General Bi-tri Logic SPWM for Current Source Converter with Optimized Zero-state Replacement
Bibliographic record
Abstract
This paper proposed an optimized zero-state replacement (ZSR) method for general Bi-tri logic SPWM (BTSPWM) to deal with the common-mode voltage (CMV) issue. The BTSPWM can take advantage of the well-developed modulation strategies adopted in voltage source converters (VSCs) and transfer them into a current source converter (CSC) system through logic translation, which shares more general features compared with other modulations developed for CSC. However, all-off gating signals can be generated through the logic translation, which should be replaced with the redundant zero-states to avoid open-circuit. Most of the ZSR strategies are focused on switching time minimization while not considering the CMV excited by the replaced zero-states. To address the CMV issue, an optimized ZSR method for general BTSPWM was proposed to suppress the CMV while not influence the pulse width modulation (PWM) features. Not only the CMV peak value but also the third-order component can be effectively reduced. Moreover, the proposed method can be easily applied to a parallel CSC system due to the inherent modularity. The validity of the proposed methods was verified on both single and parallel CSC systems.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".