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Record W2076450057 · doi:10.1109/pesc.2008.4592198

A power electronic interface for a battery supercapacitor hybrid energy storage system for wind applications

2008· article· en· W2076450057 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

VenuePESC record · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMcGill University
FundersMcGill UniversityU.S. Department of Defense
KeywordsSupercapacitorBattery (electricity)Wind powerEnergy storageElectrical engineeringAutomotive engineeringPower (physics)VoltageElectric power systemComputer scienceEngineeringCapacitancePhysics

Abstract

fetched live from OpenAlex

An energy storage system (ESS) in a wind farm is required to be able to absorb wind power surges during gusts, and have sufficient energy storage capacity to level wind fluctuations lasting for longer periods. ESS using a single technology, such as batteries, or supercapacitors, will have difficulties providing both large power and energy capacities. This paper proposes a flow-battery supercapacitor hybrid ESS, which takes advantage of the two complementary technologies to provide large power and energy capacities. The flow-battery is directly coupled to the WTG dc bus while the supercapacitor has a dc/dc IGBT converter interface. The dc bus voltage varies within a certain limit determined by the variable battery terminal voltage. With the supercapacitor absorbing high frequency power surges, the battery power rating, degree of discharge, and power losses are all reduced. Therefore the battery in the hybrid ESS has low cost and high longevity; and the system overall efficiency is improved.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.619

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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