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Record W2185526110 · doi:10.1260/0309-524x.39.5.549

On Electrodynamic Braking for Small Wind Turbines

2015· article· en· W2185526110 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

VenueWind Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsTrinity College
FundersTrinity College Dublin
KeywordsWind powerTurbineAutomotive engineeringRotor (electric)Transient (computer programming)BrakeTorqueGenerator (circuit theory)Dynamic brakingPower (physics)Induction generatorElectric generatorEngineeringControl theory (sociology)Computer scienceElectrical engineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Straightforward analysis can show that it is difficult to implement a successful electrodynamic braking system for a small wind turbine system, i.e. of swept area less than 200 m 2 and power rating of 50 kW. Two principal difficulties are: (i) the peak short-circuit torque of the electrical generator can be far too low to overcome the torques associated with the wind turbine rotor, even at wind speeds close to rated; (ii) the energy dumped into the generator during braking is significant and can cause swift heating to high temperatures. Transient electrical effects can also lead to electrical and electronic component failures. Documented failures in machines of up to 10 kW indicate that it is the case that electrodynamic braking is not well understood throughout the industry. Additionally, the academic literature on the topic is sparse. In this paper, we show how very straightforward analysis can shed light on the edge cases for electrodynamic brake systems and help to avoid expensive errors.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score1.000

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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