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Record W2112761594 · doi:10.1109/delta.2011.55

Performance Testing and Control of a Small Wind Energy Converter

2011· article· en· W2112761594 on OpenAlexaff
Riadh Habash, Voicu Groza, Yeu-Haw Yang, Charles Blouin, Pierre Guillemette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWind powerTurbinePower optimizerAutomotive engineeringRotor (electric)Induction generatorRenewable energyEngineeringElectricity generationElectrical engineeringPower (physics)Computer scienceMechanical engineeringMaximum power point trackingVoltageInverter

Abstract

fetched live from OpenAlex

Responding to more demand in coming years, the task of the small wind energy industry requires progress on several fronts-from public policy initiatives, to technology development, to market growth. Enhanced technologies such as contra-rotating blades, transmission systems, lubrication, airfoils, generators, and power electronics will lower cost and increase energy production. This paper mainly considers two key technological points of a small wind energy converter (SWEC) namely, the performance of the rotor system and induction generator. Small-scale prototypes have been built to experimentally verify the performance of the SWEC. Wind tunnel tests of the power output, power coefficient, and turbine speed were carried out to ascertain the aerodynamic power conversion and the operation capability at lower wind speeds. The results demonstrated a significant increase in performance compared to a single-rotor system of the same type. Another aspect of development and test is to present a comparative performance evaluation between a standard induction generator and an efficient but with modified design (TRIAS Generator) as a realistic solution of clean power for grid-connected SWECs. The paper also discusses issues related to control and monitoring of SWEC.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.185

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 head0.024
GPT teacher head0.171
Teacher spread0.147 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2011
Admission routes1
Has abstractyes

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