Parameter Identification of Induction Motor Double-Cage Model Using Exchange Market Algorithm

Abstract

A newly established method, called the Exchange Market Algorithm (EMA), is provided to identify the
parameters of the three-phase induction motor from the manufacturing information. For the suggested technique, input
data such as rated output power, beginning torque, breakdown torque, complete load torque, power factor and
effectiveness at rated output energy are needed. Using the squared error between the identified and the manufacturing
data as the objective function, the parameter identification problem is assigned to an optimization process where the
parameters of the double cage model are identified to minimize the defined objective function. The EMA algorithm is
used to iteratively minimize the objective function.Two sample engines tested the EMA strategy. The achievement of the
EMA algorithm is contrasted with the technique of classical parameter determination (CPD) and other techniques of
optimization, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Simulation findings show the
ability of the suggested method to capture the real values of the machine parameters and the dominance of the outcomes
obtained using the EMA over other methods to parameter identification.