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Abstract

The current paper presents an adaptive system identification/parameter estimation algorithm for a three-phase cage induction motor based on particle swarm optimization (PSO). The performance of the proposed algorithm is emphasized by comparing its results with those of the well-known stochastic optimization techniques of genetic algorithm (GA) and simulated annealing (SA) for the benchmark application with six unknown parameters to identify. The dynamic inertia-weighted PSO algorithm significantly outperformed the GA and SA techniques. The achievement of the presented methodology in confronting a rather complicated non-linear dynamic engineering application underlines the ability of the algorithm to be used for a range of real-world problems, and moreover justifies and motivates the development of more advanced techniques.

Keywords

particle swarm optimization induction machine parameter identification swarm intelligence evolutionary algorithms

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Parameter identification of a cage induction motor using particle swarm optimization

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