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基于改进粒子群优化算法的永磁同步电机传递函数识别

发表时间:2017-01-23  浏览量:1583  下载量:333
全部作者: 陶之雨,张波,郝跃红,崔家瑞,胡广大
作者单位: 北京科技大学自动化学院;中国电力科学研究院配电研究所
摘 要: 在工程应用中,针对提高永磁同步电机(permanent magnet synchronous motors,PMSM)参数识别的准确度问题,提出改进适应度函数的粒子群优化(particle swarm optimization,PSO)算法。首先建立包含电流控制和空间电压矢量调制的传递函数模型,然后对PMSM施加多频率速度正弦信号,利用传统PSO算法拟合实际速度曲线的幅值、频率和初相位,根据第一次拟合结果构造权重函数,提出改进的粒子群优化(improved particle swarm optimization,IPSO)算法,进行第二次拟合,得到该传递函数模型在不同频率下的频率特性,并利用列维(Levy)算法求出传递函数的参数。最后,利用TMS320F2812平台进行验证。实验结果表明,所提出的IPSO算法比传统PSO算法在拟合电机转速正弦信号时误差更小,结果更稳定,辨识出的传递函数与电机的静态、动态特性一致,验证了该方法辨识结果的准确性和有效性。
关 键 词: 电机与电器;频率响应;粒子群优化;列维算法;传递函数
Title: Identification of transfer function of permanent magnet synchronous motor based on improved particle swarm optimization algorithm
Author: TAO Zhiyu, ZHANG Bo, HAO Yuehong, CUI Jiarui, HU Guangda
Organization: School of Automation and Electrical Engineering, University of Science and Technology Beijing; Power Distribution Department, China Electric Power Research Institute
Abstract: In order to improve the accuracy of identification of permanent magnet synchronous motor (PMSM) parameters in engineering applications, a particle swarm optimization (PSO) algorithm with improved fitness function is proposed. Firstly, the transfer function model including current control and space voltage vector modulation is established. Then the multi-frequency velocity sinusoidal signals are injected into the PMSM. The amplitude, frequency and initial phase of the actual velocity curve are fitted by the traditional PSO algorithm. The weight functions at different frequency are constructed according to the fitting results. Then the improved particle swarm optimization (IPSO) algorithm is proposed. The frequency characteristics of the transfer function model at different frequencies are obtained. Finally, the platform is verified by TMS320F2812. The experimental results show that the proposed IPSO algorithm has smaller error values and more stable results than the traditional PSO algorithm in fitting with the sinusoidal signal of the motor speed. The transfer function identified is consistent with both the static and dynamic characteristics of the motor.
Key words: electrical machines and apparatus; frequency response; particle swarm optimization; Levy method; transfer function
发表期数: 2017年1月第2期
引用格式: 陶之雨,张波,郝跃红,等. 基于改进粒子群优化算法的永磁同步电机传递函数识别[J]. 中国科技论文在线精品论文,2017,10(2):149-157.
 
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