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Record W2153079538

Method for studying and mitigating the effects of wind variability on frequency regulation

2009· article· en· W2153079538 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

Venue2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources Into the Power Delivery System · 2009
Typearticle
Languageen
FieldEngineering
TopicFrequency Control in Power Systems
Canadian institutionsResearch ManitobaManitoba HydroUniversity of Manitoba
Fundersnot available
KeywordsControl theory (sociology)Automatic frequency controlInertiaWind powerAutomatic Generation ControlTurbineElectric power systemGovernorSystem dynamicsController (irrigation)Representation (politics)Power (physics)Variation (astronomy)EngineeringFrequency responseSensitivity (control systems)Control engineeringComputer scienceControl (management)TelecommunicationsElectronic engineering
DOInot available

Abstract

fetched live from OpenAlex

This paper presents a simulation-based approach to study the effect of wind energy variation on the frequency regulation of a power system. In North America, the quality of frequency regulation is defined in terms of two indices known as Control Performance indices (CPS1 and CPS2). The power system is modelled as a control system with equivalent representation of turbine-governor dynamics. The system inertia is modelled as a single equivalent inertia. The power system external to the system under consideration is modelled as a single equivalent. The model is then used to study the sensitivity of CPS indices to wind variation and the settings of the Automatic Generation Controller parameters. The model also gives the amount of regulation reserves utilized in each simulated scenario.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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