Modelling and Simulations (IREMOS) REPRINT - scu.ac.ir

International Review on Modelling and Simulations

(IREMOS)

Contents:

T-Connected Autotransformer Based AC-DC Converters for Power Quality Improvement by Rohollah Abdollahi

2047

Fault Detection and Reconfiguration of a Modular Multilevel Inverter Using Histogram Analysis and Neural Network by S. Sedghi, A. Dastfan, A. Ahmadyfard

2057

Modeling and Control of Five-Level Three-Phase Flying Capacitors Inverter by O. Bouhali, N. Rizoug, A. Talha

2066

Cost-Effective Resonant Driving Method for High-Voltage CMOS Driver IC by Hyun-Lark Do

2074

Single-Stage High-Power-Factor Electronic Ballast for Fluorescent Lamps by Hyun-Lark Do

2081

Digital Σ∆/PFM Controller Combined with IVFF Used for Synchronous Buck Converters by H. Pakniat, B. Abdi, J. S. Moghani

2088

Impact of Inverter Based Distributed Generation on Network Resonance and Harmonic Distortion by A. F. A. Kadir, A. Mohamed, H. Shareef, M. Z. C. Wanik

2095

Practical Medium Voltage Multi-Level Converter Topologies by Fazel Seyed Saeed

2102

Comparison of Two EKF Based Observers Optimized Online by Both Simulated Annealing and Big Bang-Big Crunch Methods for Sensorless Estimations in Induction Motor by M. Aydin, M. Gokasan, S. Bogosyan

2111

Inter Turn Stator Winding Fault Estimation of Induction Generator by Wavelet Analysis by E. Gharibreza, S. Gh. Seifossadat, M. Joorabian, M. Heidari Orejloo

2122

Detection and Localization of Transformer Internal Fault During Impulse Test by J. Beiza, M. Salaynaderi, N. Taghizadegan, A. A. Dadjouyan, J. Rabbaani

2129

Rotor Resistance Estimation Methods for Performance Enhancement of Induction Motor Drives - A Survey by M. Nandhini Gayathri, S. Himavathi, R. Sankaran

2138

(continued on inside back cover)

ISSN 1974-9821Vol. 4 N. 5

October 2011

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 [email protected]

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 Mathématiques et de Modélisation de Montpellier Pradipta Kumar Panigrahi (India) Indian Institute of Technology, Kanpur - Mechanical Engineering Adrian Traian Pleşca (Romania) "Gh. Asachi" Technical University of Iasi Ľubomír Šooš (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, 2011. Copyright © 2011 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: [email protected] 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: [email protected] 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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International Review on Modelling and Simulations (I.RE.MO.S.), Vol. 4, N. 5

October 2011

Manuscript received and revised September 2011, accepted October 2011 Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved

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Inter Turn Stator Winding Fault Estimation of Induction Generator by Wavelet Analysis

E. Gharibreza, S. Gh. Seifossadat, M. Joorabian, M. Heidari Orejloo Abstract – The most probable fault in induction generator is stator winding inter-turn fault. In the proposed method, Extended Park’s vector approach with the variance of the wavelet coefficients are used for detect of inter-turn fault. Also in this article Adaptive Neuro Fuzzy Inference System is used for estimation of the percentage of shorted turns in the phase under fault. Simulation studies are carried out for a induction generator to validate the proposed method. Results show high accuracy of proposed method in detection and estimation of number of shorted turns of stator winding under fault. the simulation results will show that the proposed method is very accurate. Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved. Keywords: Adaptive Neuro Fuzzy Inference System, Extended Park’s Vector Approach, Induction

Generator, Inter-Turn Fault

Nomenclature v i R λ L φ ψ

Voltage Current Resistance Flux Linkage Inductance Scaling Function Wavelet Function

I. Introduction Nowadays using of renewable energies especially

wind energy have been increased rapidly. Different technologies have been used in the protection of wind generator. Wounded rotor induction generator has been applied in Iranian wind farm such as Manjil farms that in witch rotor current can be controlled. Protection of generators is an important issue because they play vital role in wind farms. Stator winding faults are considered serious problems.

