State estimation for induction machines using an neural network backpropagation technique

Author

D´Angel, M.F.S.V. ; Costa, Pyramo P., Jr.

Author_Institution

Graduate Program in Electr. Eng., Pontifical Catholic Univ. of Minas Gerais, Brazil

Volume

4

fYear

2000

fDate

2000

Firstpage

2613

Abstract

The paper presents a proposal for the implementation of speed and electromagnetic torque estimators in induction machines using a neural network backpropagation technique. Models of estimators based on the dynamic equations of the induction motor are developed and simulated in a digital computer. The results of the simulation are used to implement the neural network estimators. Finally, results of the comparative analysis of performance between the proposed estimators and those obtained from the dynamic model of the motor are presented

Keywords

asynchronous machines; backpropagation; neural nets; power system simulation; power system state estimation; comparative analysis; digital computer; dynamic equations; dynamic model; electromagnetic torque estimators; estimator models; induction machines; induction motor; neural network backpropagation technique; neural network estimators; state estimation; Backpropagation; Computational modeling; Electromagnetic induction; Equations; Induction machines; Induction motors; Neural networks; Proposals; State estimation; Torque;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 2000 IEEE International Conference on

Conference_Location

Nashville, TN

ISSN

1062-922X

Print_ISBN

0-7803-6583-6

Type

conf

DOI

10.1109/ICSMC.2000.884388

Filename

884388