Online Parameter Identification of PMSM Based on LAWPSO

被引：0

作者：

Cheng, Yong ^{[1
]}

Zhao, Mengying ^{[1
]}

Liu, Qian ^{[1
]}

机构：

[1] Xian Univ Sci & Technol, Sch Elect & Control Engn, Xian, Peoples R China

来源：

PROCEEDINGS OF 2020 IEEE 4TH INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2020) | 2020年

关键词：

large-scale network; survivability association; set pair analysis theory; complex system;

D O I：

10.1109/itnec48623.2020.9084925

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the parameter identification of permanent magnet synchronous motor (PMSM), traditional strategies are complex and low efficiency. In the paper, a logistic function-based adaptive inertia weight particle swarm optimization algorithm is adopted to achieve online identification of electrical parameters and mechanical parameters of PMSM. The experimental results show the algorithm can accurately identify five motor parameters online, which have attributes of faster convergence and accuracy. Finally, experiments verify effectiveness and feasibility of the algorithm.

引用

页码：2188 / 2192

页数：5

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