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Record W4362514067 · doi:10.3103/s0003701x22060093

Novel Hybrid Photovoltaic Array Arrangement to Mitigate Partial Shading Effects

2022· article· en· W4362514067 on OpenAlexaboutno aff
Nouhaila Jariri, Elhassan Aroudam

Bibliographic record

VenueApplied Solar Energy · 2022
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsShadingPhotovoltaic systemOpticsEnvironmental scienceMaterials scienceOptoelectronicsComputer sciencePhysicsElectrical engineeringEngineeringComputer graphics (images)

Abstract

fetched live from OpenAlex

Abstract This paper presents a Novel Hybrid reconfiguration scheme to mitigate partial shading effects in the photovoltaic array and minimize ties number and wiring complexity. The proposed topology is based on a combination of Total-Cross-Tied and Bridge-Linked classic topologies. Ten possible shadowing scenarios have been considered for this investigation in 6x6 photovoltaic array. Further, the proposed topology performance is assessed and compared to various typical photovoltaic array topologies, namely, Series-Parallel (S‑P), Honey-Comb (H-C), Bridge-Linked (B-L), and Total-Cross-Tied (T-C-T) by considering the several parameters like Global Maximum Power Point (GMPP), Current at GMPP, Voltage at GMPP, Fill Factor, Mismatch Loss, and Efficiency. The Canadian Solar CS5A-200M PV module parameters are used in MATLAB-Simulink software to simulate photovoltaic array configurations. Finally, the findings of this work demonstrates that the suggested topology minimizes the shading losses and generates the highest maximum power regarding the other array configurations under most shadowing cases. The power enhancement percentage of the Novel arrangement can reach up to 20.25% for the S-P configuration, and up to 16% for both the H-C and B-L topologies. In addition, the proposed scheme is characterized by the minimum wiring loss when compared to the Total-Cross-Tied topology.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.214
Teacher spread0.205 · 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 designBench or experimental
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

Citations4
Published2022
Admission routes1
Has abstractyes

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