Parameter estimation of a single-phase induction machine using a dynamic particle swarm optimization algorithm

Duy Huynh, Bach Dinh, Mathew Walter Dunnigan, Thu Nguyen, Nam Le

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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 a dynamic particle swarm optimization (Dynamic PSO) algorithm. The dynamic PSO algorithm modifies 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 and dynamic PSO algorithms. The results show that the dynamic PSO algorithm is better than the standard PSO algorithm for parameter estimation of the SPIM.
Original languageEnglish
Title of host publicationPower Engineering and Automation Conference (PEAM), 2011 IEEE (Volume:3 )
PublisherIEEE
Pages183-186
Number of pages4
ISBN (Print)978-1-4244-9691-4
DOIs
Publication statusPublished - 2011

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