MétaCan
Menu
Back to cohort
Record W4285252168 · doi:10.1109/ojpel.2022.3174445

An Efficient PV Battery Charger/Harvester for Low Power Applications, Suitable for Heavily Overcast Operations

2022· article· en· W4285252168 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Open Journal of Power Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBoost converterDuty cycleVoltageElectrical engineeringInductorOvercastBattery (electricity)Power (physics)Electronic engineeringReliability (semiconductor)Computer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

A DC-DC boost converter is a vital part of any power harvesting system. Coupled inductors have been utilized to increase and extend the voltage gain of the boost converter. Although the duty cycle impedance matching method can accommodate the efficiency regulation in a boost converter, it suffers from voltage loss during shading. This paper introduces a modified interleaved coupled boost converter Photo-Voltaic (PV) for low power applications with increased voltage gain to solve this issue. The proposed approach facilitates the operating condition of the power harvesting during a strong overcast by improving the efficiency and output voltage. It responds to a wider range of solar irradiations and extends the solar operational range of the charger/harvester with an improved output current, reliability, and functionality in Continuous Current Mode (CCM). A prototype has been implemented and successfully tested to verify and validate the proposed topology. An efficiency of 87.4% is retained for the harvester during an overcast.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.833

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.306
Teacher spread0.292 · 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