2022
DOI: 10.1109/tmag.2021.3080119
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Kriging Surrogate Model-Based Design of an Ultra-High-Speed Surface-Mounted Permanent-Magnet Synchronous Motor Considering Stator Iron Loss and Rotor Eddy Current Loss

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Cited by 16 publications
(4 citation statements)
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“…A surrogate model is a type of functional relationship between input and output variables that serves as an approximation of a complex optimization objective model. In this study, the kriging surrogate model was adopted as a surrogate model, which is widely used in electric motor optimization owing to its high predictability when a non-linear input is used [25]. The kriging surrogate model is defined as follows:…”
Section: A Design Of Pmsmmentioning
confidence: 99%
“…A surrogate model is a type of functional relationship between input and output variables that serves as an approximation of a complex optimization objective model. In this study, the kriging surrogate model was adopted as a surrogate model, which is widely used in electric motor optimization owing to its high predictability when a non-linear input is used [25]. The kriging surrogate model is defined as follows:…”
Section: A Design Of Pmsmmentioning
confidence: 99%
“…Compared with conventional speed motors of the same specification, high-speed motors generally have higher loss, and in order to facilitate speed regulation, variable frequency power supply is generally used, which introduces higher harmonics that will increase the loss of the motor and cause temperature rise. Especially for rotors, the added difficulty in heat dissipation leads to a significant increase in rotor eddy current loss [4][5][6] . Reference [7] conducted finite element simulation calculations on the electromagnetic losses of the high-speed motor and found that compared to sinusoidal power supply, variable frequency power supply increased the total loss of the motor by more than 45%, mainly on the rotor.…”
Section: Introductionmentioning
confidence: 99%
“…The accuracy of the surrogate model is a controversial problem, especially in the high-dimensional optimizations. As a consequence, various fitting approaches, such as response surface methodology (RSM) [27], or Kriging method [28], have recently been developed to create an approximate model with reasonable accuracy in terms of data prediction. In addition to the modern fitting approaches, the proper sampling of data can significantly reduce the potential inaccuracies in the resultant surrogate model.…”
Section: Introductionmentioning
confidence: 99%