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- W4310969574 abstract "The effectiveness of optimization can be significantly increased by using precise multi-physics field surrogate computational models for electrical machines. This paper developed an analytically guided Kriging (AG Kriging) model construction method in order to optimize the temperature of the electrical machine. Depending on the solution domains, analytical relationships between the stator geometrical parameters and the losses and temperatures of the windings and cores were derived, and these relationships served as the basis function for the Kriging model. The data set produced by sampling the Taguchi orthogonal array was used to train the Kriging model. Several Kriging models were evaluated with the proposed AG Kriging model in order to acquire an accurate fit to the design space. On the basis of these, a multi-objective genetic optimization algorithm was used to optimize a high-power synchronous generator. The results show that the AG Kriging model has a fitting error of less than 2% and that the improved stator can reduce temperatures by up to 13°C." @default.
- W4310969574 created "2022-12-21" @default.
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- W4310969574 date "2022-10-17" @default.
- W4310969574 modified "2023-10-18" @default.
- W4310969574 title "Analytic Guided Magnetic-Thermal Kriging Surrogate Model and Multi-Objective Optimization of Synchronous Generator" @default.
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- W4310969574 doi "https://doi.org/10.1109/iecon49645.2022.9968336" @default.
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