international journal of marketing and technology (issn ......

International Journal of Marketing and Technology

(ISSN: 2249-1058)

CONTENTS Sr.

No. TITLE & NAME OF THE AUTHOR (S) Page

No.

1 Threshold effects in the relationship between oil revenues and growth in oil exporting countries.

Mohsen Mehrara and Seyed Mohammad Hossein Sadr 1-19

2 Management of E-Waste- Black to Green.

Dr. D. Sudharani Ravindran, Hari Sundar. G. Ram and M. Sathish 20-33

3 Customer Retention In Nationalised Banks In Erode of Tamilnadu.

Dr. S. M. Venkatachalam and Ms. R. Anuradha 34-67

4 The Use of Visual Cues and Metaphors in Advertising.

Vikram Kapoor 68-84

5 Library Administration Vs Management.

J. B. Parmar and A. B. Parmar 85-98

6 Crisis Management.

Dr. (Mrs.) A. Kumudha and Mr. K. Prabakar 99-110

7 Organizational Competency Management: A Competence Performance Approach.

Dr. A. Kumuudha, K. Prabakar and Benny J. Godwin 111-121

8 Impact of Just-In-Time Production Towards Global Competitiveness Through Competency

Management.

Dr. A. Kumuudha, Benny J. Godwin and K. Prabakar

122-133

9 Data Mining Issues and Key to Success.

Deep Mala Sihint 134-149

10 Synthesis of MCM-41 via different routes.

Ranajyoti Das and Bharat Modhera 150-171

11 Green Marketing – A Bridge Between Consumerism And Conservation.

Richa Arora and Nitin R. Mahankale 172-184

12 An Empirical Study On The Effect of Payment Mechanism For Purchasing Intention – The Moderating

Effect of Consumer Involvement in Selected Product Categories in Vadodara.

CS. Ashutosh A. Sandhe, Dr. Amit R. Pandya and DR. Abhijeet Chatterjee

185-213

13 A New approach to Sensor less vector control of induction motors.

G. Srinivas and Dr. S. Tarakalyani 214-228

14 Eyes Bamboozling the Mind: Use of Optical Illusion in Advertising.

Vikram Kapoor 229-247

15 Perception Of Employees Towards Performance Appraisal In Insurance Sector.

Rita Goyal 248-276

16 Process Improvement of an Organization Enhancing Better Quality System – Applying TQM.

Bhupender Singh, O. P. Mishra and Surender Singh 277-289

IJMT Volume 1, Issue 7 ISSN: 2249-1058 __________________________________________________________

A Monthly Double-Blind Peer Reviewed Refereed Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage, India as well as in Cabell’s Directories of Publishing Opportunities, U.S.A.

International Journal of Marketing and Technology http://www.ijmra.us

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Chief Patron Dr. JOSE G. VARGAS-HERNANDEZ

Member of the National System of Researchers, Mexico

Research professor at University Center of Economic and Managerial Sciences,

University of Guadalajara

Director of Mass Media at Ayuntamiento de Cd. Guzman

Ex. director of Centro de Capacitacion y Adiestramiento

Patron Dr. Mohammad Reza Noruzi

PhD: Public Administration, Public Sector Policy Making Management,

Tarbiat Modarres University, Tehran, Iran

Faculty of Economics and Management, Tarbiat Modarres University, Tehran, Iran

Young Researchers' Club Member, Islamic Azad University, Bonab, Iran

Chief Advisors Dr. NAGENDRA. S. Senior Asst. Professor,

Department of MBA, Mangalore Institute of Technology and Engineering, Moodabidri

Dr. SUNIL KUMAR MISHRA Associate Professor,

Dronacharya College of Engineering, Gurgaon, INDIA

Mr. GARRY TAN WEI HAN Lecturer and Chairperson (Centre for Business and Management),

Department of Marketing, University Tunku Abdul Rahman, MALAYSIA

MS. R. KAVITHA

Assistant Professor,

Aloysius Institute of Management and Information, Mangalore, INDIA

Dr. A. JUSTIN DIRAVIAM

Assistant Professor,

Dept. of Computer Science and Engineering, Sardar Raja College of Engineering,

Alangulam Tirunelveli, TAMIL NADU, INDIA




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Editorial Board

Dr. CRAIG E. REESE Professor, School of Business, St. Thomas University, Miami Gardens

Dr. S. N. TAKALIKAR Principal, St. Johns Institute of Engineering, PALGHAR (M.S.)

