MétaCan
Menu
Back to cohort
Record W3098627470 · doi:10.18280/ijdne.150504

Use of an Indoor Solar Flash Test Device to Evaluate Production Loss Associated to Specific Defects on Photovoltaic Modules

2020· article· en· W3098627470 on OpenAlexvenueno aff
Silvia Luciani, Gianluca Coccia, Sebastiano Tomassetti, Mariano Pierantozzi, Giovanni Di Nicola

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2020
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemReliability engineeringFlash (photography)ThermographyAutomotive engineeringProduction (economics)Power (physics)Computer scienceElectronic engineeringEngineeringElectrical engineeringInfraredOptics

Abstract

fetched live from OpenAlex

During their lifetime, photovoltaic (PV) plants are subject to a normal degradation of their components, and they are consequently characterized by decrease of the expected production. In order to prevent and evaluate failures and loss of production, specific tests can be carried out on the PV modules. Non-destructive methods, such as visual inspection and infrared thermography, can be performed in order to determine production failures or defects on the PV modules. I-V curves allow to estimate the performance of photovoltaic modules and strings, estimating the deviation between the power of the examined module and that declared by the manufacturer. The aim of this work is to evaluate the efficiency loss of photovoltaic modules associated to specific defects, causing in a systematic way some faults on a set of brand-new modules and assessing the relative decrease of power. The set of brand-new photovoltaic modules, after being damaged, was experimentally characterized determining their I-V curves by means of an indoor solar flash test device based on a class A+ AM 1.5 solar simulator. Using the I-V curves as a dataset, it was possible to estimate the incidence of different defects on the power of the photovoltaic module being considered.

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)0.001
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.031
GPT teacher head0.275
Teacher spread0.244 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Design & Nature and EcodynamicsSame topicPhotovoltaic System Optimization TechniquesFrench-language works237,207