Control of high-performance switched-mode rectifier system
Bibliographic record
Abstract
A novel high-performance switched-mode rectifier system for high-power applications is introduced. The rectifier system comprises a three-level neutral-point-diode clamped (NPC) AC/DC rectifier, which is interfaced to the DC load through a DC/DC buck converter. The NPC converter provides a tightly regulated DC voltage at the input of the buck converter; the buck converter provides a rapid current control for the load. This novel arrangement has the following technical merits: (i) fast transient response, (ii) operation at unity power factor, (iii) low distortion AC-side currents, and (iv) capability to operate at high-voltage levels with commercially available power switches rated at lower voltage levels, (v) elimination of multi-winding transformers, and (vi) decoupling between the DC load and the supply system in terms of rapid transients of AC and DC sides. Dynamic models and control schemes for the proposed rectifier system are presented. The control schemes include the buck converter control, the NPC capacitor voltage balancer, the DC-bus voltage control and the AC-side current control. Superior performance of the proposed rectifier system is validated through digital time-domain simulation studies in the PSCAD/EMTDC environment.
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 | 0.000 |
| 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 | 0.001 | 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".