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Record W2150160113

EMULATION OF A SMALL WIND TURBINE SYSTEM WITH A SEPARATELY-EXCITED DC MACHINE

2008· article· en· W2150160113 on OpenAlexaff
Md Arifujjaman, M. Tariq Iqbal, John E. Quaicoe

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTurbineControl theory (sociology)DC motorWind powerTorqueRotational speedInertiaEngineeringArmature (electrical engineering)Rotor (electric)Power optimizerElectrical engineeringComputer sciencePhysicsMechanical engineeringMaximum power point trackingElectromagnetic coilVoltage
DOInot available

Abstract

fetched live from OpenAlex

A small wind turbine emulator based on a separately excited DC machine is described in this paper. The experimental rig consists of a PC, Lab Master I/O board, power electronics circuitry and a 3HP separately excited DC motor which drives a synchronous generator. Varying aerodynamic power of the wind turbine due to furling action and its resulting dynamics are incorporated in the emulator with the use of a PC based wind turbine model. The shaft torque of the dc motor is determined from the armature current and parameters of the DC drive obtained by experimentation. In order to reflect the inertia of a small wind turbine an inertia disk is designed and coupled to the generator shaft and separately- excited DC motor. A digital PI controller is designed which makes sure that the actual rotational speed of the motor is tracking the theoretical rotational speed of the wind turbine rotor. The system design, model used and experimental results of the small wind turbine emulator are presented in the paper.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0050.001

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 head0.275
GPT teacher head0.465
Teacher spread0.190 · 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 designSimulation or modeling
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

Citations28
Published2008
Admission routes1
Has abstractyes

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