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Record W2154860395 · doi:10.1109/tpwrs.2009.2030282

Supply Adequacy Assessment of Distribution System Including Wind-Based DG During Different Modes of Operation

2009· article· en· W2154860395 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 Transactions on Power Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIslandingReliability engineeringWind powerMonte Carlo methodDistributed generationReliability (semiconductor)Electric power systemComputer scienceEngineeringPower (physics)Renewable energyElectrical engineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

Keen interest in the development and utilization of wind-based distributed generation (DG) has been currently observed worldwide. The reliability impact of this highly variable energy source is an important aspect that needs to be assessed as wind power penetration becomes increasingly significant. Distribution system adequacy assessment including wind-based DG units during different modes of operation is described in this paper. Monte Carlo simulation (MCS) and analytical technique are used in this work with a new implementation of the islanding mode of operation in the assessment. The results show that there is no significant difference between the outcomes of the two proposed techniques; however, MCS requires much longer computational time. Moreover, the effect of islanding appears in the improvement of the loss of load expectation (LOLE) and loss of energy expectation (LOEE).

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.666
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.234
Teacher spread0.224 · 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