Artificial immune system for parameter estimation of induction motor

The conventional method of determining the steady state equivalent circuit parameters of an induction motor utilizes no-load and locked rotor tests. The values so determined may not give satisfactory results for a wide variation in the operating conditions. This paper presents a new algorithm based on the immune algorithm (IA) to optimize the parameters of three different induction motor models from the manufacturer data and/or from the tests. The non-linear equations of induction motor to be solved for the parameter estimation are formulated as a minimization problem. The equivalent circuit parameters are obtained as the solution of minimization of a normalized square error function of the difference between estimated and manufacturer data. The proposed IA approach has been tested and examined on two different sample motors. The proposed approach results have been compared with the classical parameter estimation technique and the genetic algorithm (GA). The results show the effectiveness and the robustness of the proposed approach.