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Record W4212871485 · doi:10.1109/access.2022.3152536

Comparative Assessment of Multi-Port MMCs for High-Power Applications

2022· article· en· W4212871485 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Access · 2022
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence Fund
KeywordsConvertersModular designPort (circuit theory)Computer scienceElectrical engineeringPower (physics)Electronic engineeringInterconnectionVoltageElectric power systemHigh-voltage direct currentEnergy storageNetwork topologyEngineeringDirect currentTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Multi-port converters can interconnect different dc and ac systems while reducing semiconductor requirements and losses by eliminating redundant power conversion stages. Modular multilevel converter (MMC)-based multiport systems are well suited for application in mixed ac-dc grids containing high-voltage dc (HVDC) and medium-voltage dc (MVDC) systems. This paper conducts a detailed comparative assessment of multi-port dc-dc-ac MMCs for high power applications. Four representative topologies are chosen for study due to their contrasting internal power processing characteristics. Three different network scenarios are investigated that include HVDC and MVDC applications, covering several different power flow cases. The multi-port MMCs are compared in terms of losses, semiconductor effort, internal energy storage and magnetics requirements. The results are extensively discussed and general conclusions are summarized.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.296

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 head0.052
GPT teacher head0.368
Teacher spread0.316 · 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