Open AccessImprovements in the permanent magnet synchronous motor torque model using incremental inductance
Author(s) -
Wang Xinjian,
Yi Peng,
Zhou Zishen,
Sun Zechang,
Ruan Wenjin
Publication year - 2020
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
ISSN - 1751-8679
DOI - 10.1049/iet-epa.2019.0187
Subject(s) - flux linkage , inductance , control theory (sociology) , cogging torque , torque , finite element method , synchronous motor , torque ripple , magnet , counter electromotive force , direct torque control , physics , engineering , computer science , current (fluid) , induction motor , mechanical engineering , voltage , electrical engineering , structural engineering , control (management) , artificial intelligence , thermodynamics
This study proposes a method to improve the permanent magnet synchronous motor (PMSM) dq 0 model for torque using incremental inductance. The torque model is essentially a partial differential equation considering flux linkage and magnetic energy. In practice, non‐ideal factors, such as saturation non‐linearity, the cogging structure, and the distortion current, increase the difficulty of solving this equation. In this study, two parameters related to flux linkage and magnetic energy are derived using incremental inductance. In the differential range, they are independent of the currents. Therefore, the partial derivatives of flux linkage and magnetic energy can be expressed in the form of derivatives under the saturation non‐linearity conditions. Thereby a dq 0 model that describes the torque ripple of a PMSM is established. This model can be used to calculate and analyse the transient characteristics of torque regardless of steady or unsteady current excitation. The main parameters of the model are obtained from finite‐element analysis (FEA). To reduce the dependency on FEA in the application, a derived version of this model is obtained by extending the range of the linearisation assumption. Finally, the rationality and effectiveness of the models are proven by comparison with the FEA and experiments, respectively.