On line parameter identification of an induction motor, using improved particle swarm optimization

被引：0
|
作者：
Chen Guangyi [1 ]
Wei, Guo [1 ,2 ]
Huang Kaisheng [2 ]
机构：
[1] Foshan Univ, Dept Automat, Foshan 528200, Peoples R China
[2] Guangdong Univ Technol, Fac Automat, Guangzhou 510090, Peoples R China
来源：
PROCEEDINGS OF THE 26TH CHINESE CONTROL CONFERENCE, VOL 5 | 2007年
关键词：
improved particle swarm optimization; induction motor; parameter identification; saturable model;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The paper introduces a improved particle swarm optimization (IPSO) algorithm with dynamic inertia weight and applies this method to parameter identification of induction machine including the effects of saturation. The machine dynamics can be presented as a set of time-varying differential equations with machine saturated inductances modeled by nonlinear functions of exciting. current ([9]). Based on the data acquired from the 1.1 kw induction motor, a comparison between the real parameters response with that determined by the proposed algorithm have been presented, and the result of identification using the GA(genetic algorithm) and standard particle swarm optimization algorithm have also been provided. The results show that the performance of the IPSO is better than other techniques. It is concluded that IPSO is a effective algorithm for parameters identification.
引用
收藏
页码：745 / +
页数：3
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