Parameters identification in induction motors following hyperplanes optimization

In this work, it is proposed a simple off-line identification algorithm for an induction motor (IM) based on a novel optimization scheme, called hyperplanes optimization. The main idea of the identification scheme is to convert the problem of parameters characterization to a finite dimensional optimization problem over a bounded set. The proposed approach relies on the information of a hard or soft startup of the motor, in order to identify all 7 IM parameters: stator and rotor leakage inductances, stator and rotor resistances, mutual inductance, mechanical inertia and friction coefficient. Finally, the hyperplanes optimization is extended using an iterative approximation to the optimal parameters, and compared to an stochastic search algorithm. Experimental results in 1 HP and 3 HP IM test-rigs show an accurate characterization with the proposed identification scheme, and validate the approach illustrated in this work.