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Record W4360993993 · doi:10.1049/gtd2.12821

Investigating the impact of a dynamic thermal rating on wind farm integration

2023· article· en· W4360993993 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIET Generation Transmission & Distribution · 2023
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWind powerTransformerThermalRevenueRenewable energyElectric power systemAutomotive engineeringEnvironmental scienceReliability engineeringComputer scienceEngineeringElectrical engineeringMeteorologyBusinessPower (physics)VoltageFinance

Abstract

fetched live from OpenAlex

Abstract With an increased focus on renewable power generation, dynamic thermal ratings of various power equipment are being investigated to connect the new intermittent generation to the grid without needing to replace existing infrastructure. Previous research is mainly focused on a dynamic thermal rating for overhead lines. End‐of‐line equipment are also restricted by a thermal limit. This paper combines the thermal model for overhead lines with the transformer thermal model, to investigate the impact of environmental conditions on both. A wind farm case study is used to determine the potential increase in capacity using a combined thermal model. The impact of a higher thermal limit on the loss of life of the transformer is compared to the amount of curtailed wind and the potential revenue from the added wind farm. This analysis serves to provide a method to analyse the risks of using a higher transformer thermal limit, compared to the benefits of increased wind penetration and additional revenue.

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)0.001
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: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.270
Teacher spread0.253 · 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