Simultaneous DC Current Balance and CMV Reduction for Parallel CSC System With Interleaved Carrier-Based SPWM
Bibliographic record
Abstract
Parallel current source converter (CSC) has attracted increasing attention due to the potential advantages to increase the system power capacity, reliability, and output quality. Compared with multilevel space vector modulation (SVM) and selective harmonic elimination, carrier-based sinusoidal pulsewidth modulation (SPWM) enjoys inherent scalability and modularity, which is very easy to be implemented in N-CSC parallel system by interleaving the carriers. The dc current balance and common-mode voltage (CMV) are the main concerns in parallel CSC system and most of the research was focused on SVM. However, dc current balance and CMV reduction methods with interleaved SPWM were not well addressed. In this article, we mainly investigate three different interleaved SPWM methods, namely, bi-tri logic SPWM, six-step direct PWM, and direct duty-ratio PWM (DDPWM). The comparison results show that the proposed interleaved DDPWM can balance the dc current and suppress CMV simultaneously, which can improve the output quality and reduce load-side CMV stress effectively. The validity and effectiveness of the proposed methods are verified on a parallel CSC system with shared dc-link by simulation and experimental results.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".