同步磁阻电机交叉饱和特性参数辨识与建模  

Parameter Identification and Modeling of Cross-Saturated Synchronous Reluctance Motor

作　　者：鲍崇 隋宇 孙海纳 程启原 宋受俊[1] BAO Chong;SUI Yu;SUN Haina;CHENG Qiyuan;SONG Shoujun(School of Automation,Northwestern Polytechnical University,Xi’an 710072,China)

机构地区：[1]西北工业大学自动化学院,西安710072

出　　处：《南京航空航天大学学报》2024年第5期834-846,共13页Journal of Nanjing University of Aeronautics & Astronautics

基　　金：陕西省杰出青年科学基金(2023-JC-JO-44)。

摘　　要：同步磁阻电机(Synchronous reluctance motor,SynRM)由于其独特的磁路结构,在运行过程中存在显著的交叉饱和效应,这给电机的精确控制和性能优化带来了挑战。本文针对由磁路自饱和及交叉饱和引起的同步磁阻电机参数非线性变化问题,提出了电机静止状态下的交叉饱和特性参数辨识方法和改进的数学模型建模方法。首先,通过离线静止辨识,获取电机在不同饱和状态下的特性数据;然后,利用神经网络及数值优化技术对数据进行拟合,得到SynRM在不同工作状态下的交叉饱和参数;最后,建立了考虑交叉饱和效应的改进数学模型,并通过与有限元仿真结果对比及仿真验证了该模型的准确性和有效性。Due to its unique magnetic circuit structure,the synchronous reluctance motor(SynRM)exhibits significant cross-saturation effects during operation,posing challenges for accurate control and performance optimization of the motor.This paper presents a method for identifying cross-saturation characteristic parameters and an improved mathematical modeling method for the nonlinear changes in SynRM parameters caused by magnetic circuit self-saturation and cross-saturation in the static state of the motor.Offline stand still tests are conducted to acquire characteristic data of the motor under different saturation levels.Subsequently,neural networks learn the complex relationships between input currents and output flux linkages,while numerical optimization techniques refine the extracted parameters to minimize model discrepancies.These identified parameters are then integrated into an enhanced mathematical model that effectively incorporates cross-saturation effects.The accuracy and effectiveness of the proposed model are rigorously validated through comprehensive comparisons with finite element analysis(FEA)results and further simulations.

关 键 词：同步磁阻电机 交叉饱和 参数辨识 神经网络 数学建模 

分 类 号：TM351[电气工程—电机]