基于SAPSO算法的异步电动机参数辨识

异步电动机等效电路参数的准确辨识对电动机的控制具有重要作用,同时,等效电路参数的变化可以反映电动机的运行状态,故参数辨识也被运用到电机故障诊断中。将现代最优化算法应用到三相异步电动机的等效电路参数辨识中。通过将粒子群优化算法(PSO)和模拟退火算法(SA)相结合,可以准确有效地对异步电动机的6个等效参数进行辨识,与遗传算法相比,SAPSO算法易于实现且收敛速度快。算法采用考虑铁耗的异步电机dq坐标系下的模型来实现,将温度对电阻参数的影响考虑在内。通过算例证明了算法能够有效地对电机参数进行辨识及跟踪电阻的变化。

Parameter Identification of Asynchronous Motor Based onSAPSO Algorithm

The accurate identification of the equivalent circuit parameters of the asynchronous motor played an important role in motor control. At the same time, the variation of the equivalent circuit parameters could reflect the running state of the motor, so the parameter identification was also applied to the motor fault diagnosis. The modern optimization algorithm was applied to the equivalent circuit parameter identification of threephase asynchronous motor. By combining particle swarm optimization PSO algorithm with simulated annealing (SA) algorithm, six equivalent parameters of asynchronous motor could be identified accurately and effectively. Compared with genetic algorithm, the proposed algorithm was easier to implement and converged more rapidly. The algorithm was implemented by using the model in dqcoordinate system of asynchronous motor considering iron loss. Taking into account the influence of temperature on the resistance parameters, the algorithm was proved to be effective in identifying the motor parameters and tracking resistance changes through examples.