A Step-Up Transformerless, ZV–ZCS High-Gain DC/DC Converter With Output Voltage Regulation Using Modular Step-Up Resonant Cells for DC Grid in Wind Systems
Bibliographic record
Abstract
The use of medium voltage dc (MVDC) grid in offshore wind farms has been presented as an alternative solution to eliminate the conventional bulky low frequency step-up transformers used in the medium voltage ac grid. In order to reduce the transmission losses, the trend is to increase the wind turbine output voltage to at least thousands of volts. In this paper, a variable frequency controlled MVDC converter with modular approach of combining multiple step-up resonant circuits and multistring of switches configuration is proposed for MVDC grid. The proposed converter has the following features: 1) the multistring arrangement of the switches allows much lower voltage stress across each transistor; 2) the modular step-up resonant circuits are able to achieve high voltage gain so that transformer with large turns ratio is not required; 3) zero voltage switching turn-on and zero current switching turn-off are achieved in all the switches; 4) the output MVDC level is controlled by variable frequency control of the step-up converter; and 5) circulating energy in all the resonant circuit modules is minimized through close-to-resonant operation for different load conditions. Simulation results are provided on a 4 kVac/50 kV, 2 MW converter for a wide range of load conditions. Experimental results are provided on a laboratory-scale 200 V/1.6 kV proof-of-concept prototype to highlight the merits of this paper.
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 | 0.000 | 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".