Trade-Offs Between Efficiency and Output Voltage of a Single Boost DC-DC Converter for Photo-Voltaic Low Power Harvesting Applications
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".