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Record W6980756522

Control of the Current Shaping MMC to Enable High Step-down AC/DC Power Conversion

2021· dissertation· W6980756522 on OpenAlexfundno aff

Bibliographic record

VenueTSpace · 2021
Typedissertation
Language
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModular designTopology (electrical circuits)Power (physics)VoltageCurrent (fluid)InductorDistortion (music)Control (management)
DOInot available

Abstract

fetched live from OpenAlex

This thesis introduces a new control strategy for the Current Shaping Modular Multilevel Converter (CS-MMC), a recently proposed DC/DC converter intended for medium voltage applications. This new control formulation allows the same CS-MMC to accommodate a vastly wider range of input voltages while also reducing the output current ripple. Experimental results are presented using a 1 kW laboratory scale prototype. It is then shown that this introduced control strategy enables the CS-MMC to also accommodate AC input voltages. To achieve this, the topology must be slightly modified, with the new topology termed the AC/DC CS-MMC. While the CS-MMC is intended for the future DC grid, the AC/DC CS-MMC can be powered from existing AC distribution feeders. Simulation results for a 10 kW system are presented. The input current distortion is shown to be in compliance with the IEEE 519-2014 standard.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0290.000

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 head0.034
GPT teacher head0.292
Teacher spread0.259 · 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.

Study designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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