Digital implementation of one-cycle controller (OCC) for AC-DC converters
Bibliographic record
Abstract
In this paper, a novel method for the digital implementation of one-cycle controller (OCC) is introduced for the control of AC-DC full-bridge converter. The use of this technique eliminates the need of high sampling rates for data acquisition or of using averaged input parameters for control, which is usually the case for digital implementation of OCC. Since, the instantaneous result of integration of switched variable is compared with the reference signal, sampling rates much higher than the switching frequency are required. In the proposed implementation technique, the current ripple is mathematically estimated based on the measured values of input current and voltages sampled at the beginning of each switching cycle. Since the choice of carrier affects the performance of the converter, a generalized triangular carrier is used in the analysis of converter operation using this technique. It allows us to choose an appropriate carrier suitable for this application. This leads to obtaining accurate duty cycles by switching action, while sampling rates remain the same as the switching frequency. The above technique is then used to perform reactive power control on a lab prototype of a boost based AC-DC converter. The simulation and experimental results show satisfactory performance of the converter with the proposed implementation technique.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".