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Record W2461611690 · doi:10.1049/iet-rpg.2016.0252

Wind power plants grid code compliance tests – Hydro‐Québec TransÉnergie experience

2016· article· en· W2461611690 on OpenAlexaffabout
Mohamed Asmine, Charles‐Éric Langlois

Bibliographic record

VenueIET Renewable Power Generation · 2016
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsGrid codeWind powerPower gridGridCompliance (psychology)Environmental scienceComputer scienceNuclear engineeringPower (physics)Marine engineeringElectrical engineeringPhysicsEngineeringAC powerGeologyPsychology

Abstract

fetched live from OpenAlex

This study presents the Hydro‐Québec TransÉnergie experience regarding the grid code compliance tests that have been performed for the integration of wind power plants using type‐III and type‐IV technologies. Tests discussed in this study were conducted in two phases: validation of the performance requirements for each project and type tests on a single wind turbine generator for each technology involved. The study presents examples of results for each test module and measurements from online monitoring. Technical challenges are discussed and performance issues that were raised through the process are explained.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.234
Teacher spread0.208 · 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 designObservational
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

Citations12
Published2016
Admission routes2
Has abstractyes

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