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Wind Turbine and Photovoltaic Hybrid Generations under Extreme Operating Gust

2014· article· en· W2038130747 on OpenAlexvenueno aff
Ahmad Eid, Mamdouh Abdel‐Akher

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMaximum power point trackingTurbinePhotovoltaic systemWind powerAutomotive engineeringWind speedVariable speed wind turbineInduction generatorEngineeringPower optimizerRotor (electric)Electrical engineeringControl theory (sociology)VoltageMarine engineeringComputer sciencePermanent magnet synchronous generatorMeteorologyInverterAerospace engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

A micro-grid consisting of a 300kW wind turbine and a 40kW photovoltaic array is investigated under extreme operating gust (EOG) wind scenarios. The micro-grid is connected to the 380-V utility through a utility-side converter using natural-frame control, which provides a constant voltage of the DC link between the micro-grid and utility. The impact of the extreme gust wind variations on the micro-grid performance is studied for variable speed wind energy system equipped with a squirrel-cage induction generator. The Hurghada city, Red Sea, Egypt is taken as a case study for the wind speed profile. A detailed model of extreme gust-wind speed variation is implemented and simulated using PSIM commercial software package, based on climate characteristics of Hurghada city. The indirect rotor field oriented control (FOC) method is implemented to the generator-side converter to keep the system stable under the extreme gust wind conditions and to control the squirrel-cage induction generator (SCIG) speed for maximum power-point tracker (MPPT) regime. Power quality of the utility-side converter in terms of operation at different power factors, voltage value and THD are verified.

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 score0.438
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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