Study of IM Parameter Identification Using Multi-objective Particle Swarm Optimization with Proportional Guided Strategy

Abstract

Abstract: A multi-parameter and multi-objective identification model of induction motor was established, and a multi-objective particle swarm optimization based on Pareto set and all personal-best positions guided strategy was proposed and applied to the identification model. Not considerring the weighted coefficient of each objective, Pareto set is able to avoid subjective choice of the coefficients of multi-objective identification and proportion strategy with all personal-best positions guided could balance the learning ability from personal-best positions and global-best position. Having verified the performance on Matlab/Simulink, the results show that the proposed algorithm is able to improve parameter identification accuracy, and has a better performance.

Key words: particle swarm optimization, personal-best, induction motor, parameter identification, Pareto set

CLC Number:

TP18
TM85

Huang Song, Tian Na, Wang Yan, Ji Zhicheng. Study of IM Parameter Identification Using Multi-objective Particle Swarm Optimization with Proportional Guided Strategy[J]. Journal of System Simulation, 2016, 28(7): 1489-1496.

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