Electrical actuators and high-freguency magnetic components (review of "High-Frequency Magnetic Components"; Kazimierczuk, M.K.; 2009) [Book News]

Kazimierkowski, Marian K.

doi:10.1109/mie.2010.937932

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“…The main methods for parameter prediction during transformer design include 1-D electronmagnetic analysis (1-D EM) [2,3] and 3-D finite-element analysis (3-D FEA) [4,5]. However, due to the complexity of modelling and high simulation time cost, several methods have been proposed for better prediction of parasitic parameters, including curve fitting [6] and evolutionary algorithms [7].…”

Section: Introductionmentioning

confidence: 99%

Parasitic Parameter Prediction for Planar Transformers Based on Neural Network

Tan,

Hong,

et al. 2023

J. Phys.: Conf. Ser.

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Parasitic parameters such as leakage inductance and distributed capacitance of planar transformers have a direct impact on the performance and efficiency of transformers. Traditional methods for parasitic parameter prediction are commonly based on empirical formulas or simulation software, but they have problems of high computational complexity, time-consuming and low accuracy. In this paper, a method for predicting parasitic parameters of planar transformers based on a multilayer perceptron (MLP) under a specific winding structure is proposed, which can improve the efficiency of transformer design. The experiments demonstrate that the model can effectively predict the leakage inductance, distributed capacitance, and AC loss of planar transformers.

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