebrahim Rahimpor, Alireza Gorzin

1987

Parameters Calculation of Transformer Winding Detailed Model Based on Finite Element Method to Study Partial Discharge by S. M. Hassan Hosseini, M. Vakilian, S. M. Enjavimadar

1995

Permanent Magnet Power Inductor Circuit and Physical Modeling by Zhigang Dang, Jaber A. Abu Qahouq, Shashank Wattal

2001

Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Simulation of Induction Motor Drives by P. M. Menghal, A. Jaya Laxmi

2007

Control of Isolated Self-Excited Induction Generator with DTFC Strategy and DC Voltage Fuzzy Controller Used in Wind Turbine by A. Abbou, M. Akherraz, H. Mahmoudi, M. Barara

2017

Simulation and DSP Based Real Time Implementation of Closed LoopVariable Speed Operation of a PMBLDC Motor Drive System by Lekshmi A., Sankaran R., Ushakumari S

2026

Position Sensorless Direct Torque Control of Brushless DC Motor Drive Using Four-Switch, Three Phase Inverter by S. M. Seyedi, A. Halvaei Niasar, H. Moghbelli

2036

Improved Dynamic Performance of IPMSM Over Wide Speed Range Based on Numerical Computation of id in the Field Weakening Region by S. Pervin, M. Nasir Uddin, Z. Siri

2042

Torque Profile Analysis of In-Wheel Switched Reluctance Motor with Pole Shape Modification by G. Nalina Shini, V. Kamaraj, M. Balaji

2049

A Study of Various Drive Train Models of Fixed Speed Wind Generators and their Simulation Using Simscape by Rajiv Singh, Asheesh Kumar Singh

2056

Self-Scheduling of a Plug-in Electric Vehicles Aggregator in Spinning Reserve Market by Mehdi Rahmani-Andebili

2066

(continued on outside back cover) Abstracting and Indexing Information:

Academic Search Complete - EBSCO Information Services Cambridge Scientific Abstracts - CSA/CIG Elsevier Bibliographic Database SCOPUS Index Copernicus (Journal Master List): Impact Factor 6.55

Autorizzazione del Tribunale di Napoli n. 78 del 1/10/2008

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(continued from inside back cover)

Robust Neural Adaptive Control for a Class of Uncertain Nonlinear Complex Dynamical Multivariable Systems by Farouk Zouari, Kamel Ben Saad, Mohamed Benrejeb

2075

Investigate Balancing in Rotating Structures Using Modal Testing and Analysis Results for Structural Health Monitoring Purpose and Design by H. Al-Khazali, M. Askari

2104

Investigation by Simulation of Closed-Loop Optimal Control Law Implementation Using Artificial Neural Network by Xavier Matieni, Stephen J. Dodds, Sin Wee Lee

2119

Based Feature Comparison between Two Topologies of an Axial-Flux Permanent-Magnet Synchronous Motor Intended for Traction Application by Helmi Aloui, Nadia Chaker, Rafik Neji

2128

Advanced Simulation Model of Five Phase PMBLDC Motor Drive for Low Power High Torque Applications by Inayathullaah M. A., Anita R.

2136

(continued on Part B)

This volume cannot be sold separately by Part B

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