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Record W4293238985 · doi:10.24084/repqj20.425

Transition between Stand-Alone and Grid Connected Solar PV Microgrids

2022· article· en· W4293238985 on OpenAlexafffund
F.H. Sarker, Adel Merabet, Tareq Salameh, C. Ghenai

Bibliographic record

VenueRenewable Energy and Power Quality Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsSaint Mary's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrogridPhotovoltaic systemConvertersPower electronicsGridComputer scienceBattery (electricity)Energy storagePower (physics)MATLABElectrical engineeringVoltageControl engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

In this study, a solar photovoltaic microgrid, to sustainable energy and system efficiency, is being studied under different configurations. Solar modules, energy storage devices (battery), power electronics converters are used to validate the desired results. The study of stand-alone to grid-connected systems will be analyzed and tuned for a stable system performance. In the stand-alone tuning, the voltage and the frequency are the key driving parameters to be controlled and evaluated for proper operation of the microgrid. Also, the grid connected configuration will be studied and then both systems will be compiled together using a switching mechanism. The aim is developing a control system that enables the transition between the two configurations. The microgrid is simulated and tested using MATLAB/Simulink platform. The objective is to create a smooth transition of system with control mechanisms in different modes of the microgrid

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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