改进综合学习粒子群算法的PMSM参数辨识

发布时间：2018-07-01 20:54

本文选题：参数辨识 + 永磁同步电机　；参考：《电机与控制学报》2015年01期

【摘要】：为了解决永磁同步电机(permanent magnet synchronous motor,PMSM)多参数辨识问题,提出一种改进综合学习粒子群优化算法。针对综合学习粒子群算法后期搜索效率低的缺陷,所提算法引入反映粒子状态的增长率算子,通过该算子动态调整综合学习粒子群算法的关键参数,并根据增长率算子判断种群中粒子所处状态,对处于停滞状态的粒子实施高斯扰动,使粒子能在解空间中进行有效搜索。将所提改进算法应用于永磁同步电机多参数辨识,该方法仅需采样电机的定子电流、电压和转速信号。实验结果表明,改进综合学习粒子群优化方法能够准确地辨识PMSM的定子电阻、d轴和q轴电感和永磁体磁链等参数。
[Abstract]:In order to solve the problem of multi-parameter identification of permanent magnet synchronous motor (permanent magnet synchronous motor PMSM), an improved synthetic learning particle swarm optimization algorithm is proposed. Aiming at the defect of low searching efficiency in the late stage of the synthetic learning particle swarm optimization algorithm, the proposed algorithm introduces the growth rate operator which reflects the particle state, and dynamically adjusts the key parameters of the synthetic learning particle swarm optimization algorithm. According to the growth rate operator, the state of the particles in the population is judged, and the particles in the stationary state are disturbed by Gao Si, so that the particles can be searched effectively in the solution space. The proposed improved algorithm is applied to the multi-parameter identification of PMSM. The proposed method only needs to sample the stator current, voltage and rotational speed signals of the PMSM. The experimental results show that the improved synthetic learning particle swarm optimization method can accurately identify the stator resistance d axis and Q axis inductance and permanent magnet flux of PMSM.
【作者单位】：湖南大学电气与信息工程学院;湖南工程学院电气信息学院;湖南科技大学信息与电气工程学院;
【基金】：国家自然科学基金(61174140) 中国博士后面上基金(2013M540628) 湖南省重点实验室开放基金(10K017)
【分类号】：TM341

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