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Single-Stage MMC for Single-Phase AC–DC Applications Using Anti-Series Half-Bridge Submodules with Ultra-Low Capacitance

2025· article· en· W4413319892 on OpenAlexaff
Philippe Gray, Matin Nabizadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCapacitanceSingle stageSeries (stratigraphy)Single phaseElectrical engineeringMaterials scienceCapacitorBridge (graph theory)Parasitic capacitanceStage (stratigraphy)Electronic engineeringOptoelectronicsPhysicsEngineeringElectrodeVoltage

Abstract

fetched live from OpenAlex

In this paper, a self-balancing single-stage modular multilevel converter (MMC) is proposed for medium-voltage AC to low-voltage DC applications. The proposed structure utilizes a novel type of submodule which consists of two half-bridges connected in an anti-series arrangement. The proposed converter features minimized stored energy requirements. This is a consequence of (i) operating the converter in such a way that the submodule capacitor voltages naturally follow the AC network voltage, and (ii) that the twice line-frequency power pulsations from the AC network are buffered by the low-voltage DC network. Additionally, the converter employs an interleaved structure that facilitates the natural circulation of switching harmonics within the converter, which reduces the AC network filtering requirements. The topology exhibits operational similarities to the dual active bridge (DAB) converter used in DC-DC applications, and as a result, it requires a control of relatively low-complexity. This paper introduces the proposed topology and its operating principles. Experimental results from a laboratory-scale prototype are provided for 273 Vrms to 150Vdc and 1.3kW.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.772
Threshold uncertainty score0.880

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 communication0.0000.000
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 head0.028
GPT teacher head0.261
Teacher spread0.232 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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