The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 27-374
Full Name: Constantin Apostoaia
Position: Associate Professor
Age: ON
Sex: Male
Address: 2200 169th Street, Hammond, IN 46323
Country: UNITED STATES
Tel: 1-219-989-2472 -extension 2268
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E-mail address: apostoai@calumet.purdue.edu
Other E-mails: capostoaia@yahoo.com
Title of the Paper: Flux, Torque, and Speed Estimators of an Induction Machine Drive
Authors as they appear in the Paper: Constantin Apostoaia,Zoltan Szekely,Donald Gray,Mariana Hentea
Email addresses of all the authors: apostoai@calumet.purdue.edu,szekelyz1@calumet.purdue.edu,dmmgray@comcast.net,mhentea@excelsior.edu
Number of paper pages: 11
Abstract: This paper presents the study of flux, torque and speed estimation of an induction machine drive. The d-q model of the induction machine based on space vector theory is used to determine the various matrices of the state space representation of the system. The modeling and simulation is carried out by using the MATLAB/Simulink software. Five estimators are analyzed using as inputs different machine voltage, current, or speed measurements. First, two estimators are derived by direct synthesis from state equations. Then, two of the major types of observers, a Luenberger observer and an optimal extended Kalman filter (EKF) are investigated. Finally a trained neural network is used to estimate the speed of the induction machine drive. The performances of all estimators are presented and good results were obtained for the Luenberger and the EKF, even in the presence of the system and measurements noise in the latter case.
Keywords: Induction Motor, Estimator, Luenberger Observer, Kalman Filter, Neural Network
EXTENSION of the file: .pdf
Special (Invited) Session: Feedback Signals Estimation of an Induction Machine Drive
Organizer of the Session: 591-099
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