FEM-Based Statistical Data-Driven Modeling Approach for MFT Design Optimization

Mogorovic, Marko; Dujić, Dražen

doi:10.1109/tpel.2020.2982130

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…The prototype transformer, constructed with N87 SIFERRIT ferrite U-cores was evaluated to have an efficiency above 99.3% at the rated condition. Other aspects of MF transformer design are leakage inductance modeling, local electric field distribution inside the transformer [27], and inductor-integrated MF transformer design [20].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Ferrite and Nanocrystalline Cores for Medium-Frequency Transformer of Dual Active Bridge DC-DC Converter

et al. 2021

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This article reports an investigation into ferrite and nanocrystalline materials for the medium-frequency transformer of a dual active bridge DC-DC converter, which plays a key role in the converter’s efficiency and power density. E65 MnZn ferrite cores and toroidal and cut nanocrystalline cores are selected for the construction of 20-kHz transformers. Transformer performance is evaluated with a 1.1-kW (42–54 V)/400 V dual active bridge DC-DC converter with single-phase shift and extended phase shift modulations. The experimental results indicate that the toroidal nanocrystalline transformer had the best performance with an efficiency range of 98.5–99.2% and power density of 12 W/cm3, whereas the cut-core nanocrystalline transformer had an efficiency range of 98.4–99.1% with a power density of 9 W/cm3, and the ferrite transformer had an efficiency range of 97.6–98.8% with a power density of 6 W/cm3. A small mismatch in the circuit parameters is found to cause saturation in the nanocrystalline toroidal core, due to its high permeability. The analytical and experimental results suggest that cut nanocrystalline cores are suitable for the dual active bridge DC-DC converter transformers with switching frequencies up to 100 kHz.

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Section: Introductionmentioning

confidence: 99%

Performance Comparison of Ferrite and Nanocrystalline Cores for Medium-Frequency Transformer of Dual Active Bridge DC-DC Converter

et al. 2021

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“…ITH the dawn of artificial intelligence era, the prospect of utilizing Neural Network (NN) technology to reshape human perception of the world is compelling, especially where the physical mechanism is not fully understood [1], [2]. Compared to the ineluctable trade-off between accuracy and efficiency in conventional modeling methods, these data-driven approaches can elevate the model accuracy comparable to the fine-tuned simulation, while confining the compute time to a level akin to the analytical method [3].…”

Section: Introductionmentioning

confidence: 99%

“…lp-c-dInsulation distance between L p and core along d win[3,6] mmg lp-c-hInsulation distance between L p and core along h win[3,6] mmg ls-c-dInsulation distance between L s and core along d win[1,4] mmg ls-c-hInsulation distance between L s and core along h win[1,4] mmg ls-lpInsulation distance between L s and L p [0.5, 6] mm unavoidable discrepancies between the reference value and real power loss, the correctors C iw-k and C ow-k produced by NN models are implemented to refine the output accuracy. It should be noted that the independent outputs for the power loss in each layer benefit the further thermal optimization of MFTs.…”

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confidence: 99%

Hierarchical Physics-Informed Neural Network Framework for 3D Magnetic Modeling of Medium Frequency Transformers

Yang,

Shu,

Yang

2024

Preprint

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Neural Network (NN) technology is revolutionizing the modeling paradigm in the engineering arena, enhancing simultaneously model precision and process speed. Nevertheless, the deficiency of physical interpretability in current NN models causes an unattainable demand for the quantity of training data, especially when dealing with high-dimension physical behaviours. In this work, a Hierarchical Physics-Informed Neural Network (HPINN) framework is proposed to address the 3D magnetic modeling issue of Medium Frequency Transformers (MFTs). By establishing the knowledge transfer channel between the cross section and the equivalent space, the inferior 2D model parameter search space, which is restricted by Dowell’s equation and accessible 2D magnetic data information, can be mapped into the superior 3D search space. Accordingly, with limited 3D training data, the performance of the HPINN framework is still guaranteed around the optimal state. The structural embedding of physics knowledge reduces the model’s reliance on 3D training data, thereby relieving the strict computing requirement at the data generation stage. Finally, according to the training results and experiment verifications, with only one-third computational burden used in the classic NN model, the average error of this proposed HPINN framework is reduced to 1%, and the maximum error does not exceed 10%.

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“…Umamaheswara Vemulapati and Thomas Stiasny are with Hitachi ABB Power Grids, Semiconductors, 5600, Lenzburg, Switzerland (e-mail: umamaheswara.vemulapati@hitachi-powergrids.com; thomas.stiasny@hitachi-powergrids.com). capabilities of modern power semiconductors, with switching frequencies of several kHz having been demonstrated [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Resonant IGCT Soft-Switching: Zero-Voltage Switching or Zero-Current Switching?

Ulissi

Kucka

Vemulapati³

et al. 2022

IEEE Trans. Power Electron.

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In the last thirty years the IGCT has been employed in medium voltage MW power level converters and has demonstrated some of the lowest conduction losses of any actively controlled device in traditional hard-switched, sub-kHz applications. Extending the use of the device to medium frequency applications such as DC transformers is possible through soft-switched operation, where low turnoff current and zero voltage turn-on significantly reduce switching losses. For this purpose, this paper explores the trade-offs in the transition between zero voltage and zero current switching conditions of reverse conducting IGCTs. Switching conditions resulting in minimal switching loss are identified for standard commercial devices and engineering samples subjected to increased electron irradiation to reduce switching energy at the expense of on-state voltage. The operation of the IGCTs at the record frequency of 5 kHz is thermally validated under load in the identified switching conditions.

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FEM-Based Statistical Data-Driven Modeling Approach for MFT Design Optimization

Cited by 12 publications

References 17 publications

Performance Comparison of Ferrite and Nanocrystalline Cores for Medium-Frequency Transformer of Dual Active Bridge DC-DC Converter

Performance Comparison of Ferrite and Nanocrystalline Cores for Medium-Frequency Transformer of Dual Active Bridge DC-DC Converter

Hierarchical Physics-Informed Neural Network Framework for 3D Magnetic Modeling of Medium Frequency Transformers

Resonant IGCT Soft-Switching: Zero-Voltage Switching or Zero-Current Switching?

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