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Record W2013398018 · doi:10.1109/ccece.2008.4564811

A novel speed control algorithm for PMSG wind turbines aimed at output power smoothing

2008· article· en· W2013398018 on OpenAlexvenueno aff
A. Abedini, Tin Luu, Adel Nasiri

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsTurbineWind powerPermanent magnet synchronous generatorControl theory (sociology)Rotor (electric)SmoothingWind speedComputer sciencePower (physics)Power BalanceControl engineeringEngineeringControl (management)MagnetElectrical engineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents an idea of utilizing the rotor kinetic energy of the wind turbine to smooth the output power. The mathematical expressions of the idea are developed based on the energy balance of the system. A control algorithm is presented to control the speed of the wind turbine based on the presented method. The control algorithm is applied to a Permanent Magnet Synchronous Generator (PMSG) wind turbine to evaluate the performance of the algorithm.

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: 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)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.178
Teacher spread0.164 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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