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Effect of Series and Parallel Shading on the Photovoltaic Performance of Silicon Based Solar Panels

2015· article· en· W2228345715 on OpenAlexvenueno aff
Fahmi Fariq Muhammad, Shalaw Sdeeq, Aso Ameen

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2015
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsShadingPhotovoltaic systemEquivalent series resistanceSeries and parallel circuitsVoltageSeries (stratigraphy)Materials scienceOptoelectronicsEnvironmental scienceComputer scienceElectrical engineeringEngineeringComputer graphics (images)Biology

Abstract

fetched live from OpenAlex

In this research work silicon based solar panels were used to investigate the impact of series and parallel shading on the photovoltaic performance of inorganic solar panels. The results showed that voltage, current and power of the solar panels were reduced upon shading the series and parallel cells. This decrement was seen to be larger for the series shading compared to that of the parallel shading. This was attributed to the adverse effect of the series resistance of the shaded cells, which is acted as a bottleneck in front of the passage of current. This was not very effective in the parallel shading because current is readily capable to pass through the illuminated parallel cells and neglect the pathway of parallel shaded cells. From the results, it was concluded that the lower performance of solar panels due to shading effect is because of the change in the internal resistance of the panels. This situation is possible to occur during daily life use of solar panels as a result of the shading by clouds, dusts or trees. Hence, considerable investigation towards solving this problem is of great importance.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
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 head0.017
GPT teacher head0.249
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2015
Admission routes1
Has abstractyes

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