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Record W1988187037 · doi:10.1109/ipec.2010.5542011

Sensorless control of PMSG in variable speed wind energy conversion systems

2010· article· en· W1988187037 on OpenAlexaff
J. S. Thongam, Pierre Bouchard, Rachid Beguenane, I. Fofana, Mohand Ouhrouche

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsUniversité du Québec à ChicoutimiRoyal Military College of Canada
Fundersnot available
KeywordsControl theory (sociology)Permanent magnet synchronous generatorStatorWind speedMaximum power point trackingVector controlRotor (electric)Wind powerComputer scienceController (irrigation)Electronic speed controlVoltageEngineeringPhysicsInverterInduction motorControl (management)

Abstract

fetched live from OpenAlex

A sensorless control strategy for a permanent magnet synchronous generator (PMSG) in variable speed wind energy conversion system (WECS) is proposed. The proposed method is based on recursive least square estimation (RLSE) algorithm for rotor speed estimation using an equation containing only stator flux and current. Further, rotor position information used for coordinate transformation is computed using the estimated speed. Stator flux required for speed estimation is obtained using a programmable low pass filter (PLPF). The estimated speed is used as the feedback signal for the speed control loop of the vector controlled machine side converter control system whose command speed is obtained from a wind speed sensorless maximum power point tracking (MPPT) controller. The front end converter is vector controlled in grid voltage reference frame. Simulation is carried out in order to verify the proposed 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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.003
GPT teacher head0.162
Teacher spread0.159 · 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

Citations9
Published2010
Admission routes1
Has abstractyes

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