Dr. RAMPRATAP SINGH Professor, Bangalore Institute of International Management, KARNATAKA

Dr. P. MALYADRI Principal, Government Degree College, Osmania University, TANDUR

Dr. Y. LOKESWARA CHOUDARY Asst. Professor Cum, SRM B-School, SRM University, CHENNAI

Prof. Dr. TEKI SURAYYA Professor, Adikavi Nannaya University, ANDHRA PRADESH, INDIA

Dr. T. DULABABU Principal, The Oxford College of Business Management, BANGALORE

Dr. A. ARUL LAWRENCE SELVAKUMAR Professor, Adhiparasakthi Engineering College, MELMARAVATHUR, TN

Dr. S. D. SURYAWANSHI

Lecturer, College of Engineering Pune, SHIVAJINAGAR

Dr. S. KALIYAMOORTHY Professor & Director, Alagappa Institute of Management, KARAIKUDI

Prof S. R. BADRINARAYAN

Sinhgad Institute for Management & Computer Applications, PUNE

Mr. GURSEL ILIPINAR ESADE Business School, Department of Marketing, SPAIN

Mr. ZEESHAN AHMED Software Research Eng, Department of Bioinformatics, GERMANY




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2011

Mr. SANJAY ASATI Dept of ME, M. Patel Institute of Engg. & Tech., GONDIA(M.S.)

Mr. G. Y. KUDALE N.M.D. College of Management and Research, GONDIA(M.S.)

Editorial Advisory Board

Dr. MANJIT DAS Assistant Professor, Deptt. of Economics, M.C.College, ASSAM

Dr. ROLI PRADHAN Maulana Azad National Institute of Technology, BHOPAL

Dr. N. KAVITHA Assistant Professor, Department of Management, Mekelle University, ETHIOPIA

Prof C. M. MARAN Assistant Professor (Senior), VIT Business School, TAMIL NADU

Dr. RAJIV KHOSLA Associate Professor and Head, Chandigarh Business School, MOHALI

Dr. S. K. SINGH Asst. Professor, R. D. Foundation Group of Institutions, MODINAGAR

Dr. (Mrs.) MANISHA N. PALIWAL Associate Professor, Sinhgad Institute of Management, PUNE

Dr. (Mrs.) ARCHANA ARJUN GHATULE Director, SPSPM, SKN Sinhgad Business School, MAHARASHTRA

Dr. NEELAM RANI DHANDA Associate Professor, Department of Commerce, kuk, HARYANA

Dr. FARAH NAAZ GAURI Associate Professor, Department of Commerce, Dr. Babasaheb Ambedkar Marathwada

University, AURANGABAD




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Prof. Dr. BADAR ALAM IQBAL Associate Professor, Department of Commerce, Aligarh Muslim University, UP

Dr. CH. JAYASANKARAPRASAD Assistant Professor, Dept. of Business Management, Krishna University, A. P., INDIA

Technical Advisors Mr. Vishal Verma

Lecturer, Department of Computer Science, Ambala, INDIA

Mr. Ankit Jain Department of Chemical Engineering, NIT Karnataka, Mangalore, INDIA

Associate Editors Dr. SANJAY J. BHAYANI

Associate Professor ,Department of Business Management, RAJKOT, INDIA

MOID UDDIN AHMAD Assistant Professor, Jaipuria Institute of Management, NOIDA

Dr. SUNEEL ARORA Assistant Professor, G D Goenka World Institute, Lancaster University, NEW DELHI

Mr. P. PRABHU Assistant Professor, Alagappa University, KARAIKUDI

Mr. MANISH KUMAR Assistant Professor, DBIT, Deptt. Of MBA, DEHRADUN

Mrs. BABITA VERMA Assistant Professor, Bhilai Institute Of Technology, DURG

Ms. MONIKA BHATNAGAR Assistant Professor, Technocrat Institute of Technology, BHOPAL

Ms. SUPRIYA RAHEJA Assistant Professor, CSE Department of ITM University, GURGAON




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A New approach to Sensor less vector

control of induction motors

G. Srinivas

Assistant professor

Gitam University Hyderabad

Dr. S. Tarakalyani

Assoc Professor

J.N.T.U.H Hyderabad

Title

Author(s)




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Abstract:

Induction motors are used in variable speed drive applications with the development of vector

control technology. There are two forms of vector or field oriented control: direct field

orientation, which relies on direct measurement or estimation of the rotor flux, and indirect field

orientation, which utilizes an inherent slip relation. Though indirect field orientation essentially

uses the command (reference) rotor flux, some recent works using the actual rotor flux are

reported to achieve perfect decoupling Estimation rather than measurement of the rotor flux is an

alternative approach for both direct and indirect field orientation that has received considerable

attention. In this paper the rotor flux and speed control of induction motor is presented using a

novel technique in which the induction motor model in rotor reference frame is considered.

Controllers used for sensor less control of the drive. The estimation technique works well and the

sensor less speed control scheme can achieve fast transient response as good as that of the

induction motor with sensors and at the same time maintain a wide speed control range. This

paper presents work on flux and speed estimation of induction motor by considering the rotor

reference frame of the Induction motor using MATLAB / Simulink.