When there is an insulation failure between the winding inter-turns they get short circuited. The differential protection schemes cannot detect the inter-turn stator winding faults [1].

At the first step, an appropriate model for induction generator to study the inter-turn faults has to be chosen that be able to cover all modes of operation. Various models such as Finite-element, Coupled-circuit, Transfer function and three phase models have been suggested for machine operation studies. magnetic flux, vibration, Noise/Acoustic Noise, Instantaneous Angular Speed, Temperature, Air-Gap Torque, motor current signature analysis, Induced Voltage, power, partial discharge, gas analysis, rotor current analysis have been applied for

condition monitoring[2]-[5]. FFT, Bispectrum, High-Resolution spectral analysis, Park’s vector approach[6], ANN[7], Nero-fuzzy[1] and wavelet[8]-[11] analysis are analysis methods that have been used for detection of the inter-turn faults.

In this paper three phases ABC/abc model is used. In this work, a new method based on wavelet analysis and variance of its coefficients for detection of inter-turn faults. Also a new ANFIS is proposed to estimate the percentage of shorted turns. To validate the method developed in this paper, an induction generator used in Iranian wind farms is simulated using MATLAB software.

II. System Modelling Starting from the concept of magnetically coupled

circuits, the WRIG can be modeled by at least two coupled circuits [5], [6].

For a WRIM, a simple circuit model can be given by assuming individual stator, rotor coils (abc) and their couplings. Inter-turn short circuit fault modeling consists on considering the turn itself as a separate coil in series with the phase (a) of the stator. It has been assumed that phase (a) of the stator has divided into two windings in series comprising unshorted turns (Nunsh) and shorted turn (Nsh), where Na =Nunsh +Nsh=Ns, that Ns is the overall number of turns. The phases (b) and (c) of the stator have the same turns (Nb =Nc =Ns).

The parameters of shorted turns are given using the d subscript. Taking into account the distributed nature of the windings, stator and rotor equivalent circuits can be represented on a coil to coil basis. Resistive and inductive effects are only taken into account and the

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E. Gharibreza, S. Gh. Seifossadat, M. Joorabian, M. Heidari Orejloo

Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 4, N. 5

2123

capacitive effect is neglected in order to considering the machine in the low frequency range.

In this work, it is assumed that the stator and the rotor have sinusoidally distributed windings [12].

In the following equations, the upper script s is related to stator circuits, the upper script r to the rotor circuits and the d subscript to the shorted turn circuits. Voltages equations in the machine can be given as follows:

s

s s s abcdabcd abcd

ddt

= +λ

v R i (1)

r

r r r abcabc abc

ddt

= +λ

v R i (2)

both Rs

and Rr are diagonal matrices:

0 0 0

0 0 0

0 0 0

0 0 0

sa

sbs

sc

sd

R

R

R

R

⎡ ⎤⎢ ⎥⎢ ⎥

= ⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

R

0 0

0 0

0 0

ra

r rb

rc

R

R

R

⎡ ⎤⎢ ⎥

= ⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

R

the flux linkages λs

abcd and λrabc can be expressed by:

s ss srabcd abcdr rrs rabc abc

⎡ ⎤ ⎡ ⎤⎡ ⎤=⎢ ⎥ ⎢ ⎥⎢ ⎥

⎢ ⎥ ⎢ ⎥⎢ ⎥⎣ ⎦⎣ ⎦ ⎣ ⎦

λ iL L

λ iL L (3)

the winding inductances are given by:

asas asbs ascs asds

bsas bsbs bscs bsdss

csas csbs csc s csds

dsas dsbs dscs dsds

L L L LL L L LL L L LL L L L

⎡ ⎤⎢ ⎥⎢ ⎥=⎢ ⎥⎢ ⎥⎣ ⎦

L

arar arbr arcr

rbrar brbr brcr

crar crbr crcr

L L LL L LL L L

⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦

L

The mutual inductance between stator and rotor

winding can be given by:

