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Record W2148895869 · doi:10.1109/tpwrd.2007.899276

Locating and Isolating DC Faults in Multi-Terminal DC Systems

2007· article· en· W2148895869 on OpenAlexaff
Lianxiang Tang, Boon‐Teck Ooi

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2007
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsMcGill UniversityHydro One (Canada)
Fundersnot available
KeywordsCircuit breakerTerminal (telecommunication)ConvertersHandshakingElectrical engineeringFault (geology)Line (geometry)DC biasEngineeringVoltageComputer scienceTelecommunicationsComputer network

Abstract

fetched live from OpenAlex

A VSC-MTDC (multi-terminal dc) system consists of voltage-source converters (VSCs) connected to a dc network at their dc terminals. The MTDC is most vulnerable to a dc fault which paralyses all the VSCs until the dc fault is cleared. As dc circuit breakers are expensive, this paper proposes a solution based on extinguishing the dc fault current by opening all the ac-circuit breakers (ac-CBs) which the VSCs are already equipped with on the ac-sides. However, it is necessary to identify which dc line is the faulted line (in case it is a permanent fault) so that it can be isolated by fast dc switches (which are much more economical than the dc circuit breakers), prior to restoring the MTDC system by re-closing all the ac-CBs. This paper presents the handshaking method, which locates and isolates the faulted dc line and restores the MTDC without telecommunication.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.237
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations637
Published2007
Admission routes1
Has abstractyes

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