Parameter estimation of an induction machine using advanced particle swarm optimisation algorithms

Abstract

This study proposes a new application of two advanced particle swarm optimisation (PSO) algorithms for parameter estimation of an induction machine (IM). The inertia weight, cognitive and social parameters and two independent random sequences are the main parameters of the standard PSO algorithm which affect the search characteristics, convergence capability and solution quality in a particular application. Two advanced PSO algorithms, known as the dynamic particle swarm optimisation (dynamic PSO) and chaos PSO algorithms modify those parameters to improve the performance of the standard PSO algorithm. The algorithms use the measurements of the three-phase stator currents, voltages and the speed of the IM as the inputs to the parameter estimator. The experimental results obtained compare the estimated parameters with the IM 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, dynamic PSO and chaos PSO algorithms. The results show that the dynamic PSO and chaos PSO algorithms are better than the standard PSO algorithm and GA for parameter estimation of the IM.