By Topic

Nonlinear neural-network modeling of an induction machine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Seung-Ill Moon ; Sch. of Electr. Eng., Seoul Nat. Univ., South Korea ; Keyhani, A. ; Pillutla, S.

Presents an approach to identify the nonlinear model of an induction machine. The free acceleration test is performed on a 5-HP induction machine, and the resulting stator voltages, stator currents and rotor angular velocity are measured. Using the maximum likelihood (ML) algorithm, the parameter sets of the nonlinear model at various operating conditions are estimated. Then the nonlinear model parameters are represented by feedforward neural networks (FNNs). For validation, the simulated responses of the identified model using the measured and the simulated input patterns for the FNN models are performed. The identified model can be utilized for power system transient stability analysis and for online computer controlled electric drives

Published in:

Control Systems Technology, IEEE Transactions on  (Volume:7 ,  Issue: 2 )