@inproceedings{f50ea1d9542f409198006465a23d5cf6,
title = "Thermal Prediction of Induction Machines Based on Finite Element and Analytical Methods",
abstract = "Since thermal performance heavily influences the efficiency of electric machines, it is important to predict the temperature rise and distribution in the design and optimisation stages. This paper predicts the thermal performances of a commercial 2.2kW induction motor (IM) at different loads. It includes an analytical model and its verification via an electromagnetic-thermal model using finite-element method. In the proposed equivalent thermal network, forced convective heat transfer coefficients were estimated by empirical correlations. The other parameters, including the thermal resistances between stator windings and stator, between stator and housing frame and between housing frame and ambient air, were determined in DC heating tests. The experimentally obtained thermal model in DC heating tests was verified against the prediction in finite-element analysis software. The temperature rise in the machine was obtained at different loads. This electromagnetic-thermal analysis makes it easier for machine designers to optimise the design of IMs considering their thermal performances.",
keywords = "analytical methods, electromagnetic fields, finite element method, thermal analysis",
author = "Bingjian Li and Amin Mahmoudi and Solmaz Kahourzade and Soong, {Wen L.} and Zhi Cao",
year = "2023",
month = aug,
day = "21",
doi = "10.1109/AUPEC58309.2022.10215567",
language = "English",
series = "2022 32nd Australasian Universities Power Engineering Conference, AUPEC 2022",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2022 32nd Australasian Universities Power Engineering Conference, AUPEC 2022",
address = "United States",
note = "32nd Australasian Universities Power Engineering Conference, AUPEC 2022 ; Conference date: 26-09-2022 Through 28-09-2022",
}
