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Record W2107545046 · doi:10.1109/tpwrd.2002.804013

Flicker study using a novel arc furnace model

2002· article· en· W2107545046 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2002
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsnot available
FundersUniversity of TorontoUniversity College DublinUniversity of GlasgowTexas A and M University
KeywordsFlickerElectric arc furnaceHarmonicsArc (geometry)VoltageEngineeringElectric arcElectric power systemPower (physics)ChaoticPower qualityAutomotive engineeringElectronic engineeringControl theory (sociology)Electrical engineeringComputer scienceMechanical engineeringMaterials scienceElectrodeMetallurgy

Abstract

fetched live from OpenAlex

Voltage flicker and harmonics are the types of power-quality problems that are introduced to the power system as a result of arc furnace operation. Utilities are concerned about these effects and try to take precautions to minimize them. Therefore, an accurate model of an arc furnace is needed to test and verify proposed solutions to this end. In this paper, an arc furnace model is developed and implemented in a Simulink environment by using chaotic and deterministic elements. Moreover, the modeling and simulation of an IEC flickermeter are also performed to evaluate the severity of fluctuations in the simulated arc furnace voltage.

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 categoriesMeta-epidemiology (narrow)
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 score0.612
Threshold uncertainty score1.000

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 head0.077
GPT teacher head0.252
Teacher spread0.174 · 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