文章基本信息

标题：Estimation of Stator Resistance and Rotor Flux Linkage in SPMSM Using CLPSO with Opposition-Based-Learning Strategy
本地全文：下载
作者：Jian He ; Zhao-Hua Liu
期刊名称：Journal of Control Science and Engineering
印刷版ISSN：1687-5249
电子版ISSN：1687-5257
出版年度：2016
卷号：2016
DOI：10.1155/2016/5781467
出版社：Hindawi Publishing Corporation
摘要：Electromagnetic parameters are important for controller design and condition monitoring of permanent magnet synchronous machine (PMSM) system. In this paper, an improved comprehensive learning particle swarm optimization (CLPSO) with opposition-based-learning (OBL) strategy is proposed for estimating stator resistance and rotor flux linkage in surface-mounted PMSM; the proposed method is referred to as CLPSO-OBL. In the CLPSO-OBL framework, an opposition-learning strategy is used for best particles reinforcement learning to improve the dynamic performance and global convergence ability of the CLPSO. The proposed parameter optimization not only retains the advantages of diversity in the CLPSO but also has inherited global exploration capability of the OBL. Then, the proposed method is applied to estimate the stator resistance and rotor flux linkage of surface-mounted PMSM. The experimental results show that the CLPSO-OBL has better performance in estimating winding resistance and PM flux compared to the existing peer PSOs. Furthermore, the proposed parameter estimation model and optimization method are simple and with good accuracy, fast convergence, and easy digital implementation.