Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors
Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors
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DOI：
10.1016/j.engappai.2007.10.002
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发表时间：
2008-10
期刊：
影响因子：
--
通讯作者：
Li Liu;Wenxin Liu;D. Cartes
中科院分区：
文献类型：
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作者：
Li Liu;Wenxin Liu;D. Cartes
大功率永磁同步电动机（PMSM）的高性能应用日益增多。具有准确参数的永磁同步电机模型对于精确控制系统的设计具有重要意义。由于电机动力学固有的非线性，在电机运行过程中获取这些参数是一项具有挑战性的任务。提出了一种基于粒子群算法的模型参数智能识别方法。粒子群算法是一种基于随机搜索的智能计算方法，是解决这一复杂工程问题的一种通用而有效的工具。通过仿真和实验验证了该方法在永磁同步电机模型参数识别中的有效性。具体地说，定子电阻和负载转矩扰动在这个永磁同步电机应用中被识别。虽然提出了永磁同步电机，但该方法一般适用于其他类型的电动机，以及其他具有非线性模型结构的动态系统。
The high-performance application of high-power permanent magnet synchronous motors (PMSM) is increasing. PMSM models with accurate parameters are significant for precise control system designs. Acquisition of these parameters during motor operation is a challenging task due to the inherent nonlinearity of motor dynamics. This paper proposes an intelligent model parameter identification method using particle swarm optimization (PSO). PSO, an intelligent computational method based on stochastic search, is shown to be a versatile and efficient tool for this complicated engineering problem. Through both simulation and experiment, this paper verifies the effectiveness of the proposed method in identification of PMSM model parameters. Specifically, stator resistance and load torque disturbance are identified in this PMSM application. Though PMSM is presented, the method is generally applicable to other types of electrical motors, as well as other dynamic systems with nonlinear model structure.