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Record W2132745496 · doi:10.1109/tpwrs.2009.2016485

Unit Commitment Risk Analysis of Wind Integrated Power Systems

2009· article· en· W2132745496 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

VenueIEEE Transactions on Power Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWind powerPower system simulationElectric power systemReliability engineeringPower (physics)Wind speedEngineeringAutomotive engineeringNameplate capacityBase load power plantElectricity generationComputer scienceOperations researchControl theory (sociology)Electrical engineeringMeteorologyControl (management)

Abstract

fetched live from OpenAlex

The utilization of wind power generation is increasing throughout the world and it is therefore important that these facilities be integrated in the existing generating capacity planning and operating protocols and procedures. This paper presents an approach to evaluate the contribution that wind power can make to the load carrying capability of a power generating system in an operating scenario. The basic concepts of unit commitment risk analysis are extended to include the inherent variability associated with wind power by developing short-term probability distributions of the wind speed and wind power output using auto-regressive moving average (ARMA) time series models. The operating capacity contributions attributable to wind power are illustrated by application to a small test system and are expressed in terms of the increased load carrying capability due to the wind power generating facilities.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.010
GPT teacher head0.215
Teacher spread0.206 · 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