On-line generation of gating signals for current source converter topologies
Bibliographic record
Abstract
PWM rectifiers and current source inverters (CSI's) are usually gated using optimized patterns stored in EPROMs. Patterns are therefore mostly of fixed modulation index, since variable modulation index requires storing patterns for a finite numbr of indices. Furthermore, the modulation index can only be changed once per cycle, thus reducing the dynamic response of the system. This paper proposes a technique of generating gating patterns online for CSI topologies based on carrier PWM techniques (PWM rectifiers or inverters). The proposed technique is designed and implemented for the standard three-phase six-switch configuration. It delivers the appropriate gating signals for all switching patterns and modulation indices including overmodulation. It is therefore possible to extend the concept of duality in the power circuit topology between VSI's and CSI's to carrier PWM modulation techniques. In the proposed approach, the requirements imposed on gating signals are satisfied by the appropriate combinations of single phase switching patterns and the introduction of the required complementary and overlap pulses. Implementation in a mixed analog/digital circuit is described and experimental results obtained on a 2 kVA voltage-controlled current source inverter unit are given.>
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 | 0.001 |
| 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) | 0.003 | 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".