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Record W2021836804 · doi:10.1109/compel.2013.6626408

A modular bidirectional DC power flow controller with fault blocking capability for DC networks

2013· article· en· W2021836804 on OpenAlexaff
Gregory J. Kish, Peter W. Lehn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConvertersController (irrigation)Modular designComputer scienceFault (geology)Blocking (statistics)Power (physics)CapacitorElectronic engineeringVoltageControl theory (sociology)EngineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

This paper introduces a power flow controller that can be deployed to interconnect DC networks of different or similar voltage levels and manage the power flow between them. Its key features include: 1) bidirectional power flow 2) both step-up and step-down operation and 3) bidirectional fault blocking similar to a DC circuit breaker. Moreover, its modular architecture makes it aptly suited for high-voltage DC (HVDC) applications. The kernel of the power flow controller is a new class of single-stage bidirectional DC/DC converters utilizing interleaved strings of cascaded submodules. The converter operation is analyzed and a simplified model is implemented in PSCAD/EMTDC. A control strategy that ensures steady-state power balance of each submodule capacitor via circulating AC currents is introduced. Simulation results validate the converter's principle of operation and the proposed control strategy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), 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

Citations55
Published2013
Admission routes1
Has abstractyes

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