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Record W2160587035 · doi:10.1109/naps.2008.5307339

Load carrying capability of wind integrated power systems

2008· article· en· W2160587035 on OpenAlex
B. Karki, R. Billinton, Rajesh Karki, Ramakrishna Gokaraju

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWind powerElectric power systemRenewable energyReliability engineeringProbabilistic logicBase load power plantWind speedComputer scienceAutomotive engineeringPower (physics)Power system simulationEnvironmental scienceEngineeringMarine engineeringDistributed generationElectrical engineeringMeteorology

Abstract

fetched live from OpenAlex

Wind power is a valuable renewable source of electrical energy. Due to its stochastic nature, wind power is usually considered as an energy source rather than as a power contributor to the system generating capacity. Worldwide installation of wind power is expected to continue to grow in the coming years. It is therefore necessary to recognize the operating capacity value of the added wind power. This paper presents an approach to predicting short term wind speed distributions that can be used in a probabilistic analysis of the unit commitment risk. The development of performance indices such as the Peak Load Carrying Capability (PLCC) and the PLCC benefit ratio in wind integrated power systems is demonstrated using two published 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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.366
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.185
Teacher spread0.175 · 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

Quick stats

Citations2
Published2008
Admission routes1
Has abstractyes

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