基于迭代式粒子群优化的永磁同步电机热网络模型参数辨识研究

Iterative particle swarm optimization based parameter identification of lumped-parameter thermal network for permanent magnet synchronous motors

针对集总参数热网络模型未知参数多、参数辨识收敛困难的问题,利用永磁同步电机在不同工况下的特性,提出迭代式粒子群优化辨识框架,用实验测量的电机温度场数据,以各节点估计温度与实测温度的均方误差作为目标函数,将并行优化转化为三步串行迭代优化,减少每一步优化变量数,缩小种群规模,避免陷入局部最优。应用于某额定功率70 kW电机,得到一般热阻和热容随温度变化的规律,电机损耗、绕组涡流系数和气隙热阻随转速变化的规律。台架实验表明,在综合驾驶工况下,以槽内绕组、端部绕组、永磁体、定子齿和定子轭的温度估计最大误差和平均误差作为评价指标,与实测结果以及传统的采用固定参数的集总参数模型相比,提出的模型精确度高,工况适应性好。

Addressing the problem of non-convergent parameter identification due to many unknown parameters,a frame of iterative particle swarm optimization was proposed to identify the parameters in three iterative steps.Using experimentally measured motor temperature field data,with the mean square error between the estimated and measured temperatures at each node as the objective function,parallel optimization was transformed into a three-step serial iterative optimization.The number of optimization variables were reduced at each step,the population size was shrunk,and falling into local optima was avoided.The frame was applied to a five-node lumped-parameter thermal network(LPTN)model of a motor with rated power at 70 kW.The identified parameters demonstrate the change of the ordinary thermal resistance and capacities with temperature,the change of the motor losses,the eddy-current coefficient of stator resistance and air gap thermal resistance with the motor speed.The identification performance is evaluated in terms of the maximum error and average error of temperature at the active-winding,end-winding,permanent magnet,stator tooth and stator yoke.Dynamometer experiments conducted under comprehensive operating conditions validate that,compared with experimental measurements and traditional lumped parameter models that adopt fixed parameter values,the proposed model gains higher accuracy and better adaptability to different operating conditions compared with the conventional LPTN model with fixed parameters.