General Bi-Tri Logic SPWM for Current Source Converter With Optimized Zero-State Replacement
Bibliographic record
Abstract
This article proposes 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 transform the well-developed modulation strategies for voltage source converters (VSCs) into current source converter (CSC) system through the logic translation, which can utilize the analysis concepts developed for VSC. With BTSPWM, all-offgating signals can be generated through the logic translation, which should be replaced with the redundant zero-states to avoid open-circuit, which provides the flexibility for ZSR. Existing ZSR strategies are mainly focused on switching time minimization while the CMV excited by the replaced zero-states is not considering. To address the CMV issue, optimized ZSR methods for general BTSPWM are proposed to address the CMV while not influence the regular PWM features. With the proposed methods, both the CMV peak value and 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 performances of the proposed methods have been experimentally verified on both single and parallel CSC systems.
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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) | 0.001 | 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) | 0.003 | 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".