A Low Q Three-Phase Series Resonant Converter for PV Applications
Bibliographic record
Abstract
In this paper, a soft-switched three-phase resonant converter with a low quality factor (Q) design is proposed for Photovoltaic (PV) energy harvesting systems with a very wide range of operating conditions. Due to the low quality factor (Q ≤ 1), the voltage stress across the capacitors is drastically reduced. However, a major challenge of using a low Q design is the wide switching frequency range required for operation over a wide range of load and input conditions. In order to reduce the switching frequency range, this paper introduces a hybrid modulation technique employing asymmetric pulse width modulation (APWM) along with variable frequency modulation. The proposed modulation scheme also substantially extends the soft-switching range of the converter from full-load upto 10–35% load condition over a wide range of line voltages. To sustain soft-switching in the entire operating region of the converter, the converter is operated in a hybrid structure with single-phase and burst modes at light load conditions. A comprehensive time-domain analysis of the proposed converter is presented, which greatly improves accuracy over conventional frequency-domain modeling. Experimental results from a 1 kW prototype are presented to verify the performance of the converter and validate the theoretical analysis.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".