Application of intelligent search techniques to identify single-phase induction motor's parameters

This paper presents an intelligent approach to identify parameters of single-phase induction motors. Because of the complication of space-phasor equations describing its dynamic behaviors, the parameters of single-phase induction motors could be roughly estimated via conventional tests based on the steady-state analysis. Therefore, they may cause inaccurate estimation. In this paper, some efficient intelligent search techniques, i.e. (i) Genetic Algorithm (GA), (ii) Particle Swarm Optimization (PSO), and Adaptive Tabu Search (ATS), are employed to demonstrate the intelligent identification. The effectiveness of the proposed approach is assured when comparing with the conventional parameter tests.

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