By Topic

Genetic algorithm-based induction machine characterization procedure with application to maximum torque per amp control

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
$33 $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

2 Author(s)
C. Kwon ; Dept. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA ; S. D. Sudhoff

There has been considerable research in developing improved induction motor models. One recently developed model simultaneously includes magnetizing path saturation, leakage saturation, and a highly flexible transfer function approach to represent the rotor circuits. This alternate QD model (AQDM) is also computationally efficient in that it is noniterative at each time step. It is considerably more accurate than the classical QD model (CQDM). However, the suggested characterization procedure is complicated and time consuming. This paper proposes a new characterization procedure for the AQDM. The proposed procedure employs a genetic algorithm (GA) as an optimization engine to identify the parameters of the AQDM by simultaneously considering per-phase fundamental frequency impedance and stand-still frequency response (SSFR) impedance. The proposed approach is validated by comparison of current ripple predictions (to validate high-frequency model behavior) and by application to maximum torque per ampere control design (to validate fundamental frequency model behavior). The proposed procedure is significantly more straightforward than the other published method of obtaining AQDM parameters.

Published in:

IEEE Transactions on Energy Conversion  (Volume:21 ,  Issue: 2 )