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Record W2064323657 · doi:10.1080/15325008.2011.639129

Operating Risk Analysis of Wind-integrated Power Systems

2012· article· en· W2064323657 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

VenueElectric Power Components and Systems · 2012
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWind powerElectric power systemReliability engineeringRenewable energyPower optimizerWind power forecastingAutomotive engineeringEngineeringPower (physics)Computer scienceMaximum power point trackingElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Abstract Growing environmental concerns associated with electric power generation, and the awareness toward the use of renewable energy has caused widespread and rapid increase in the installation of wind-power systems. The uncertain and intermittent nature of wind power has led to growing problems in integrating wind power in power systems as the wind-power penetration continues to increase. One of the prime requirements of operating a power system with wind power is maintaining the system reliability by committing an appropriate amount of power from available generation sources in the lead time considered. Commitment of wind power is a very crucial task that requires accurate wind-power forecasting. This article presents a time series model to recognize the variability in wind and presents a conditional probabilistic method to quantify the wind-power commitment risk during system operation. The method is applied to assess the short-term operational risks with wind power and the operating capacity credits of a wind farm using the Roy Billinton Test System test system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
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
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.202
Teacher spread0.192 · 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