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Record W4285099745 · doi:10.18280/jesa.550309

The Design and Simulation of a Photovoltaic System Connected to the Grid Using a Boost Converter

2022· article· en· W4285099745 on OpenAlexvenueno aff
Moulay Fatima, Assia Habbati, Abdelkader Lousdad

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2022
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMaximum power point trackingPhotovoltaic systemMATLABMaximum power principleComputer scienceBoost converterPower (physics)Grid-connected photovoltaic power systemSolar irradianceGridInverterControl theory (sociology)Power optimizerElectronic engineeringAC powerVoltageEngineeringElectrical engineeringControl (management)MathematicsPhysics

Abstract

fetched live from OpenAlex

The objective of this paper is to present the modelling and simulation of the PV generation system connected to the network under MATLAB/Simulink. Firstly, a mathematical model of the Photovoltaic Module is developed taking meteorological data namely the irradiance and temperature as input variables needed to model this device. The output can be current, voltage or power. The model allows the prediction of the behavior and characteristics of the PV module based on the equivalent circuit of the mathematical model under different temperatures and solar radiation readings. Secondly the PV array is connected to the boost DC-DC converter, the control systems based on Maximum Power Point Tracking (MPPT) with P&O algorithm helps the PV array to generate the maximum power to the grid in case of changing weather conditions, then in the third step, it is integrated into AC power grid by DC/AC inverter to control active and reactive power to achieve unity power factor which is validated by satisfactory results. In this paper, different cases are simulated and the results verified the validity of the models.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.697
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.259
Teacher spread0.232 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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