PMSM Combination Modeling for Multiparameter Estimation Using Bayesian Learning With Inverter Distortion Cancellation and Temperature Compensation
PMSM Combination Modeling for Multiparameter Estimation Using Bayesian Learning With Inverter Distortion Cancellation and Temperature Compensation
复制标题
使用贝叶斯学习、逆变器失真消除和温度补偿进行多参数估计的 PMSM 组合建模
DOI：
10.1109/tec.2022.3220943
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发表时间：
2023-06
影响因子：
4.9
通讯作者：
Narayan C. Kar
中科院分区：
文献类型：
--
作者：
Kaide Huang;Beichen Ding;Chunyan Lai;Guodong Feng;Narayan C. Kar
永磁同步电机（PMSM）驱动器具有更高的效率是非常需要的，精确的磁链和电感模型或地图是实现这种驱动器的关键。然而，这些参数的精确建模和估计应采用冗余数据，并受到磁饱和和逆变器失真的影响。本文首先从电机模型推导出磁链和电感估计的磁链组合模型，其中逆变器失真被抵消，从而逆变器的影响最小化，以提高性能。为了考虑磁饱和的影响，采用径向基函数对磁链的非线性进行建模，并使用少量的相关向量，有效地描述了磁链的非线性变化。在此基础上，采用贝叶斯学习方法对磁链模型的稀疏系数进行估计，该方法能够有效地处理非高斯噪声，提高磁链模型的估计精度，保证磁链模型具有更高的计算效率和更少的内存占用。此外，考虑了温度效应，以保证模型在温度上升时的精度。实验室永磁同步电机驱动器上的实验和比较，所提出的方法进行了验证。
Permanent magnet synchronous machine (PMSM) drives with better efficiency are highly demanded, and accurate flux linkage and inductance models or maps are critical to achieve such drives. However, precise modeling and estimation of these parameters should employ redundant data and are affected by magnetic saturation and inverter distortion. This paper firstly derives a flux linkage combination model from machine model for flux linkage and inductance estimation, in which inverter distortion is cancelled and thus inverter influence is minimized for performance improvement. To consider magnetic saturation, radial basis functions are employed to model the nonlinear flux linkages with a small number of relevance vectors, which can effectively depict the nonlinear variation. Bayesian learning approach is then explored to estimate the sparse coefficients of the flux linkage model in the context of radial basis functions, which can deal with non-Gaussian noise to improve the estimation accuracy and guarantee flux linkage model with better computation efficiency and less memory occupation. Moreover, temperature effect is considered to ensure the model accuracy under temperature rise. The proposed approach is validated with experiments and comparisons on a laboratory PMSM drive.
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