asar asbr ascr

bsar bsbr bscrsr

csar csbr csc r

dsar dsbr dscr

L L LL L LL L LL L L

⎡ ⎤⎢ ⎥⎢ ⎥=⎢ ⎥⎢ ⎥⎣ ⎦

L

Tasar bsar csar dsar

rs srasbr bsbr csbr dsbr

ascr bscr cscr dscr

L L L LL L L LL L L L

⎡ ⎤⎢ ⎥= = ⎢ ⎥⎢ ⎥⎣ ⎦

L L

Matrix elements of Rs, Rr, Ls, Lr, Lsr and Lrs are given

in Appendix. Then the machine voltage equation can be expressed

by equation (5). The electromagnetic torque is expressed as a function

of stator currents, rotor currents and mutual inductance derivative with respect to rotor position θ, according to the following equation:

d c

xsrys re x y

x a y a

dLT i i

dθ= =

= ⋅∑ ∑ (4)

( )s s s s s s s r r r sa a a asas a asbs b ascs c asds d asar a asbr b ascr c d

dV R i L i L i L i L i L i L i L i Vdt

= + + + + + + + +

( )s s s s s s s r r rb b b bsas a bsbs b bscs c bsds d bsar a bsbr b bscr c

dV R i L i L i L i L i L i L i L idt

= + + + + + + +

( )s s s s s s s r r rc c c csas a csbs b csc s c csds d csar a csbr b csc r c


= + + + + + + +

( )r r r s s s s r r ra a a asar a bsar b csar c dsar d arar a arbr b arcr c


= + + + + + + + (5)

( )r r r s s s s r r ra a a asar a bsar b csar c dsar d arar a arbr b arcr c


= + + + + + + +

( )r r r s s s s r r rb b b asbr a bsbr b csbr c dsbr d brar a brbr b brcr c


= + + + + + + +

( )r r r s s s s r r rc c c ascr a bscr b csc r c dscr d crar a crbr b crcr c


= + + + + + + +

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III. The Extended Park’s Vector Approach (EPVA)

The EPVA is a new detection technique, which has been successfully applied in the steady-state diagnosis of rotor faults, inter-turn stator faults and unbalanced supply voltage and mechanical load-misalignment. This technique is based on the Park’s Vector Approach; however it provides greater insight into this severity of the faults. The instantaneous line currents of the stator are transformed into the Park’s vector using (6) and (7). An undamaged machine theoretically shows a perfect circle where the instantaneous magnitude is constant as shown in Fig. 1. An unbalance due to turn faults results in an elliptic representation of the Park’s Vector as shown in Fig. 2:

2 1 13 6 6

s s s sD a b ci i i i= − − (6)

1 12 2

s s sQ b ci i i= − (7)

Fig. 1. The Park’s Vector (left) and magnitude (right) for a healthy

Fig. 2. The Park’s Vector (left) and magnitude (right) for a damaged machine

IV. Wavelet Analysis Wavelet analysis is a signal processing method that

decomposed the signal into several wavelet with different scales and positions. In this paper DWT has been used. Wavelet analysis integrated the product of function and scale function:

( ) ( ) ( )*a,bW f a,b f t ψ t dt f ,ψψ = × =∫ (8)

where:

1a,b

t baa

ψ ψ −⎛ ⎞= ⎜ ⎟⎝ ⎠

a and b are scale and position parameters respectively

and ψ is wavelet. Mother wavelet has been chosen according to problem. Equation (9) is applied for DWT:

( ) ( ) 0 0

00

1 m*

mmk

k nb aDWT f ,m,n f k ψ

aa

+∞

=−∞

⎛ ⎞−= ⎜ ⎟⎜ ⎟

⎝ ⎠∑ (9)

in this equation a and b are replaced by 0

ma and 0mka ,

( k ,m Z∈ ). In DWT original signal passed through to

complementary filters and emerges as to signals in various levels. each level content of approximation and detail components. wavelet and scaling function have been used for extracting detail (high frequency) and approximation (low frequency) components respectively, so:

( ) ( ) ( )

( ) ( )1

2

02 2

oj

j jj

j k

f t c k t k

d k t k

ϕ

ψ−

=

= − +

+ −∑∑ (10)

In this equation φ and ψ are scaling and wavelet

function, dj in detail coefficient at level j and c0 is the first approximation coefficient.

