Electrical machine fault analysis and modelling by using electromagnetic approach
Zhang, Meng Qi
Date of Issue2016-08-31
School of Electrical and Electronic Engineering
Although electrical machines have been used in a wide range of industrial applications for many decades, the demand for machine reliability in the present day is higher than ever before and it is growing continuously. Currently, due to increasing motivation to enhance machine operation reliability, the area of machine fault analysis has attracted considerable attention. This thesis introduces a less reported approach by using electromagnetic characteristics for machine fault analysis and modelling. The technical contents can be briefly abstracted into four aspects. Firstly, a generalized approach to design magnetic equivalent circuit model for the interior permanent magnet machine is proposed. The numerical modelling by finite element method is popular in predicting machine operating performance, due to its high degree of accuracy. However, its long computation time, especially when the machine is connected to external circuit, makes it less promising in repeatedly assessing the machine performance. Here, another numerical modeling technique based on the magnetic equivalent circuit method is used to develop machine model. Compared to the finite element method, this model gives consistent prediction of machine flux distribution and exhibits faster computational speed. Secondly, a lumped MEC model has been proposed to predict machine performance incorporating rotor motion. This lumped model has also been used to investigate the influence of partial inter-turn winding short-circuit fault. Machine operating characteristics such as flux linkage, inductance, and electromagnetic torque have been evaluated in good agreement with finite element analysis under both healthy and faulty conditions. This investigation broadens the applications of magnetic equivalent circuit method which was mainly used for machine design optimization purpose in the previously reported studies. Thirdly, a simplified method to assess machine irreversible demagnetization risk caused by stator inter-turn winding short-circuit fault is proposed. It has been re-ported in the previous researches that the stator phase short-circuit is the main cause of machine irreversible demagnetization fault. The study in this thesis indicates that the stator inter-turn winding short-circuit fault can also result in irreversi-ble demagnetization for the bar-wounded permanent magnet machines. Moreover, an analytical model is designed to predict winding short-circuit current, which significantly reduce the time required by finite element method to assess machine irreversible demagnetization. Finally, a dual search coil method is proposed to detect machine airgap eccentricity condition. Machine flux distribution is not symmetric in the presence of airgap eccentricity, and the situation can be reflected as variations of search coil terminal voltage induced by machine magnetic field. In this study, the time-stepping FEM analysis is used to model machine static-, dynamic-, and mixed- airgap eccentricity fault conditions. The dual search coils are placed at both airgap main magnetic field and stray magnetic field to detect machine operating abnormality due to eccentric airgap. The results show that airgap eccentricity can be recognized by comparing the fundamental voltage component of search coils. This configuration is suitable for the eccentricity fault detection of low-cost machines.