A Balanced, Unity Power Factor, 3-phase Bridgeless AC/DC Step-up Transformer-less Converter with Magnetic-Coupled Soft-Switched Step-up Rectifiers for Wind Farm with a MVDC Grid
Bibliographic record
Abstract
High voltage gain, high-power AC/DC converters are the key components for medium voltage (MV) step-up power conversion in wind systems with a MVDC grid. This paper presents a new, three-phase bridgeless AC/DC soft-switched, step-up resonant converter with magnetically coupled high-gain output rectifier modules for MV step-up conversion. In the proposed circuit, the output high-gain rectifier stage in all phases are coupled together to improve the output current and voltage balancing issues. While each phase (i.e. each module) has its own dedicated controller to regulate the output voltage in each phase, the coupled magnetics is able to ensure that the output current in each phase is balanced. Similarly, the inductor in the input power factor correction stage in all three phases are also coupled together to reduce the overall number of magnetic components. The operating stages of the proposed converter, and the design of the coupled magnetics will be discussed in this paper. Simulation results are first given on a 1.8MW, 690VAC/44kVDCsystem to highlight the performance of the proposed converter. Hardware experimental results on a laboratory-scale prototype with an output voltage of 1.2kVDC are finally presented to verify with the proposed converter's performance.
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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 | 0.001 | 0.001 |
| 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".