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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 OpenAlexafffund
Leanne Dawson, Andrew M. Knight

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.

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)0.001
metaresearch head score (Gemma)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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

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

Citations10
Published2023
Admission routes2
Has abstractyes

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