基于鲁棒UKF 的永磁同步电机转子速度估计

针对无位置传感器直驱永磁同步电机系统(DD-PMSG: Direct-Driven Permanent Magnetic Synchronous Generator), 无迹Kalman 滤波(UKF: Unscented Kalman Filtering)方法鲁棒性不高的问题, 提出了基于一种新的鲁棒UKF 的转子速度估计方法。该方法将DD-PMSG 模型转化为由发电机机电状态与定子参数组成的增广方程形式。运用鲁棒UKF 算法, 可同时辨识发电机的机电状态和定子不确定参数动态, 使估计发电机参数不确定的鲁棒性得以改善。仿真结果表明, 与传统UKF 方法相比, 该方法对存在不确定性及非高斯噪声的系统模型具有较强的鲁棒性。

Estimation of Speed for Sensorless DD-PMSG Based on Robust UKF Method

To solve the weak robustness of UKF(Unscented Kalman Filtering) of the DD-PMSG(Direct-Driven Permanent Synchronous Magnetic Generator) without position sensor, a rotor speed estimation method based on RUKF(Robust Unscented Kalman Filtering) algorithm is proposed. The DD-PMSG model is transformed into the augmented equations with electromechanical state and stator parameters. By applying the RUKF to the augmented model, the PMSG states will be estimated, and the uncertain parameters dynamic, in which the latter participates in the iterative process of state estimation and improves the robustness of state estimation against parameter uncertainties. The stronger robustness can be shown by comparing with the UKF method in simulation results.