基于AKF的永磁同步电机关联关系模型研究

Research on Incidence Relation Model of Permanent Magnet Synchronous Machine Based on Adaptive Kalman Filtering

建立永磁同步电机的关联关系模型有两个关键点:电机模型的确立和模型中未知参数的估计。在永磁同步电机模型的确立方面,建立了能同时考虑铁耗和转子磁场谐波的d-q轴模型;在电机模型参数估计方面,提出了一种采用自适应卡尔曼滤波算法的系统辨识方法。仿真结果表明:能同时考虑铁耗和转子磁场谐波的永磁同步电机模型具有较高的建模准确度;采用自适应卡尔曼滤波的算法能够有效跟踪实际测量噪声特性的变化,精确估计参数,且快速收敛。实验测试结果进一步验证了该模型和算法的有效性和实用性。

There are two pivotal points in the course of building a permanent magnet synchronous machine（ PMSM） ＇s incidence relation model： validation of the PMSM model and estimation of parameters in the model. In the respect of validation of the PMSM model,a d-q model of PMSM which takes iron loss and flux harmonics into account was builded. In the respect of estimation of parameters in the PMSM model,the identification method for the motor based on adaptive Kalman filtering（ AKF） algorithm was proposed. The simulations revealed that the PMSM model which takes iron loss and flux harmonics into account offers an extremely high accuracy and the AKF method can follow the change of actual measurement noise,estimate parameters accurately and have fast convergence. Experiment results further verified the effectiveness and practicability of the model and method.