EFFICIENT PARAMETER ESTIMATION PROCEDURE USING SUNFLOWER OPTIMIZATION ALGORITHM FOR SIX-PHASE INDUCTION MOTOR

The accuracy of studying the performance of the six-phase induction motors (SPIMs) depends on the accurate estimation of the motor parameters. This article examines the performance evaluation of SPIMs among several optimization algorithms using parameter optimization. The competitive algorithms are differential evolution (DE), genetic algorithm (GA), Jaya optimization algorithm (JOA), particle swarm optimization (PSO), and sunflower optimization (SFO) algorithms. Parameter estimation is extracted from the performance curves based on manufacturer data. Laboratory verifications are performed on a SPIM modified from a three-phase induction motor. It also shows that using SFO gives convergence between measured and estimated parameters with small errors and fast response compared to many optimization algorithms. The statistical analysis of the results shows the effectiveness of the proposed SFO algorithm compared to other methods at different values of iterations.