V. Detection and Estimate When observing the coefficients of d1 for each

condition, it was found to be an extremely difficult task to distinguish damaged mode from the healthy mode by using these wavelet coefficients directly.

For this reason a statistical approach was attempted. Generating a histogram of the d1 coefficients has shown to give a better insight into the machine’s condition. In this work, the simplest wavelet, Haar wavelet (see Figs. 3 and 4), is used.

In the case of a healthy machine the coefficients produce a Gaussian distribution at all speed as shown in Figures 5(a) and 6(a). In the case of the damaged machine, the distribution is bimodal as shown in Figures 5(b), 5(c), 5(d) and 6(b), 6(c), 6(d). clearly these distribution plots are useful for determining if a machine is healthy. Fig. 5(a) displays histogram of coefficients for healthy operation and figures 5(b), 5(c) and 5(d) show 30, 60 and 90 percents of inter-turned fault of phase 'a' respectively witch slip equal to -0.01.

In addition, in Figs. 6, histogram of d1 coefficients of wavelet transformation for healthy condition and 30, 60

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and 90 percents of inter-turned fault are depicted in Figs. 5(a)-(d) respectively which slip equal to -0.1.

Variance of Gaussian distribution is equal to zero while variance of bimodal distribution is not. So as a result, if variance of wavelet coefficients is zero, it has Gaussian distribution and machine is working well, on the other hand, Machine is working under inter-turn fault condition when variance of wavelet coefficients is different from zero.

The flowchart of proposed algorithm has been shown in Fig. 7. Pointed block is related to shorted turns percentage estimation unit.

Fig. 3. Haar scaling function

Fig. 4. Haar wavelet function

(a)

(b)

(c)

(d)

Figs. 5. Histogram of coefficients for (a)healthy operation, (b) 30%, (c)

60% , (d) 90% of inter-turned fault of phase 'a' for s=0

(a)

(b)

(c)

(d)

Fig. 6. Histogram of coefficients for (a) healthy operation, (b) 30%, (c)

60%, (d) 90% of inter-turned fault of phase 'a' for s=0.1

Fig. 7. Flowchart of proposed algorithm

According to Figs. 5 and 6, the variance of wavelet coefficients goes up with increasing percentage of shorted turns. This relationship can be used as a suitable criterion for shorted turn percentage estimation. For this purpose, ANFIS is used in this article. The ANFIS is a fuzzy Sugeno model of integration where the final fuzzy inference system is optimized via the ANNs training[1]. The inputs have are passed through input membership functions and associated parameters, and then the same procedure is used for outputs. The initial membership functions and rules for the fuzzy inference system can be designed by employing human expertise about the target system to be modeled.

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2126

Then ANFIS can refine the fuzzy if–then rules and membership functions to describe the input/output behavior of a complex system. Machine slip and variance of wavelet coefficients are considered as inputs of ANFIS and shorted turn percentage estimation as output.

VI. Simulation Results Equations In this paper vestas 47 wind turbine that is

commonly installed in wind farms such as manjil in iran has been simulated by Matlab software. Technical information of generator is presented in Table I.

TABLE I

TECHNICAL INFORMATION OF GENERATOR

Type Asynchronous, Variable Slip

Rated Power 660 kW Voltage 690 V, AC

Frequency 50 Hz Number of Poles 4 Rotational Speed 1515-1650 rpm

Induction generator has been tested for over 200

faulty and healthy operation conditions and the results have proved the accuracy of proposed algorithm in fault detection.

The result of percentage of shorted turns estimation by ANFIS have been shown in Table II, that they are closed to real values.

TABLE II

THE RESULT OF PERCENTAGE OF SHORTED TURNS ESTIMATION BY ANFIS

Slip Variance Percentage of shorted turn

Estimate by ANFIS

-0.01 2.05 15 15.2 -0.05 15.95 55 54.9 -0.07 88.04 85 85.2

VII. Conclusion Wavelet analysis has been successfully applied to the

detection of stator turn faults in induction generator. The detection algorithm is a combination of the

Extended Park’s Vector, with variance of wavelet coefficients and statistics. The 1th detail scale has been used for analyzing. Simplest wavelet, i.e. Haar wavelet, can be used to successfully detect the inter-turn fault.

