A Comprehensive Mathematical Model for Health and Multiple Faults of Permanent Magnet Synchronous Motor Based on Coil Subunit

The existing inter-turn short circuit fault (ISF) mathematical models of permanent magnet synchronous motors (PMSM) fail to comprehensively take into account multiple factors such as short-circuit location and winding structure, therefore they suffer from low modeling accuracy and poor universality. Furthermore, most fault models are developed for single fault conditions of ISF and demagnetization fault (DMF), thus cannot analyze composite fault (CF) composed of ISF and DMF. Moreover, it is necessary to repeat modeling for each type of fault and its each fault configuration, which results in a substantial amount of time and effort. To address these issues, this paper proposes a comprehensive mathematical model (CMM) for health and multiple faults of PMSM based on coil sub-unit, which suggests a new way to easily carry out different types of health, ISF, DMF, and CF. Firstly, a coil is divided into three coil sub-units, and a coil sub-units based CMM for health and multiple faults is developed. It not only eliminates the effect of short-circuit locations on the modeling accuracy but also has excellent universality. Additionally, it is capable of precisely analyzing the electromagnetic characteristics of individual coils. Secondly, the inductance matrix and the no-load back electromotive force (EMF) matrix of wire turns are established by the finite-element (FEM) and surface polynomial function fitting methods, thereby considering the effect of leakage flux on wire turns at different locations, further ensuring the accuracy of the model. Then, the physical model of CMM and its graphical interface is created in the Matlab/Simulink, which allows for the convenient and rapid modeling of ISF, DMF, and CF by parametric fault setting framework without changing the internal topological structure of the model, achieving single-time modeling, multi-faults reuse. Finally, FEM simulation and experimental results verify the correctness and efficiency of the model. The proposed CMM can accurately and efficiently analyze the electromagnetic characteristics of both healthy and faulty PMSMs, and generate large healthy and faulty samples, which provides an important foundation for fault diagnosis.