Genetic Algorithm-Based Induction Machine Characterization Procedure With Application to Maximum Torque Per Amp Control

Abstract

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.