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Record W4205188099 · doi:10.18280/i2m.200603

Shading Effect on Flow Rate of Solar DC Water Pump

2021· article· en· W4205188099 on OpenAlexvenueno aff
Ali N. Hamoodi, Fawaz S. Abdullah, Farah I. Hameed, Bashar Mohammed Salih

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2021
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsShadingPhotovoltaic systemMaximum power point trackingMaximum power principleDC motorSolar energyPower (physics)VoltageElectrical engineeringMaterials scienceAutomotive engineeringEnvironmental scienceComputer sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

Partial shading (PS) has a negative impact not only on the shaded Photo Voltaic PV modules/arrays but also on the power generated by the partially shaded photovoltaic (PSPV) system. It decreases the output power of the (PV) system and adds to hot fleck issues, which may result in the thermal breakdown of shaded PV modules. Solar water pumping is based on a (PV) system that converts solar energy into electrical energy to power a DC motor. This paper includes a practical investigation on the DC pump performance, as well as the influence of (1/4, 1/2, 3/4, and completely) shading on the performance of the DC pump motor. The current study shows that by Using maximum power point tracking (MPPT) control, the motor voltage will be stabilized and not affected by the increase in the shading area.

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)0.000
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 score0.039
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.018
GPT teacher head0.274
Teacher spread0.256 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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