MétaCan
Menu
Back to cohort
Record W2996402562 · doi:10.1109/iemcon.2019.8936146

Trade-Offs Between Efficiency and Output Voltage of a Single Boost DC-DC Converter for Photo-Voltaic Low Power Harvesting Applications

2019· article· en· W2996402562 on OpenAlexaff
Maziar Rastmanesh, E.I. El-Masry, Kamal El‐Sankary

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDuty cycleBoost converterVoltageElectrical impedancePhotovoltaic systemElectrical efficiencyPower electronicsOutput impedancePower (physics)Computer scienceElectrical engineeringElectronic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Solar power harvesting is one of the most effective ways of providing clean energy. However, the power transfer is a major issue due to the shading effect. Besides; the photo voltaic (PV) output impedance is non-constant and it changes in a nonlinear fashion. This creates a challenge to ensure a maximum power transfer and hence, degrades the efficiency. A recent trend to regulate the efficiency has employed a boost DC- DC converter through adjusting its duty cycle. This paper presents an impedance matrix and analytical study on a single boost converter in continuous current mode (CCM) for low power harvesting application with its limitations to adapt PV's impedance change. It establishes the relationship between efficiency and output voltage by analyzing the dynamics between load, duty cycle, constantly changing impedance of the PV's cell and the impact of optimal duty cycle on the efficiency and output voltage as well. It further sheds some light into the over-sensitivity of efficiency with respect to duty cycle. To verify the analysis, an example simulation of 1.4V/17.1mW single boost converter for wearable electronics has been simulated and the result confirms the proposed model and analysis. Furthermore, the paper highlights the trade-off between the efficiency and its impact on the voltage regulation as well.

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)0.000
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: none
Teacher disagreement score0.854
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicPhotovoltaic System Optimization TechniquesFrench-language works237,207