The coefficient distribution for the 1th detail scale is Gaussian when there are no turn faults.

The distribution is bimodal with a flattened interior after occurring inter-turn faults.

Variance of Gaussian distribution is equal to zero while variance of bimodal distribution is not. Variance of wavelet coefficients goes up with increasing the shorted turns percentage. This relationship has been used as a suitable criterion for shorted turn percentage estimation.

In this article, ANFIS has been used for this purpose.

Appendix Matrix elements for induction motor with inter-turn

stator short circuit. The Rs matrix elements are:

s unsha s

a

NR R

N⎛ ⎞

= ⎜ ⎟⎝ ⎠

s sb c sR R R= =

s shd s

a

NR R

N⎛ ⎞

= ⎜ ⎟⎝ ⎠

Because of rotor symmetry, the Rr matrix elements

are:

r r ra b c rR R R R= = =

The stator self- and mutual inductances Ls matrix

elements are:

2unsh

asas sa

NL L

N⎛ ⎞

= ⎜ ⎟⎝ ⎠

bsbs sL L=

2

shdsds s

a

NL L

N⎛ ⎞

= ⎜ ⎟⎝ ⎠

2unsh ms

asbsa

N LL

N⎛ ⎞

= −⎜ ⎟⎝ ⎠

2ms

bscsL

L = −

sh unshasds ms

a a

N NL L

N N⎛ ⎞⎛ ⎞

= ⎜ ⎟⎜ ⎟⎝ ⎠⎝ ⎠

2sh ms

bsdsa

N LL

N⎛ ⎞

= −⎜ ⎟⎝ ⎠

and:

Lascs=Lcsas=Lbsas=Lasbs, Lcscs=Lbsbs, Lcsbs=Lbscs, Ldsas=Lasds

and:

Lcsds=Ldsbs=Ldscs=Lbsds

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The rotor self- and mutual inductances Lr matrix elements are:

asas rL L=

2ms

arbrL

L = −

and: Lbrbr=Lcrcr=Larar and Larcr=Lbrar=Lbrcr=Lcrar=Lcrbr The stator-to-rotor mutual inductances can be defined

as follows:

unshasar m r

a

NL L cos

Nθ

⎛ ⎞= ⎜ ⎟

⎝ ⎠

( )2 3unshasbr m r

a

NL L cos

Nθ π

⎛ ⎞= +⎜ ⎟

⎝ ⎠

( )2 3unshascr m r

a

NL L cos

Nθ π

⎛ ⎞= −⎜ ⎟

⎝ ⎠

( )2 3bsar m rL L cos θ π= −

bsbr m rL L cosθ=

( )2 3bscr m rL L cos θ π= +

shdsar m r

a

NL L cos

Nθ

⎛ ⎞= ⎜ ⎟

⎝ ⎠

2unsh ms

asbsa

N LL

N⎛ ⎞

= −⎜ ⎟⎝ ⎠

2unsh ms

asbsa

N LL

N⎛ ⎞

= −⎜ ⎟⎝ ⎠

( )2 3shdsbr m r

a

NL L cos

Nθ π

⎛ ⎞= +⎜ ⎟

⎝ ⎠

( )2 3shdscr m r

a

NL L cos

Nθ π

⎛ ⎞= −⎜ ⎟

⎝ ⎠

and:

Lcsar=Lbsar, Lcscr=Lbsbr and Lcsar=Lbscr

References [1] R.Rajeswari, N.Kamaraj, “Diagnosis of Inter Turn Fault in the

stator of synchronous Generator using Wavelet Based ANFIS", World Academy of science, Engineering and Technology, 2007.

[2] Y. Amirat, M.E.H. Benbouzid, E. Al-Ahmar, B. Bensaker, S. Turri, “A brief status on condition monitoring and fault diagnosis in wind energy conversion systems" , Renewable and Sustainable Energy Reviews 13, p: 2629–2636, 2009.

[3] Arfat Siddique, G. S. Yadava, Bhim Singh, “A Review of Stator Fault Monitoring Techniques of Induction Motors" , IEEE Transactions on energy conversion, VOL. 20, NO. 1, MARCH 2005.