Index Terms— Induction motor, Kalman filter, Sensor less control, Simulink.

INTRODUCTION:

In a field oriented induction motor drive, the field flux and armature mmf are separately created

and controlled based on the vector coordinate transformations. These projections lead to a

structure similar to that of a DC machine control[1]-[3]. The field oriented control is used in

most of the induction motor drive applications in order to obtain high control performance, but it

needs motor flux position (rotor flux angle) information and utilizes AC excitation voltages for

the current regulation. Current regulation is provided with advanced feedback control methods

based on the current measurements taken at the output of excitation voltages supplied from

voltage source inverter (VSI). The rotor flux angle can be measured by using shaft sensor and

that information is utilized by field orientation scheme. However, as discussed in the current

study, sensor less control algorithms[5]-[8] eliminate the need for a shaft sensor. The induction




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machine drives without the speed sensor are attractive due to low cost and high reliability.

Therefore, flux and speed estimations have become particular issues of the field oriented control

in the recent years. The main advantages of speed sensor less induction motor drives are lower

cost, reduced size of the drive machine, elimination of sensor cable and increased reliability. As

it is stated, for implementing vector control, the determination of the rotor flux position is

required. Two basic approaches to determine the rotor flux position angle have evolved. One of

them is the direct field orientation which depends on direct measurement or estimation of rotor

flux magnitude and angle. From the feasibility point of view, implementation of the direct

method is difficult. The other one is the indirect field orientation which makes use of slip relation

in computing the angle of the rotor flux relative to rotor axis.

The Field Oriented Control (Vector Control) of Induction Machines:

Basically, field oriented control[4],[9] (FOC) is a method based on vector coordinates. The term

“vector” refers to the control technique that controls both the amplitude and the phase of AC

excitation voltage. Vector control is used for controllers that maintain 90° spatial orientation

between the two field components which are d and q co-ordinates of a time invariant system. In a

field oriented induction motor drive, the field flux and armature mmf are separately created and

controlled based on the vector coordinate transformations. These projections lead to a structure

similar to that of a DC machine control. The field oriented control is used in most of the

induction motor drive applications in order to obtain high control performance, but it needs

motor flux position (rotor flux angle) information and utilizes AC excitation voltages for the

current regulation. Current regulation is provided with advanced feedback control methods based

on the current measurements taken at the output of excitation voltages supplied from voltage

source inverter (VSI). The rotor flux angle can be measured by using shaft sensor and that

information is utilized by field orientation scheme. However, as discussed in the current study,

sensor less control algorithms eliminates the need for a shaft sensor. The induction machine

drives without the speed sensor are attractive due to low cost and high reliability. Therefore, flux

and speed estimations have become particular issues of the field oriented control in the recent

years. The main advantages of speed sensor less induction motor drives are lower cost, reduced

size of the drive machine, elimination of sensor cable and increased reliability. As it is stated,




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for implementing vector control, the determination of the rotor flux position is required. Two

basic approaches to determine the

rotor flux position angle have evolved. One of them is the direct field orientation which depends

on direct measurement or estimation of rotor flux magnitude and angle. From the feasibility

point of view, implementation of the direct method is difficult. The other one is the

indirect field orientation which makes use of slip relation in computing the angle of the rotor flux

relative to rotor axis.

Fig.2 Field orientation control of Induction Motor

Field orientation control of Induction Motor

Stator quadrature axis flux linkage

e

= ids/1+τrs

τrs=




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Stator direct axis current reference

Rotor flux position :

)dt

Sensor less Vector Control of Induction Machine:

To implement vector control, determination of the rotor flux position is required. Rotor speed or

position could be measured by a shaft sensor. Moreover, rotor flux position could be taken by

sensing the air-gap flux with the flux sensing coils. The main drawbacks of using speed/position

sensor are high cost, lower system reliability and special attention to noise. Such problems make

sensor less drives popular. The recent trend in field-oriented control [11]-[14] is towards

avoiding the use of speed sensors and using algorithms based on the terminal quantities of the

machine for the estimation of the fluxes. Different solutions for sensor less drives have been

proposed in the past few years. Saliency based fundamental or high frequency signal injection is

one of the flux and speed estimation techniques. A method involving modulation of the rotor

slots results in a salient rotor, and the saliency can be tracked by imposing a balanced, three-

phase, high-frequency set of harmonics from the inverter. An alternative method is to use

saliency caused by magnetic saturation the absolute rotor position can be detected. The

advantage of the saliency technique is that the saliency is not sensitive to actual motor

parameters the rotor speed[10] can be estimated through nonlinear observers alternatively, the

rotor speed can be considered as a parameter and estimated using recursive identification. The

latter method can also be augmented to include machine parameter estimation (inductances,

resistances, and time constants). These methods do not need to rely on harmonics or saliency,

and the hardware requirements are the same as for the digital implementation of vector control,

given that the estimation algorithm is not too complex. Their drawback is that the rotor speed

estimate will be inaccurate if the non-estimated machine parameters are not known..




