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Record W2958223066 · doi:10.18280/ejee.210107

Modeling of New Architecture of Photovoltaic Generator Based on a-Si: H/c-Si Materials

2019· article· en· W2958223066 on OpenAlexvenueno aff
Mourad Talbi, Rached Ganouni, Hatem Ezzaouia

Bibliographic record

VenueEuropean Journal of Electrical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemGenerator (circuit theory)ArchitectureMaterials scienceComputer scienceOptoelectronicsEngineeringElectrical engineeringPhysicsPower (physics)Art

Abstract

fetched live from OpenAlex

The objective of this study is to investigate the shading effect on a new architecture of Photovoltaic Generator (PVG) proposed in this work. This new architecture is constitutes of three PV modules in series connected. Two of them are constitute of amorphous silicon cells in series connected. The third Module is constitutes of monocristallin silicon cells in series connected. This architecture is conceived as a PV concentrator where the two Amorphous PV Modules are in the lower position and the third one is in the upper position (located in the Focus). The role of the upper Module consists in absorbing the solar rays reflected by the two others modules in order to gain the maximum of solar energy. This architecture aims to solve the problems existing with the architecture of tandem solar cells proposed in literature. Those problems are the mismatch between cells and the tunnel junction costs and fabrication. In this paper, we use Matlab/Simulink for modeling this architecture and studying their characteristics ( I -V and P -V ) in case of partial shading. Through this study, it was found that the maximum PV power is affected by the partial shading. The findings of this research can serve as in the real construction of this new PVG architecture.

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 score0.083
Threshold uncertainty score0.657

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 head0.009
GPT teacher head0.178
Teacher spread0.169 · 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
Published2019
Admission routes1
Has abstractyes

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