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Record W2782093952 · doi:10.1109/chilecon.2017.8229726

Sensitivity analysis of the synchronous generation repowering system in parallel with induction generator

2017· article· en· W2782093952 on OpenAlexaff
Alana da Silva Magalhães, Júnio S. Bulhões, Calebe Abrenhosa Matias, Alan H. F. Silva, Geovanne P. Furriel, Márcio R. C. Reis, Gabriel Wainer, Viviane M. Gomes, Wesley Pacheco Calixto, Aylton J. Alves

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsCarleton University
FundersFundação de Amparo à Pesquisa do Estado de Goiás
KeywordsSensitivity (control systems)Permanent magnet synchronous generatorControl theory (sociology)AC powerInduction generatorGenerator (circuit theory)Nonlinear systemElectric generatorElectric power systemPower (physics)Computer scienceWork (physics)Electricity generationVoltageElectronic engineeringEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

In this work, it is proposed to calculate the sensitivity of parameters, based on analytical calculation, of repowered system between two electric generators, an induction generator and a synchronous generator, connected to a common bus in permanent regime, subject to a non-linear load. It is proposed a repowered system with simulated data where, based on percentage variations in the base values of the parameters, measurements are made for the analytical calculation that expresses the impact at the output caused by variations in the input. The experimental tests confirmed quantitatively the most sensitive variables for the system for the interval of interest. The results show that the generators and the nonlinear load are the input parameters with the highest sensitivity in the active power output and that the synchronous generator is responsible for the greater sensitivity in the reactive power output.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.011
GPT teacher head0.200
Teacher spread0.189 · 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 teacher head, 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

Citations1
Published2017
Admission routes1
Has abstractyes

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