改进粒子群算法在永磁同步电动机参数识别中的应用

Parameter Identification of Permanent Magnet Synchronous Motors Using Improved Particle Swarm Optimization

应用改进粒子群优化算法(IPSO)来识别异步起动永磁同步电动机起动时的动态模型参数。永磁同步电动机异步起动时的电机动态模型采用状态微分方程表示。与遗传算法(GA)和标准粒子群算法(SPSO)相比较,仿真试验表明改进粒子群算法明显提高了识别参数的准确性,同时表明改进粒子群算法能更好地识别永磁同步电动机起动时动态模型的参数。

This paper introduces an improved particle swarm optimization （IPSO） algorithm and applies this method to parameter identification of permanent magnet synchronous motors （PMSM）. The machine dynamics was presented as a set of time-varying differential equations with machine inductances modeled by nonlinear functions of exciting current. For comparison, the results of identification using the genetic algorithm （GA） and the standard particle swarm optimization （SPSO） were also provided. It is concluded that the IPSO algorithm remarkably improves the accuracy of identification parameters and IPSO is more effective for parameter identification of permanent magnet synchronous motors.