[4] Subhasis Nandi Hamid A. Toliyat, “CONDITION MONITORING AND FAULT DIAGNOSIS OF ELECTRICAL MACHINES - A REVIEW”, IEEE, Industry Applications Conference, Vol. 1, P: 197 – 204, 3-7 Oct. 1999.

[5] Y. Gritli, A. Stefani, F. Filippetti, A. Chatti, “Stator Fault Analysis Based on Wavelet Technique for Wind Turbines Equipped with DFIG”, IEEE, Clean Electrical Power, International Conference, P: 485 – 491, 9-11 June 2009.

[6] M.El Hachemi Benbouzid, “A Review of Induction Motors Signature Analysis as a Medium for Faults Detection" , IEEE Transactions on industrial electronics, VOL. 47, NO. 5, OCTOBER 2000.

[7] Z.E. Gketsis, M.E. Zervakis, G.Stavrakakis, “Detection and classification of winding faults in windmill generators using Wavelet Transform and ANN" , Electric Power Systems Research , 2009.

[8] H. Douglas,P. Pillay,P. Barendse, “The Detection of Interturn Stator Faults in Doubly-Fed Induction Generators" , IEEE, Industry Applications Conference, Vol. 2, p: 1097- 1102 , 2005.

[9] N. Uchaipichat, “Detection of Sleep Apnea from Electrocardiogram Using Wavelet Transform”, International Review of Modelling and Simulations (IREMOS), Vol. 2. N. 5, p: 606-609, October 2009.

[10] Manef Bourogaoui, Houda Ben Attia Sethom, “Discrete Wavelet Decomposition Applied to Position Sensor Default Detection on a PMSM Traction Drive”, International Review of Modelling and Simulations (IREMOS), Vol. 4 N. 1, p: 279-286, February 2011.

[11] Sendilkumar Subramanian, Mathur Badrilal, Joseph Henry, “Wavelet Transform Based Differential Protection For Power Transformer and Classification of Faults Using SVM and PNN” , International Review of Electrical Engineering (IREE), Vol. 5, p:2186-2198, September-October 2010.

[12] M. Rachek, Y. Méssaoudi, B. Oukacine , S. Naitlarbi, “Accurate Multi-Turn Model of Induction Motors Under Stator Short Circuits and Phases Breakdown Faults”, International Review of Modelling and Simulations (IREMOS), Vol. 1 n. 1, p: 154 – 162, October 2008.

Authors’ information Shahid Chamran University, Ahvaz, Iran.

E. Gharibreza was born in Ahvaz (Iran), in Sep 21, 1986. He obtained his B.Sc. degree in 2008 and M.Sc. in 2011 in Electrical Engineering from Shahid Chamran University, Ahvaz (Iran) and teaches in Islamic Azad University, Abadan branch, Iran. His research interests are in power system protection and power system analysis.

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S. Gh. Seifossadat was born in Ahwaz,Iran, in1963. He received the B.Sc. degree in electrical engineering from the Iran University of Science and Technology (IUST), Tehran, Iran in 1989 and the M.Sc. degree in electrical engineering from Ferdosi University of Mashhad, Mashhad, Iran, in 1992 and the Ph.D. degree from IUST, Tehran (Iran) in 2006.

Currently, he is with the Department of Electrical Engineering of Shahid Chamran University of Ahvaz, where he has been there since 1992. His research interests are power electronics, protection relay, and electric machinery.

Mahmood Joorabian was born Iran, on April 29, 1961. He graduated in B.E.E. from University of New Haven, CT, USA, in 1983. He received M.Sc. in Electrical Power Engineering from Rensselaer Polytechnic Institute, NY, USA, in 1985, and received Ph.D. in Electrical Engineering from University of Bath, Bath, UK, in 1996. Since 1985, he has been with the Department of Electrical

Engineering, Shahid Chamran University, as Senior Lecturer, since 1996 as assistant professor, since 2004 as associate professor and since 2009 is a professor of Electrical Engineering. His research interests are power system modeling, power quality, power system protection, renewable energy, and applications of intelligence technique in power systems.

