Low Power Photo-Voltaic Harvesting Matrix Based Boost DC–DC Converter with Recycled and Synchro-Recycled Scheme
Bibliographic record
Abstract
Photo-voltaic (PV) power harvest can have decent efficiency when dealing with high power. When operating with a DC–DC boost converter during the low-power harvest, its efficiency and output voltage are degraded due to excessive losses in the converter components. The objective of this paper is to present a systematic approach to designing an efficient low-power photo-voltaic harvesting topology with an improved efficiency and output voltage. The proposed topology uses a boost converter with and extra inductor in recycled and synchro-recycled techniques in continuous current mode (CCM). By exploiting the non-linearity of the PV cell, it reduces the power loss and using the current stored in the second inductor, it enhances the output voltage and output power simultaneously. Further, by utilizing the Metal Oxide Silicon Field Effect Transistor’s (MOSFET) body diode as a switch, it maintains a minimum hardware, and introduces a negligible impact on the reliability. The test results of the proposed boost converters show that it achieves a decent power and output voltage. Theoretical and experimental results of the proposed topologies with a tested prototype are presented along with a strategy to maximize power and voltage conversion efficiencies and output voltage.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".