Parameter Identification of Induction Motor by Using Cooperative-Coevolution and a Nonlinear Estimator

Abstract

Induction motors are one of the critical industrial drivers due to its simplicity, inexpensiveness, and high resistance. Such motors have a nonlinear model divided into two electrical and mechanical equations in terms of modeling. Knowing the values of electric parameters and mechanical moment of inertia is critically important for speed controlling and induction motors’ position. In many algorithms, electric parameters can be obtained by the locked rotor and unloaded tests, conducting these methods in laboratory would probably cost a lot of time and money. In this paper electrical parameters and moment of inertia are used approximately, without doing the above test by currents, voltages, and motor speed sampling in motor normal operation. This paper applies cooperative co-evolution method to remove certain costly tests that are required for induction motors. Two identification algorithms are suggested for all electrical parameters and moment of inertia. All inductances and resistances which are the two input parameters measured in electric equations using Cooperative-Coevolution algorithm. Mechanical model estimated the moment of inertia and load torque by using a nonlinear method based on Lyapunov. Computerized numerical simulations show that electric parameters, moment of inertia, and load torque were properly estimated by integrating the two smart and classic methods. The results show that the stator inductance error is about 1% and rotor inductance error is around 20%. Rotor and stator resistance error and self-Inductance is also less than one percent.