AC Resistance and Leakage Inductance Estimation for Planar Transformers With Parallel-Connected Windings

Pniak, Lucas; Quéval, Loïc; Revol, Bertrand; Teu, Jean-Sylvio Ngoua; Gautier, Cyrille; Béthoux, Olivier

doi:10.1109/tpel.2022.3203515

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…It can be written as: Copper losses in transformer windings are estimated based on R ac which has to be accurately determined. The latter, as well as L ac , highly depends on the current distribution in the windings connected in parallel as it is demonstrated in [35]. Noting I = [i 1 .…”

Section: In Magnetic Componentsmentioning

confidence: 95%

“…At high frequency, the correct distribution of the currents in these windings is no longer certain and must be thoroughly assessed. To this purpose, a simplified version of the harmonic model presented in [35] is developped in this paper. The approach is quite similar to the method proposed by Weichen in [36], but much easier to implement.…”

Section: In Magnetic Componentsmentioning

confidence: 99%

“…where † is the transpose conjugate operator. According to [35], the resistance matrix R is expressed as follows…”

Section: In Magnetic Componentsmentioning

confidence: 99%

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“…Various methods exist for predicting winding losses, including analytical and numerical approaches [ 26 , 27 ]. Notably, loss models for Litz wire devices [ 28 ] and 2D copper foil devices are continuously optimized [ 29 , 30 , 31 ]. Analytical prediction models for core losses encompass mathematical models, time-domain approximation models, loss separation models, and empirical models [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Loss-Optimized Design of Magnetic Devices

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Maximizing efficiency, power density, and reliability stands as paramount objectives in the advancement of power electronic systems. Notably, the dimensions and losses of magnetic components emerge as primary constraints hindering the miniaturization of such systems. Researchers have increasingly focused on the design of loss minimization and size optimization of magnetic devices. In this paper, with the objective of minimizing the loss of magnetic devices, an optimal design method for the winding structure of devices is proposed based on the coupling relationship between the loss prediction model and the design variables. The method examines the decoupling conditions between the design variables and the loss model, deriving optimized design closure equations for the design variables. This approach furnishes a technical foundation for the miniaturized design of miniature apparatuses incorporating magnetic components, offering a straightforward and adaptable design methodology. The finite element method simulation results and experimental measurement data verify the accuracy of the prediction of the proposed method and the validity of the optimal design theory of device loss.

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“…As a power transmission device in power electronics, planar transformers are often used in switching power supplies. Parasitic parameters such as leakage inductance, distributed capacitance, and AC losses have a direct impact on the performance of planar transformers [1]. Leakage inductance and distributed capacitance lead to lower transformer efficiency and damage switching tubes by forming voltage spikes and current spikes on the primary side.…”

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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AC Resistance and Leakage Inductance Estimation for Planar Transformers With Parallel-Connected Windings

Cited by 7 publications

References 31 publications

Pre-sizing of a modular high power density DC/DC converter with GaN components

Pre-sizing of a modular high power density DC/DC converter with GaN components

Loss-Optimized Design of Magnetic Devices

Parasitic Parameter Prediction for Planar Transformers Based on Neural Network

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