M. Heidari Orejloo was born in Ghachsaran, (Iran) in 1976. He received the B.Sc degree in electrical engineering from Shiraz University, shiraz, (Iran) in1999, and after some year’s employment with KWPA corporation, the M.Sc. degree in electrical power engineering from Shahid Chamran University of Ahvaz, Ahvaz (Iran) in 2005.

Now he is a Ph.D. student in Shahid Chamran University of Ahvaz, Ahvaz (Iran). His research interest is power system protection and insulation and high voltage.

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International Review on Modelling and Simulations (IREMOS)

(continued from outside front cover)

A New Induction Motor Model for Fault Analysis by F. J. Villalobos-Piña, R. Alvarez-Salas, N. Visairo, V. Cardenas

2145

Adaptive Selective Current Harmonic Cancellation Algorithm for PMBLDC Motor Drive by V. M. Varatharaju, B. L. Mathur, K. Udhayakumar

2153

Detection and Localization of Turn-to-Turn Short Fault in Power Transformers by Analyzing of Transfer Function Using an Artificial Neural Network by Vahid Rashtchi, Ebrahim Rahimpour

2159

Modeling of Multi-Phase Transformer - Equivalent Circuit by Subhash Kumar Joshi, Hari Om Gupta, Pramod Agarwal

2164

Design and Modelling of Multi-Pulse Phase-Shifting Transformers for Medium Voltage Applications by Fazel Seyed Saeed

2172

A New Method for Stator Winding Turn-Fault Diagnosis of Induction Motor by Space Vector Model Based on Neural Network by Mehdi Samiei Sarkhanloo, Davar Ghalledar, Akbar Danandeh, Mohsen Ghorbani

2182

Modeling and Analysis of Saturated Induction Machines by Ezzine Walid, Khlaifi Mohamed Larbi, Habib Rehaoulia

2190

Using Multiple Scales Method and Chaos Theory for Detecting Route to Chaos in Chaotic Oscillations in Voltage Transformer with Nonlinear Core Loss Models by H. R. Abbasi, A. Gholami, S. H. Fathi, A. Abbasi

2195

Direct Torque Control of Two-Phase Induction Motors Fed by Two- and Three-Leg Inverters by S. Ziaeinejad, Y. Sangsefidi, A. Shoulaie

2211

Analysis of Commutation Torque Ripple of BLDC Motors and Presenting Two Methods for its Reductionby Y. Sangsefidi, S. Ziaeinejad, A. Shoulaie

2219

Furan Analysis on Power Transformers in Malaysia: a Field Investigation by Zulkurnain Abdul-Malek, Nouruddeen Bashir, Hasree Ismail

2227

Design of Sample Based Filtering Schemes for a Three Phase Induction Motor Model by J. Ravikumar, S. Subramanian

2234

An ANFIS-Based Neuro-Fuzzy Controller with Supervisory Learning for Speed Control of Brushless DC Motor by M. R. Mosavi, A. Rahmati, A. Khoshsaadat

2246

Maximum Power Tracking of Wind Turbine Based on Doubly Fed Induction Generator by M. Hilal, M. Benchagra, Y. Errami, M. Maaroufi, M. Ouassaid

2255

(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)

Transient Finite Element Analysis of a Low Power Composite Solid Rotor Induction Motor by Akbar Rezaie Sardarabadi, Mohsen Hosseini

2264

An Excellent Technique for Hot Spot Temperature Reduction in 3 Phase Transformers Using Auxiliary Windings by Diako Azizi, Ahmad Gholami, Dear Azizi

2269

Online Energy Efficient Control of Three-Phase Induction Motor Drive Using PIC-Microcontroller by Hussein S. Sarhan

2278

Power Measurement for the AUFL by Rasha M. El Azab, E. H. Shehab Eldin, P. Lataire, M. M. Sallam

2285

Modelling of PEM Fuel Cell Systems Using ANN by Inmaculada Zamora, José I. San Martín, José J. San Martín, Víctor Aperribay, Pablo Eguía

2291

(continued on Part B)

This volume cannot be sold separately by Parts B, C

1974-9821(201110)4:5;1-T

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