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Record W3192932428 · doi:10.14447/jnmes.v24i2.a08

Maximum Power Point Tracking in Solar Cells with Power Quality Preservation Based on Impedance Matching Concept for Satellite Electrical Energy Supply

2021· article· en· W3192932428 on OpenAlexvenueno aff
Li Fu, Xiuwei Fu, Yang Ping

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2021
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMaximum power point trackingPower (physics)Maximum power principleMATLABImpedance matchingElectricity generationPhotovoltaic systemElectrical impedanceMatching (statistics)Electronic engineeringElectrical engineeringEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

The power generation subsystem represents one of the principal components of a space system and is usually assembled from solar arrays and PPT topology. This paper aims to design and implement an algorithm to continuously extract the maximum power from the solar cells and deliver it to the consumer with minimum loss. In this regard, a brief introduction to the various parts of the power generation subsystem is provided and through accurate modeling of the solar cell and exploring the contributing factors to its power generation (including temperature, radiation, and space radiation), the algorithm is proposed based on the impedance matching concept. Simulation studies using MATLAB software have estimated an approximate 0.25065W per square centimeter power extraction and delivery to the customer. Our results suggest that the proposed approach can reduce steady-state losses while ensuring accuracy.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0010.001
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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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 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
Published2021
Admission routes1
Has abstractyes

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