Hybrid optimization algorithm for parameter estimation of poly-phase induction motors with experimental verification

The estimated parameters accuracy of poly-phase induction motors is crucial for effective performance prediction and/or control in various manufacturing applications. This study investigates hybrid algorithm between particle swarm optimization and Jaya optimization algorithms for finding the optimal parameters estimation of poly-phase induction motors. It is carried out using the manufacturer's operation characteristics on two poly-phase induction motors. Numerical results show the capability of the proposed hybrid optimization algorithm. The proposed algorithm has competitive performance compared with conventional algorithms as well as with differential evolution and genetic algorithms. Experimental verifications are carried out on three-phase and six-phase induction motors. Also, it emulates the closeness between experimental and estimated parameters with fast convergence compared to other algorithms. Also, the results reflect the high robustness of the proposed algorithm compared with other algorithms for varied iteration numbers, population size and convergence.