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Record W1822743446 · doi:10.1109/tdc.2006.1668731

Power System Response to Wind Power Fluctuations

2006· article· en· W1822743446 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
TopicPower System Optimization and Stability
Canadian institutionsMcGill University
Fundersnot available
KeywordsGovernorWind powerPower (physics)Electric power systemControl theory (sociology)Frequency deviationThermal power stationTransfer functionWind speedEnvironmental scienceComputer scienceElectrical engineeringAutomatic frequency controlEngineeringMeteorologyControl (management)PhysicsAerospace engineering

Abstract

fetched live from OpenAlex

This paper reports on a study aimed at analysis of the power grid response to fluctuations in the wind farms output powers. Using transfer function analysis of thermal generation plants, the paper demonstrates that for an allowable 1% deviation from 60 Hz, the peak fluctuating power at a single wind farm can go as high as 5% of the total power ratings of the thermal plants. The paper draws attention to the inadequacy of frequency regulation alone in handling the fluctuations and to the increase of wear and tear on hardware, when the governor systems serve as filters to wind power fluctuations. It also calls for more pertinent policies for filtering the fluctuations, prior to entry at the points of connection

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score0.962

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)0.0010.001

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 head0.004
GPT teacher head0.196
Teacher spread0.191 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

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