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Record W2148979021 · doi:10.1109/cjece.2015.2420995

Energy Storage in Flywheels: An Overview

2015· article· en· W2148979021 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFlywheelEnergy storageFlywheel energy storageRenewable energyUpgradeComputer scienceElectric power systemDeferralAutomotive engineeringReliability engineeringEngineeringElectrical engineeringPower (physics)Business

Abstract

fetched live from OpenAlex

In a deregulated power market with increasing penetration of distributed generators and renewable sources, energy storage becomes a necessity. Renewable energy sources are characterized by a fluctuating and intermittent nature, which simply means that energy may be available when it is not needed, and vice versa. Energy storage devices can help rectify the mismatch between generation and demand at any loading condition. Such devices can also provide some ancillary services, such as frequency regulation, voltage support, power quality improvement, transmission congestion relief, and system upgrade deferral. This paper presents an overview of the flywheel as a promising energy storage element. Electrical machines used with flywheels are surveyed along with their control techniques. Loss minimization and bearing system development are introduced. In addition, power system applications of flywheels are summarized. Finally, some outstanding technical difficulties are recommended for future research work.

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 score0.791
Threshold uncertainty score0.348

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 head0.012
GPT teacher head0.174
Teacher spread0.163 · 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