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Detection of Demagnetization Fault in Interior Permanent Magnet Synchronous Motors for EV Based on UKF

机译:基于UKF的EV内部永磁同步电动机中的退磁故障检测

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In most applications, the permanent magnet flux linkage of interior permanent magnet synchronous motors is treated as a known constant value. However, high operation temperature, stator winding starting current impulsion, armature reaction and frequent field weakening control all may cause irreversible demagnetization of permanent magnet which has direct impacts on motor control performance. In order to implement demagnetization fault detection accurately, the nonlinear observation model is first set up allowing for the effects of magnetic saturation and cross saturation of IPMSM, and then the unscented Kalman filter (UKF), based on dynamic data processing technology, is presented to implement flux linkage estimation and overcome the disadvantages of traditional extended Kalman filter (EKF). Finally, the simulation results are presented to verify the effectiveness of the proposed method, it is pointed that this method can estimate the permanent magnet flux linkage with the estimation error less than 3% in the full speed range of IPMSM, so it is fully competent for demagnetization fault detection, and consequently, can also prevent the deterioration of demagnetization and improve the reliability of drive system.
机译:在大多数应用中,内部永磁同步电动机的永磁通量连杆被视为已知的恒定值。然而,高操作温度,定子绕组启动电流脉冲,电枢反应和频繁的场弱化控制都可能导致永磁体的不可逆的退磁,这对电机控制性能直接影响。为了精确地实现退磁故障检测,首先建立非线性观察模型,允许IPMSM的磁饱和度和交叉饱和度的影响,然后基于动态数据处理技术的Unscented Kalman滤波器(UKF)呈现给实现助焊剂连杆估计并克服传统扩展卡尔曼滤波器(EKF)的缺点。最后,提出了仿真结果以验证所提出的方法的有效性,指出,该方法可以在IPMSM的全速范围内与估计误差估计小于3%的估计误差,因此它是完全称职的对于退磁故障检测,因此,还可以防止退磁的劣化并提高驱动系统的可靠性。

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