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Induction motor modeling:

Fig.2 Induction motor block diagram

Fig.3 Speed and Estimator block

Fig.4 control and logic block




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Fig.5sensorless vector control induction motor Mat lab/Simulink model

Table.1

Ratings and parameters of induction motors

SIMULATION RESULTS:

Direct Torque Control of Induction machine is simulated on MATLAB or SIMULINK- platform

to study various aspects of the controller. This chapter discusses a realization of difference in

response of induction motor with regular input and the implementation of Direct Torque Control

(DTC) technique.




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In this Simulink block diagram the induction motor is fed from a balanced three phase supply.

For the induction motor block torque and speed estimation block is added in order to observe

response of an induction motor. The balance three phase supply is converted into the voltages of

the d-q axis components for the estimation of the motor parameters. In the fallowing block only

the subsystems of induction motor and torque & speed estimation block are presented for the

simplicity purpose. From the above simulation results it is observed that the induction motor

parameters can be estimated by simulating the induction motor model equations. Fig 6. shows

the plot between rotor speed versus time. Fig 7-9 shows the estimated value of the induction

motor. For all the results the initial time taken is 2 msec after that it comes to steady state.

Fig.6. rotor torque

Fig.7. stator currents direct axis and quadrature axis (Ids, iqs)




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Fig.8. rotor speed

Fig.9. Rotor currents direct axis and quadrature axis (idr, iqr)

Conclusion:

The estimation of rotor flux and speed of induction motor is presented by the rotor reference

frame model equations by using the flux and speed estimator. The dynamic response of sensor

less speed control is as fast as that of the machine with physical sensors. Sensor less speed

control scheme works for a wide speed control range.

Reference:

G. C. Verghese, and S. R. Sanders, “Observers for flux estimation in induction machines”,

IEEE Trans. on Indust. Elect., vol. 35, no. 1, pp. 85-94, 1988.

H. Kubota, K. Matsuse and T. Nakano, “DSP-based speed adaptive flux observer of

induction motor,” IEEE Trans. on Ind. Applicat., vol. 29, pp. 344-348, March/April 1993.




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2011

L. Harnefors and H.P. Nee, “Full-order observers for flux and parameter estimation of

induction motors,” EPE'97, vol. 3, Trondheim, 1997, pp. 375-381.

J.H. Kim, J.W. Choi and S.K. Sul, “Novel rotor-flux observer using observer characteristic

function in complex vector space for field-oriented induction motor drives,” IEEE Trans. Ind.

Applicat., vol. 38, pp. 1334-1343, Sept./Oct. 2002.

M. Hinkkanen, “Analysis and design of full-order flux observers for sensorless induction

motors,” IEEE Trans. Ind. Electron., vol. 51, pp. 1033- 1040, Oct. 2004.

F. Alonge, F. D’Ippolito, G. Giardina and T. Scaffidi, “Design and low cost implementation

of an optimal robust reduced order rotor flux observer for induction motor control,” IEEE

Transaction on Industrial Electronics, vol. 54, n.6, December 2007.

T. Du, P. Vas and F. Stronach, “Design and applications of extended observers for joint

states and parameter estimation in high-performance AC drives,” IEE Proc. Electric Power

Applicat., vol. 142, No. 2, pp. 71-78, March 1995.

M. Barut, S. Bogosyan, and M. Gokasan, “Speed-sensorless estimation for induction motors

using Extented Kalman Finters,” IEEE Trans. Ind. Electron., vol. 54, NO. 1, February 2007,

pp. 272-280.

S. Bogosyan, M. Barut, and M. Gokasan, “Braided extended Kalman filters for sensorless

estimation in induction motors at high-low/zero speed,”IET Control Theory Appl., vol. 1, no.

4, 2007, pp. 987-998.

L. Cai, Y. Zhang, Z. Zhang, C. Liu and Z. Lu, “Application of Genetic Algorithms in EKF

for Speed Estimation of an Inducion Motor, Proc. on IEEE Power Electronics Specialist

Conference, Vol. 1, 15-19 June 2003, pp. 345-349

F. Alonge, F. D'Ippolito and F.M. Raimondi, “Least squares and genetic algorithms for

parameters identification of induction motors,” Control Engineering Practice, June 2001, vol.

9/6, pp.647-657.

K. Rajashekara, A. Kawamura, K. Maytsuse, Sensorless Control ofAC Motor Drives – Speed

and Position Sensorless Operation, IEEE Press, 1996.

P. Vas, Sensorless Vector and Direct Torque Control, Oxford University Press, New York,

1998.

B. K. Bose, Modern Power Electronics and AC Drives, Prentice Hall PTR, Upper Saddle

River: 2002.

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