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Record W2145236771 · doi:10.1109/ccece.2003.1226416

Compensation for load and speed variation of self-excited induction generator

2004· article· en· W2145236771 on OpenAlexaff
Tarek H. M. EL-Fouly, Ehab F. El‐Saadany, M.M.A. Salama, A.Y. Chikhani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInduction generatorControl theory (sociology)Prime moverWind powerTurbineWind speedPulse-width modulationInverterRotor (electric)VoltageCompensation (psychology)EngineeringComputer scienceElectrical engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

The generation of electrical power in isolated remote areas depends on the utilization of renewable energy resources such as wind, solar and geothermal energies. Self-excited induction generators (SEIG) have been widely used in electric power plants utilizing wind energy. This paper introduces a digital simulation and a control technique of a stand-alone SEIG scheme driven by a wind turbine as an external mechanical prime mover. The SEIG scheme will be simulated under the PSCAD/EMTDC environment. The proposed control scheme consists of two parts. The first part controls the turbine blades pitch angle (/spl beta/) to compensate the variations in the rotor speed due to the variations in the wind velocity. The second part regulates the load voltage waveform in magnitude and frequency within the acceptable limits using a PWM inverter. Two Pl controllers will be used to provide the control signal for each of the two parts. Results are presented in the paper to demonstrate the effectiveness of the proposed control scheme.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.237

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 head0.010
GPT teacher head0.195
Teacher spread0.185 · 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

Citations3
Published2004
Admission routes1
Has abstractyes

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