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Record W1803807889 · doi:10.1109/ccece.2002.1015169

Adequacy evaluation of generation systems including wind energy

2003· article· en· W1803807889 on OpenAlexaff
R. Billinton, Guang Chen Bai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWind speedWind powerReliability (semiconductor)Monte Carlo methodComputer scienceAutoregressive–moving-average modelReliability engineeringEngineeringAutoregressive modelMarine engineeringMeteorologyMathematicsPower (physics)Statistics

Abstract

fetched live from OpenAlex

Wind energy is an intermittent resource as wind speed is highly variable and site-specific. This paper examines the capacity adequacy of a representative system containing both conventional generation units and wind energy conversion systems (WECS). An autoregressive moving average (ARMA) time series model is used to simulate the hourly wind speeds considering the chronological wind velocity characteristics. A reliability test system (RBTS) containing 11 conventional generation units is utilized to illustrate the proposed concepts. The analysis is conducted using a sequential Monte Carlo simulation approach. A WECS can make a contribution to the load carrying capacity of a conventional generating system given a reasonably high wind speed. The results show that the performance of a WECS is highly site-specific.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.258
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations12
Published2003
Admission routes1
Has abstractyes

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