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Record W2126687810 · doi:10.1109/ias.2005.1518727

Short-term energy storage for wind energy applications

2005· article· en· W2126687810 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFourtieth IAS Annual Meeting. Conference Record of the 2005 Industry Applications Conference, 2005. · 2005
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMcGill University
FundersNatural Resources Canada
KeywordsWind powerEnergy storageTurbineInduction generatorTransient (computer programming)Pumped-storage hydroelectricityLow voltage ride throughComputer scienceVoltageEmtpControl theory (sociology)Electric power systemEngineeringAC powerAutomotive engineeringElectrical engineeringDistributed generationPower (physics)Renewable energyPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

The need to limit power fluctuations resulting from wind turbine generators (WTG) is becoming more important as wind energy reaches higher levels of penetration. This paper considers the integration of a short-term energy storage device in a doubly-fed induction generator (DFIG) design in order to smooth the fast, wind induced power variations. This storage feature can also enhance the low voltage ride through (LVRT) capability. The topology is evaluated in terms of its ability to improve both the steady state and transient performance. The system is modeled in EMTP and a number of cases and configurations are illustrated. Results show that when storage is sized based upon the LVRT requirement, it can provide improved performance both under steady state and during transients when compared with conventional technologies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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