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Record W2157676431 · doi:10.1109/pes.2011.6039636

Novel configurations for photovoltaic farms to reduce partial shading losses

2011· article· en· W2157676431 on OpenAlexaff
M. Z. Shams El-Dein, Mehrdad Kazerani, M.M.A. Salama

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsShadingPhotovoltaic systemComputer scienceElectrical engineeringEnvironmental scienceEngineeringComputer graphics (images)

Abstract

fetched live from OpenAlex

Partial shading is the condition of having different insolation levels at different parts of a photovoltaic structure. This structure can be a module, array or farm. The difference in insolation levels causes miss-match in the elements of the photovoltaic structure. This miss-match has undesirable effects such as reduction in generated power from the elements of the structure and hot spots inside the structure. The severity of these effects can be considerably reduced by reconnecting the structure in such a way that miss-match is reduced. This paper proposes novel configurations for modules inside farms, that result in considerable reduction in partial shading losses. Also this paper introduces a novel mathematical formulation for insolation level miss-match. This formula can be used for comparative evaluation of different photovoltaic configurations. The improvement over the existing photovoltaic configurations has been demonstrated by simulation results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.081
GPT teacher head0.298
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2011
Admission routes1
Has abstractyes

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