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Record W2071684125 · doi:10.1109/ipec.2010.5543649

Analysis of distributed peak power tracking in photovoltaic systems

2010· article· en· W2071684125 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaximum power point trackingPhotovoltaic systemModular designMaximum power principlePower (physics)Tracking (education)Solar micro-inverterController (irrigation)Power optimizerComputer scienceRange (aeronautics)Control theory (sociology)Electronic engineeringEngineeringElectrical engineeringPhysicsControl (management)VoltageArtificial intelligenceAerospace engineering

Abstract

fetched live from OpenAlex

It has been demonstrated that performing localized maximum peak power tracking (MPPT) on each photovoltaic (PV) panel, instead of using a single MPPT controller across the PV string can substantially increase the total harvested power, since each panel experiences unique illumination and temperature conditions. In this work, the effect of the dc-dc converter efficiency on the power savings from distributed MPPT (DMPPT) is analyzed for a wide range of test cases and different PV panel parameters. The benefit of DMPPT for a practical system is shown to be up to 25% for a standard deviation of σ= 0.2 A set of modular hardware-based PV panel emulators (ePVs) is presented. The ePVs can be programmed to match the unique i/v curves of real panels under various conditions and can therefore be used to optimize future DMPPT systems.

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.

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: Empirical
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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