采用改进粒子群算法的异步电机参数辨识

Parameter identification of asynchronous motor based on improved particle swarm optimization algorithm

为提高异步电机参数辨识精度,提出一种具有麻雀群预警机制的粒子群算法(SPSO)的异步电机参数辨识方法。利用麻雀群搜索算法中的随机预警机制对粒子群原始算法(PSO)搜索过程中进行随机扰动,避免了粒子群算法出现早熟。在Simulink中搭建三相异步电机模型,对该模型施加载荷,分别采用PSO算法和SPSO算法对异步电机参数进行辨识并对比,结果表明:SPSO辨识算法输出误差较小,被识别参数与实际值更接近。研究结论验证了SPSO算法在异步电机参数辨识中的有效性与优越性。

In order to improve the accuracy of asynchronous motor parameter identification,an asynchronous motor parameter identification method based on particle swarm optimization(SPSO)with sparrow swarm warning mechanism is proposed.The random early warning mechanism in the sparrow swarm search algorithm is used to randomly disturb the search process of the original particle swarm optimization(PSO)algorithm,which avoids the premature convergence of the PSO algorithm.The model of three-phase asynchronous motor is built in Simulink,and the load is applied to the model.The parameters of asynchronous motor are identified and compared by PSO algorithm and SPSO algorithm respectively.The results show that the output error of SPSO algorithm is small,and the identified parameters are closer to the actual values.The research conclusion verifies the effectiveness and superiority of the improved particle swarm optimization algorithm.