학술논문

Multiphysics and Multiobjective Design Optimization of High-Frequency Transformers for Solid-State Transformer Applications
Document Type
Periodical
Source
IEEE Transactions on Industry Applications IEEE Trans. on Ind. Applicat. Industry Applications, IEEE Transactions on. 57(1):1014-1023 Jan, 2021
Subject
Power, Energy and Industry Applications
Signal Processing and Analysis
Fields, Waves and Electromagnetics
Components, Circuits, Devices and Systems
Windings
Design optimization
Transformer cores
Inductance
Wires
Density measurement
High-frequency transformers (HFTs)
multiobjective optimization
multiphysics
Pareto optimization
solid-state transformer (SST)
Language
ISSN
0093-9994
1939-9367
Abstract
This article proposes a multiphysics-based and multiobjective design optimization of high-frequency transformers (HFT) for solid-state transformer (SST) applications. Achieving an efficient SST, regardless of its topology, highly depends on the design optimization of its HFT design parameters. Also, a high-power-density. The proposed algorithm (based on time-harmonic electromagnetic, thermal, and fluid physics model coupling) minimizes the volume of the HFT, total cost as well maximizes its efficiency. A case study of $20 \text{ kW}$, $10 \text{ kHz}$ is investigated and its Pareto optimal solutions (POS) presented. The simulation results show the various dependencies of the design variables on the proposed objective functions which verifies effectiveness of the proposed algorithm. The Pareto optimal solutions (POSs) show that efficiencies above $99\%$ can be achieved with appropriate selection of the design variables. From the POS, two case studies of the HFTs (referred to as $HFT_1$ and $HFT_2$ using $AMCC-100$ and $AMCC-250$ amorphous cores, respectively) are further investigated based on multiphysics numerical models. An experimental implementation of the optimized HFTs ($HFT_1$ and $HFT_2$) is integrated with a self-tuned dual active bridge converter to validate their performance. The experimental measurements from the HFTs are in very good agreement with the optimization results.