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Record W1493729624 · doi:10.1109/pes.2004.1373120

Optimal reactive power allocation in a wind powered doubly-fed induction generator

2004· article· en· W1493729624 on OpenAlexaff
Chad Abbey, G. Joós

Bibliographic record

VenueIEEE Power Engineering Society General Meeting, 2004. · 2004
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsAC powerConvertersPower factorControl theory (sociology)Induction generatorCompensation (psychology)Power (physics)Power controlRotor (electric)Generator (circuit theory)Wind powerComputer scienceEngineeringElectrical engineeringControl (management)VoltagePhysics

Abstract

fetched live from OpenAlex

The doubly-fed induction machine has been shown to be a popular choice for wind power generators, due to the desirable features of variable speed operation, reduced converter size and the ability to control the real and reactive output power. Typically the reactive power compensation is performed only from the rotor side converter while the supply side converter maintained at unity power factor. This work explains the concept of reactive power compensation in this generator and shows that it can be performed using both converters. In addition, it is shown that through proper choice of the distribution of reactive compensation between the two converters, an overall reduction in the total KVA rating of the converters can be achieved. A methodology for the design of the components and the control is presented. The theoretical predictions are supported by simulations using EMTP-RV.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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

Citations13
Published2004
Admission routes1
Has abstractyes

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Same venueIEEE Power Engineering Society General Meeting, 2004.Same topicWind Turbine Control SystemsFrench-language works237,207