Parameter identification of induction motors using Ant Colony Optimization

In this paper, the ant colony optimization (ACO) is introduced and applied to the parameter identification of an induction motor for vector control. The error between the actual stator current output of an induction motor and the stator current output of the model is used as the criterion to correct the model parameters, so as to identify all the parameters of an induction motor. Digital simulations are conducted on speed-varying operation with no load The ACO is compared with the genetic algorithm (GA) and adaptive genetic algorithm (AGA). Consequently, the ACO is shown to acquire more precise parameter values and need much less computing time than the GA and AGA.