基于比例策略的多目标PSO的感应电机参数辨识

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

建立了感应电机多参数多目标辨识模型,提出了一种基于Pareto最优集和比例策略个体最优项的多目标粒子群算法对感应电机参数进行辨识。Pareto最优集不需要考虑各个目标的加权系数,避免了感应电机辨识目标系数选择的主观性,比例策略能更好地平衡从个体最优和全局最优学习经验的能力。通过在Matlab/Simulink中进行验证,结果证明该算法能提高感应电机参数的辨识精度,具有更好的性能。

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.