基于天体系统粒子群算法的异步电机参数辨识-Journalof.PDF

第29 卷第9期 东 北 大 学 学 报 ( 自 然 科 学 版 ) Vol29, No. 9 200 8 年 9 月 Journal of Northeastern U niversity( Natural cience) ep. 2 0 0 8 1 1 1 2 李 丹, 高立群 , 黄 越 , 王 珂 ( 1. , 110004; 2. , 110000) : (P O) , ( C P O) C P O , , , , , C P O , C P O GA P O : ; ; ; ; : TP 18 : A : 1005-3026( 2008) 09- 1245-04 Induction Motor Parameter Identification Based on Celestial System Particle Swarm Optimization Algorithm 1 1 1 2 L I Dan , GA O L i-qun , H UA N G Yue , WAN G K e ( 1. chool of Information cience Engineering, Nort heastern University, henyang 110004, China; 2. Liaoning Electric Power Company Limited, henyang 110000, China. Correspondent : LI Dan, E-mail: neulidan @ 163. com) Abstract: Aiming at the problem that the particle sw arm optimization( P O) algorithm tends to precocious convergence, a new algorithm of celestial system particle swarm optimization ( C P O) is presented. With reference to the celestial system model in astronomy , the C P O algorithm divides the population into multiple independent celestial systems of w hich each and every one orbits in space in accordance w ith its ow n rules. T he chaotic optimization is introduced in the later half of the algorithm to get the globe optimum solution. The C P O algorithm was applied to the identification of induction motor parameters, and the simulation results showed that it has higher identifiability parameters than GA and P O algorithms. Key words: celestial system; particle sw arm optimization; induction motor; parameter identification; chaotic optimization , ,