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Parameter estimation of induction motors using PWM inverters

IP.com Disclosure Number: IPCOM000128121D
Original Publication Date: 1999-Dec-31
Included in the Prior Art Database: 2005-Sep-15
Document File: 7 page(s) / 24K

Publishing Venue

Software Patent Institute

Related People

Evangelista, Dennis J: AUTHOR [+3]

Related Documents

http://theses.mit.edu:80/Dienst/UI/2.0/Describe/0018.mit.theses/1999-65: URL

Abstract

Induction motor parameter identification algorithms utilizing the DSP controlled 3 phase PWNI inverter constructed in [121 are discussed. A single stator voltage test is carried out with the machine's rotor standing still. The resulting transient is used to determine machine electrical parameters. Several algorithms for this are discussed, extending work discussed in [211, [34], and [35]. The final algorithm tested uses a radial basis function network to approximate a function mapping from a time lag space consisting of the dq transformed stator currents to a space of machine electrical parameters, This algorithm is attractive because it is very easy to perform, consisting of one nonlinear step and one linear step. The original goal for the hardware of [121 was to perform adaptive field-oriented control on small induction motors. Several observers and online flux estimation methods, as well as alternative algorithms including neural networks and genetic algorithms, were looked at over the course of this year. This background is provided in the appendix, in order to aid future work on this project. Thesis Supervisor: Steven B. Leeb Title: Associate Professor Thesis Supervisor: Steven R. Shaw Title: Graduate Research Assistant [3]

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 This record is the front matter from a document that appears on a server at MIT and is used through permission from MIT. See http://theses.mit.edu:80/Dienst/UI/2.0/Describe/0018.mit.theses/1999-65 for copyright details and for the full document in image form.

Parameter Estimation of Induction Motors using PWM Inverters

by

Dennis J. Evangelista
Submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Electrical Engineering and Computer

at the Massachusetts Institute of Technology

May 1999 [June 1999]

SIGNATURE OF author: [[signature omitted]]

Department of Electrical Engineering and Computer Science May 21, 1999
CERTIFIED BY: [[SIGNATURE OMITTED]]

Steven B. Leeb

Associate Professor Thesis Supervisor

Steven R. Shaw

Graduate Research Assistant Thesis Supervisor

ACCEPTED BY: [[SIGNATURE OMITTED]]

Arthur C. Smith Chairman, Department Committee on Graduate Students ARCHIVES MASSACHUSETTS INSTITUTE OF TECHNOLOGY LIBRARIES JUL 15 1999

Massachusetts Institute of Technology Page 1 Dec 31, 1999

Page 2 of 7

Parameter estimation of induction motors using PWM inverters

[2]

Parameter Estimation of Induction Motors using PWM

Inverters

by

Dennis J. Evangelista

Submitted to the Department of Electrical Engineering and Computer Science on May 21, 1999, in partial fulfillment of the requirements for the degrees of Bachelor of Science in Electrical Science and Engineering and Master of Engineering in Electrical Engineering and Computer Science

Abstract

Induction motor parameter identification algorithms utilizing the DSP controlled 3 phase PWNI inverter constructed in [121 are discussed. A single stator voltage test is carried out with the machine's rotor standing still. The resulting transient is used to determine machine electrical parameters. Several algorithms for this are discussed, extending work discussed in [211, [34], and [35]. The final algorithm tested uses a radial basis function network to approximate a function mapping from a time lag space consisting of the dq transformed stator currents to a space of machine electrical parameters, This algorithm is attractive because it is very easy to perform, consisting of one nonlinear step and one linear step.

The original goal for the hardware of [121 was to perform adaptive field-oriented control on small induction motors. Several observers and online flux estimation methods, as well as alternative algorithms including neural networks and genetic algorithms, were looked at over the course of this year. This background is provided in the appendix, in order to aid future work on this project.

Thesis Supervisor: Steven B. Leeb Title: Associate Professor Thesis Supervisor: Steven R. Shaw Title: Graduate Research Assistant

[3]

Acknowledgments

I owe a tremendous debt to my father, Bernie Evangelista. without him, I would not be where I am in life today, and this thesis is dedicated to his memory. Thanks, Dad, I hope I've made you, proud.

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