Abstract

A temperature rise occurs when an electrical machine is fully loaded. The winding insulations of high-speed permanent magnet synchronous machines (PMSMs) are the most temperature-sensitive components, which can impact the machine’s longevity. Thus, recently, prediction for high-speed PMSM winding temperature has been given more and more attention. Thermal analysis by numerical (computational fluid dynamics (CFD) and finite element method (FEM)) and analytical lumped parameter thermal network (LPTN) methods have been widely used to estimate the temperature of totally enclosed fan cooled axial ventilation system (TEFCAVS) machines. Although numerical methods have more accuracy, their computation wastes time. Therefore, LPTN is being utilized in this paper due to the fastness of its computation. Firstly, estimated losses of high-speed PMSM with TEFCAVS via electromagnetic analysis, including copper, iron, permanent magnet (PM) eddy current, sleeve eddy current, and mechanical, are coupled to the LPTN model, which acts as heat sources for temperature prediction. Moreover, analysis shows that slot windings’ maximum temperature exceeds the winding insulation class with initial cooling configuration. Secondly, in order to mitigate slot winding temperature, the sensitivity study for liner conductivity, air gap’s heat transfer and lamination to housing’s contact is conducted by LPTN to identify which thermal parameter has more influence on mitigating the maximum temperature of the slot winding. Investigation shows that improving an air gap’s heat transfer has more influence than other parameters for mitigating slot winding maximum temperature below its insulation class. Lastly, the machine is designed and tested with the best thermal-sensitive parameters. Then test results for the maximum temperature of the winding are compared with estimated results to ensure the proposed LPTN correctness, and the calibration process confirms LPTN accuracy.

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