Natural Sampling SVM-Based Common-Mode Voltage Reduction in Medium-Voltage Current Source Rectifier
Bibliographic record
Abstract
Conventional space vector modulation (SVM)-based common-mode voltage (CMV) reduction in medium-voltage (MV) current source rectifier (CSR) cannot be used in practice. Conventional SVM contains high-magnitude low-order harmonics, particularly the fifth and seventh harmonics, that are lying closely to the resonance frequency (4.5-5.5 p.u.) of the LC filter of the converter, thus, introducing resonance as the grid-side damping is small. Recently, a natural sampling SVM (NS-SVM) with superior low-order harmonics performance has been proposed for MV CSR. On this basis, a NS-SVM-based CMV reduction method is proposed for MV CSR in this paper. The proposed scheme achieves both good CMV reduction and superior low-order harmonics performance simultaneously. Additionally, effort to lower computational burden on calculating dwell times is made. Experiments are finally provided.
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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 | 0.000 | 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".