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Record W1993605403 · doi:10.1049/iet-rpg.2010.0054

Steady-state performance analysis and modelling of directly driven interior permanent magnet wind generators

2011· article· en· W1993605403 on OpenAlexaff
S. A. Saleh, M. A. S. K. Khan, M.A. Rahman

Bibliographic record

VenueIET Renewable Power Generation · 2011
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsStatorRectifier (neural networks)HarmonicVoltageResistive touchscreenControl theory (sociology)Terminal (telecommunication)Harmonic analysisSteady state (chemistry)InverterMagnetWind powerElectrical engineeringPhysicsEngineeringComputer scienceElectronic engineeringAcoustics

Abstract

fetched live from OpenAlex

This study presents a systematic approach for modelling, analysing and evaluating steady-state performances of directly driven interior permanent magnet generators (IPMG) for wind energy conversion systems. The proposed approach for modelling and analysing the performance of PMGs is based on relating the harmonic components present in the stator currents to the harmonic components present in the terminal voltages. Three laboratory IPMG of 1, 5 and 50 kW are tested at different shaft speeds for supplying a resistive load, a rectifier and a resistive load and rectifier–inverter with a resistive load. Experimental performances of the tested IPMGs demonstrate direct relationships between terminal voltage harmonic components and harmonic components present in stator currents. Also, investigated experimental performances show significant impacts of stator currents harmonic components on the IPMG efficiency.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.191
Teacher spread0.171 · 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

Citations30
Published2011
Admission routes1
Has abstractyes

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