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Record W2028619570 · doi:10.1109/epec.2009.5420710

Precipitation-based conductor cooling model for Dynamic Thermal Rating systems

2009· article· en· W2028619570 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAmpacityConductorPrecipitationElectric power transmissionLine (geometry)Transmission lineWater coolingWork (physics)Power (physics)Environmental scienceThermalElectrical conductorMaterials scienceMeteorologyNuclear engineeringMechanical engineeringElectrical engineeringEngineeringThermodynamicsPhysicsMathematics

Abstract

fetched live from OpenAlex

This paper presents a precipitation-based conductor cooling model for use in power line ampacity rating applications. It is aimed at better modelling a conductor's temperature by incorporating line cooling resulting from precipitation falling on power lines. The improved calculations provide gains in additional line capacity for power transmission networks incorporating advanced Dynamic Thermal Rating systems. Depending on the precipitation rate and other atmospheric variables, the initial work presented in this paper suggests that line cooling gains between 1°C to over 20°C may be obtained. The precipitation based cooling model shows that the highest gains are observed for largest line loads, thus providing cooling where it is needed the most.

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 score0.785
Threshold uncertainty score0.484

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 head0.014
GPT teacher head0.244
Teacher spread0.231 · 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

Quick stats

Citations23
Published2009
Admission routes1
Has abstractyes

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