MétaCan
Menu
Back to cohort
Record W2787363896 · doi:10.1109/epec.2017.8286223

Dynamic model of an interleaved modular multilevel DC-DC converter for MVDC and HVDC systems

2017· article· en· W2787363896 on OpenAlex
Yang Gao, Nicolas Faria, Gregory J. Kish

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConvertersModular designElectronic engineeringHarmonicsForward converterCapacitorComputer scienceDC biasEngineeringVoltageControl theory (sociology)Electrical engineeringBoost converter

Abstract

fetched live from OpenAlex

The first comprehensive dynamic state-space model of an interleaved DC-MMC is presented. The DC-MMC is one of a new class of non-isolated modular multilevel dc-dc converters that are rapidly gaining traction for deployment in MVDC and HVDC systems. A non-linear time-averaged model of a two-string interleaved DC-MMC is derived that fully represents internal capacitor voltage dynamics and converter terminal dynamics. Simulation results verify the time-averaged model exactly captures the low-frequency dynamics of a full switched converter model implemented in PLECS. The model can be used for a variety of applications, for example, rapid time-domain simulation of the DC-MMC, study of internal cell power transfer mechanisms, and analysis of harmonics and circulating currents.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.770
Threshold uncertainty score0.464

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 head0.023
GPT teacher head0.249
Teacher spread0.227 · 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

Quick stats

Citations6
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicHVDC Systems and Fault ProtectionFrench-language works237,207