模拟退火粒子群优化算法在异步电动机参数辨识中的应用

Parameter Identification of an Induction Motor Using Simulated Annealing-Particle Swarm Optimization Algorithm

分析了模拟退火算法和粒子群算法的优缺点,提出了一种利用两种算法优点的混合优化算法,并给出了详细应用步骤。利用异步电动机直接空载起动特性,对异步电动机动态数学模型参数进行了辨识,通过与其他几种算法辨识的结果进行比较,说明模拟退火粒子群优化算法有效地结合了模拟退火算法的全局寻优能力和粒子群算法的快速收敛的特点。

The paper analyzed the advantages and the disadvantages of simulated annealing algorithms and particle swarm optimization algorithms, and proposed the detailed application steps of a new mixed optimization algorithm using their advantages. Parameter identification of the induction motor dynamic model was done using no load starting of the motor. Compared to other algorithms, the simulation results of simulated annealing-particle swarm optimization algorithms （SA- PSO） are much better,which means SA-PSO has effectively combined the global optimization ability of simulated annealing and the fast searching ability of particle swarm optimization.