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Record W1996330381 · doi:10.1243/1748006xjrr90

Impacts of repair state residence time distributions in an electric power generating capacity adequacy assessment

2007· article· en· W1996330381 on OpenAlexaff
D. Huang, R. Billinton

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · 2007
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsReliability engineeringWeibull distributionElectric power systemComputer scienceState (computer science)Power system simulationPower (physics)EngineeringMathematicsStatisticsAlgorithm

Abstract

fetched live from OpenAlex

The primary function of an electric power system is to satisfy the load requirement as economically as possible with an acceptable assurance of continuity and quality. A generating capacity adequacy evaluation involves the determination of the total system generation required to satisfy the load requirement. In these studies, a generating unit is usually represented by a two-state model in which the unit is either available or unavailable for service. These models are valid representations for base load units but do not adequately represent intermittent operating units used to meet peak load conditions. The two-state model for a base load unit has been extended to a four-state peaking unit model that is widely used in practice. The generating unit state residence time distributions in these models are assumed to be exponential in form in virtually all practical system studies. This may not be a valid assumption for the repair state in some situations. A sequential Monte Carlo simulation technique is utilized to incorporate Weibull distributed generating unit state residence times in the two-state and four-state models. The effects on the adequacy indices and the adequacy index distributions are illustrated by application to two practical test systems.

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.

How this classification was reachedexpand

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.087
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
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 integrity0.0000.001
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 head0.229
Teacher spread0.222 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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