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Power Generation Improvement in Partially Shaded Series-Parallel PV Arrays through Junction Wires

2022· article· en· W4293234230 on OpenAlexaff
Khaja Izharuddin, Belqasem Aljafari, Rupendra Kumar Pachauri, Karthik Balasubramanian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsShadingSeries and parallel circuitsDiodeSeries (stratigraphy)VoltagePower (physics)Electricity generationComputer scienceJunction temperatureReduction (mathematics)Maximum power principleMaterials scienceTopology (electrical circuits)OptoelectronicsElectronic engineeringElectrical engineeringPhysicsEngineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

In this paper, the effect of partial shading in the most hitherto known and widely applied PV array configuration has been studied. Also, an approach of adding additional wires to the junctions of series-parallel modules for losses reduction due to shading is proposed and validated using various shading scenarios. The additional wires in the module junctions allow the flowing of higher current through additional paths avoiding the bypass diodes activation. The complete study is conducted in the simulation for a 6x5 array and both the connection type i.e. simple series-parallel and series-parallel with junction wires are tested under six partial shading scenarios using power generation, mismatch and power losses, efficiency and power-voltage characteristics curves. From the conducted study, it has been found that the array with junction wires is capable of improving the PV array generation capacity under shading and can be applied to arbitrary sized arrays with very less complexity and cost.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.996

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)0.0050.000

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 head0.021
GPT teacher head0.241
Teacher spread0.221 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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