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Record W4200195871 · doi:10.18280/jesa.540606

Performance Evaluation of PV Panel Configurations Considering PSC’s for PV Standalone Applications

2021· article· en· W4200195871 on OpenAlexvenueno aff
Asadi Suresh Kumar, Vyza Usha Reddy

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2021
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemShadingMaximum power principleIrradianceShadow (psychology)DiagonalEconomic shortagePower (physics)ElectricityComputer scienceAutomotive engineeringEnvironmental scienceElectrical engineeringEngineeringMathematicsPhysicsOpticsGeometry

Abstract

fetched live from OpenAlex

One of the major concerns for continuous solar photovoltaic (PV) generation is partial shading. The movement of clouds, shadow of buildings, trees, birds, litter and dust, etc., can lead to partial shadow conditions (PSCs). The PSCs have caused inconsistent power losses in the PV modules. This leads to a shortage of electricity production and the presence in the PV curve of several peaks. One of the simplest solutions to PSC’s is the PV configurations. The objective of this paper is modelling and simulation of solar PV system in various shading scenarios for KC200GT 200 W, 5 x 5 configurations that includes Series/Parallel (SP), Total-Cross-Tied (TCT), Triple-Tied (TT), Bridge-Link (BL) configurations. Real time PSC’s such as corner, center, frame, random, diagonal, right side end shading conditions are evaluated under all PV array configurations. A comparative analysis is carried out for the parameters such as open circuit voltage, short circuit current, maximum power point, panel mismatch losses, fill factor, efficiency under all PV configurations considering PSC’s. From the comparison analysis best configuration will be presented.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.668
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.063
GPT teacher head0.303
Teacher spread0.240 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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