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Record W2119297662 · doi:10.1109/pes.2007.386052

Severity Index for Estimating the Impact of Wind Generation on System Vulnerability

2007· article· en· W2119297662 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 Power Engineering Society General Meeting · 2007
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsMcGill University
Fundersnot available
KeywordsWind powerReliability engineeringElectric power systemProbabilistic logicComputer scienceIndex (typography)Vulnerability (computing)Vulnerability indexElectricity generationVulnerability assessmentOperations researchEngineeringComputer securityPower (physics)Electrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes a severity index for estimating the impact of penetration of wind energy on the vulnerability of a power system. This index is the risk of not meeting the load and is in fact the risk of failure of the probability distributions of the system load and the system generation including the wind penetration. This probabilistic approach is appropriate for a system with large wind generation installation due to the random nature of the wind generation output. The severity index is proposed to recognize the possible existence of an area of vulnerability at the point of common coupling (PCC). This approach does not preclude applying this methodology to a specific PCC bus in order to verify the impact on the security at each PCC bus in the planning stage and to alert the system operator of any possible shortfall of generation. A case study is provided to demonstrate the proposed approach.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0000.000

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 head0.012
GPT teacher head0.253
Teacher spread0.241 · 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