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Record W2118972853 · doi:10.1109/pesc.2007.4342177

Comparison of Energy Storage System Technologies and Configurations in a Wind Farm

2007· article· en· W2118972853 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsWind powerEnergy storageElectric power systemPower (physics)VoltageDistributed generationElectrical engineeringComputer scienceSmoothingFrequency deviationRenewable energyAutomotive engineeringElectronic engineeringEnvironmental scienceReliability engineeringEngineeringAutomatic frequency controlPhysics

Abstract

fetched live from OpenAlex

Wind power fluctuations have adverse impacts on power quality, such as local voltage and system frequency. Integrating an energy storage system (ESS) in a wind farm reduces wind power fluctuations. Various ESS technologies and configurations are viable for this application. This paper examines aggregated and distributed connection topologies of the ESS technologies within the wind farm. The power ratings of the ESSs and their power electronic interfaces are estimated. The abilities of the aggregated and distributed ESSs to improve local voltage and reduce system frequency deviations are evaluated and compared. Simulation results show that the two ESS configurations have effective and similar performance to suppress wind power fluctuations, and thus to improve the power quality. Compared to its distributed counterpart, the aggregated ESS could have a lower power rating due to smoothing effect in a wind farm, but a higher converter VA rating due to the switch count.

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 score0.598
Threshold uncertainty score0.164

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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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

Citations132
Published2007
Admission routes1
Has abstractyes

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