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Record W2129398593 · doi:10.1109/tia.2007.895768

Supercapacitor Energy Storage for Wind Energy Applications

2007· article· en· W2129398593 on OpenAlexafffund
Chad Abbey, Gza Joos

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2007
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsEnergy storageWind powerLow voltage ride throughSupercapacitorTransient (computer programming)Electrical engineeringTurbineCapacitorAutomotive engineeringSuperconducting magnetic energy storageInduction generatorEngineeringIntermittencyComputer scienceVoltageElectronic engineeringPower (physics)AC powerCapacitanceMechanical engineeringPhysicsElectromagnetic coil

Abstract

fetched live from OpenAlex

As wind energy reaches higher penetration levels, there is a greater need to manage intermittency associated with the individual wind turbine generators. This paper considers the integration of a short-term energy storage device in a doubly fed induction generator design in order to smooth the fast wind-induced power variations. This storage device can also be used to reinforce the dc bus during transients, thereby enhancing its low-voltage ride through (LVRT) capability. The topology is evaluated in terms of its ability to improve the performance both during normal operation and during transients. Results show that when storage is sized based upon the LVRT requirement, it can effectively damp short-term power oscillations, and it provides superior transient performance when compared with conventional topologies

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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

Citations827
Published2007
Admission routes2
Has abstractyes

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