This study proposes a novel inter-turn fault diagnosis method for a permanent magnet synchronous machine. Search coils are set on every stator tooth to measure the tooth fluxes. The high-order harmonics produced by the inverter are adopted to diagnose the inter-turn fault existence and locate the fault tooth. The fault severity coefficient is proposed on the basis of theoretical analysis and can identify the fault severity. A comparative analysis of a traditional method based on co-simulation shows that the proposed method is sensitive and that the fault severity coefficient is accurate, intuitive and independent of the operating condition of the motor. An experimental platform is set up, and it validates the effectiveness of the proposed method.
A 3D electromagnetic model of the end region of a 1550 MW nuclear generator is set up. The electromagnetic forces on the involute and nose parts of the end winding under a rated operation are obtained through the 3D time-step finite element method. The electromagnetic forces on different coils in the same phase are analyzed. By changing the rotor's relative length and stator coil's linear length in the 3D electromagnetic model, the electromagnetic force distributions on the end winding are obtained. The influence of each structure change on the electromagnetic force in different directions is studied in detail. Conclusions that can be helpful in decreasing the electromagnetic forces on the end winding through optimizing the end region design are presented.
Eccentricity fault is a common fault of permanent magnet synchronous machines. It is hard to repair the fault efficiently if it is not diagnosed accurately. This paper presents a quantitative analysis of tooth flux with eccentricity fault based on co-simulation. Moreover, set up an experiment platform to validate the analysis. The analysis result reflects quantitative relationship of the eccentricity ratio and the tooth fluxes. It is helpful for improving the accuracy of the eccentricity diagnosis based on the tooth flux and the reliability of PMSMs.
scite is a Brooklyn-based startup that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.