Parameter estimation of an induction machine using a chaos particle swarm optimization algorithm

This presentation proposes a new application of a chaos particle swarm optimization (PSO) algorithm for parameter estimation of an induction machine. A chaos PSO with a logistic map has been used for initializing random values of the estimated parameters, as well as the inertia weight in the velocity update equation of the PSO. This creates the best balance for the inertia weight during the evolution process of the PSO which results in the best convergence capability and search performance. Additionally, the algorithm has also been improved with regards to the diversity in the solution space through two independent chaotic random sequences. 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 chaos PSO. The results show that the chaos PSO is better than the GA and standard PSO for parameter estimation of the induction machine.