Parameter Estimation of an Induction Machine using a Dynamic Particle Swarm Optimization Algorithm

This paper proposes a new application of a dynamic particle swarm optimization (PSO) algorithm for parameter estimation of an induction machine. The dynamic PSO is one of the PSO variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the PSO as linear time-varying parameters. The acceleration coefficients are varied during the evolution process of the PSO to improve the global search capability of particles in the early stage of the optimization process and direct the global optima at the end stage. The algorithm uses the measurements of the three-phase stator currents, voltages, and the speed of the induction machine as the inputs to the parameter estimator. The experimental results obtained compare the estimated parameters with the induction machine parameters achieved using traditional tests such as the dc, no-load, and locked-rotor tests. There is also a comparison of the solution quality between a genetic algorithm (GA), standard PSO, and dynamic PSO. The results show that the dynamic PSO is better than the standard PSO and GA for parameter estimation of the induction machine.