改进粒子群算法在异步电机静态参数识别中的应用  

Application of Improved PSO in Induction Motors Static Parameters Identification

作　　者：覃建波[1] 邱小华[1] 许宁[1] 宋湛华[1] 

机构地区：[1]广东工业大学,510006

出　　处：《电机技术》2008年第6期11-14,共4页Electrical Machinery Technology

摘　　要：提出一种新的粒子群协同优化算法(PSCO)。应用该粒子群协同优化算法实现异步电机静态参数辨识。在有噪声的情况下,该算法明显改善了标准粒子群算法和基于遗传的粒子群算法,在解决异步电机静态参数辨识问题时,存在识别参数准确性不高、辨识成功率低的问题。This paper introduced a new improved particle swarm collaborative optimization （PSCO） algorithm and applied this method to static parameter identification of induction motor. Compared with the standard particle swarm optimization algorithm and the particle swarm optimization based on genetic algorithm, It had obvious advantage. Emulation experiments demonstrated that the PSCO algorithm remarkably improved the accuracy of identification parameters and increased the success rate of identification results when the noise existed.

关 键 词：粒子群算法 异步电机 参数辨识 

分 类 号：O322[理学—一般力学与力学基础] TS210.3[理学—力学]