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Record W4399071570 · doi:10.1049/icp.2024.1009

Modified empirical arc model for simulating HVdc open conductor faults

2024· article· en· W4399071570 on OpenAlex
MK Perera, Athula Rajapakse, A.M. Gole

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

VenueIET conference proceedings. · 2024
Typearticle
Languageen
FieldEngineering
TopicElectrical Fault Detection and Protection
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsArc (geometry)ConductorElectrical conductorFault (geology)Arc flashElectric arcSkin effectTransient (computer programming)Arc-fault circuit interrupterMechanicsElectrical engineeringHigh-voltage direct currentVoltageShort circuitEngineeringComputer sciencePhysicsMaterials scienceMechanical engineeringDirect currentElectrodeGeology

Abstract

fetched live from OpenAlex

One fault that has hitherto not widely been investigated is the open circuiting of an HVdc conductor, as its occurrence is relatively rare. Using electromagnetic transient (EMT) simulation, this paper addresses the void in HVdc fault analysis by giving useful insight into the impact of HVdc open conductor faults on current and voltage transients. In order to simulate the resulting series arc between falling HVdc conductors, the Schavemaker model, a well-known empirical model, is adapted by introducing the dynamics of varying arc length. The modified Schavemaker arc model is implemented in PSCAD/ EMTDC. This study analyzes the impact of each parameter of the Schavemaker arc model on the arc conductance variation during an open conductor fault. This study shows that the arc time constant is the most influential parameter on the arc conductance variation while the time-varying reference arc voltage and arc cooling power introduces the effect of variable arc lengths to the Schawmaker model.

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 score0.820
Threshold uncertainty score0.806

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 communication0.0010.001
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 head0.120
GPT teacher head0.355
Teacher spread0.236 · 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