Advanced particle swarm optimisation algorithms for parameter estimation of a single-phase induction machine

This paper proposes a new parameter estimation approach for a single-phase induction machine (SPIM) whose parameters are usually obtained using several traditional techniques such as the DC, no-load, load and locked-rotor tests. The proposal is based on using two advanced particle swarm optimisation (PSO) algorithms. In the PSO algorithm, the inertia weight, cognitive and social parameters and two independent random sequences are the main parameters which affect the search characteristics and convergence capability, as well as the solution quality in each application. Two advanced PSO algorithms, known as the dynamic particle swarm optimisation (dynamic PSO) and the chaos particle swarm optimisation (chaos PSO) algorithms modify the algorithm parameters to improve the performance of the standard PSO algorithm. The algorithms use the experimental measurements of the currents and active powers in the SPIM main and auxiliary windings as the inputs to the parameter estimator. The experimental results obtained compare the estimated SPIM parameters with the SPIM parameters achieved using the traditional tests. There is also a comparison of the solution quality between the 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 for parameter estimation of the SPIM.