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- W3211851898 abstract "Despite some recent efforts towards multi-objective design optimization of multilevel converters, design optimization of solid-state-transformers (SSTs) are not presented much in the literature mainly because of the lack of computationally feasible techniques. This paper is dedicated towards a computational feasibility study of multi-objective design optimization techniques for medium-voltage (MV) grid-connected SSTs. After defining the application and scope of SST design optimization problem, a brief description of the possible solution techniques are discussed which shows the merits of semi-numerical/hybrid design optimization techniques. Subsequently, a machine learning (ML) aided hybrid optimization technique is executed for a 15 kVA single-stage SiC-based SST design. Suitable component modelling is presented and a strong agreement is observed between theoretical optimization and experimental results. Finally, a comparative evaluation of the analytical, numerical, standalone hybrid and ML-aided hybrid optimization techniques (deployed for the same 15 kVA SiC-based SST design) reveals that the ML-aided hybrid strategy is best suited for SST design optimization as it requires feasible computational time for <5% error." @default.
- W3211851898 created "2021-11-22" @default.
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- W3211851898 date "2021-10-13" @default.
- W3211851898 modified "2023-10-17" @default.
- W3211851898 title "Computational Feasibility of Multi-objective Optimal Design Techniques for Grid-Connected Multi-cell Solid-State-Transformers" @default.
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- W3211851898 doi "https://doi.org/10.1109/iecon48115.2021.9589779" @default.
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