基于改进社交网络搜索算法的永磁同步电机参数辨识

田德; 吴晓璇; 苏怡; 孟慧雯

doi:10.19912/j.0254-0096.tynxb.2024-1852

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (4) : 604-611. DOI: 10.19912/j.0254-0096.tynxb.2024-1852

基于改进社交网络搜索算法的永磁同步电机参数辨识

田德, 吴晓璇, 苏怡, 孟慧雯

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PARAMETER IDENTIFICATION FOR PERMANENT MAGNET SYNCHRONOUS MOTOR BASED ON IMPROVED SOCIAL NETWORK SEARCH ALGORITHM

Tian De, Wu Xiaoxuan, Su Yi, Meng Huiwen

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文章历史 +

摘要

针对传统永磁同步电机参数辨识方法中存在辨识速度慢、精度低、易陷入局部最优等缺点,提出一种基于改进社交网络搜索算法（ISNS）的永磁同步电机参数辨识方法。在SNS算法基础上引入Tent混沌初始化策略、莱维飞行策略、黄金正弦策略以及高斯变异策略,并基于测试函数验证所提ISNS算法的优越性。仿真结果表明,与其他算法相比,所提出的基于ISNS算法的参数辨识方法辨识速度更快,精度更高,对永磁同步电机的定子电阻、d轴电感、q轴电感以及磁链辨识精度分别为99.996%、99.934%、99.947%、99.962%。

Abstract

Aiming at the shortcomings of traditional PMSM parameter identification methods, including slow identification speed, low precision, and proneness to fall into local optimality, a PMSM parameter identification approach based on the improved social network search （ISNS） algorithm in this paper is proposed. The ISNS algorithm incorporate the Tent chaotic initialization strategy, Levy flight strategy, golden sine strategy, and Gaussian mutation strategy in the fundamental SNS algorithm. With reference to the test function, the superiority of the proposed ISNS algorithm is validated.The simulation results show that compared with other optimization algorithms, the proposed PMSM parameter identification method based on ISNS algorithm is capable of obtaining more accurate, high-quality, and stable identification outcomes.The identification accuracy of stator resistance, d-axis inductance, q-axis inductance, and flux linkage of PMSM is 99.996%, 99.934%, 99.947% and 99.962%, respectively.

导出引用

田德, 吴晓璇, 苏怡, 孟慧雯. 基于改进社交网络搜索算法的永磁同步电机参数辨识[J]. 太阳能学报. 2025, 46(4): 604-611 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1852

Tian De, Wu Xiaoxuan, Su Yi, Meng Huiwen. PARAMETER IDENTIFICATION FOR PERMANENT MAGNET SYNCHRONOUS MOTOR BASED ON IMPROVED SOCIAL NETWORK SEARCH ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 604-611 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1852

中图分类号： TM351

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