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Modeling and Soft-Switching Operation of an Isolated Modular-Multilevel-Converter-Based DC-DC Converter

2022· article· en· W4310475788 on OpenAlex
Hossein Saeedifard, Amirnaser Yazdani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2022 IEEE Energy Conversion Congress and Exposition (ECCE) · 2022
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsModular designTopology (electrical circuits)CapacitorModulation (music)Electronic engineeringComputer scienceVoltageForward converterĆuk converterInterconnectionBoost converterEngineeringControl theory (sociology)Electrical engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

This paper presents the results of an in-depth investigation into the operation, modulation, control, and performance assessment of a DC-DC converter, which is based upon a hybrid combination of the conventional dual-active-bridge topology and the modular multi-level converter (MMC) configuration. The proposed converter is a potential candidate topology for interconnection of medium- and low-voltage DC grids. The paper first introduces the circuit topology and presents the basics of operation and governing steady-state equations for the converter. Then, based on the developed mathematical model, it identifies a suitable modulation strategy for the converter bridges and submodules, as well as strategies for the regulation of the MMC submodule capacitor voltages and soft switching of the constituent semiconductor devices. The validity of the mathematical model, effectiveness of the proposed modulation and control strategies, and realization of soft switching are verified through simulation and experiments on an experimental setup.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.203
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it