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Record W2906954192 · doi:10.1109/iecon.2018.8591168

Bank Switching Technique in Supercapacitor Energy Storage Systems for Line Voltage Regulation in Pulsed Power Applications

2018· article· en· W2906954192 on OpenAlexaff
Sidhu Navbir, L.M. Patnaik, Najath Abdul Azeez, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSupercapacitorEnergy storageCapacitorRenewable energyVoltageElectrical engineeringElectric double-layer capacitorPower densityComputer sciencePower (physics)CapacitanceMaterials scienceEngineeringElectrodeChemistryPhysics

Abstract

fetched live from OpenAlex

Electric Energy Storage Systems are pivotal for cleaner and sustainable development, applications ranging from transport electrification to electric power generation from renewable sources require some form of energy storage. Supercapacitors (SC) or Electric Double Layer Capacitors (EDLCs) with their remarkably long life cycles and high power handling capabilities have a growing presence in the energy storage market. With their complementary attributes compared to batteries, EDLCs provide a potent solution for hybrid electrical energy storage and even replace batteries in certain power applications. The lower energy density and a linear voltage-charge relation, limits the use of supercapacitors. In this paper a simple and efficient method to overcome these issues and increase the energy utilization of SCs and thereby downsizing the number of SCs required for an application has been presented with simulation and experimental results.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.249
Teacher spread0.234 · 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 designBench or experimental
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

Citations7
Published2018
Admission routes1
Has abstractyes

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