DC current balance with common-mode voltage reduction for parallel current source converters
Bibliographic record
Abstract
Current source converter (CSC) is mainly applied in high medium voltage drives. To increase the system power region and reduce the output harmonics, parallel connection is an easy-implement and practical method. There are mainly two types of parallel based topologies. The direct parallel connect of two back-to-back CSCs is the first type, the two DC links can be controlled from the rectifier side independently. The second type constructed by one current source rectifier (CSR) on the grid side and two parallel current source inverters (CSIs) on the motor side, which can reduce the size and cost of the system. With two inverters sharing the same DC link, the DC current should be guaranteed with proper modulation to make the system work properly. Moreover, common-mode voltage (CMV) increases the line-to-ground voltage and damage the insulation system, which should also be suppressed. In this paper, the two types of topologies are compared from the DC current balance, CMV and circulating current point of views. An optimized multilevel based DC current balance method considering the CMV reduction is applied in the second type of topology. The effectiveness of proposed method is verified with simulation and experimental results.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".